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Personal Computers in Clinical Research

Personal computers have proved useful in several areas of clinical research support, but they typically fail to meet all of the traditional data processing requirements of serious clinical investigators — usually because of software restrictions. Evaluation of the strengths and weaknesses of these small systems, and particularly of their software, ensures effective use of research budgets and personnel. [For detailed discussion please see the paper, “Personal Computers in Clinical Research” by the same authors, elsewhere in these proceedings.]

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Proceedings of the 1983 Academy of Marketing Science (AMS) Annual Conference pp 594 Cite as

The Personal Computer as a Research Tool in Consumer Behavior Classes

  • Fred Miller 4  
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For the professional marketer of the 1980’s and 90’s, the personal computer will be increasingly important tool of the trade. Relevant applications will include: 1) local statistical analysis of data, 2) interfacing with a mainframe for more elaborate statistical procedures or larger samples, and 3) accessing national data bases in bibliographic research and/or the collection of demographic data. For this reason, it is important that exposure to the personal computer as a research tool be incorporated into marketing curricula. This paper describes and evaluates a set of assignments that seeks to integrate this objective into a Consumer Behavior class.

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Miller, F. (2015). The Personal Computer as a Research Tool in Consumer Behavior Classes. In: Rogers III, J., Lamb, Jr., C. (eds) Proceedings of the 1983 Academy of Marketing Science (AMS) Annual Conference. Developments in Marketing Science: Proceedings of the Academy of Marketing Science. Springer, Cham. https://doi.org/10.1007/978-3-319-16937-8_167

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  • Published: 02 October 2017

Computer-based technology and student engagement: a critical review of the literature

  • Laura A. Schindler   ORCID: orcid.org/0000-0001-8730-5189 1 ,
  • Gary J. Burkholder 2 , 3 ,
  • Osama A. Morad 1 &
  • Craig Marsh 4  

International Journal of Educational Technology in Higher Education volume  14 , Article number:  25 ( 2017 ) Cite this article

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Computer-based technology has infiltrated many aspects of life and industry, yet there is little understanding of how it can be used to promote student engagement, a concept receiving strong attention in higher education due to its association with a number of positive academic outcomes. The purpose of this article is to present a critical review of the literature from the past 5 years related to how web-conferencing software, blogs, wikis, social networking sites ( Facebook and Twitter ), and digital games influence student engagement. We prefaced the findings with a substantive overview of student engagement definitions and indicators, which revealed three types of engagement (behavioral, emotional, and cognitive) that informed how we classified articles. Our findings suggest that digital games provide the most far-reaching influence across different types of student engagement, followed by web-conferencing and Facebook . Findings regarding wikis, blogs, and Twitter are less conclusive and significantly limited in number of studies conducted within the past 5 years. Overall, the findings provide preliminary support that computer-based technology influences student engagement, however, additional research is needed to confirm and build on these findings. We conclude the article by providing a list of recommendations for practice, with the intent of increasing understanding of how computer-based technology may be purposefully implemented to achieve the greatest gains in student engagement.


The digital revolution has profoundly affected daily living, evident in the ubiquity of mobile devices and the seamless integration of technology into common tasks such as shopping, reading, and finding directions (Anderson, 2016 ; Smith & Anderson, 2016 ; Zickuhr & Raine, 2014 ). The use of computers, mobile devices, and the Internet is at its highest level to date and expected to continue to increase as technology becomes more accessible, particularly for users in developing countries (Poushter, 2016 ). In addition, there is a growing number of people who are smartphone dependent, relying solely on smartphones for Internet access (Anderson & Horrigan, 2016 ) rather than more expensive devices such as laptops and tablets. Greater access to and demand for technology has presented unique opportunities and challenges for many industries, some of which have thrived by effectively digitizing their operations and services (e.g., finance, media) and others that have struggled to keep up with the pace of technological innovation (e.g., education, healthcare) (Gandhi, Khanna, & Ramaswamy, 2016 ).

Integrating technology into teaching and learning is not a new challenge for universities. Since the 1900s, administrators and faculty have grappled with how to effectively use technical innovations such as video and audio recordings, email, and teleconferencing to augment or replace traditional instructional delivery methods (Kaware & Sain, 2015 ; Westera, 2015 ). Within the past two decades, however, this challenge has been much more difficult due to the sheer volume of new technologies on the market. For example, in the span of 7 years (from 2008 to 2015), the number of active apps in Apple’s App Store increased from 5000 to 1.75 million. Over the next 4 years, the number of apps is projected to rise by 73%, totaling over 5 million (Nelson, 2016 ). Further compounding this challenge is the limited shelf life of new devices and software combined with significant internal organizational barriers that hinder universities from efficiently and effectively integrating new technologies (Amirault, 2012 ; Kinchin, 2012 ; Linder-VanBerschot & Summers 2015 ; Westera, 2015 ).

Many organizational barriers to technology integration arise from competing tensions between institutional policy and practice and faculty beliefs and abilities. For example, university administrators may view technology as a tool to attract and retain students, whereas faculty may struggle to determine how technology coincides with existing pedagogy (Lawrence & Lentle-Keenan, 2013 ; Lin, Singer, & Ha, 2010 ). In addition, some faculty may be hesitant to use technology due to lack of technical knowledge and/or skepticism about the efficacy of technology to improve student learning outcomes (Ashrafzadeh & Sayadian, 2015 ; Buchanan, Sainter, & Saunders, 2013 ; Hauptman, 2015 ; Johnson, 2013 ; Kidd, Davis, & Larke, 2016 ; Kopcha, Rieber, & Walker, 2016 ; Lawrence & Lentle-Keenan, 2013 ; Lewis, Fretwell, Ryan, & Parham, 2013 ; Reid, 2014 ). Organizational barriers to technology adoption are particularly problematic given the growing demands and perceived benefits among students about using technology to learn (Amirault, 2012 ; Cassidy et al., 2014 ; Gikas & Grant, 2013 ; Paul & Cochran, 2013 ). Surveys suggest that two-thirds of students use mobile devices for learning and believe that technology can help them achieve learning outcomes and better prepare them for a workforce that is increasingly dependent on technology (Chen, Seilhamer, Bennett, & Bauer, 2015 ; Dahlstrom, 2012 ). Universities that fail to effectively integrate technology into the learning experience miss opportunities to improve student outcomes and meet the expectations of a student body that has grown accustomed to the integration of technology into every facet of life (Amirault, 2012 ; Cook & Sonnenberg, 2014 ; Revere & Kovach, 2011 ; Sun & Chen, 2016 ; Westera, 2015 ).

The purpose of this paper is to provide a literature review on how computer-based technology influences student engagement within higher education settings. We focused on computer-based technology given the specific types of technologies (i.e., web-conferencing software, blogs, wikis, social networking sites, and digital games) that emerged from a broad search of the literature, which is described in more detail below. Computer-based technology (hereafter referred to as technology) requires the use of specific hardware, software, and micro processing features available on a computer or mobile device. We also focused on student engagement as the dependent variable of interest because it encompasses many different aspects of the teaching and learning process (Bryson & Hand, 2007 ; Fredricks, Blumenfeld, & Parks, 1994; Wimpenny & Savin-Baden, 2013 ), compared narrower variables in the literature such as final grades or exam scores. Furthermore, student engagement has received significant attention over the past several decades due to shifts towards student-centered, constructivist instructional methods (Haggis, 2009 ; Wright, 2011 ), mounting pressures to improve teaching and learning outcomes (Axelson & Flick, 2011 ; Kuh, 2009 ), and promising studies suggesting relationships between student engagement and positive academic outcomes (Carini, Kuh, & Klein, 2006 ; Center for Postsecondary Research, 2016 ; Hu & McCormick, 2012 ). Despite the interest in student engagement and the demand for more technology in higher education, there are no articles offering a comprehensive review of how these two variables intersect. Similarly, while many existing student engagement conceptual models have expanded to include factors that influence student engagement, none highlight the overt role of technology in the engagement process (Kahu, 2013 ; Lam, Wong, Yang, & Yi, 2012 ; Nora, Barlow, & Crisp, 2005 ; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ).

Our review aims to address existing gaps in the student engagement literature and seeks to determine whether student engagement models should be expanded to include technology. The review also addresses some of the organizational barriers to technology integration (e.g., faculty uncertainty and skepticism about technology) by providing a comprehensive account of the research evidence regarding how technology influences student engagement. One limitation of the literature, however, is the lack of detail regarding how teaching and learning practices were used to select and integrate technology into learning. For example, the methodology section of many studies does not include a pedagogical justification for why a particular technology was used or details about the design of the learning activity itself. Therefore, it often is unclear how teaching and learning practices may have affected student engagement levels. We revisit this issue in more detail at the end of this paper in our discussions of areas for future research and recommendations for practice. We initiated our literature review by conducting a broad search for articles published within the past 5 years, using the key words technology and higher education , in Google Scholar and the following research databases: Academic Search Complete, Communication & Mass Media Complete, Computers & Applied Sciences Complete, Education Research Complete, ERIC, PsycARTICLES, and PsycINFO . Our initial search revealed themes regarding which technologies were most prevalent in the literature (e.g., social networking, digital games), which then lead to several, more targeted searches of the same databases using specific keywords such as Facebook and student engagement. After both broad and targeted searches, we identified five technologies (web-conferencing software, blogs, wikis, social networking sites, and digital games) to include in our review.

We chose to focus on technologies for which there were multiple studies published, allowing us to identify areas of convergence and divergence in the literature and draw conclusions about positive and negative effects on student engagement. In total, we identified 69 articles relevant to our review, with 36 pertaining to social networking sites (21 for Facebook and 15 for Twitter ), 14 pertaining to digital games, seven pertaining to wikis, and six pertaining to blogs and web-conferencing software respectively. Articles were categorized according to their influence on specific types of student engagement, which will be described in more detail below. In some instances, one article pertained to multiple types of engagement. In the sections that follow, we will provide an overview of student engagement, including an explanation of common definitions and indicators of engagement, followed by a synthesis of how each type of technology influences student engagement. Finally, we will discuss areas for future research and make recommendations for practice.

  • Student engagement

Interest in student engagement began over 70 years ago with Ralph Tyler’s research on the relationship between time spent on coursework and learning (Axelson & Flick, 2011 ; Kuh, 2009 ). Since then, the study of student engagement has evolved and expanded considerably, through the seminal works of Pace ( 1980 ; 1984 ) and Astin ( 1984 ) about how quantity and quality of student effort affect learning and many more recent studies on the environmental conditions and individual dispositions that contribute to student engagement (Bakker, Vergel, & Kuntze, 2015 ; Gilboy, Heinerichs, & Pazzaglia, 2015 ; Martin, Goldwasser, & Galentino, 2017 ; Pellas, 2014 ). Perhaps the most well-known resource on student engagement is the National Survey of Student Engagement (NSSE), an instrument designed to assess student participation in various educational activities (Kuh, 2009 ). The NSSE and other engagement instruments like it have been used in many studies that link student engagement to positive student outcomes such as higher grades, retention, persistence, and completion (Leach, 2016 ; McClenney, Marti, & Adkins, 2012 ; Trowler & Trowler, 2010 ), further convincing universities that student engagement is an important factor in the teaching and learning process. However, despite the increased interest in student engagement, its meaning is generally not well understood or agreed upon.

Student engagement is a broad and complex phenomenon for which there are many definitions grounded in psychological, social, and/or cultural perspectives (Fredricks et al., 1994; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ). Review of definitions revealed that student engagement is defined in two ways. One set of definitions refer to student engagement as a desired outcome reflective of a student’s thoughts, feelings, and behaviors about learning. For example, Kahu ( 2013 ) defines student engagement as an “individual psychological state” that includes a student’s affect, cognition, and behavior (p. 764). Other definitions focus primarily on student behavior, suggesting that engagement is the “extent to which students are engaging in activities that higher education research has shown to be linked with high-quality learning outcomes” (Krause & Coates, 2008 , p. 493) or the “quality of effort and involvement in productive learning activities” (Kuh, 2009 , p. 6). Another set of definitions refer to student engagement as a process involving both the student and the university. For example, Trowler ( 2010 ) defined student engagement as “the interaction between the time, effort and other relevant resources invested by both students and their institutions intended to optimize the student experience and enhance the learning outcomes and development of students and the performance, and reputation of the institution” (p. 2). Similarly, the NSSE website indicates that student engagement is “the amount of time and effort students put into their studies and other educationally purposeful activities” as well as “how the institution deploys its resources and organizes the curriculum and other learning opportunities to get students to participate in activities that decades of research studies show are linked to student learning” (Center for Postsecondary Research, 2017 , para. 1).

Many existing models of student engagement reflect the latter set of definitions, depicting engagement as a complex, psychosocial process involving both student and university characteristics. Such models organize the engagement process into three areas: factors that influence student engagement (e.g., institutional culture, curriculum, and teaching practices), indicators of student engagement (e.g., interest in learning, interaction with instructors and peers, and meaningful processing of information), and outcomes of student engagement (e.g., academic achievement, retention, and personal growth) (Kahu, 2013 ; Lam et al., 2012 ; Nora et al., 2005 ). In this review, we examine the literature to determine whether technology influences student engagement. In addition, we will use Fredricks et al. ( 2004 ) typology of student engagement to organize and present research findings, which suggests that there are three types of engagement (behavioral, emotional, and cognitive). The typology is useful because it is broad in scope, encompassing different types of engagement that capture a range of student experiences, rather than narrower typologies that offer specific or prescriptive conceptualizations of student engagement. In addition, this typology is student-centered, focusing exclusively on student-focused indicators rather than combining student indicators with confounding variables, such as faculty behavior, curriculum design, and campus environment (Coates, 2008 ; Kuh, 2009 ). While such variables are important in the discussion of student engagement, perhaps as factors that may influence engagement, they are not true indicators of student engagement. Using the typology as a guide, we examined recent student engagement research, models, and measures to gain a better understanding of how behavioral, emotional, and cognitive student engagement are conceptualized and to identify specific indicators that correspond with each type of engagement, as shown in Fig. 1 .

Conceptual framework of types and indicators of student engagement

Behavioral engagement is the degree to which students are actively involved in learning activities (Fredricks et al., 2004 ; Kahu, 2013 ; Zepke, 2014 ). Indicators of behavioral engagement include time and effort spent participating in learning activities (Coates, 2008 ; Fredricks et al., 2004 ; Kahu, 2013 ; Kuh, 2009 ; Lam et al., 2012 ; Lester, 2013 ; Trowler, 2010 ) and interaction with peers, faculty, and staff (Coates, 2008 ; Kahu, 2013 ; Kuh, 2009 ; Bryson & Hand, 2007 ; Wimpenny & Savin-Baden, 2013 : Zepke & Leach, 2010 ). Indicators of behavioral engagement reflect observable student actions and most closely align with Pace ( 1980 ) and Astin’s ( 1984 ) original conceptualizations of student engagement as quantity and quality of effort towards learning. Emotional engagement is students’ affective reactions to learning (Fredricks et al., 2004 ; Lester, 2013 ; Trowler, 2010 ). Indicators of emotional engagement include attitudes, interests, and values towards learning (Fredricks et al., 2004 ; Kahu, 2013 ; Lester, 2013 ; Trowler, 2010 ; Wimpenny & Savin-Baden, 2013 ; Witkowski & Cornell, 2015 ) and a perceived sense of belonging within a learning community (Fredricks et al., 2004 ; Kahu, 2013 ; Lester, 2013 ; Trowler, 2010 ; Wimpenny & Savin-Baden, 2013 ). Emotional engagement often is assessed using self-report measures (Fredricks et al., 2004 ) and provides insight into how students feel about a particular topic, delivery method, or instructor. Finally, cognitive engagement is the degree to which students invest in learning and expend mental effort to comprehend and master content (Fredricks et al., 2004 ; Lester, 2013 ). Indicators of cognitive engagement include: motivation to learn (Lester, 2013 ; Richardson & Newby, 2006 ; Zepke & Leach, 2010 ); persistence to overcome academic challenges and meet/exceed requirements (Fredricks et al., 2004 ; Kuh, 2009 ; Trowler, 2010 ); and deep processing of information (Fredricks et al., 2004 ; Kahu, 2013 ; Lam et al., 2012 ; Richardson & Newby, 2006 ) through critical thinking (Coates, 2008 ; Witkowski & Cornell, 2015 ), self-regulation (e.g., set goals, plan, organize study effort, and monitor learning; Fredricks et al., 2004 ; Lester, 2013 ), and the active construction of knowledge (Coates, 2008 ; Kuh, 2009 ). While cognitive engagement includes motivational aspects, much of the literature focuses on how students use active learning and higher-order thinking, in some form, to achieve content mastery. For example, there is significant emphasis on the importance of deep learning, which involves analyzing new learning in relation previous knowledge, compared to surface learning, which is limited to memorization, recall, and rehearsal (Fredricks et al., 2004 ; Kahu, 2013 ; Lam et al., 2012 ).

While each type of engagement has distinct features, there is some overlap across cognitive, behavioral, and emotional domains. In instances where an indicator could correspond with more than one type of engagement, we chose to match the indicator to the type of engagement that most closely aligned, based on our review of the engagement literature and our interpretation of the indicators. Similarly, there is also some overlap among indicators. As a result, we combined and subsumed similar indicators found in the literature, where appropriate, to avoid redundancy. Achieving an in-depth understanding of student engagement and associated indicators was an important pre-cursor to our review of the technology literature. Very few articles used the term student engagement as a dependent variable given the concept is so broad and multidimensional. We found that specific indicators (e.g., interaction, sense of belonging, and knowledge construction) of student engagement were more common in the literature as dependent variables. Next, we will provide a synthesis of the findings regarding how different types of technology influence behavioral, emotional, and cognitive student engagement and associated indicators.

Influence of technology on student engagement

We identified five technologies post-literature search (i.e., web-conferencing, blogs, wikis, social networking sites , and digital games) to include in our review, based on frequency in which they appeared in the literature over the past 5 years. One commonality among these technologies is their potential value in supporting a constructivist approach to learning, characterized by the active discovery of knowledge through reflection of experiences with one’s environment, the connection of new knowledge to prior knowledge, and interaction with others (Boghossian, 2006 ; Clements, 2015 ). Another commonality is that most of the technologies, except perhaps for digital games, are designed primarily to promote interaction and collaboration with others. Our search yielded very few studies on how informational technologies, such as video lectures and podcasts, influence student engagement. Therefore, these technologies are notably absent from our review. Unlike the technologies we identified earlier, informational technologies reflect a behaviorist approach to learning in which students are passive recipients of knowledge that is transmitted from an expert (Boghossian, 2006 ). The lack of recent research on how informational technologies affect student engagement may be due to the increasing shift from instructor-centered, behaviorist approaches to student-centered, constructivist approaches within higher education (Haggis, 2009 ; Wright, 2011 ) along with the ubiquity of web 2.0 technologies.

  • Web-conferencing

Web-conferencing software provides a virtual meeting space where users login simultaneously and communicate about a given topic. While each software application is unique, many share similar features such as audio, video, or instant messaging options for real-time communication; screen sharing, whiteboards, and digital pens for presentations and demonstrations; polls and quizzes for gauging comprehension or eliciting feedback; and breakout rooms for small group work (Bower, 2011 ; Hudson, Knight, & Collins, 2012 ; Martin, Parker, & Deale, 2012 ; McBrien, Jones, & Cheng, 2009 ). Of the technologies included in this literature review, web-conferencing software most closely mimics the face-to-face classroom environment, providing a space where instructors and students can hear and see each other in real-time as typical classroom activities (i.e., delivering lectures, discussing course content, asking/answering questions) are carried out (Francescucci & Foster, 2013 ; Hudson et al., 2012 ). Studies on web-conferencing software deployed Adobe Connect, Cisco WebEx, Horizon Wimba, or Blackboard Collaborate and made use of multiple features, such as screen sharing, instant messaging, polling, and break out rooms. In addition, most of the studies integrated web-conferencing software into courses on a voluntary basis to supplement traditional instructional methods (Andrew, Maslin-Prothero, & Ewens, 2015 ; Armstrong & Thornton, 2012 ; Francescucci & Foster, 2013 ; Hudson et al., 2012 ; Martin et al., 2012 ; Wdowik, 2014 ). Existing studies on web-conferencing pertain to all three types of student engagement.

Studies on web-conferencing and behavioral engagement reveal mixed findings. For example, voluntary attendance in web-conferencing sessions ranged from 54 to 57% (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ) and, in a comparison between a blended course with regular web-conferencing sessions and a traditional, face-to-face course, researchers found no significant difference in student attendance in courses. However, students in the blended course reported higher levels of class participation compared to students in the face-to-face course (Francescucci & Foster, 2013 ). These findings suggest while web-conferencing may not boost attendance, especially if voluntary, it may offer more opportunities for class participation, perhaps through the use of communication channels typically not available in a traditional, face-to-face course (e.g., instant messaging, anonymous polling). Studies on web-conferencing and interaction, another behavioral indicator, support this assertion. For example, researchers found that students use various features of web-conferencing software (e.g., polling, instant message, break-out rooms) to interact with peers and the instructor by asking questions, expressing opinions and ideas, sharing resources, and discussing academic content (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ; Hudson et al., 2012 ; Martin et al., 2012 ; Wdowik, 2014 ).

Studies on web-conferencing and cognitive engagement are more conclusive than those for behavioral engagement, although are fewer in number. Findings suggest that students who participated in web-conferencing demonstrated critical reflection and enhanced learning through interactions with others (Armstrong & Thornton, 2012 ), higher-order thinking (e.g., problem-solving, synthesis, evaluation) in response to challenging assignments (Wdowik, 2014 ), and motivation to learn, particularly when using polling features (Hudson et al., 2012 ). There is only one study examining how web-conferencing affects emotional engagement, although it is positive suggesting that students who participated in web-conferences had higher levels of interest in course content than those who did not (Francescucci & Foster, 2013 ). One possible reason for the positive cognitive and emotional engagement findings may be that web-conferencing software provides many features that promote active learning. For example, whiteboards and breakout rooms provide opportunities for real-time, collaborative problem-solving activities and discussions. However, additional studies are needed to isolate and compare specific web-conferencing features to determine which have the greatest effect on student engagement.

A blog, which is short for Weblog, is a collection of personal journal entries, published online and presented chronologically, to which readers (or subscribers) may respond by providing additional commentary or feedback. In order to create a blog, one must compose content for an entry, which may include text, hyperlinks, graphics, audio, or video, publish the content online using a blogging application, and alert subscribers that new content is posted. Blogs may be informal and personal in nature or may serve as formal commentary in a specific genre, such as in politics or education (Coghlan et al., 2007 ). Fortunately, many blog applications are free, and many learning management systems (LMSs) offer a blogging feature that is seamlessly integrated into the online classroom. The ease of blogging has attracted attention from educators, who currently use blogs as an instructional tool for the expression of ideas, opinions, and experiences and for promoting dialogue on a wide range of academic topics (Garrity, Jones, VanderZwan, de la Rocha, & Epstein, 2014 ; Wang, 2008 ).

Studies on blogs show consistently positive findings for many of the behavioral and emotional engagement indicators. For example, students reported that blogs promoted interaction with others, through greater communication and information sharing with peers (Chu, Chan, & Tiwari, 2012 ; Ivala & Gachago, 2012 ; Mansouri & Piki, 2016 ), and analyses of blog posts show evidence of students elaborating on one another’s ideas and sharing experiences and conceptions of course content (Sharma & Tietjen, 2016 ). Blogs also contribute to emotional engagement by providing students with opportunities to express their feelings about learning and by encouraging positive attitudes about learning (Dos & Demir, 2013 ; Chu et al., 2012 ; Yang & Chang, 2012 ). For example, Dos and Demir ( 2013 ) found that students expressed prejudices and fears about specific course topics in their blog posts. In addition, Yang and Chang ( 2012 ) found that interactive blogging, where comment features were enabled, lead to more positive attitudes about course content and peers compared to solitary blogging, where comment features were disabled.

The literature on blogs and cognitive engagement is less consistent. Some studies suggest that blogs may help students engage in active learning, problem-solving, and reflection (Chawinga, 2017 ; Chu et al., 2012 ; Ivala & Gachago, 2012 ; Mansouri & Piki, 2016 ), while other studies suggest that students’ blog posts show very little evidence of higher-order thinking (Dos & Demir, 2013 ; Sharma & Tietjen, 2016 ). The inconsistency in findings may be due to the wording of blog instructions. Students may not necessarily demonstrate or engage in deep processing of information unless explicitly instructed to do so. Unfortunately, it is difficult to determine whether the wording of blog assignments contributed to the mixed results because many of the studies did not provide assignment details. However, studies pertaining to other technologies suggest that assignment wording that lacks specificity or requires low-level thinking can have detrimental effects on student engagement outcomes (Hou, Wang, Lin, & Chang, 2015 ; Prestridge, 2014 ). Therefore, blog assignments that are vague or require only low-level thinking may have adverse effects on cognitive engagement.

A wiki is a web page that can be edited by multiple users at once (Nakamaru, 2012 ). Wikis have gained popularity in educational settings as a viable tool for group projects where group members can work collaboratively to develop content (i.e., writings, hyperlinks, images, graphics, media) and keep track of revisions through an extensive versioning system (Roussinos & Jimoyiannis, 2013 ). Most studies on wikis pertain to behavioral engagement, with far fewer studies on cognitive engagement and none on emotional engagement. Studies pertaining to behavioral engagement reveal mixed results, with some showing very little enduring participation in wikis beyond the first few weeks of the course (Nakamaru, 2012 ; Salaber, 2014 ) and another showing active participation, as seen in high numbers of posts and edits (Roussinos & Jimoyiannis, 2013 ). The most notable difference between these studies is the presence of grading, which may account for the inconsistencies in findings. For example, in studies where participation was low, wikis were ungraded, suggesting that students may need extra motivation and encouragement to use wikis (Nakamaru, 2012 ; Salaber, 2014 ). Findings regarding the use of wikis for promoting interaction are also inconsistent. In some studies, students reported that wikis were useful for interaction, teamwork, collaboration, and group networking (Camacho, Carrión, Chayah, & Campos, 2016 ; Martínez, Medina, Albalat, & Rubió, 2013 ; Morely, 2012 ; Calabretto & Rao, 2011 ) and researchers found evidence of substantial collaboration among students (e.g., sharing ideas, opinions, and points of view) in wiki activity (Hewege & Perera, 2013 ); however, Miller, Norris, and Bookstaver ( 2012 ) found that only 58% of students reported that wikis promoted collegiality among peers. The findings in the latter study were unexpected and may be due to design flaws in the wiki assignments. For example, the authors noted that wiki assignments were not explicitly referred to in face-to-face classes; therefore, this disconnect may have prevented students from building on interactive momentum achieved during out-of-class wiki assignments (Miller et al., 2012 ).

Studies regarding cognitive engagement are limited in number but more consistent than those concerning behavioral engagement, suggesting that wikis promote high levels of knowledge construction (i.e., evaluation of arguments, the integration of multiple viewpoints, new understanding of course topics; Hewege & Perera, 2013 ), and are useful for reflection, reinforcing course content, and applying academic skills (Miller et al., 2012 ). Overall, there is mixed support for the use of wikis to promote behavioral engagement, although making wiki assignments mandatory and explicitly referring to wikis in class may help bolster participation and interaction. In addition, there is some support for using wikis to promote cognitive engagement, but additional studies are needed to confirm and expand on findings as well as explore the effect of wikis on emotional engagement.

Social networking sites

Social networking is “the practice of expanding knowledge by making connections with individuals of similar interests” (Gunawardena et al., 2009 , p. 4). Social networking sites, such as Facebook, Twitter, Instagram, and LinkedIn, allow users to create and share digital content publicly or with others to whom they are connected and communicate privately through messaging features. Two of the most popular social networking sites in the educational literature are Facebook and Twitter (Camus, Hurt, Larson, & Prevost, 2016 ; Manca & Ranieri, 2013 ), which is consistent with recent statistics suggesting that both sites also are exceedingly popular among the general population (Greenwood, Perrin, & Duggan, 2016 ). In the sections that follow, we examine how both Facebook and Twitter influence different types of student engagement.

Facebook is a web-based service that allows users to create a public or private profile and invite others to connect. Users may build social, academic, and professional connections by posting messages in various media formats (i.e., text, pictures, videos) and commenting on, liking, and reacting to others’ messages (Bowman & Akcaoglu, 2014 ; Maben, Edwards, & Malone, 2014 ; Hou et al., 2015 ). Within an educational context, Facebook has often been used as a supplementary instructional tool to lectures or LMSs to support class discussions or develop, deliver, and share academic content and resources. Many instructors have opted to create private Facebook groups, offering an added layer of security and privacy because groups are not accessible to strangers (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Clements, 2015 ; Dougherty & Andercheck, 2014 ; Esteves, 2012 ; Shraim, 2014 ; Maben et al., 2014 ; Manca & Ranieri, 2013 ; Naghdipour & Eldridge, 2016 ; Rambe, 2012 ). The majority of studies on Facebook address behavioral indicators of student engagement, with far fewer focusing on emotional or cognitive engagement.

Studies that examine the influence of Facebook on behavioral engagement focus both on participation in learning activities and interaction with peers and instructors. In most studies, Facebook activities were voluntary and participation rates ranged from 16 to 95%, with an average of rate of 47% (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Fagioli, Rios-Aguilar, & Deil-Amen, 2015 ; Rambe, 2012 ; Staines & Lauchs, 2013 ). Participation was assessed by tracking how many students joined course- or university-specific Facebook groups (Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Fagioli et al., 2015 ), visited or followed course-specific Facebook pages (DiVall & Kirwin, 2012 ; Staines & Lauchs, 2013 ), or posted at least once in a course-specific Facebook page (Rambe, 2012 ). The lowest levels of participation (16%) arose from a study where community college students were invited to use the Schools App, a free application that connects students to their university’s private Facebook community. While the authors acknowledged that building an online community of college students is difficult (Fagioli et al., 2015 ), downloading the Schools App may have been a deterrent to widespread participation. In addition, use of the app was not tied to any specific courses or assignments; therefore, students may have lacked adequate incentive to use it. The highest level of participation (95%) in the literature arose from a study in which the instructor created a Facebook page where students could find or post study tips or ask questions. Followership to the page was highest around exams, when students likely had stronger motivations to access study tips and ask the instructor questions (DiVall & Kirwin, 2012 ). The wide range of participation in Facebook activities suggests that some students may be intrinsically motivated to participate, while other students may need some external encouragement. For example, Bahati ( 2015 ) found that when students assumed that a course-specific Facebook was voluntary, only 23% participated, but when the instructor confirmed that the Facebook group was, in fact, mandatory, the level of participation rose to 94%.

While voluntary participation in Facebook activities may be lower than desired or expected (Dyson, Vickers, Turtle, Cowan, & Tassone, 2015 ; Fagioli et al., 2015 ; Naghdipour & Eldridge, 2016 ; Rambe, 2012 ), students seem to have a clear preference for Facebook compared to other instructional tools (Clements, 2015 ; DiVall & Kirwin, 2012 ; Hurt et al., 2012 ; Hou et al., 2015 ; Kent, 2013 ). For example, in one study where an instructor shared course-related information in a Facebook group, in the LMS, and through email, the level of participation in the Facebook group was ten times higher than in email or the LMS (Clements, 2015 ). In other studies, class discussions held in Facebook resulted in greater levels of participation and dialogue than class discussions held in LMS discussion forums (Camus et al., 2016 ; Hurt et al., 2012 ; Kent, 2013 ). Researchers found that preference for Facebook over the university’s LMS is due to perceptions that the LMS is outdated and unorganized and reports that Facebook is more familiar, convenient, and accessible given that many students already visit the social networking site multiple times per day (Clements, 2015 ; Dougherty & Andercheck, 2014 ; Hurt et al., 2012 ; Kent, 2013 ). In addition, students report that Facebook helps them stay engaged in learning through collaboration and interaction with both peers and instructors (Bahati, 2015 ; Shraim, 2014 ), which is evident in Facebook posts where students collaborated to study for exams, consulted on technical and theoretical problem solving, discussed course content, exchanged learning resources, and expressed opinions as well as academic successes and challenges (Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Esteves, 2012 Ivala & Gachago, 2012 ; Maben et al., 2014 ; Rambe, 2012 ; van Beynen & Swenson, 2016 ).

There is far less evidence in the literature about the use of Facebook for emotional and cognitive engagement. In terms of emotional engagement, studies suggest that students feel positively about being part of a course-specific Facebook group and that Facebook is useful for expressing feelings about learning and concerns for peers, through features such as the “like” button and emoticons (Bowman & Akcaoglu, 2014 ; Dougherty & Andercheck, 2014 ; Naghdipour & Eldridge, 2016 ). In addition, being involved in a course-specific Facebook group was positively related to students’ sense of belonging in the course (Dougherty & Andercheck, 2014 ). The research on cognitive engagement is less conclusive, with some studies suggesting that Facebook participation is related to academic persistence (Fagioli et al., 2015 ) and self-regulation (Dougherty & Andercheck, 2014 ) while other studies show low levels of knowledge construction in Facebook posts (Hou et al., 2015 ), particularly when compared to discussions held in the LMS. One possible reason may be because the LMS is associated with formal, academic interactions while Facebook is associated with informal, social interactions (Camus et al., 2016 ). While additional research is needed to confirm the efficacy of Facebook for promoting cognitive engagement, studies suggest that Facebook may be a viable tool for increasing specific behavioral and emotional engagement indicators, such as interactions with others and a sense of belonging within a learning community.

Twitter is a web-based service where subscribers can post short messages, called tweets, in real-time that are no longer than 140 characters in length. Tweets may contain hyperlinks to other websites, images, graphics, and/or videos and may be tagged by topic using the hashtag symbol before the designated label (e.g., #elearning). Twitter subscribers may “follow” other users and gain access to their tweets and also may “retweet” messages that have already been posted (Hennessy, Kirkpatrick, Smith, & Border, 2016 ; Osgerby & Rush, 2015 ; Prestridge, 2014 ; West, Moore, & Barry, 2015 ; Tiernan, 2014 ;). Instructors may use Twitter to post updates about the course, clarify expectations, direct students to additional learning materials, and encourage students to discuss course content (Bista, 2015 ; Williams & Whiting, 2016 ). Several of the studies on the use of Twitter included broad, all-encompassing measures of student engagement and produced mixed findings. For example, some studies suggest that Twitter increases student engagement (Evans, 2014 ; Gagnon, 2015 ; Junco, Heibergert, & Loken, 2011 ) while other studies suggest that Twitter has little to no influence on student engagement (Junco, Elavsky, & Heiberger, 2013 ; McKay, Sanko, Shekhter, & Birnbach, 2014 ). In both studies suggesting little to no influence on student engagement, Twitter use was voluntary and in one of the studies faculty involvement in Twitter was low, which may account for the negative findings (Junco et al., 2013 ; McKay et al., 2014 ). Conversely, in the studies that show positive findings, Twitter use was mandatory and often directly integrated with required assignments (Evans, 2014 ; Gagnon, 2015 ; Junco et al., 2011 ). Therefore, making Twitter use mandatory, increasing faculty involvement in Twitter, and integrating Twitter into assignments may help to increase student engagement.

Studies pertaining to specific behavioral student engagement indicators also reveal mixed findings. For example, in studies where course-related Twitter use was voluntary, 45-91% of students reported using Twitter during the term (Hennessy et al., 2016 ; Junco et al., 2013 ; Ross, Banow, & Yu, 2015 ; Tiernan, 2014 ; Williams & Whiting, 2016 ), but only 30-36% reported making contributions to the course-specific Twitter page (Hennessy et al., 2016 ; Tiernan, 2014 ; Ross et al., 2015 ; Williams & Whiting, 2016 ). The study that reported a 91% participation rate was unique because the course-specific Twitter page was accessible via a public link. Therefore, students who chose only to view the content (58%), rather than contribute to the page, did not have to create a Twitter account (Hennessy et al., 2016 ). The convenience of not having to create an account may be one reason for much higher participation rates. In terms of low participation rates, a lack of literacy, familiarity, and interest in Twitter , as well as a preference for Facebook , are cited as contributing factors (Bista, 2015 ; McKay et al., 2014 ; Mysko & Delgaty, 2015 ; Osgerby & Rush, 2015 ; Tiernan, 2014 ). However, when the use of Twitter was required and integrated into class discussions, the participation rate was 100% (Gagnon, 2015 ). Similarly, 46% of students in one study indicated that they would have been more motivated to participate in Twitter activities if they were graded (Osgerby & Rush, 2015 ), again confirming the power of extrinsic motivating factors.

Studies also show mixed results for the use of Twitter to promote interactions with peers and instructors. Researchers found that when instructors used Twitter to post updates about the course, ask and answer questions, and encourage students to tweet about course content, there was evidence of student-student and student-instructor interactions in tweets (Hennessy et al., 2016 ; Tiernan, 2014 ). Some students echoed these findings, suggesting that Twitter is useful for sharing ideas and resources, discussing course content, asking the instructor questions, and networking (Chawinga, 2017 ; Evans, 2014 ; Gagnon, 2015 ; Hennessy et al., 2016 ; Mysko & Delgaty, 2015 ; West et al., 2015 ) and is preferable over speaking aloud in class because it is more comfortable, less threatening, and more concise due to the 140 character limit (Gagnon, 2015 ; Mysko & Delgaty, 2015 ; Tiernan, 2014 ). Conversely, other students reported that Twitter was not useful for improving interaction because they viewed it predominately for social, rather than academic, interactions and they found the 140 character limit to be frustrating and restrictive. A theme among the latter studies was that a large proportion of the sample had never used Twitter before (Bista, 2015 ; McKay et al., 2014 ; Osgerby & Rush, 2015 ), which may have contributed to negative perceptions.

The literature on the use of Twitter for cognitive and emotional engagement is minimal but nonetheless promising in terms of promoting knowledge gains, the practical application of content, and a sense of belonging among users. For example, using Twitter to respond to questions that arose in lectures and tweet about course content throughout the term is associated with increased understanding of course content and application of knowledge (Kim et al., 2015 ; Tiernan, 2014 ; West et al., 2015 ). While the underlying mechanisms pertaining to why Twitter promotes an understanding of content and application of knowledge are not entirely clear, Tiernan ( 2014 ) suggests that one possible reason may be that Twitter helps to break down communication barriers, encouraging shy or timid students to participate in discussions that ultimately are richer in dialogue and debate. In terms of emotional engagement, students who participated in a large, class-specific Twitter page were more likely to feel a sense of community and belonging compared to those who did not participate because they could more easily find support from and share resources with other Twitter users (Ross et al., 2015 ). Despite the positive findings about the use of Twitter for cognitive and emotional engagement, more studies are needed to confirm existing results regarding behavioral engagement and target additional engagement indicators such as motivation, persistence, and attitudes, interests, and values about learning. In addition, given the strong negative perceptions of Twitter that still exist, additional studies are needed to confirm Twitter ’s efficacy for promoting different types of behavioral engagement among both novice and experienced Twitter users, particularly when compared to more familiar tools such as Facebook or LMS discussion forums.

  • Digital games

Digital games are “applications using the characteristics of video and computer games to create engaging and immersive learning experiences for delivery of specified learning goals, outcomes and experiences” (de Freitas, 2006 , p. 9). Digital games often serve the dual purpose of promoting the achievement of learning outcomes while making learning fun by providing simulations of real-world scenarios as well as role play, problem-solving, and drill and repeat activities (Boyle et al., 2016 ; Connolly, Boyle, MacArthur, Hainey, & Boyle, 2012 ; Scarlet & Ampolos, 2013 ; Whitton, 2011 ). In addition, gamified elements, such as digital badges and leaderboards, may be integrated into instruction to provide additional motivation for completing assigned readings and other learning activities (Armier, Shepherd, & Skrabut, 2016 ; Hew, Huang, Chu, & Chiu, 2016 ). The pedagogical benefits of digital games are somewhat distinct from the other technologies addressed in this review, which are designed primarily for social interaction. While digital games may be played in teams or allow one player to compete against another, the focus of their design often is on providing opportunities for students to interact with academic content in a virtual environment through decision-making, problem-solving, and reward mechanisms. For example, a digital game may require students to adopt a role as CEO in a computer-simulated business environment, make decisions about a series of organizational issues, and respond to the consequences of those decisions. In this example and others, digital games use adaptive learning principles, where the learning environment is re-configured or modified in response to the actions and needs of students (Bower, 2016 ). Most of the studies on digital games focused on cognitive and emotional indicators of student engagement, in contrast to the previous technologies addressed in this review which primarily focused on behavioral indicators of engagement.

Existing studies provide support for the influence of digital games on cognitive engagement, through achieving a greater understanding of course content and demonstrating higher-order thinking skills (Beckem & Watkins, 2012 ; Farley, 2013 ; Ke, Xie, & Xie, 2016 ; Marriott, Tan, & Marriott, 2015 ), particularly when compared to traditional instructional methods, such as giving lectures or assigning textbook readings (Lu, Hallinger, & Showanasai, 2014 ; Siddique, Ling, Roberson, Xu, & Geng, 2013 ; Zimmermann, 2013 ). For example, in a study comparing courses that offered computer simulations of business challenges (e.g, implementing a new information technology system, managing a startup company, and managing a brand of medicine in a simulated market environment) and courses that did not, students in simulation-based courses reported higher levels of action-directed learning (i.e., connecting theory to practice in a business context) than students in traditional, non-simulation-based courses (Lu et al., 2014 ). Similarly, engineering students who participated in a car simulator game, which was designed to help students apply and reinforce the knowledge gained from lectures, demonstrated higher levels of critical thinking (i.e., analysis, evaluation) on a quiz than students who only attended lectures (Siddique et al., 2013 ).

Motivation is another cognitive engagement indicator that is linked to digital games (Armier et al., 2016 ; Chang & Wei, 2016 ; Dichev & Dicheva, 2017 ; Grimley, Green, Nilsen, & Thompson, 2012 ; Hew et al., 2016 ; Ibáñez, Di-Serio, & Delgado-Kloos, 2014 ; Ke et al., 2016 ; Liu, Cheng, & Huang, 2011 ; Nadolny & Halabi, 2016 ). Researchers found that incorporating gamified elements into courses, such as giving students digital rewards (e.g., redeemable points, trophies, and badges) for participating in learning activities or creating competition through the use of leaderboards where students can see how they rank against other students positively affects student motivation to complete learning tasks (Armier et al., 2016 ; Chang & Wei, 2016 ; Hew et al., 2016 ; Nadolny & Halabi, 2016 ). In addition, students who participated in gamified elements, such as trying to earn digital badges, were more motivated to complete particularly difficult learning activities (Hew et al., 2016 ) and showed persistence in exceeding learning requirements (Ibáñez et al., 2014 ). Research on emotional engagement may help to explain these findings. Studies suggest that digital games positively affect student attitudes about learning, evident in student reports that games are fun, interesting, and enjoyable (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Hew et al., 2016 ; Liu et al., 2011 ; Zimmermann, 2013 ), which may account for higher levels of student motivation in courses that offered digital games.

Research on digital games and behavioral engagement is more limited, with only one study suggesting that games lead to greater participation in educational activities (Hew et al., 2016 ). Therefore, more research is needed to explore how digital games may influence behavioral engagement. In addition, research is needed to determine whether the underlying technology associated with digital games (e.g., computer-based simulations and virtual realities) produce positive engagement outcomes or whether common mechanisms associated with both digital and non-digital games (e.g., role play, rewards, and competition) account for those outcomes. For example, studies in which non-digital, face-to-face games were used also showed positive effects on student engagement (Antunes, Pacheco, & Giovanela, 2012 ; Auman, 2011 ; Coffey, Miller, & Feuerstein, 2011 ; Crocco, Offenholley, & Hernandez, 2016 ; Poole, Kemp, Williams, & Patterson, 2014 ; Scarlet & Ampolos, 2013 ); therefore, it is unclear if and how digitizing games contributes to student engagement.

Discussion and implications

Student engagement is linked to a number of academic outcomes, such as retention, grade point average, and graduation rates (Carini et al., 2006 ; Center for Postsecondary Research, 2016 ; Hu & McCormick, 2012 ). As a result, universities have shown a strong interest in how to increase student engagement, particularly given rising external pressures to improve learning outcomes and prepare students for academic success (Axelson & Flick, 2011 ; Kuh, 2009 ). There are various models of student engagement that identify factors that influence student engagement (Kahu, 2013 ; Lam et al., 2012 ; Nora et al., 2005 ; Wimpenny & Savin-Baden, 2013 ; Zepke & Leach, 2010 ); however, none include the overt role of technology despite the growing trend and student demands to integrate technology into the learning experience (Amirault, 2012 ; Cook & Sonnenberg, 2014 ; Revere & Kovach, 2011 ; Sun & Chen, 2016 ; Westera, 2015 ). Therefore, the primary purpose of our literature review was to explore whether technology influences student engagement. The secondary purpose was to address skepticism and uncertainty about pedagogical benefits of technology (Ashrafzadeh & Sayadian, 2015 ; Kopcha et al., 2016 ; Reid, 2014 ) by reviewing the literature regarding the efficacy of specific technologies (i.e., web-conferencing software, blogs, wikis, social networking sites, and digital games) for promoting student engagement and offering recommendations for effective implementation, which are included at the end of this paper. In the sections that follow, we provide an overview of the findings, an explanation of existing methodological limitations and areas for future research, and a list of best practices for integrating the technologies we reviewed into the teaching and learning process.

Summary of findings

Findings from our literature review provide preliminary support for including technology as a factor that influences student engagement in existing models (Table 1 ). One overarching theme is that most of the technologies we reviewed had a positive influence on multiple indicators of student engagement, which may lead to a larger return on investment in terms of learning outcomes. For example, digital games influence all three types of student engagement and six of the seven indicators we identified, surpassing the other technologies in this review. There were several key differences in the design and pedagogical use between digital games and other technologies that may explain these findings. First, digital games were designed to provide authentic learning contexts in which students could practice skills and apply learning (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Ke et al., 2016 ; Liu et al., 2011 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ), which is consistent with experiential learning and adult learning theories. Experiential learning theory suggests that learning occurs through interaction with one’s environment (Kolb, 2014 ) while adult learning theory suggests that adult learners want to be actively involved in the learning process and be able apply learning to real life situations and problems (Cercone, 2008 ). Second, students reported that digital games (and gamified elements) are fun, enjoyable, and interesting (Beckem & Watkins, 2012 ; Farley, 2013 ; Grimley et al., 2012 ; Hew et al., 2016 ; Liu et al., 2011 ; Zimmermann, 2013 ), feelings that are associated with a flow-like state where one is completely immersed in and engaged with the activity (Csikszentmihalyi, 1988 ; Weibel, Wissmath, Habegger, Steiner, & Groner, 2008 ). Third, digital games were closely integrated into the curriculum as required activities (Farley, 2013 ; Grimley et al., 2012 , Ke et al., 2016 ; Liu et al., 2011 ; Marriott et al., 2015 ; Siddique et al., 2013 ) as opposed to wikis, Facebook , and Twitter , which were often voluntary and used to supplement lectures (Dougherty & Andercheck, 2014 Nakamaru, 2012 ; Prestridge, 2014 ; Rambe, 2012 ).

Web-conferencing software and Facebook also yielded the most positive findings, influencing four of the seven indicators of student engagement, compared to other collaborative technologies, such as blogs, wikis, and Twitter . Web-conferencing software was unique due to the sheer number of collaborative features it offers, providing multiple ways for students to actively engage with course content (screen sharing, whiteboards, digital pens) and interact with peers and the instructor (audio, video, text chats, breakout rooms) (Bower, 2011 ; Hudson et al., 2012 ; Martin et al., 2012 ; McBrien et al., 2009 ); this may account for the effects on multiple indicators of student engagement. Positive findings regarding Facebook ’s influence on student engagement could be explained by a strong familiarity and preference for the social networking site (Clements, 2015 ; DiVall & Kirwin, 2012 ; Hurt et al., 2012 ; Hou et al., 2015 ; Kent, 2013 ; Manca & Ranieri, 2013 ), compared to Twitter which was less familiar or interesting to students (Bista, 2015 ; McKay et al., 2014 ; Mysko & Delgaty, 2015 ; Osgerby & Rush, 2015 ; Tiernan, 2014 ). Wikis had the lowest influence on student engagement, with mixed findings regarding behavioral engagement, limited, but conclusive findings, regarding one indicator of cognitive engagement (deep processing of information), and no studies pertaining to other indicators of cognitive engagement (motivation, persistence) or emotional engagement.

Another theme that arose was the prevalence of mixed findings across multiple technologies regarding behavioral engagement. Overall, the vast majority of studies addressed behavioral engagement, and we expected that technologies designed specifically for social interaction, such as web-conferencing, wikis, and social networking sites, would yield more conclusive findings. However, one possible reason for the mixed findings may be that the technologies were voluntary in many studies, resulting in lower than desired participation rates and missed opportunities for interaction (Armstrong & Thornton, 2012 ; Fagioli et al., 2015 ; Nakamaru, 2012 ; Rambe, 2012 ; Ross et al., 2015 ; Williams & Whiting, 2016 ), and mandatory in a few studies, yielding higher levels of participation and interaction (Bahati, 2015 ; Gagnon, 2015 ; Roussinos & Jimoyiannis, 2013 ). Another possible reason for the mixed findings is that measures of variables differed across studies. For example, in some studies participation meant that a student signed up for a Twitter account (Tiernan, 2014 ), used the Twitter account for class (Williams & Whiting, 2016 ), or viewed the course-specific Twitter page (Hennessy et al., 2016 ). The pedagogical uses of the technologies also varied considerably across studies, making it difficult to make comparisons. For example, Facebook was used in studies to share learning materials (Clements, 2015 ; Dyson et al., 2015 ), answer student questions about academic content or administrative issues (Rambe, 2012 ), prepare for upcoming exams and share study tips (Bowman & Akcaoglu, 2014 ; DiVall & Kirwin, 2012 ), complete group work (Hou et al., 2015 ; Staines & Lauchs, 2013 ), and discuss course content (Camus et al., 2016 ; Kent, 2013 ; Hurt et al., 2012 ). Finally, cognitive indicators (motivation and persistence) drew the fewest amount of studies, which suggests that research is needed to determine whether technologies affect these indicators.

Methodological limitations

While there appears to be preliminary support for the use of many of the technologies to promote student engagement, there are significant methodological limitations in the literature and, as a result, findings should be interpreted with caution. First, many studies used small sample sizes and were limited to one course, one degree level, and one university. Therefore, generalizability is limited. Second, very few studies used experimental or quasi-experimental designs; therefore, very little evidence exists to substantiate a cause and effect relationship between technologies and student engagement indicators. In addition, in many studies that did use experimental or quasi-experimental designs, participants were not randomized; rather, participants who volunteered to use a specific technology were compared to those who chose not to use the technology. As a result, there is a possibility that fundamental differences between users and non-users could have affected the engagement results. Furthermore, many of the studies did not isolate specific technological features (e.g, using only the breakout rooms for group work in web-conferencing software, rather than using the chat feature, screen sharing, and breakout rooms for group work). Using multiple features at once could have conflated student engagement results. Third, many studies relied on one source to measure technological and engagement variables (single source bias), such as self-report data (i.e., reported usage of technology and perceptions of student engagement), which may have affected the validity of the results. Fourth, many studies were conducted during a very brief timeframe, such as one academic term. As a result, positive student engagement findings may be attributed to a “novelty effect” (Dichev & Dicheva, 2017 ) associated with using a new technology. Finally, many studies lack adequate details about learning activities, raising questions about whether poor instructional design may have adversely affected results. For example, an instructor may intend to elicit higher-order thinking from students, but if learning activity instructions are written using low-level verbs, such as identify, describe, and summarize, students will be less likely to engage in higher-order thinking.

Areas for future research

The findings of our literature review suggest that the influence of technology on student engagement is still a developing area of knowledge that requires additional research to build on promising, but limited, evidence, clarify mixed findings, and address several gaps in the literature. As such, our recommendations for future areas of research are as follows:

Examine the effect of collaborative technologies (i.e., web-conferencing, blogs, wikis, social networking sites ) on emotional and cognitive student engagement. There are significant gaps in the literature regarding whether these technologies affect attitudes, interests, and values about learning; a sense of belonging within a learning community; motivation to learn; and persistence to overcome academic challenges and meet or exceed requirements.

Clarify mixed findings, particularly regarding how web-conferencing software, wikis, and Facebook and Twitter affect participation in learning activities. Researchers should make considerable efforts to gain consensus or increase consistency on how participation is measured (e.g., visited Facebook group or contributed one post a week) in order to make meaningful comparisons and draw conclusions about the efficacy of various technologies for promoting behavioral engagement. In addition, further research is needed to clarify findings regarding how wikis and Twitter influence interaction and how blogs and Facebook influence deep processing of information. Future research studies should include justifications for the pedagogical use of specific technologies and detailed instructions for learning activities to minimize adverse findings from poor instructional design and to encourage replication.

Conduct longitudinal studies over several academic terms and across multiple academic disciplines, degree levels, and institutions to determine long-term effects of specific technologies on student engagement and to increase generalizability of findings. Also, future studies should take individual factors into account, such as gender, age, and prior experience with the technology. Studies suggest that a lack of prior experience or familiarity with Twitter was a barrier to Twitter use in educational settings (Bista, 2015 , Mysko & Delgaty, 2015 , Tiernan, 2014 ); therefore, future studies should take prior experience into account.

Compare student engagement outcomes between and among different technologies and non-technologies. For example, studies suggest that students prefer Facebook over Twitter (Bista, 2015 ; Osgerby & Rush, 2015 ), but there were no studies that compared these technologies for promoting student engagement. Also, studies are needed to isolate and compare different features within the same technology to determine which might be most effective for increasing engagement. Finally, studies on digital games (Beckem & Watkins, 2012 ; Grimley et al., 2012 ; Ke et al., 2016 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ) and face-to-face games (Antunes et al., 2012 ; Auman, 2011 ; Coffey et al., 2011 ; Crocco et al., 2016 ; Poole et al., 2014 ; Scarlet & Ampolos, 2013 ) show similar, positive effects on student engagement, therefore, additional research is needed to determine the degree to which the delivery method (i.e.., digital versus face-to-face) accounts for positive gains in student engagement.

Determine whether other technologies not included in this review influence student engagement. Facebook and Twitter regularly appear in the literature regarding social networking, but it is unclear how other popular social networking sites, such as LinkedIn, Instagram, and Flickr, influence student engagement. Future research should focus on the efficacy of these and other popular social networking sites for promoting student engagement. In addition, there were very few studies about whether informational technologies, which involve the one-way transmission of information to students, affect different types of student engagement. Future research should examine whether informational technologies, such as video lectures, podcasts, and pre-recorded narrated Power Point presentations or screen casts, affect student engagement. Finally, studies should examine the influence of mobile software and technologies, such as educational apps or smartphones, on student engagement.

Achieve greater consensus on the meaning of student engagement and its distinction from similar concepts in the literature, such as social and cognitive presence (Garrison & Arbaugh, 2007 )

Recommendations for practice

Despite the existing gaps and mixed findings in the literature, we were able to compile a list of recommendations for when and how to use technology to increase the likelihood of promoting student engagement. What follows is not an exhaustive list; rather, it is a synthesis of both research findings and lessons learned from the studies we reviewed. There may be other recommendations to add to this list; however, our intent is to provide some useful information to help address barriers to technology integration among faculty who feel uncertain or unprepared to use technology (Ashrafzadeh & Sayadian, 2015 ; Hauptman, 2015 ; Kidd et al., 2016 ; Reid, 2014 ) and to add to the body of practical knowledge in instructional design and delivery. Our recommendations for practice are as follows:

Consider context before selecting technologies. Contextual factors such as existing technological infrastructure and requirements, program and course characteristics, and the intended audience will help determine which technologies, if any, are most appropriate (Bullen & Morgan, 2011 ; Bullen, Morgan, & Qayyum, 2011 ). For example, requiring students to use a blog that is not well integrated with the existing LMS may prove too frustrating for both the instructor and students. Similarly, integrating Facebook- and Twitter- based learning activities throughout a marketing program may be more appropriate, given the subject matter, compared to doing so in an engineering or accounting program where social media is less integral to the profession. Finally, do not assume that students appreciate or are familiar with all technologies. For example, students who did not already have Facebook or Twitter accounts were less likely to use either for learning purposes and perceived setting up an account to be an increase in workload (Bista, 2015 , Clements, 2015 ; DiVall & Kirwin, 2012 ; Hennessy et al., 2016 ; Mysko & Delgaty, 2015 , Tiernan, 2014 ). Therefore, prior to using any technology, instructors may want to determine how many students already have accounts and/or are familiar with the technology.

Carefully select technologies based on their strengths and limitations and the intended learning outcome. For example, Twitter is limited to 140 characters, making it a viable tool for learning activities that require brevity. In one study, an instructor used Twitter for short pop quizzes during lectures, where the first few students to tweet the correct answer received additional points (Kim et al., 2015 ), which helped students practice applying knowledge. In addition, studies show that students perceive Twitter and Facebook to be primarily for social interactions (Camus et al., 2016 ; Ross et al., 2015 ), which may make these technologies viable tools for sharing resources, giving brief opinions about news stories pertaining to course content, or having casual conversations with classmates rather than full-fledged scholarly discourse.

Incentivize students to use technology, either by assigning regular grades or giving extra credit. The average participation rates in voluntary web-conferencing, Facebook , and Twitter learning activities in studies we reviewed was 52% (Andrew et al., 2015 ; Armstrong & Thornton, 2012 ; Bahati, 2015 ; Bowman & Akcaoglu, 2014 ; Divall & Kirwin, 2012 ; Dougherty & Andercheck, 2014 ; Fagioli et al., 2015 ; Hennessy et al., 2016 ; Junco et al., 2013 ; Rambe, 2012 ; Ross et al., 2015 ; Staines & Lauchs, 2013 ; Tiernan, 2014 ; Williams & Whiting, 2016 ). While there were far fewer studies on the use of technology for graded or mandatory learning activities, the average participation rate reported in those studies was 97% (Bahati2015; Gagnon, 2015 ), suggesting that grading may be a key factor in ensuring students participate.

Communicate clear guidelines for technology use. Prior to the implementation of technology in a course, students may benefit from an overview the technology, including its navigational features, privacy settings, and security (Andrew et al., 2015 ; Hurt et al., 2012 ; Martin et al., 2012 ) and a set of guidelines for how to use the technology effectively and professionally within an educational setting (Miller et al., 2012 ; Prestridge, 2014 ; Staines & Lauchs, 2013 ; West et al., 2015 ). In addition, giving students examples of exemplary and poor entries and posts may also help to clarify how they are expected to use the technology (Shraim, 2014 ; Roussinos & Jimoyiannis, 2013 ). Also, if instructors expect students to use technology to demonstrate higher-order thinking or to interact with peers, there should be explicit instructions to do so. For example, Prestridge ( 2014 ) found that students used Twitter to ask the instructor questions but very few interacted with peers because they were not explicitly asked to do so. Similarly, Hou et al., 2015 reported low levels of knowledge construction in Facebook , admitting that the wording of the learning activity (e.g., explore and present applications of computer networking) and the lack of probing questions in the instructions may have been to blame.

Use technology to provide authentic and integrated learning experiences. In many studies, instructors used digital games to simulate authentic environments in which students could apply new knowledge and skills, which ultimately lead to a greater understanding of content and evidence of higher-order thinking (Beckem & Watkins, 2012 ; Liu et al., 2011 ; Lu et al., 2014 ; Marriott et al., 2015 ; Siddique et al., 2013 ). For example, in one study, students were required to play the role of a stock trader in a simulated trading environment and they reported that the simulation helped them engage in critical reflection, enabling them to identify their mistakes and weaknesses in their trading approaches and strategies (Marriott et al., 2015 ). In addition, integrating technology into regularly-scheduled classroom activities, such as lectures, may help to promote student engagement. For example, in one study, the instructor posed a question in class, asked students to respond aloud or tweet their response, and projected the Twitter page so that everyone could see the tweets in class, which lead to favorable comments about the usefulness of Twitter to promote engagement (Tiernan, 2014 ).

Actively participate in using the technologies assigned to students during the first few weeks of the course to generate interest (Dougherty & Andercheck, 2014 ; West et al., 2015 ) and, preferably, throughout the course to answer questions, encourage dialogue, correct misconceptions, and address inappropriate behavior (Bowman & Akcaoglu, 2014 ; Hennessy et al., 2016 ; Junco et al., 2013 ; Roussinos & Jimoyiannis, 2013 ). Miller et al. ( 2012 ) found that faculty encouragement and prompting was associated with increases in students’ expression of ideas and the degree to which they edited and elaborated on their peers’ work in a course-specific wiki.

Be mindful of privacy, security, and accessibility issues. In many studies, instructors took necessary steps to help ensure privacy and security by creating closed Facebook groups and private Twitter pages, accessible only to students in the course (Bahati, 2015 ; Bista, 2015 ; Bowman & Akcaoglu, 2014 ; Esteves, 2012 ; Rambe, 2012 ; Tiernan, 2014 ; Williams & Whiting, 2016 ) and by offering training to students on how to use privacy and security settings (Hurt et al., 2012 ). Instructors also made efforts to increase accessibility of web-conferencing software by including a phone number for students unable to access audio or video through their computer and by recording and archiving sessions for students unable to attend due to pre-existing conflicts (Andrew et al., 2015 ; Martin et al., 2012 ). In the future, instructors should also keep in mind that some technologies, like Facebook and Twitter , are not accessible to students living in China; therefore, alternative arrangements may need to be made.

In 1985, Steve Jobs predicted that computers and software would revolutionize the way we learn. Over 30 years later, his prediction has yet to be fully confirmed in the student engagement literature; however, our findings offer preliminary evidence that the potential is there. Of the technologies we reviewed, digital games, web-conferencing software, and Facebook had the most far-reaching effects across multiple types and indicators of student engagement, suggesting that technology should be considered a factor that influences student engagement in existing models. Findings regarding blogs, wikis, and Twitter, however, are less convincing, given a lack of studies in relation to engagement indicators or mixed findings. Significant methodological limitations may account for the wide range of findings in the literature. For example, small sample sizes, inconsistent measurement of variables, lack of comparison groups, and missing details about specific, pedagogical uses of technologies threaten the validity and reliability of findings. Therefore, more rigorous and robust research is needed to confirm and build upon limited but positive findings, clarify mixed findings, and address gaps particularly regarding how different technologies influence emotional and cognitive indicators of engagement.


Learning management system

Amirault, R. J. (2012). Distance learning in the 21 st century university. Quarterly Review of Distance Education, 13 (4), 253–265.

Google Scholar  

Anderson, M. (2016). More Americans using smartphones for getting directions, streaming TV . Washington, D.C.: Pew Research Center Retrieved from http://www.pewresearch.org/fact-tank/2016/01/29/us-smartphone-use/ .

Anderson, M., & Horrigan, J. B. (2016). Smartphones help those without broadband get online, but don’t necessary bridge the digital divide . Washington, D.C.: Pew Research Center Retrieved from http://www.pewresearch.org/fact-tank/2016/10/03/smartphones-help-those-without-broadband-get-online-but-dont-necessarily-bridge-the-digital-divide/ .

Andrew, L., Maslin-Prothero, S., & Ewens, B. (2015). Enhancing the online learning experience using virtual interactive classrooms. Australian Journal of Advanced Nursing, 32 (4), 22–31.

Antunes, M., Pacheco, M. R., & Giovanela, M. (2012). Design and implementation of an educational game for teaching chemistry in higher education. Journal of Chemical Education, 89 (4), 517–521. doi: 10.1021/ed2003077 .

Article   Google Scholar  

Armier, D. J., Shepherd, C. E., & Skrabut, S. (2016). Using game elements to increase student engagement in course assignments. College Teaching, 64 (2), 64–72 https://doi.org/10.1080/87567555.2015.1094439 .

Armstrong, A., & Thornton, N. (2012). Incorporating Brookfield’s discussion techniques synchronously into asynchronous online courses. Quarterly Review of Distance Education, 13 (1), 1–9.

Ashrafzadeh, A., & Sayadian, S. (2015). University instructors’ concerns and perceptions of technology integration. Computers in Human Behavior, 49 , 62–73. doi: 10.1016/j.chb.2015.01.071 .

Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 25 (4), 297–308.

Auman, C. (2011). Using simulation games to increase student and instructor engagement. College Teaching, 59 (4), 154–161. doi: 10.1080/87567555 .

Axelson, R. D., & Flick, A. (2011). Defining student engagement. Change: The magazine of higher learning, 43 (1), 38–43.

Bahati, B. (2015). Extending student discussions beyond lecture room walls via Facebook. Journal of Education and Practice, 6 (15), 160–171.

Bakker, A. B., Vergel, A. I. S., & Kuntze, J. (2015). Student engagement and performance: A weekly diary study on the role of openness. Motivation and Emotion, 39 (1), 49–62. doi: 10.1007/s11031-014-9422-5 .

Beckem, J. I., & Watkins, M. (2012). Bringing life to learning: Immersive experiential learning simulations for online and blended courses. Journal if Asynchronous Learning Networks, 16 (5), 61–70 https://doi.org/10.24059/olj.v16i5.287 .

Bista, K. (2015). Is Twitter an effective pedagogical tool in higher education? Perspectives of education graduate students. Journal of the Scholarship Of Teaching And Learning, 15 (2), 83–102 https://doi.org/10.14434/josotl.v15i2.12825 .

Boghossian, P. (2006). Behaviorism, constructivism, and Socratic pedagogy. Educational Philosophy and Theory, 38 (6), 713–722 https://doi.org/10.1111/j.1469-5812.2006.00226.x .

Bower, M. (2011). Redesigning a web-conferencing environment to scaffold computing students’ creative design processes. Journal of Educational Technology & Society, 14 (1), 27–42.

MathSciNet   Google Scholar  

Bower, M. (2016). A framework for adaptive learning design in a Web-conferencing environment. Journal of Interactive Media in Education, 2016 (1), 11 http://doi.org/10.5334/jime.406 .

Article   MathSciNet   Google Scholar  

Bowman, N. D., & Akcaoglu, M. (2014). “I see smart people!”: Using Facebook to supplement cognitive and affective learning in the university mass lecture. The Internet and Higher Education, 23 , 1–8. doi: 10.1016/j.iheduc.2014.05.003 .

Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., et al. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of computer games and serious games. Computers & Education, 94 , 178–192. doi: 10.1016/j.compedu.2015.11.003 .

Bryson, C., & Hand, L. (2007). The role of engagement in inspiring teaching and learning. Innovations in Education and Teaching International, 44 (4), 349–362. doi: 10.1080/14703290701602748 .

Buchanan, T., Sainter, P., & Saunders, G. (2013). Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computer in Higher Education, 25 (1), 1–11.

Bullen, M., & Morgan, T. (2011). Digital learners not digital natives. La Cuestión Universitaria, 7 , 60–68.

Bullen, M., Morgan, T., & Qayyum, A. (2011). Digital learners in higher education: Generation is not the issue. Canadian Journal of Learning and Technology, 37 (1), 1–24.

Calabretto, J., & Rao, D. (2011). Wikis to support collaboration of pharmacy students in medication management workshops -- a pilot project. International Journal of Pharmacy Education & Practice, 8 (2), 1–12.

Camacho, M. E., Carrión, M. D., Chayah, M., & Campos, J. M. (2016). The use of wiki to promote students’ learning in higher education (Degree in Pharmacy). International Journal of Educational Technology in Higher Education, 13 (1), 1–8 https://doi.org/10.1186/s41239-016-0025-y .

Camus, M., Hurt, N. E., Larson, L. R., & Prevost, L. (2016). Facebook as an online teaching tool: Effects on student participation, learning, and overall course performance. College Teaching, 64 (2), 84–94 https://doi.org/10.1080/87567555.2015.1099093 .

Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the linkages. Research in Higher Education, 47 (1), 1–32. doi: 10.1007/s11162-005-8150-9 .

Cassidy, E. D., Colmenares, A., Jones, G., Manolovitz, T., Shen, L., & Vieira, S. (2014). Higher Education and Emerging Technologies: Shifting Trends in Student Usage. The Journal of Academic Librarianship, 40 , 124–133. doi: 10.1016/j.acalib.2014.02.003 .

Center for Postsecondary Research (2016). Engagement insights: Survey findings on the quality of undergraduate education . Retrieved from http://nsse.indiana.edu/NSSE_2016_Results/pdf/NSSE_2016_Annual_Results.pdf .

Center for Postsecondary Research (2017). About NSSE. Retrieved on February 15, 2017 from http://nsse.indiana.edu/html/about.cfm

Cercone, K. (2008). Characteristics of adult learners with implications for online learning design. AACE Journal, 16 (2), 137–159.

Chang, J. W., & Wei, H. Y. (2016). Exploring Engaging Gamification Mechanics in Massive Online Open Courses. Educational Technology & Society, 19 (2), 177–203.

Chawinga, W. D. (2017). Taking social media to a university classroom: teaching and learning using Twitter and blogs. International Journal of Educational Technology in Higher Education, 14 (1), 3 https://doi.org/10.1186/s41239-017-0041-6 .

Chen, B., Seilhamer, R., Bennett, L., & Bauer, S. (2015). Students’ mobile learning practices in higher education: A multi-year study. In EDUCAUSE Review Retrieved from http://er.educause.edu/articles/2015/6/students-mobile-learning-practices-in-higher-education-a-multiyear-study .

Chu, S. K., Chan, C. K., & Tiwari, A. F. (2012). Using blogs to support learning during internship. Computers & Education, 58 (3), 989–1000. doi: 10.1016/j.compedu.2011.08.027 .

Clements, J. C. (2015). Using Facebook to enhance independent student engagement: A case study of first-year undergraduates. Higher Education Studies, 5 (4), 131–146 https://doi.org/10.5539/hes.v5n4p131 .

Coates, H. (2008). Attracting, engaging and retaining: New conversations about learning . Camberwell: Australian Council for Educational Research Retrieved from http://research.acer.edu.au/cgi/viewcontent.cgi?article=1015&context=ausse .

Coffey, D. J., Miller, W. J., & Feuerstein, D. (2011). Classroom as reality: Demonstrating campaign effects through live simulation. Journal of Political Science Education, 7 (1), 14–33.

Coghlan, E., Crawford, J. Little, J., Lomas, C., Lombardi, M., Oblinger, D., & Windham, C. (2007). ELI Discovery Tool: Guide to Blogging . Retrieved from https://net.educause.edu/ir/library/pdf/ELI8006.pdf .

Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of empirical evidence on computer games and serious games. Computers & Education, 59 , 661–686. doi: 10.1016/j.compedu.2012.03.004 .

Cook, C. W., & Sonnenberg, C. (2014). Technology and online education: Models for change. ASBBS E-Journal, 10 (1), 43–59.

Crocco, F., Offenholley, K., & Hernandez, C. (2016). A proof-of-concept study of game-based learning in higher education. Simulation & Gaming, 47 (4), 403–422. doi: 10.1177/1046878116632484 .

Csikszentmihalyi, M. (1988). The flow experience and its significance for human psychology. In M. Csikszentmihalyi & I. Csikszentmihalyi (Eds.), Optimal experience: Psychological studies of flow in consciousness (pp. 15–13). Cambridge, UK: Cambridge University Press.

Chapter   Google Scholar  

Dahlstrom, E. (2012). ECAR study of undergraduate students and information technology, 2012 (Research Report). Retrieved from http://net.educause.edu/ir/library/pdf/ERS1208/ERS1208.pdf

de Freitas, S. (2006). Learning in immersive worlds: A review of game-based learning . Retrieved from https://curve.coventry.ac.uk/open/file/aeedcd86-bc4c-40fe-bfdf-df22ee53a495/1/learning%20in%20immersive%20worlds.pdf .

Dichev, C., & Dicheva, D. (2017). Gamifying education: What is known, what is believed and what remains uncertain: A critical review. International Journal of Educational Technology in Higher Education, 14 (9), 1–36. doi: 10.1186/s41239-017-0042-5 .

DiVall, M. V., & Kirwin, J. L. (2012). Using Facebook to facilitate course-related discussion between students and faculty members. American Journal of Pharmaceutical Education, 76 (2), 1–5 https://doi.org/10.5688/ajpe76232 .

Dos, B., & Demir, S. (2013). The analysis of the blogs created in a blended course through the reflective thinking perspective. Educational Sciences: Theory & Practice, 13 (2), 1335–1344.

Dougherty, K., & Andercheck, B. (2014). Using Facebook to engage learners in a large introductory course. Teaching Sociology, 42 (2), 95–104 https://doi.org/10.1177/0092055x14521022 .

Dyson, B., Vickers, K., Turtle, J., Cowan, S., & Tassone, A. (2015). Evaluating the use of Facebook to increase student engagement and understanding in lecture-based classes. Higher Education: The International Journal of Higher Education and Educational Planning, 69 (2), 303–313 https://doi.org/10.1007/s10734-014-9776-3.

Esteves, K. K. (2012). Exploring Facebook to enhance learning and student engagement: A case from the University of Philippines (UP) Open University. Malaysian Journal of Distance Education, 14 (1), 1–15.

Evans, C. (2014). Twitter for teaching: Can social media be used to enhance the process of learning? British Journal of Educational Technology, 45 (5), 902–915 https://doi.org/10.1111/bjet.12099 .

Fagioli, L., Rios-Aguilar, C., & Deil-Amen, R. (2015). Changing the context of student engagement: Using Facebook to increase community college student persistence and success. Teachers College Record, 17 , 1–42.

Farley, P. C. (2013). Using the computer game “FoldIt” to entice students to explore external representations of protein structure in a biochemistry course for nonmajors. Biochemistry and Molecular Biology Education, 41 (1), 56–57 https://doi.org/10.1002/bmb.20655 .

Francescucci, A., & Foster, M. (2013). The VIRI classroom: The impact of blended synchronous online courses on student performance, engagement, and satisfaction. Canadian Journal of Higher Education, 43 (3), 78–91.

Fredricks, J., Blumenfeld, P., & Paris, A. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74 (1), 59–109. doi: 10.3102/00346543074001059 .

Gagnon, K. (2015). Using twitter in health professional education: A case study. Journal of Allied Health, 44 (1), 25–33.

Gandhi, P., Khanna, S., & Ramaswamy, S. (2016). Which industries are the most digital (and why?) . Retrieved from https://hbr.org/2016/04/a-chart-that-shows-which-industries-are-the-most-digital-and-why .

Garrison, D. R., & Arbaugh, J. B. (2007). Researching the community of inquiry framework: Review, issues, and future directions. The Internet and Higher Education, 10 (3), 157–172 http://dx.doi.org/10.1016/j.iheduc.2007.04.001 .

Garrity, M. K., Jones, K., VanderZwan, K. J., de la Rocha, A. R., & Epstein, I. (2014). Integrative review of blogging: Implications for nursing education. Journal of Nursing Education, 53 (7), 395–401. doi: 10.3928/01484834-20140620-01 .

Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19 , 18–26 http://dx.doi.org/10.1016/j.iheduc.2013.06.002 .

Gilboy, M. B., Heinerichs, S., & Pazzaglia, G. (2015). Enhancing student engagement using the flipped classroom. Journal of Nutrition Education and Behavior, 47 (1), 109–114 http://dx.doi.org/10.1016/j.jneb.2014.08.008 .

Greenwood, S., Perrin, A., & Duggan, M. (2016). Social media update 2016 . Washington.: Pew Research Center Retrieved from http://www.pewinternet.org/2016/11/11/social-media-update-2016/ .

Grimley, M., Green, R., Nilsen, T., & Thompson, D. (2012). Comparing computer game and traditional lecture using experience ratings from high and low achieving students. Australasian Journal of Educational Technology, 28 (4), 619–638 https://doi.org/10.14742/ajet.831 .

Gunawardena, C. N., Hermans, M. B., Sanchez, D., Richmond, C., Bohley, M., & Tuttle, R. (2009). A theoretical framework for building online communities of practice with social networking tools. Educational Media International, 46 (1), 3–16 https://doi.org/10.1080/09523980802588626 .

Haggis, T. (2009). What have we been thinking of? A critical overview of 40 years of student learning research in higher education. Studies in Higher Education, 34 (4), 377–390. doi: 10.1080/03075070902771903 .

Hauptman, P.H. (2015). Mobile technology in college instruction. Faculty perceptions and barriers to adoption (Doctoral dissertation). Retrieved from ProQuest. (AAI3712404).

Hennessy, C. M., Kirkpatrick, E., Smith, C. F., & Border, S. (2016). Social media and anatomy education: Using twitter to enhance the student learning experience in anatomy. Anatomical Sciences Education, 9 (6), 505–515 https://doi.org/10.1002/ase.1610 .

Hew, K. F., Huang, B., Chu, K. S., & Chiu, D. K. (2016). Engaging Asian students through game mechanics: Findings from two experiment studies. Computers & Education, 93 , 221–236. doi: 10.1016/j.compedu.2015.10.010 .

Hewege, C. R., & Perera, L. R. (2013). Pedagogical significance of wikis: Towards gaining effective learning outcomes. Journal of International Education in Business, 6 (1), 51–70 https://doi.org/10.1108/18363261311314953 .

Hou, H., Wang, S., Lin, P., & Chang, K. (2015). Exploring the learner’s knowledge construction and cognitive patterns of different asynchronous platforms: comparison of an online discussion forum and Facebook. Innovations in Education and Teaching International, 52 (6), 610–620. doi: 10.1080/14703297.2013.847381 .

Hu, S., & McCormick, A. C. (2012). An engagement-based student typology and its relationship to college outcomes. Research in Higher Education, 53 , 738–754. doi: 10.1007/s11162-012-9254-7 .

Hudson, T. M., Knight, V., & Collins, B. C. (2012). Perceived effectiveness of web conferencing software in the digital environment to deliver a graduate course in applied behavior analysis. Rural Special Education Quarterly, 31 (2), 27–39.

Hurt, N. E., Moss, G. S., Bradley, C. L., Larson, L. R., Lovelace, M. D., & Prevost, L. B. (2012). The ‘Facebook’ effect: College students’ perceptions of online discussions in the age of social networking. International Journal for the Scholarship of Teaching & Learning, 6 (2), 1–24 https://doi.org/10.20429/ijsotl.2012.060210 .

Ibáñez, M. B., Di-Serio, A., & Delgado-Kloos, C. (2014). Gamification for engaging computer science students in learning activities: A case study. IEEE Transactions on Learning Technologies, 7 (3), 291–301 https://doi.org/10.1109/tlt.2014.2329293 .

Ivala, E., & Gachago, D. (2012). Social media for enhancing student engagement: The use of facebook and blogs at a university of technology. South African Journal of Higher Education, 26 (1), 152–167.

Johnson, D. R. (2013). Technological change and professional control in the professoriate. Science, Technology & Human Values, 38 (1), 126–149. doi: 10.1177/0162243911430236 .

Junco, R., Elavsky, C. M., & Heiberger, G. (2013). Putting Twitter to the test: Assessing outcomes for student collaboration, engagement and success. British Journal of Educational Technology, 44 (2), 273–287. doi: 10.1111/j.1467-8535.2012.01284.x .

Junco, R., Heibergert, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27 (2), 119–132. doi: 10.1111/j.1365-2729.2010.00387.x .

Kahu, E. R. (2013). Framing student engagement in higher education. Studies in Higher Education, 38 (5), 758–773. doi: 10.1080/03075079.2011.598505 .

Kaware, S. S., & Sain, S. K. (2015). ICT Application in Education: An Overview. International Journal of Multidisciplinary Approach & Studies, 2 (1), 25–32.

Ke, F., Xie, K., & Xie, Y. (2016). Game-based learning engagement: A theory- and data-driven exploration. British Journal of Educational Technology, 47 (6), 1183–1201 https://doi.org/10.1111/bjet.12314 .

Kent, M. (2013). Changing the conversation: Facebook as a venue for online class discussion in higher education. Journal of Online Learning & Teaching, 9 (4), 546–565 https://doi.org/10.1353/rhe.2015.0000 .

Kidd, T., Davis, T., & Larke, P. (2016). Experience, adoption, and technology: Exploring the phenomenological experiences of faculty involved in online teaching at once school of public health. International Journal of E-Learning, 15 (1), 71–99.

Kim, Y., Jeong, S., Ji, Y., Lee, S., Kwon, K. H., & Jeon, J. W. (2015). Smartphone response system using twitter to enable effective interaction and improve engagement in large classrooms. IEEE Transactions on Education, 58 (2), 98–103 https://doi.org/10.1109/te.2014.2329651 .

Kinchin. (2012). Avoiding technology-enhanced non-learning. British Journal of Educational Technology, 43 (2), E43–E48.

Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development (2nd ed.). Upper Saddle River: Pearson Education, Inc..

Kopcha, T. J., Rieber, L. P., & Walker, B. B. (2016). Understanding university faculty perceptions about innovation in teaching and technology. British Journal of Educational Technology, 47 (5), 945–957. doi: 10.1111/bjet.12361 .

Krause, K., & Coates, H. (2008). Students’ engagement in first-year university. Assessment and Evaluation in Higher Education, 33 (5), 493–505. doi: 10.1080/02602930701698892 .

Kuh, G. D. (2009). The National Survey of Student Engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 141 , 5–20.

Lam, S., Wong, B., Yang, H., & Yi, L. (2012). Understanding student engagement with a contextual model. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of Research on Student Engagement (pp. 403–419). New York: Springer.

Lawrence, B., & Lentle-Keenan, S. (2013). Teaching beliefs and practice, institutional context, and the uptake of Web-based technology. Distance Education, 34 (1), 4–20.

Leach, L. (2016). Enhancing student engagement in one institution. Journal of Further and Higher Education, 40 (1), 23–47.

Lester, D. (2013). A review of the student engagement literature. Focus on Colleges, Universities, and Schools, 7 (1), 1–8.

Lewis, C. C., Fretwell, C. E., Ryan, J., & Parham, J. B. (2013). Faculty use of established and emerging technologies in higher education: A unified theory of acceptance and use of technology perspective. International Journal of Higher Education, 2 (2), 22–34 http://dx.doi.org/10.5430/ijhe.v2n2p22 .

Lin, C., Singer, R., & Ha, L. (2010). Why university members use and resist technology? A structure enactment perspective. Journal of Computing in Higher Education, 22 (1), 38–59. doi: 10.1007/s12528-010-9028-1 .

Linder-VanBerschot, J. A., & Summers, L. L. (2015). Designing instruction in the face of technology transience. Quarterly Review of Distance Education, 16 (2), 107–118.

Liu, C., Cheng, Y., & Huang, C. (2011). The effect of simulation games on the learning of computational problem solving. Computers & Education, 57 (3), 1907–1918 https://doi.org/10.1016/j.compedu.2011.04.002 .

Lu, J., Hallinger, P., & Showanasai, P. (2014). Simulation-based learning in management education: A longitudinal quasi-experimental evaluation of instructional effectiveness. Journal of Management Development, 33 (3), 218–244. doi: 10.1108/JMD-11-2011-0115 .

Maben, S., Edwards, J., & Malone, D. (2014). Online engagement through Facebook groups in face-to-face undergraduate communication courses: A case study. Southwestern Mass Communication Journal, 29 (2), 1–27.

Manca, S., & Ranieri, M. (2013). Is it a tool suitable for learning? A critical review of the literature on Facebook as a technology-enhanced learning environment. Journal of Computer Assisted Learning, 29 (6), 487–504. doi: 10.1111/jcal.12007 .

Mansouri, S. A., & Piki, A. (2016). An exploration into the impact of blogs on students’ learning: Case studies in postgraduate business education. Innovations in Education And Teaching International, 53 (3), 260–273 http://dx.doi.org/10.1080/14703297.2014.997777 .

Marriott, P., Tan, S. W., & Marriot, N. (2015). Experiential learning – A case study of the use of computerized stock market trading simulation in finance education. Accounting Education, 24 (6), 480–497 http://dx.doi.org/10.1080/09639284.2015.1072728 .

Martin, F., Parker, M. A., & Deale, D. F. (2012). Examining interactivity in synchronous virtual classrooms. International Review of Research in Open and Distance Learning, 13 (3), 227–261.

Martin, K., Goldwasser, M., & Galentino, R. (2017). Impact of Cohort Bonds on Student Satisfaction and Engagement. Current Issues in Education, 19 (3), 1–14.

Martínez, A. A., Medina, F. X., Albalat, J. A. P., & Rubió, F. S. (2013). Challenges and opportunities of 2.0 tools for the interdisciplinary study of nutrition: The case of the Mediterranean Diet wiki. International Journal of Educational Technology in Higher Education, 10 (1), 210–225 https://doi.org/10.7238/rusc.v10i1.1341 .

McBrien, J. L., Jones, P., & Cheng, R. (2009). Virtual spaces: Employing a synchronous online classroom to facilitate student engagement in online learning. International Review of Research in Open and Distance Learning, 10 (3), 1–17 https://doi.org/10.19173/irrodl.v10i3.605 .

McClenney, K., Marti, C. N., & Adkins, C. (2012). Student engagement and student outcomes: Key findings from “CCSSE” validation research . Austin: Community College Survey of Student Engagement.

McKay, M., Sanko, J., Shekhter, I., & Birnbach, D. (2014). Twitter as a tool to enhance student engagement during an interprofessional patient safety course. Journal of Interprofessional Care, 28 (6), 565–567 https://doi.org/10.3109/13561820.2014.912618 .

Miller, A. D., Norris, L. B., & Bookstaver, P. B. (2012). Use of wikis in pharmacy hybrid elective courses. Currents in Pharmacy Teaching & Learning, 4 (4), 256–261. doi: 10.1016/j.cptl.2012.05.004 .

Morley, D. A. (2012). Enhancing networking and proactive learning skills in the first year university experience through the use of wikis. Nurse Education Today, 32 (3), 261–266.

Mysko, C., & Delgaty, L. (2015). How and why are students using Twitter for #meded? Integrating Twitter into undergraduate medical education to promote active learning. Annual Review of Education, Communication & Language Sciences, 12 , 24–52.

Nadolny, L., & Halabi, A. (2016). Student participation and achievement in a large lecture course with game-based learning. Simulation and Gaming, 47 (1), 51–72. doi: 10.1177/1046878115620388 .

Naghdipour, B., & Eldridge, N. H. (2016). Incorporating social networking sites into traditional pedagogy: A case of facebook. TechTrends, 60 (6), 591–597 http://dx.doi.org/10.1007/s11528-016-0118-4 .

Nakamaru, S. (2012). Investment and return: Wiki engagement in a “remedial” ESL writing course. Journal of Research on Technology in Education, 44 (4), 273–291.

Nelson, R. (2016). Apple’s app store will hit 5 million apps by 2020, more than doubling its current size . Retrieved from https://sensortower.com/blog/app-store-growth-forecast-2020 .

Nora, A., Barlow, E., & Crisp, G. (2005). Student persistence and degree attainment beyond the first year in college. In A. Seidman (Ed.), College Student Retention (pp. 129–154). Westport: Praeger Publishers.

Osgerby, J., & Rush, D. (2015). An exploratory case study examining undergraduate accounting students’ perceptions of using Twitter as a learning support tool. International Journal of Management Education, 13 (3), 337–348. doi: 10.1016/j.ijme.2015.10.002 .

Pace, C. R. (1980). Measuring the quality of student effort. Current Issues in Higher Education, 2 , 10–16.

Pace, C. R. (1984). Student effort: A new key to assessing quality . Los Angeles: University of California, Higher Education Research Institute.

Paul, J. A., & Cochran, J. D. (2013). Key interactions for online programs between faculty, students, technologies, and educational institutions: A holistic framework. Quarterly Review of Distance Education, 14 (1), 49–62.

Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation, and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, 35 , 157–170. doi: 10.1016/j.chb.2014.02.048 .

Poole, S. M., Kemp, E., Williams, K. H., & Patterson, L. (2014). Get your head in the game: Using gamification in business education to connect with Generation Y. Journal for Excellence in Business Education, 3 (2), 1–9.

Poushter, J. (2016). Smartphone ownership and internet usage continues to climb in emerging economies . Washington, D.C.: Pew Research Center Retrieved from http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/ .

Prestridge, S. (2014). A focus on students’ use of Twitter - their interactions with each other, content and interface. Active Learning in Higher Education, 15 (2), 101–115.

Rambe, P. (2012). Activity theory and technology mediated interaction: Cognitive scaffolding using question-based consultation on “Facebook”. Australasian Journal of Educational Technology, 28 (8), 1333–1361 https://doi.org/10.14742/ajet.775 .

Reid, P. (2014). Categories for barriers to adoption of instructional technologies. Education and Information Technologies, 19 (2), 383–407.

Revere, L., & Kovach, J. V. (2011). Online technologies for engagement learning: A meaningful synthesis for educators. Quarterly Review of Distance Education, 12 (2), 113–124.

Richardson, J. C., & Newby, T. (2006). The role of students’ cognitive engagement in online learning. American Journal of Distance Education, 20 (1), 23–37 http://dx.doi.org/10.1207/s15389286ajde2001_3 .

Ross, H. M., Banow, R., & Yu, S. (2015). The use of Twitter in large lecture courses: Do the students see a benefit? Contemporary Educational Technology, 6 (2), 126–139.

Roussinos, D., & Jimoyiannis, A. (2013). Analysis of students’ participation patterns and learning presence in a wiki-based project. Educational Media International, 50 (4), 306–324 https://doi.org/10.1080/09523987.2013.863471 .

Salaber, J. (2014). Facilitating student engagement and collaboration in a large postgraduate course using wiki-based activities. International Journal of Management Education, 12 (2), 115–126. doi: 10.1016/j.ijme.2014.03.006 .

Scarlet, J., & Ampolos, L. (2013). Using game-based learning to teach psychopharmacology. Psychology Learning and Teaching, 12 (1), 64–70 https://doi.org/10.2304/plat.2013.12.1.64 .

Sharma, P., & Tietjen, P. (2016). Examining patterns of participation and meaning making in student blogs: A case study in higher education. American Journal of Distance Education, 30 (1), 2–13 http://dx.doi.org/10.1080/08923647.2016.1119605 .

Shraim, K. Y. (2014). Pedagogical innovation within Facebook: A case study in tertiary education in Palestine. International Journal of Emerging Technologies in Learning, 9 (8), 25–31. doi: 10.3991/ijet.v9i8.3805 .

Siddique, Z., Ling, C., Roberson, P., Xu, Y., & Geng, X. (2013). Facilitating higher-order learning through computer games. Journal of Mechanical Design, 135 (12), 121004–121010.

Smith, A., & Anderson, M. (2016). Online Shopping and E-Commerce . Washington, D.C.: Pew Research Center Retrieved from http://www.pewinternet.org/2016/12/19/online-shopping-and-e-commerce/ .

Staines, Z., & Lauchs, M. (2013). Students’ engagement with Facebook in a university undergraduate policing unit. Australasian Journal of Educational Technology, 29 (6), 792–805 https://doi.org/10.14742/ajet.270 .

Sun, A., & Chen, X. (2016). Online education and its effective practice: A research review. Journal of Information Technology Education: Research, 15 , 157–190.

Tiernan, P. (2014). A study of the use of Twitter by students for lecture engagement and discussion. Education and Information Technologies, 19 (4), 673–690 https://doi.org/10.1007/s10639-012-9246-4 .

Trowler, V. (2010). Student engagement literature review . Lancaster: Lancaster University Retrieved from http://www.lancaster.ac.uk/staff/trowler/StudentEngagementLiteratureReview.pdf .

Trowler, V., & Trowler, P. (2010). Student engagement evidence summary . Lancaster: Lancaster University Retrieved from http://eprints.lancs.ac.uk/61680/1/Deliverable_2._Evidence_Summary._Nov_2010.pdf .

van Beynen, K., & Swenson, C. (2016). Exploring peer-to-peer library content and engagement on a student-run Facebook group. College & Research Libraries, 77 (1), 34–50 https://doi.org/10.5860/crl.77.1.34 .

Wang, S. (2008). Blogs in education. In M. Pagani (Ed.), Encyclopedia of Multimedia Technology and Networking (2nd ed., pp. 134–139). Hershey: Information Sciences Reference.

Wdowik, S. (2014). Using a synchronous online learning environment to promote and enhance transactional engagement beyond the classroom. Campus — Wide Information Systems, 31 (4), 264–275. doi: 10.1108/CWIS-10-2013-0057 .

Weibel, D., Wissmath, B., Habegger, S., Steiner, Y., & Groner, R. (2008). Playing online games against computer-vs. human-controlled opponents: Effects on presence, flow, and enjoyment. Computers in Human Behavior, 24 (5), 2274–2291 https://doi.org/10.1016/j.chb.2007.11.002 .

West, B., Moore, H., & Barry, B. (2015). Beyond the tweet: Using Twitter to enhance engagement, learning, and success among first-year students. Journal of Marketing Education, 37 (3), 160–170. doi: 10.1177/0273475315586061 .

Westera, W. (2015). Reframing the role of educational media technologies. Quarterly Review of Distance Education, 16 (2), 19–32.

Whitton, N. (2011). Game engagement theory and adult learning. Simulation & Gaming, 42 (5), 596–609.

Williams, D., & Whiting, A. (2016). Exploring the relationship between student engagement, Twitter, and a learning management system: A study of undergraduate marketing students. International Journal of Teaching & Learning in Higher Education, 28 (3), 302–313.

Wimpenny, K., & Savin-Baden, M. (2013). Alienation, agency, and authenticity: A synthesis of the literature on student engagement. Teaching in Higher Education, 18 (3), 311–326. doi: 10.1080/13562517.2012.725223 .

Witkowski, P., & Cornell, T. (2015). An Investigation into Student Engagement in Higher Education Classrooms. InSight: A Journal of Scholarly Teaching, 10 , 56–67.

Wright, G. B. (2011). Student-centered learning in higher education. International Journal of Teaching and Learning in Higher Education, 23 (3), 92–97.

Yang, C., & Chang, Y. (2012). Assessing the effects of interactive blogging on student attitudes towards peer interaction, learning motivation, and academic achievements. Journal of Computer Assisted Learning, 28 (2), 126–135 https://doi.org/10.1111/j.1365-2729.2011.00423.x .

Zepke, N. (2014). Student engagement research in higher education: questioning an academic orthodoxy. Teaching in Higher Education, 19 (6), 697–708 http://dx.doi.org/10.1080/13562517.2014.901956 .

Zepke, N., & Leach, L. (2010). Improving student engagement: Ten proposals for action. Active Learning in Higher Education, 11 (3), 167–177. doi: 10.1177/1469787410379680 .

Zickuhr, K., & Raine, L. (2014). E-reading rises as device ownership jumps . Washington, D.C.: Pew Research Center Retrieved from http://www.pewinternet.org/2014/01/16/e-reading-rises-as-device-ownership-jumps/ .

Zimmermann, L. K. (2013). Using a virtual simulation program to teach child development. College Teaching, 61 (4), 138–142. doi: 10.1080/87567555.2013.817377 .

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Schindler, L.A., Burkholder, G.J., Morad, O.A. et al. Computer-based technology and student engagement: a critical review of the literature. Int J Educ Technol High Educ 14 , 25 (2017). https://doi.org/10.1186/s41239-017-0063-0

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How to Write a Research Paper | A Beginner's Guide

A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research.

Research papers are similar to academic essays , but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research. Writing a research paper requires you to demonstrate a strong knowledge of your topic, engage with a variety of sources, and make an original contribution to the debate.

This step-by-step guide takes you through the entire writing process, from understanding your assignment to proofreading your final draft.

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Understand the assignment, choose a research paper topic, conduct preliminary research, develop a thesis statement, create a research paper outline, write a first draft of the research paper, write the introduction, write a compelling body of text, write the conclusion, the second draft, the revision process, research paper checklist, free lecture slides.

Completing a research paper successfully means accomplishing the specific tasks set out for you. Before you start, make sure you thoroughly understanding the assignment task sheet:

  • Read it carefully, looking for anything confusing you might need to clarify with your professor.
  • Identify the assignment goal, deadline, length specifications, formatting, and submission method.
  • Make a bulleted list of the key points, then go back and cross completed items off as you’re writing.

Carefully consider your timeframe and word limit: be realistic, and plan enough time to research, write, and edit.

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There are many ways to generate an idea for a research paper, from brainstorming with pen and paper to talking it through with a fellow student or professor.

You can try free writing, which involves taking a broad topic and writing continuously for two or three minutes to identify absolutely anything relevant that could be interesting.

You can also gain inspiration from other research. The discussion or recommendations sections of research papers often include ideas for other specific topics that require further examination.

Once you have a broad subject area, narrow it down to choose a topic that interests you, m eets the criteria of your assignment, and i s possible to research. Aim for ideas that are both original and specific:

  • A paper following the chronology of World War II would not be original or specific enough.
  • A paper on the experience of Danish citizens living close to the German border during World War II would be specific and could be original enough.

Note any discussions that seem important to the topic, and try to find an issue that you can focus your paper around. Use a variety of sources , including journals, books, and reliable websites, to ensure you do not miss anything glaring.

Do not only verify the ideas you have in mind, but look for sources that contradict your point of view.

  • Is there anything people seem to overlook in the sources you research?
  • Are there any heated debates you can address?
  • Do you have a unique take on your topic?
  • Have there been some recent developments that build on the extant research?

In this stage, you might find it helpful to formulate some research questions to help guide you. To write research questions, try to finish the following sentence: “I want to know how/what/why…”

A thesis statement is a statement of your central argument — it establishes the purpose and position of your paper. If you started with a research question, the thesis statement should answer it. It should also show what evidence and reasoning you’ll use to support that answer.

The thesis statement should be concise, contentious, and coherent. That means it should briefly summarize your argument in a sentence or two, make a claim that requires further evidence or analysis, and make a coherent point that relates to every part of the paper.

You will probably revise and refine the thesis statement as you do more research, but it can serve as a guide throughout the writing process. Every paragraph should aim to support and develop this central claim.

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A research paper outline is essentially a list of the key topics, arguments, and evidence you want to include, divided into sections with headings so that you know roughly what the paper will look like before you start writing.

A structure outline can help make the writing process much more efficient, so it’s worth dedicating some time to create one.

Your first draft won’t be perfect — you can polish later on. Your priorities at this stage are as follows:

  • Maintaining forward momentum — write now, perfect later.
  • Paying attention to clear organization and logical ordering of paragraphs and sentences, which will help when you come to the second draft.
  • Expressing your ideas as clearly as possible, so you know what you were trying to say when you come back to the text.

You do not need to start by writing the introduction. Begin where it feels most natural for you — some prefer to finish the most difficult sections first, while others choose to start with the easiest part. If you created an outline, use it as a map while you work.

Do not delete large sections of text. If you begin to dislike something you have written or find it doesn’t quite fit, move it to a different document, but don’t lose it completely — you never know if it might come in useful later.

Paragraph structure

Paragraphs are the basic building blocks of research papers. Each one should focus on a single claim or idea that helps to establish the overall argument or purpose of the paper.

Example paragraph

George Orwell’s 1946 essay “Politics and the English Language” has had an enduring impact on thought about the relationship between politics and language. This impact is particularly obvious in light of the various critical review articles that have recently referenced the essay. For example, consider Mark Falcoff’s 2009 article in The National Review Online, “The Perversion of Language; or, Orwell Revisited,” in which he analyzes several common words (“activist,” “civil-rights leader,” “diversity,” and more). Falcoff’s close analysis of the ambiguity built into political language intentionally mirrors Orwell’s own point-by-point analysis of the political language of his day. Even 63 years after its publication, Orwell’s essay is emulated by contemporary thinkers.

Citing sources

It’s also important to keep track of citations at this stage to avoid accidental plagiarism . Each time you use a source, make sure to take note of where the information came from.

You can use our free citation generators to automatically create citations and save your reference list as you go.

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The research paper introduction should address three questions: What, why, and how? After finishing the introduction, the reader should know what the paper is about, why it is worth reading, and how you’ll build your arguments.

What? Be specific about the topic of the paper, introduce the background, and define key terms or concepts.

Why? This is the most important, but also the most difficult, part of the introduction. Try to provide brief answers to the following questions: What new material or insight are you offering? What important issues does your essay help define or answer?

How? To let the reader know what to expect from the rest of the paper, the introduction should include a “map” of what will be discussed, briefly presenting the key elements of the paper in chronological order.

The major struggle faced by most writers is how to organize the information presented in the paper, which is one reason an outline is so useful. However, remember that the outline is only a guide and, when writing, you can be flexible with the order in which the information and arguments are presented.

One way to stay on track is to use your thesis statement and topic sentences . Check:

  • topic sentences against the thesis statement;
  • topic sentences against each other, for similarities and logical ordering;
  • and each sentence against the topic sentence of that paragraph.

Be aware of paragraphs that seem to cover the same things. If two paragraphs discuss something similar, they must approach that topic in different ways. Aim to create smooth transitions between sentences, paragraphs, and sections.

The research paper conclusion is designed to help your reader out of the paper’s argument, giving them a sense of finality.

Trace the course of the paper, emphasizing how it all comes together to prove your thesis statement. Give the paper a sense of finality by making sure the reader understands how you’ve settled the issues raised in the introduction.

You might also discuss the more general consequences of the argument, outline what the paper offers to future students of the topic, and suggest any questions the paper’s argument raises but cannot or does not try to answer.

You should not :

  • Offer new arguments or essential information
  • Take up any more space than necessary
  • Begin with stock phrases that signal you are ending the paper (e.g. “In conclusion”)

There are four main considerations when it comes to the second draft.

  • Check how your vision of the paper lines up with the first draft and, more importantly, that your paper still answers the assignment.
  • Identify any assumptions that might require (more substantial) justification, keeping your reader’s perspective foremost in mind. Remove these points if you cannot substantiate them further.
  • Be open to rearranging your ideas. Check whether any sections feel out of place and whether your ideas could be better organized.
  • If you find that old ideas do not fit as well as you anticipated, you should cut them out or condense them. You might also find that new and well-suited ideas occurred to you during the writing of the first draft — now is the time to make them part of the paper.

The goal during the revision and proofreading process is to ensure you have completed all the necessary tasks and that the paper is as well-articulated as possible. You can speed up the proofreading process by using the AI proofreader .

Global concerns

  • Confirm that your paper completes every task specified in your assignment sheet.
  • Check for logical organization and flow of paragraphs.
  • Check paragraphs against the introduction and thesis statement.

Fine-grained details

Check the content of each paragraph, making sure that:

  • each sentence helps support the topic sentence.
  • no unnecessary or irrelevant information is present.
  • all technical terms your audience might not know are identified.

Next, think about sentence structure , grammatical errors, and formatting . Check that you have correctly used transition words and phrases to show the connections between your ideas. Look for typos, cut unnecessary words, and check for consistency in aspects such as heading formatting and spellings .

Finally, you need to make sure your paper is correctly formatted according to the rules of the citation style you are using. For example, you might need to include an MLA heading  or create an APA title page .

Scribbr’s professional editors can help with the revision process with our award-winning proofreading services.

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Checklist: Research paper

I have followed all instructions in the assignment sheet.

My introduction presents my topic in an engaging way and provides necessary background information.

My introduction presents a clear, focused research problem and/or thesis statement .

My paper is logically organized using paragraphs and (if relevant) section headings .

Each paragraph is clearly focused on one central idea, expressed in a clear topic sentence .

Each paragraph is relevant to my research problem or thesis statement.

I have used appropriate transitions  to clarify the connections between sections, paragraphs, and sentences.

My conclusion provides a concise answer to the research question or emphasizes how the thesis has been supported.

My conclusion shows how my research has contributed to knowledge or understanding of my topic.

My conclusion does not present any new points or information essential to my argument.

I have provided an in-text citation every time I refer to ideas or information from a source.

I have included a reference list at the end of my paper, consistently formatted according to a specific citation style .

I have thoroughly revised my paper and addressed any feedback from my professor or supervisor.

I have followed all formatting guidelines (page numbers, headers, spacing, etc.).

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Computer Research Paper

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View sample computer research paper. Browse other  research paper examples and check the list of history research paper topics for more inspiration. If you need a history research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Feel free to contact our custom writing service for professional assistance. We offer high-quality assignments for reasonable rates.

Humans have always looked for “technologies” to help them count—from stick-markings prehistoric foragers made to keep track of cattle to the first programmable, room-filling mainframes employed by post–World War II business. In the twenty-first century computers do far more than calculate; forecasters predict that the computing power of today’s desktop will someday be packaged in a device the size of a shirt button and for the cost of a dime.

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Computers have transformed work, communication, and leisure activity, and they promise future changes of equal magnitude. For many years, computer technology was dominated by groups in the United States, because that nation had the largest single market and its government invested heavily in military applications and fundamental science and engineering. But many nations contributed to the technological basis on which computing arose, and with the development of the World Wide Web computing became a global phenomenon.

Mechanical Predecessors

Programmable digital computers were developed just before the middle of the twentieth century, but the more general history of devices that help people think goes back to prehistoric times, when someone first carved marks on a stick to count the cattle in a herd or mark the days in the phases of the moon. Complex additions and subtractions were done in ancient days by arranging pebbles in piles on the ground, and our word calculate derives from the Latin word calculus (pebble). The most complex known “computer” of classical civilization is the remarkable geared Antikythera device, which apparently was designed to predict the motions of the sun, moon, and planets. Found in a shipwreck on the bottom of the Mediterranean Sea, it is believed to date from about 80 BCE.

Computing has always been closely allied with mathematics, and the invention of logarithms by the Scottish mathematician John Napier around 1614 was a major advance for practical calculating. With a mechanical calculating device, it is much easier to add than to multiply, and subtraction is much easier than division. Logarithms turned multiplication into addition, and division into subtraction, at the cost of looking up numbers in vast books of tables that also had to be calculated by hand. From Napier’s time until the introduction of transistorized electronic calculators around 1970, a book of logarithm tables was a standard tool for engineers and scientists. They were cumbersome to use, so for quick estimates slide rules were employed. A slide rule is an analog calculating device based on logarithmic scales marked along rulers that slide past each other. The term analog refers to the analogy between the abstract numbers and corresponding physical distances along a line.

Digital mechanical calculators that represented numbers as precise digits were also developed—for example, by the French mathematician and philosopher Blaise Pascal in 1642. A common approach was to connect a series of wheels, each of which would turn in ten steps for the digits 0 through 9. A legend has developed that the eccentric English dilettante Charles Babbage was the father of computing because around 1835 he designed a mechanical calculator that could be programmed with punched cards. Science fiction writers William Gibson and Bruce Sterling wrote a novel imagining that Babbage succeeded in building it, launching a golden age of British scientific and technological dominance but magnifying social problems. However, in reality Babbage failed, and historian Doron Swade estimates that his influence on the development of electronic computers was insignificant.

The first comprehensive digital data-processing system using cards was developed by the American engineer Herman Hollerith, who began patenting his ideas in the 1880s. By 1902, when his machines were used to process the vast sea of information collected in the 1900 U.S. census, they already incorporated electric relays that could do conditionals (if-then operations).

The Mainframe Era

There is considerable debate among historians over which programmable, electronic digital computer was first or most influential. By 1941, professor John Atanasoff and graduate student Clifford Berry had created a demonstration machine at Iowa State University, but they did not develop it further. In Britain, a special-purpose electronic computer called Colossus began cracking German codes in 1943, but its design was kept secret for more than three decades. Perhaps the most influential early electronic digital computer was ENIAC (Electronic Numerical Integrator and Computer), completed at the University of Pennsylvania in 1946 by a team headed by physicist John W. Mauchly and engineer J. Presper Eckert.

ENIAC’s primary job was calculating accurate artillery firing tables for the U.S. Army. In the frenzy of World War II, many new models of long-range guns were being produced, and soldiers in the field needed complex tables to tell them how to aim to hit a target at a certain distance under various conditions. It was impossible to fire the guns under all the likely conditions, so data from some judiciously chosen test firings were used to anchor elaborate sets of mathematical calculations. Vannevar Bush, who was the chief science advisor to President Roosevelt, had a huge mechanical analog computer, the differential analyzer, built for this purpose in 1930. In theory, an electronic computer would be much faster and more accurate, but there were serious questions about whether it could be sufficiently reliable, because before the development of transistors they were built with vacuum tubes that tended to burn out. ENIAC weighed 30 tons, covered 1,800 square feet, and contained 18,000 vacuum tubes.

ENIAC’s data input and output employed Hollerith’s punch cards, a method that remained one of the standard approaches through the 1970s. However, programming was done manually by setting hundreds of rotary switches and plugging in wires that connected electronic components. Mauchly and Eckert designed a successor that could store a program in its memory. They formed a small company, launched a line of machines called UNIVAC, and then sold out to a private company in 1950. This example typifies mid-twentieth-century computing. The technology for large and expensive mainframe computers was developed with government funding for military purposes and then transferred to the civilian sector where it was used by large corporations for financial record-keeping and similar applications. Much of the research work was done at universities, and the availability of a few mainframe computers on campus gave scientists the chance to adapt them to many research purposes.

The Personal Computer

The birth of the computer industry involved nothing less than development of an entire computer culture, including programming languages and compilers to control the machines, networks and input-output devices to transmit information between users and machines, and new courses in universities leading to the emergence of computer science and engineering as a distinct field. For years, the dominant model was expensive mainframe computers with batch processing of data—computer runs that were carefully prepared and then placed in a queue to await time on the mainframe—although there were some experiments with time sharing in which several individuals could use a computer simultaneously in real-time. Then, in the mid-1970s, both inside information technology companies and outside among electronics hobbyists, the personal computer revolution offered a radically new concept of computing.

In April 1973, Xerox corporation’s Palo Alto Research Center ran its first test of the Alto, the prototype desktop personal computer. Alto innovated many of the technologies that would become standard for home and office computers, including the mouse, windows and icons on the screen, desktop printing with many different fonts, incorporation of images and animations, and local area networks that allowed individuals to send files back and forth between their machines. Xerox was not able to exploit the technology at the time, because of the high cost and low performance of microelectronics. In the 1960s, Gordon Moore, a founder of the Intel computer chip corporation, propounded what has become known as Moore’s Law, the observation that the performance of computer chips was doubling every eighteen or twenty-four months. Alto’s technology finally hit the home market when the first Apple Macintosh was sold in 1984, soon followed by Microsoft’s Windows operating system.

Before any of the big information technology companies offered personal computers to the public, hobbyists were building their own from kits, notably the Altair first announced in the January 1975 issue of Popular Electronics magazine. A technological social movement, drawing on some of the cultural radicalism of the 1960s, quickly spread across America and Western Europe, although in retrospect it is difficult to estimate how much this radicalism contributed to the rapid advance of the computer revolution. It is true that Apple was founded in a garage by two friends, and Bill Gates dropped out of college to help his buddies found Microsoft. For a few years after the Apple II computer appeared in 1977, an individual could write a commercially viable software program and start a small company to market it. But the greatest advances after the mid-1980s again required the combination of massive government funding and large corporations.

Internet and the World Wide Web

Internet was born in 1969 as ARPAnet, a research network funded by the Advanced Research Projects Agency of the U.S. government that connected computers at the University of California at Los Angeles, the Stanford Research Institute, the University of California at Santa Barbara, and the University of Utah. In 1972 it was first demonstrated to the public, and in the same year it began carrying email. More and more educational institutions, government agencies, and corporations began using the Internet—and finding new uses for it—until by the end of the 1980s it was an essential tool for research and had begun to demonstrate its value for business and personal applications. For example, in 1978 Roy Trubshaw and Richard Bartle invented the first online fantasy game or MUD (Multiple-User Dungeon) at Essex University in England, and in 1989 Alan Cox at the University College of Wales released his own version onto the Internet.

In 1990 at the high-energy physics laboratories of the Conseil Europeen pour la Recherche Nucleaire (CERN) near Geneva, Switzerland, Tim Berners-Lee developed the first hypertext browser and coined the term World Wide Web. Early in 1993, University of Illinois student Marc Andreessen at the National Center for Supercomputing Applications, funded by the U.S. National Science Foundation, programmed the first version of Mosaic, the easy-to-use browser that would introduce millions of people to the Web. Both the Netscape and Microsoft Internet Explorer browsers were based on Mosaic, and it is estimated that more than 10 percent of the world’s population used the Internet in 2002.

The mainframe-timesharing concept of the 1970s has evolved into what is called client-server architecture. A server is a dedicated computer, often large, that houses centralized databases (in companies, universities, or government agencies) or connects directly to the Internet. Originally, clients were dumb terminals with little or no computing power of their own, but today they are powerful personal computers connected to the server and able to access its resources. A very different approach has arisen recently, called peer-to-peer architecture—for example, the music-file-sharing programs like Napster that link personal computers over the Web, in which each computer simultaneously functions as both server and client. The grid computing concept distributes big computation jobs across many widely distributed computers, or distributes data across many archives, eroding the distinction between individual computers and the Internet.

The Era of Ubiquitous Computing

Computers today are found nearly everywhere, embedded in automobiles and grocery store checkout counters, or packaged as pocket-sized personal digital assistants that allow a user to send email or surf the Web from almost anywhere. They have begun to take over the roles of traditional devices such as telephones and televisions, while other devices have become accessories to computers, notably cameras and music players. Old forms of computing do not die, but expand. Children’s toys now have vastly greater computing power than ENIAC, but ENIAC’s direct descendents are supercomputers capable of doing dozens of trillions of calculations per second.

Computer science continues to advance, and nanotechnology promises to sustain Moore’s Law until perhaps about 2025, halting only after the smallest electronic components have shrunk to the size of a single molecule. Two decades of doubling every eighteen months means improvement by a factor of 8,000. That would imply the computing power of today’s desktop computer packaged in a shirt button and costing a dime. What will people do with such power?

In 2003, the Interagency Working Group on Information Technology Research and Development of the U.S. government identified the following “grand challenges” that computing could address in the following decade:

  • Knowledge environments for science and engineering
  • Clean energy production through improved combustion
  • High confidence infrastructure control systems
  • Improved patient safety and health quality
  • Informed strategic planning for long-term regional climate change
  • Nanoscale science and technology: explore and exploit the behavior of ensembles of atoms and molecules
  • Predicting pathways and health effects of pollutants
  • Real-time detection, assessment, and response to natural or man-made threats
  • Safer, more secure, more efficient, higher-capacity, multimodal transportation system
  • Anticipate consequences of universal participation in a digital society
  • Collaborative intelligence: integrating humans with intelligent technologies
  • Generating insights from information at your fingertips;
  • Managing knowledge-intensive dynamic systems
  • Rapidly acquiring proficiency in natural languages
  • SimUniverse [educational computer simulations]: learning by exploring
  • Virtual lifetime tutor for all


  • Austrian, G. D. (1982). Herman Hollerith: Forgotten giant of information processing. New York: Columbia University Press.
  • Bainbridge, W. S. (Ed.). (2004). Berkshire encyclopedia of human-computer interaction. Great Barrington, MA: Berkshire Publishing Group.
  • Berners-Lee, T., & Fischetti, M. (1999). Weaving the Web. New York: HarperCollins.
  • Freiberger, P., & Swaine, M. (1999). Fire in the valley: The making of the personal computer (2nd. ed.). New York: McGraw-Hill.
  • Gibson, W., & Sterling, B. (1991). The difference engine. New York: Bantam.
  • Gillies, J., & Cailliau, R. (2000). How the Web was born. Oxford, U.K.: Oxford University Press.
  • Grudin, J. (2004). History of human-computer interaction. In W. S. Bainbridge (Ed.), Berkshire Encyclopedia of human-computer interaction. Great Barrington, MA: Berkshire Publishing Group.
  • Interagency Working Group on Information Technology Research and Development. (2003). Grand challenges: Science, engineering, and societal advances requiring networking and information technology research and development. Arlington, Virginia: National Coordination Office for Information Technology Research and Development.
  • Lavendel, G. (1980). A decade of research: Xerox Palo Alto Research Center. New York: Bowker.
  • Metropolis, N., Howlett, J., & Rota, G.-C. (Eds.). (1980). A history of computing in the twentieth century. New York: Academic Press.
  • Mollenhoff, C. R. (1988). Atanasoff: Forgotten father of the computer. Ames: Iowa State University Press.
  • National Research Council. (1999). Funding a revolution: Government support for computing research. Washington, DC: National Academy Press.
  • Price, D. J. S. de. (1959). An ancient Greek computer. Scientific American 200(6), 60 –67.
  • Stern, N. (1981). From ENIAC to UNIVAC: An appraisal of the Eckert-Mauchly computers. Bedford, MA: Digital Press.
  • Swade, D. (2000). The difference engine: Charles Babbage and the quest to build the first computer. New York: Viking.
  • Waldrop, M. M. (2001). The dream machine: J. C. R. Licklider and the revolution that made computing personal. New York: Viking.


personal computer research paper

Why to Choose Mac Over Windows Personal Computer Research Paper

Introduction to mac and pc, advantages and disadvantages of mac os, advantages and disadvantages of windows os/ pc, why mac is better than pc.

The purpose of this paper is to highlight why a consumer should select a MAC operating system over the Windows PC. Both Mac and PC are popular operating systems, readily available in the mainstream market catering to the various needs of the consumers. While the basic functionality of the operating system on a computer is the same, both operating systems differ in terms of their characteristics and the specific benefits that they provide to the end user. Through this paper a justified argument, supported by evidence based on books and scholarly journals, is presented on why consumers should look towards adopting a Mac computer instead of one based on Windows PC.

The Mac operating system was a computer software developed by the company Apple Inc to provide operating systems for Apple computers. Initially the software was only available for use on Apple computers and apple devices, however now other computers can also make use of Mac OS. The original line of computers that were launched in 1984 and supported the Mac OS where named Macintosh and were sold exclusively by Apple Inc.

This was a revolutionary computer operating system as previously the only operating system that was available to consumers was MS Dos a command line and prompt in nature and was tedious for majority of the consumers. On the other hand the Macintosh computers with the Mac operating system were unique and revolutionary as they provided a graphical user interface for the consumers and enabled them to use programs, and software developed on the object oriented programming language. This form of a computer interface was more user-friendly and easy to comprehend for the consumers.

The Mac OS made use of the hierarchical directory tree approach for navigation and file addressing and enabled the users to create their own files and folders with relative ease. The system itself promoted multiple and comparative multitasking as the user could use more than one program at the same time which was previously not possible with other non graphical user interfaces. Aside from this the Mac operating system was also developed and supported by the UNIX language which meant that it was open source.

As a result the Mac users were able to customize their own systems according to their requirements and share the knowledge of new developed upgrades and programs with others in the UNIX based OS community. This enabled the Mac users to have highly customized and efficient operating systems that were based on upgrades and programs developed by other Mac users purely for their convenience and facilitation. Other application programs that are provided by the Mac operating system included image processing software, audio and video processing software as well as software for word processing, email and development of databases and spreadsheets.

While the Mac OS was launched by Apple Inc, the Windows OS was launched by Microsoft Windows in 1985. PC’s are usually associated with Microsoft Windows as they are computers that run on the Windows based operating system. The Windows operating system was also a unique operating system in the market at the time of its launch as it provided the consumers in the market to be able to use a graphical user interface for operating their computers and navigating instead of using the old command line based operating system.

The Windows operating system was launched by Microsoft Windows as an additional product/ service to the currently available MS-DOS, however the new operating system became more popular than MS-DOS amongst the users as it was more user friendly and allowed just about anyone to be able to use the computer with minimum knowledge of computers and programming languages or command line instructions. The Windows operating system also provided a range of application software for video and audio playback, video and audio recording as well as the famous Office Suite known as MS Office.

The MS Office suit provided application software for word processing (MS Word), spreadsheets, (MS Excel), data base and file management (MS Access), email management, (MS Outlook), and multimedia based presentations (MS PowerPoint). The other characteristic of the windows OS is that it is not an open source system. This means that the code of the Windows OS is restricted and accessible to only Microsoft Company. As a result it is not possible for the users to make changes to the underlying code of Windows OS in order to make customizations or change any flaws or errors that might be present in order to make the operating system more efficient.

The advantages that are associated with a Mac OS pertain to the fact that the Mac operating system is more reliable and has less chance getting a virus. Specifically after the launch of the Mac OS X in the year 2000, the number of viruses, ad ware or malicious spy ware that attack the computers has greatly reduced for computers having a Mac OS. Having a Mac does not guarantee that there are no viruses that can attack the computer, however having a Mac OS greatly reduces the chances of acquiring a virus through normal use or internet browsing.

Aside from this the Mac operating system is very friendly for the user. The users are easily able to comprehend navigation and control on the operating system. Moreover the Mac operating system is often available on machines pre-installed or can be installed at the request of the customer. This is specifically true for Apple machines or Macintosh.

Another great feature and advantage of the Mac operating System is that it allows the user to use both Mac OS as well as MS Windows OS on the same machine, therefore providing the user with a choice of dual OS usage. In fact it is also relatively easy to convert files created on the Mac OS to be transferred and used on the MS Windows OS through migration and conversion. The Mac OS and Mac computers provide the user with exceptional video, audio and photo processing technology. Using the Mac OS it is very easy for the users to run professional applications for video, audio and photo editing.

Mac OS based computers are highly capable when it comes to dealing with multimedia as their performance using multimedia is excellent. “Today, many computer users in business and industry are adopting Macintosh computers as a primary multimedia tool because of its superior video, images, and sound” (Jun Na Rajaravivarma, 2003). Aside from this the Mac OS also provides application software unique in nature like iChat for chatting using audio and video, iLife for managing files related to multimedia and the Time machine application which allows the user to schedule data backups.

The Mac OS is based on the UNIX platform in the open source. This has enabled the users to create and highly customize the Mac OS according to their specific requirements and needs. Alben highlighted in her Muriel prize winning article about the approach taken by apple for Mac OS that provides that apple conceived the Mac OS to be “a computing environment that allows people to choose what works for them. Instead of having to conform to the confines pf the computer, they will work and play and learn in a way that better fits their needs and wants. These customizable appearances take the Mac OS beyond the utilitarian operating systems currently available” (Alben, 1997).

The UNIX base of the Mac OS has made it more reliable as well as less faulty as it has been extensively tested, used and adjusted to eliminate any possible faulty code or programming by peers. The open source nature of the Linux and UNIX has allowed users to fine tune software and additional application software for the Mac OS (Lerner & Tirole, 2005).

The open source nature of the Mac OS has enabled users of Mac OS based machines and computers to create new application programs for the Mac OS system. These application software have been accessed by Apple and have been provided licensing by the company while integrating them into the Mac OS package for future users. One such development that has been developed using the Mac OS open source environment are the technical or virtual bulletin boards that were originally used by the Mac OS open source programmers to share code and advise on developing short code for Mac OS (Luca & McLoughlin, 2003). The Mac OS was specifically designed for Apple products by Apple Inc.

Therefore it is more reliable when it is run on apple products as it provides a complete solution of hardware and software that works together in a mutually cohesive manner to benefit the user and make using a computer machine easier for them.

The disadvantages that are associated with a Mac or a Mac OS pertain to upgrading the OS. As no upgrades of the Mac OS are routinely launched by Apple, it is not possible for the users to upgrade their Mac operating systems. However the users can always download shortstopped from the internet provided by other Mac users and customize and adjust their Mac Os features and application programs. Aside form this a recent trend analysis of the Mac systems on the internet has revealed that Mac systems are more expensive as opposed to PC’s to purchase as they are more multimedia oriented with additional features.

The old Mac OS is somewhat redundant and some of the applications that traditionally have worked on Windows OS might not work on the Mac OS unless the user installs the Mac OS X software. Similarly it is often difficulty to repair a Mac and resolve issues as the code for the Mac is often different and not standardized. Moreover one drawback for serious gamers is that most of the games that are readily available on the MS Windows based PC are not compatible with the Mac OS platform and therefore cannot be run on Macs. “Although many cross-platform file types are currently available, connection type depends mainly on network purpose, and media types are sufficiently diverse that uninformed users can encounter serious problems” (Jun Na Rajaravivarma, 2003).

The main advantage of a MS Windows based PC is that the MS Windows Company provides extensive and unlimited upgrades for the Windows OS. As a result the users only have to have access to the internet in able to automatically download upgrades for the Windows OS and the related application software that run on the Windows OS.

Another advantage of the PC based on MS Windows OS is that majority of the programs that are available in the market and the games that are available to the users are those which are created keeping the MS Windows in mind. As a result they run much better with MS Windows and some are only able to run if a Windows MS based platform is provided on the PC. The PC is also a very common computer that is extensively available in the market, relatively cheap as well as having a high percentage of population using it.

The disadvantages that are associated with PCs and MS Windows OS is that they are highly prone to viruses and bugs. This makes them very unstable and unreliable. The PCs as a result have to be secured with complex antivirus programs that can often be very expensive for the consumers to purchase. The system of a PC can often crash and become slow or unresponsive over time with heavy usage. This is another major problem with the PC’s that makes it unsuitable for extensive heavy usage. Moreover as PC’s have a standardized code based operating system provided by MS Windows, the hackers specifically target PC’s with their viruses and malicious code.

AS a result the MS Windows OS and PC users have to extensively take care of their computers, look after them and keep them upgraded in terms of their systems and their anti virus software in order to have smooth operations on the computer. This is especially true for the new Windows Vista which has been released. Vista makes PC’s more unreliable and unstable in terms of performance. Another disadvantage that is present to the PCs and MS Windows OS is that PCs that are bought with MS Windows OS are often provided with the most basic of application software in the package. However the Mac systems with the Mac OS have customized media applications and software that can only be run on Mac. As a result this reduces the appeal of PCs for the younger generation.

In today’s day and age a Mac is better than a PC. This is mainly because the software and media that is used on the computer by an average user is highly complex in nature with high level of multitasking taking place. In such a situation that Mac fares much better than a PC as it especially designed for multimedia applications and use of heavy duty multitasking programs. In addition to this the Mac is also exceptional for gamming and multimedia processing with the enhanced applications dedicated to this and high level of graphics provided. Moreover the design of the hardware for Mac anther GUI interface makes Mac innovative and suave choice for style conscious users. On the other hand PCs are considered to be boring with lack of stylish appeal.

The PCs are readily available as the most affordable systems in the market. However the Macs are more affordable for the users in the long term. This is because the Macs have integrated customizable application software for office as well as connectivity, multimedia and online chat that are not available on PCs. The PC users as a result have to purchase MS office suite in additional to the Windows OS and the PC, while additional security protection software and graphics cards also have to be bought in order to bring the Pc up to the level of a readily available Mac therefore increasing the cost of the PC.

The Macs are more reliable for the users as they tend to break down less often and suffer from much fewer crashes as compared to the PCs. The PCs are prone to malicious code, viruses and faulty which have to be addressed by Microsoft. This makes them unreliable. The Macs however are less prone to viruses and attacks form hackers as it is much more difficult to hack a Mac as compared to a PC. Aside from this the Macs are also well known for their high level of performance and processing Speed. The Macs are designed specifically for heavy duty usage with multitasking of multimedia applications and software.

The Macs, as a result deliver a much more efficient performance in terms of speed and reliability. On the other hand PCs tend to slow down over time and can often crash when multiple multimedia applications are being used on a PC. Similarly if high quality gamming is to be conducted on the PC, an additional graphic card is required which is not necessary for a Mac.

The Macs are also easy to use for the end users of the system. The Macs provide an interactive graphic user interface that is specifically made keeping the users’ requirements in mind. As a result the Macs are much easier to navigate and use for users as compared to PCs. Regular updates are available for Macs provided by the company as well as other users which can upgrade the Mac OS X. aside form this the Macs also feature instant connections to external devices, internet based communication devices, other apple products. The provision of the iChat software along with a web camera allows the users to have access to video based chat at the click on a button.

The Mac also features a unique capability of housing two operating systems at the same time, enabling the user to make use of Mac based OS as well as MS Windows based US on the same machine. This dual operating system is particularly useful for families where multiple people prefer different types of platforms and operating systems. Apple Inc provides the Mac computers on which Mac Os is provided by the company.

The company designs hardware which corresponds with the Mac OS and adjusts the software of the Mac OS to the changes in the hardware making both the hardware and software mutually cohesive. The integration of hardware and software provided by Apple in the Macs allows Mac to feature unique capabilities where services like chatting and internet connectivity is instantly available to the user.

The Mac users are spoilt to choice when it comes to what application they want to have on their systems and the level of customizations that they prefer for their systems. The Mac is available in unique designs and covers which cannot be rivaled by PC in terms of design or style. Moreover the Mac provides the user with a range of customized application software which can be loaded on to the machine at the time of Purchase. A limited number of such applications often are provided by MS Windows based PC at an additional charge.

The main reason as to why the Mac is so diverse and is able to provide the user with a range of benefits and customization is because of the open source nature of its UNIX and Linux platform. As the Mac OS is open sourced, users can make changes to the system code and develop new application software according to their requirements that can be used on Mac Systems. The sharing of this information enable Apple Inc to provide these newly developed application to consumers in the Mac packages, therefore making the entire Mac offering more customized for the end user. The increased performance, reliability and diverse capabilities of the Mac are also based on its open source nature.

The Macs of today are highly evolved with better reliability, speed, performance security and customizations offered to the users as opposed to the PCs. The consistency of the Macs, along with their predictability, the low level of security and virus threats as well as the increased solutions provided by Apple for Mac users makes Macs a better choice for users instead of a PCs.

Lerner, J., Tirole, J., “ The Scope of Open Source Licensing. ” Journal of Law, Economics, and Organization , 21.1 (2005): 20-26. Web.

Shaffer, G., Zettelmeyer, F., “When Good News About Your Rival Is Good for You: The Effect of Third-Party Information on the Division of Channel Profits.” Marketing Science, 21.3 (2002): 273-293. Web.

Jun Na Rajaravivarma, V., “Multimedia file sharing in multimedia home or office business networks.” System Theory Proceedings of the 35th Southeastern Symposium, (2003): 237-241. Web.

Alben L., “At the Heart of Design.” Design Management Journal , (1997): 9-27.

Luca, J., McLoughlin, C., “Peers Supporting Peers through structured bulletin boards.” Digital Voyages, (2003).

Casadesus-Masanell, R., Pankaj, G., “Dynamic Mixed Duopoly: A Model Motivated by Linux vs. Windows”, Strategy Unit Working Paper No. 04-012, (2003). Web.

Speight, E., Bennett, J.K., “Brazos: A Third Generation DSM System.” USENIX Windows NT Workshop, (1997): 95-106. Web.

Bitzer, J., “Commercial versus open source software: the role of product heterogeneity in competition”, Elsevier B.V., (2005). Web.

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How to use Google’s genAI-powered note-taking app

Google’s experimental notebooklm lacks many of the features of more established notes apps, but its generative ai core gives it analysis and summarization superpowers..

By Howard Wen

Computerworld |

google notebooklm splash screen

Create your first notebook

Add your sources, create and manage notes, chat with notebooklm about your sources, get a source summary and kick off a related chat, have notebooklm synthesize your notes, share and collaborate on a notebook, use notebooklm... with another notes app.

Among the experimental tools that Google offers is NotebookLM , a bare-bones notes app that you use in your web browser. What makes it interesting is that it uses Google’s generative AI chatbot (variously known as Bard or Duet AI and now rebranded as Gemini) to analyze sources of text that you “feed” it, and then generate notes based on the information in them.

Here are some scenarios where you might want to use NotebookLM:

  • To turn the contents of a long document into a brief summary.
  • To extract insights from several documents that cover a certain topic. For example, you can have NotebookLM analyze documents that pertain to a project that your business is working on.
  • To bring together and summarize information that you find while doing online research, such as snippets of text from web pages.

This guide will take you through setting up and using NotebookLM. You’ll need to have a Google user account to sign up for Google Workspace Labs (if you’re not already a member) and to sign in to NotebookLM. You must be at least 18 years old and live in the US to use NotebookLM.

When using NotebookLM, keep in mind that generative AI tools are in their infancy and sometimes get facts wrong. Be sure to fact-check all responses in this or any other genAI tool. Also note that this is an early beta product, and its performance can sometimes be glitchy.

In NotebookLM , a “notebook” contains one or more notes. You can create several notebooks. So each notebook represents a project that you’re working on, containing notes that are specific to that project.

On the start page of your NotebookLM account, click New Notebook . On the small panel that opens, type a name for the new notebook.

Starting a new notebook in NotebookLM. (Click image to enlarge it.)

When you click Save or press the Enter key, NotebookLM will switch to a new page showing the workspace of your new notebook.

A new blank notebook. (Click image to enlarge it.)

The sources of text that you add for NotebookLM to analyze will appear as cards on the panel that runs down the left side of the workspace.

On the Sources panel, click ADD SOURCE or the + sign to the right of “Sources.” A small panel will open that presents three ways that you can add a text source:

  • Drive: You select a Google Docs document that’s stored in your Google Drive.
  • PDF: You upload a PDF that’s stored on your PC. (Note: you cannot add a PDF that’s stored in your Google Drive.)
  • Copied text: A blank source card opens over the workspace. You can paste text into it that you’ve copied to your PC clipboard, such as text from an email or messaging chat. You can also copy and paste in a web link for NotebookLM to analyze. And you can optionally type in additional text.

Designating a new source. (Click image to enlarge it.)

After you make your choice and go through the steps that the NotebookLM interface guides you through, your source will appear as a thumbnail on the source panel.

You can have up to 20 sources in a notebook, and each source must contain fewer than 200,000 words. NotebookLM doesn’t currently support images, media files, or complex tables or charts as sources.

Google says that the data you upload is not used to train NotebookLM and will stay private, but the company also warns, “Avoid uploading documents with any personal or sensitive information” and “Avoid uploading documents you don’t have the applicable rights to.”

A notebook can have up to 1,000 notes. To create a new note manually, click the Add note button that’s at the upper right of the Notes panel. A card for a new note will appear on the Notes panel.

A card will appear on the main workspace for each note you create. (Click image to enlarge it.)

Click this card and it’ll open as a panel over the workspace. You can type text into this panel, as well as paste text saved on your PC clipboard.

Adding text to a note. (Click image to enlarge it.)

When you’re finished, click the double-arrow icon at the lower right of the panel. Your note will once again appear as a card on the Notes panel. Click it to reopen it so you can read or edit it.

To delete a note, move the pointer over its card. Click the square that appears on its upper-right corner. This will add a blue checkmark inside the square. Then click the Delete notes button at the top of the Notes panel. To delete multiple notes at once, select their individual note cards (or click the Select all button) and click Delete notes .

Deleting notes from a notebook. (Click image to enlarge it.)

With those basics out of the way, we can get to the true power of NotebookLM: its built-in chat with genAI capabilities.

Below the Notes area is a chat panel, and this is the real point of using NotebookLM. Type inside the entry box and click the arrow to the right to make a request to the AI about your text sources. The best way to word your requests is in the form of a question.

The AI also provides three suggested queries above the entry box; these are based on information in your sources. Click a suggestion and it’ll be posted in the chat window.

Type in a question or choose one of the suggested queries. (Click image to enlarge it.)

By default, NotebookLM analyzes all the sources in a notebook when formulating a response. You can click the blue checkbox on any source’s thumbnail to deselect it, and the AI will ignore that source. To remove a source entirely, move the pointer over its thumbnail, click the three-dot icon, and select Remove source .

When you enter a request, the AI will take a few seconds to process it and post a response.

NotebookLM’s response to a query. (Click image to enlarge it.)

There are a few ways to interact with response card:

Pin: Click the pin icon at the upper right to turn the AI’s response into a note card that will appear on the Notes panel. You can then click the card to open and read the note, but you cannot edit it.

Copy: Click this icon at the lower right to copy the text of the AI’s response to your PC clipboard. You can then paste the response into a document or a note you create manually, which you will be able to edit.

Thumbs Up/Down: Click either of these icons at the lower right to rate how good you think the AI’s response is. This helps to train the AI to give you better results for future requests.

Citations: At the lower left is a button telling you the number of citations — text segments from one or more sources — used to create the response. When you click the button, a number appears for each citation. Moving the pointer over a citation’s number opens a panel that shows you the source text.

Citations show specific chunks of text that Duet AI drew from to generate its response. (Click image to enlarge it.)

When you click a source’s thumbnail on the Sources panel, the source card will expand to fill the left half of the workspace and NotebookLM will generate a source guide — a summary that describes the contents of the source.

Duet AI creates a summary of the source and a list of key topics. (Click image to enlarge it.)

Note: if you created a copied text source card that includes a web link, NotebookLM will try to generate a summary of the content on that web page.

To the right of the summary is a list of key topics. Clicking any of these sends a request about the topic that the AI will respond to in the chat window.

Once you have a number of notes collected in a notebook, you can ask NotebookLM to do something new with them, such as summarize them all or create an outline from them. In the main Notes workspace, select the cards for the notes you want to draw from, and NotebookLM shows several suggested actions above the chat window, such as Summarize , Suggest related ideas , and Create outline .

NotebookLM offers suggestions for synthesizing selected notes. (Click image to enlarge it.)

Click one of the suggestions — or type your own directive into the chat window, such as “Create a bulleted list of key points” — and the AI will generate an appropriate response. Some responses you generate this way are automatically saved as a new note; in other cases you’ll need to pin or copy them if you want to save them.

You can share a notebook with others, either restricting their permissions so that they can only view its contents or allowing them to edit it, such as by adding, editing, or removing notes and sources. Everyone you share a notebook with can interact with the AI and copy its responses to their clipboard, but only those with Editor status can add a response as a note.

To share a notebook, click the angled-line icon that’s toward the upper right of your notebook’s workspace. A Share panel opens over the workspace.

Sharing a notebook. (Click image to enlarge it.)

Inside the entry box, type the name or email address of someone who’s in your Google contacts. (Note: they must already be in your Google contacts. You cannot type in any email address here.) When their name appears, click it or press the Enter key to add them to the “People who have access” list.

By default, a person you add will be granted Viewer access to your notebook. If you want them to collaborate with you on the notebook, click Viewer and change it to Editor .

When you are done adding people to share your notebook with and setting their access levels, click the Send button. They’ll be notified by email that you’re sharing this notebook with them.

To re-emphasize, NotebookLM in its current form is bare bones. It lacks several features of a typical notes app. There are no task lists, nor can you add images to your notes. And even if you add a web address to a note, it won’t turn into a clickable link.

This raises the question: will more features be added to NotebookLM? Or could its AI tool for generating notes — which is currently the sole reason for using it — be rolled into Google’s existing notes app, Google Keep ?

While Google sorts this out, remember that you can always copy the text of a response that the AI generates to your clipboard. Then you can paste it into a note in Google Keep or another notes app. In this way, NotebookLM works well as a companion tool.

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Howard Wen is a longtime contributor to Computerworld . He specializes in explainer guides, how-tos, and reviews of office applications and productivity tools.

Copyright © 2024 IDG Communications, Inc.

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Title: an interactive agent foundation model.

Abstract: The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction, enabling a versatile and adaptable AI framework. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare. Our model demonstrates its ability to generate meaningful and contextually relevant outputs in each area. The strength of our approach lies in its generality, leveraging a variety of data sources such as robotics sequences, gameplay data, large-scale video datasets, and textual information for effective multimodal and multi-task learning. Our approach provides a promising avenue for developing generalist, action-taking, multimodal systems.

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OpenAI’s Sora video-generating model can render video games, too

personal computer research paper

OpenAI’s new — and first! — video-generating model, Sora , can pull off some genuinely impressive cinematographic feats. But the model’s even more capable than OpenAI initially made it out to be, at least judging by a technical paper published this evening.

The paper, titled “Video generation models as world simulators,” co-authored by a host of OpenAI researchers, peels back the curtains on key aspects of Sora’s architecture — for instance revealing that Sora can generate videos of an arbitrary resolution and aspect ratio (up to 1080p). Per the paper, Sora’s able to perform a range of image and video editing tasks, from creating looping videos to extending videos forwards or backwards in time to changing the background in an existing video.

But most intriguing to this writer is Sora’s ability to “simulate digital worlds,” as the OpenAI co-authors put it. In an experiment, OpenAI fed Sora prompts containing the word “Minecraft” and had it render a convincingly Minecraft-like HUD and game — and the game’s dynamics, including physics — while simultaneously controlling the player character.

OpenAI Sora can simulate Minecraft I guess. Maybe next generation game console will be "Sora box" and games are distributed as 2-3 paragraphs of text. pic.twitter.com/9BZUIoruOV — Andrew White (@andrewwhite01) February 16, 2024

So how’s Sora able to do this? Well, as observed by senior Nvidia researcher Jim Fan ( via Quartz ), Sora’s more of a “data-driven physics engine” than a creative too. It’s not just generating a single photo or video, but determining the physics of each object in an environment — and rendering a photo or video (or interactive 3D world, as the case may be) based on these calculations.

“These capabilities suggest that continued scaling of video models is a promising path towards the development of highly-capable simulators of the physical and digital world, and the objects, animals and people that live within them,” the OpenAI co-authors write.

Now, Sora’s usual limitations apply in the video game domain. The model can’t accurately approximate the physics of basic interactions like glass shattering. And even with interactions it  can model, Sora’s often inconsistent — for example rendering a person eating a burger but failing to render bite marks.

Still, if I’m reading the paper correctly, it seems Sora could pave the way for more realistic — perhaps even photorealistic — procedurally generated games from text descriptions alone. That’s in equal parts exciting and terrifying (consider the deepfake implications, for one) — which is probably why OpenAI’s choosing to gate Sora behind a very limited access program for now.

Here’s hoping we learn more sooner rather than later.

OpenAI’s newest model Sora can generate videos — and they look decent

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Video generation models as world simulators.

We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of high fidelity video. Our results suggest that scaling video generation models is a promising path towards building general purpose simulators of the physical world.

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  • View Sora overview

This technical report focuses on (1) our method for turning visual data of all types into a unified representation that enables large-scale training of generative models, and (2) qualitative evaluation of Sora’s capabilities and limitations. Model and implementation details are not included in this report.

Much prior work has studied generative modeling of video data using a variety of methods, including recurrent networks, [^1] [^2] [^3] generative adversarial networks, [^4] [^5] [^6] [^7] autoregressive transformers, [^8] [^9] and diffusion models. [^10] [^11] [^12] These works often focus on a narrow category of visual data, on shorter videos, or on videos of a fixed size. Sora is a generalist model of visual data—it can generate videos and images spanning diverse durations, aspect ratios and resolutions, up to a full minute of high definition video.

Turning visual data into patches

We take inspiration from large language models which acquire generalist capabilities by training on internet-scale data. [^13] [^14] The success of the LLM paradigm is enabled in part by the use of tokens that elegantly unify diverse modalities of text—code, math and various natural languages. In this work, we consider how generative models of visual data can inherit such benefits. Whereas LLMs have text tokens, Sora has visual patches . Patches have previously been shown to be an effective representation for models of visual data. [^15] [^16] [^17] [^18] We find that patches are a highly-scalable and effective representation for training generative models on diverse types of videos and images.

Figure Patches

At a high level, we turn videos into patches by first compressing videos into a lower-dimensional latent space, [^19] and subsequently decomposing the representation into spacetime patches.

Video compression network

We train a network that reduces the dimensionality of visual data. [^20] This network takes raw video as input and outputs a latent representation that is compressed both temporally and spatially. Sora is trained on and subsequently generates videos within this compressed latent space. We also train a corresponding decoder model that maps generated latents back to pixel space.

Spacetime latent patches

Given a compressed input video, we extract a sequence of spacetime patches which act as transformer tokens. This scheme works for images too since images are just videos with a single frame. Our patch-based representation enables Sora to train on videos and images of variable resolutions, durations and aspect ratios. At inference time, we can control the size of generated videos by arranging randomly-initialized patches in an appropriately-sized grid.

Scaling transformers for video generation

Sora is a diffusion model [^21] [^22] [^23] [^24] [^25] ; given input noisy patches (and conditioning information like text prompts), it’s trained to predict the original “clean” patches. Importantly, Sora is a diffusion transformer . [^26] Transformers have demonstrated remarkable scaling properties across a variety of domains, including language modeling, [^13] [^14] computer vision, [^15] [^16] [^17] [^18] and image generation. [^27] [^28] [^29]

Figure Diffusion

In this work, we find that diffusion transformers scale effectively as video models as well. Below, we show a comparison of video samples with fixed seeds and inputs as training progresses. Sample quality improves markedly as training compute increases.

Variable durations, resolutions, aspect ratios

Past approaches to image and video generation typically resize, crop or trim videos to a standard size—e.g., 4 second videos at 256x256 resolution. We find that instead training on data at its native size provides several benefits.

Sampling flexibility

Sora can sample widescreen 1920x1080p videos, vertical 1080x1920 videos and everything inbetween. This lets Sora create content for different devices directly at their native aspect ratios. It also lets us quickly prototype content at lower sizes before generating at full resolution—all with the same model.

Improved framing and composition

We empirically find that training on videos at their native aspect ratios improves composition and framing. We compare Sora against a version of our model that crops all training videos to be square, which is common practice when training generative models. The model trained on square crops (left) sometimes generates videos where the subject is only partially in view. In comparison, videos from Sora (right) have improved framing.

Language understanding

Training text-to-video generation systems requires a large amount of videos with corresponding text captions. We apply the re-captioning technique introduced in DALL·E 3 [^30] to videos. We first train a highly descriptive captioner model and then use it to produce text captions for all videos in our training set. We find that training on highly descriptive video captions improves text fidelity as well as the overall quality of videos.

Similar to DALL·E 3, we also leverage GPT to turn short user prompts into longer detailed captions that are sent to the video model. This enables Sora to generate high quality videos that accurately follow user prompts.

Prompting with images and videos

All of the results above and in our landing page show text-to-video samples. But Sora can also be prompted with other inputs, such as pre-existing images or video. This capability enables Sora to perform a wide range of image and video editing tasks—creating perfectly looping video, animating static images, extending videos forwards or backwards in time, etc.

Animating DALL·E images

Sora is capable of generating videos provided an image and prompt as input. Below we show example videos generated based on DALL·E 2 [^31] and DALL·E 3 [^30] images.

personal computer research paper

Extending generated videos

Sora is also capable of extending videos, either forward or backward in time. Below are four videos that were all extended backward in time starting from a segment of a generated video. As a result, each of the four videos starts different from the others, yet all four videos lead to the same ending.

We can use this method to extend a video both forward and backward to produce a seamless infinite loop.

Video-to-video editing

Diffusion models have enabled a plethora of methods for editing images and videos from text prompts. Below we apply one of these methods, SDEdit, [^32] to Sora. This technique enables Sora to transform  the styles and environments of input videos zero-shot.

Connecting videos

We can also use Sora to gradually interpolate between two input videos, creating seamless transitions between videos with entirely different subjects and scene compositions. In the examples below, the videos in the center interpolate between the corresponding videos on the left and right.

Image generation capabilities

Sora is also capable of generating images. We do this by arranging patches of Gaussian noise in a spatial grid with a temporal extent of one frame. The model can generate images of variable sizes—up to 2048x2048 resolution.

personal computer research paper

Emerging simulation capabilities

We find that video models exhibit a number of interesting emergent capabilities when trained at scale. These capabilities enable Sora to simulate some aspects of people, animals and environments from the physical world. These properties emerge without any explicit inductive biases for 3D, objects, etc.—they are purely phenomena of scale.

3D consistency. Sora can generate videos with dynamic camera motion. As the camera shifts and rotates, people and scene elements move consistently through three-dimensional space.

Long-range coherence and object permanence. A significant challenge for video generation systems has been maintaining temporal consistency when sampling long videos. We find that Sora is often, though not always, able to effectively model both short- and long-range dependencies. For example, our model can persist people, animals and objects even when they are occluded or leave the frame. Likewise, it can generate multiple shots of the same character in a single sample, maintaining their appearance throughout the video.

Interacting with the world. Sora can sometimes simulate actions that affect the state of the world in simple ways. For example, a painter can leave new strokes along a canvas that persist over time, or a man can eat a burger and leave bite marks.

Simulating digital worlds. Sora is also able to simulate artificial processes–one example is video games. Sora can simultaneously control the player in Minecraft with a basic policy while also rendering the world and its dynamics in high fidelity. These capabilities can be elicited zero-shot by prompting Sora with captions mentioning “Minecraft.”

These capabilities suggest that continued scaling of video models is a promising path towards the development of highly-capable simulators of the physical and digital world, and the objects, animals and people that live within them.

Sora currently exhibits numerous limitations as a simulator. For example, it does not accurately model the physics of many basic interactions, like glass shattering. Other interactions, like eating food, do not always yield correct changes in object state. We enumerate other common failure modes of the model—such as incoherencies that develop in long duration samples or spontaneous appearances of objects—in our landing page .

We believe the capabilities Sora has today demonstrate that continued scaling of video models is a promising path towards the development of capable simulators of the physical and digital world, and the objects, animals and people that live within them.

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Please cite as Brooks, Peebles, et al., and use the following BibTeX for citation:  https://openai.com/bibtex/videoworldsimulators2024.bib

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Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

  • Will Douglas Heaven archive page

In the biggest mass-market AI launch yet, Google is rolling out Gemini , its family of large language models, across almost all its products, from Android to the iOS Google app to Gmail to Docs and more. You can also now get your hands on Gemini Ultra, the most powerful version of the model, for the first time.  

With this launch, Google is sunsetting Bard , the company's answer to ChatGPT. Bard, which has been powered by a version of Gemini since December, will now be known as Gemini too.  

ChatGPT , released by Microsoft-backed OpenAI just 14 months ago, changed people’s expectations of what computers could do. Google, which has been racing to catch up ever since, unveiled its Gemini family of models in December. They are multimodal large language models that can interact with you via voice, image, and text. Google claimed that its own benchmarking showed that Gemini could outperform OpenAI's multimodal model, GPT-4, on a range of standard tests. But the margins were slim. 

By baking Gemini into its ubiquitous products, Google is hoping to make up lost ground. “Every launch is big, but this one is the biggest yet,” Sissie Hsiao, Google vice president and general manager of Google Assistant and Bard (now Gemini), said in a press conference yesterday. “We think this is one of the most profound ways that we’re going to advance our company’s mission.”

But some will have to wait longer than others to play with Google’s new toys. The company has announced rollouts in the US and East Asia but said nothing about when the Android and iOS apps will come to the UK or the rest of Europe. This may be because the company is waiting for the EU’s new AI Act to be set in stone, says Dragoș Tudorache, a Romanian politician and member of the European Parliament, who was a key negotiator on the law.

“We’re working with local regulators to make sure that we’re abiding by local regime requirements before we can expand,” Hsiao said. “Rest assured, we are absolutely working on it and I hope we’ll be able to announce expansion very, very soon.”

How can you get it? Gemini Pro, Google’s middle-tier model that has been available via Bard since December, will continue to be available for free on the web at gemini.google.com (rather than bard.google.com). But now there is a mobile app as well.

If you have an Android device, you can either download the Gemini app or opt in to an upgrade in Google Assistant. This will let you call up Gemini in the same way that you use Google Assistant: by pressing the power button, swiping from the corner of the screen, or saying “Hey, Google!” iOS users can download the Google app, which will now include Gemini.

Gemini will pop up as an overlay on your screen, where you can ask it questions or give it instructions about whatever’s on your phone at the time, such as summarizing an article or generating a caption for a photo.  

Finally, Google is launching a paid-for service called Gemini Advanced. This comes bundled in a subscription costing $19.99 a month that the company is calling the Google One Premium AI Plan. It combines the perks of the existing Google One Premium Plan, such as 2TB of extra storage, with access to Google's most powerful model, Gemini Ultra, for the first time. This will compete with OpenAI’s paid-for service, ChatGPT Plus, which buys you access to the more powerful GPT-4 (rather than the default GPT-3.5) for $20 a month.

At some point soon (Google didn't say exactly when) this subscription will also unlock Gemini across Google’s Workspace apps like Docs, Sheets, and Slides, where it works as a smart assistant similar to the GPT-4-powered Copilot that Microsoft is trialing in Office 365.

When can you get it? The free Gemini app (powered by Gemini Pro) is available from today in English in the US. Starting next week, you’ll be able to access it across the Asia Pacific region in English and in Japanese and Korean. But there is no word on when the app will come to the UK, countries in the EU, or Switzerland.

Gemini Advanced (the paid-for service that gives access to Gemini Ultra) is available in English in more than 150 countries, including the UK and EU (but not France). Google says it is analyzing local requirements and fine-tuning Gemini for cultural nuance in different countries. But the company promises that more languages and regions are coming.

What can you do with it? Google says it has developed its Gemini products with the help of more than 100 testers and power users. At the press conference yesterday, Google execs outlined a handful of use cases, such as getting Gemini to help write a cover letter for a job application. “This can help you come across as more professional and increase your relevance to recruiters,” said Google’s vice president for product management, Kristina Behr.

Or you could take a picture of your flat tire and ask Gemini how to fix it. A more elaborate example involved Gemini managing a snack rota for the parents of kids on a soccer team. Gemini would come up with a schedule for who should bring snacks and when, help you email other parents, and then field their replies. In future versions, Gemini will be able to draw on data in your Google Drive that could help manage carpooling around game schedules, Behr said.   

But we should expect people to come up with a lot more uses themselves. “I’m really excited to see how people around the world are going to push the envelope on this AI,” Hsaio said.

Is it safe? Google has been working hard to make sure its products are safe to use. But no amount of testing can anticipate all the ways that tech will get used and misused once it is released. In the last few months, Meta saw people use its image-making app to produce pictures of Mickey Mouse with guns and SpongeBob SquarePants flying a jet into two towers. Others used Microsoft’s image-making software to create fake pornographic images of Taylor Swift .

The AI Act aims to mitigate some—but not all—of these problems. For example, it requires the makers of powerful AI like Gemini to build in safeguards, such as watermarking for generated images and steps to avoid reproducing copyrighted material. Google says that all images generated by its products will include its SynthID watermarks. 

Like most companies, Google was knocked onto the back foot when ChatGPT arrived. Microsoft’s partnership with OpenAI has given it a boost over its old rival. But with Gemini, Google has come back strong: this is the slickest packaging of this generation’s tech yet. 

Artificial intelligence

Ai for everything: 10 breakthrough technologies 2024.

Generative AI tools like ChatGPT reached mass adoption in record time, and reset the course of an entire industry.

What’s next for AI in 2024

Our writers look at the four hot trends to watch out for this year

  • Melissa Heikkilä archive page

These six questions will dictate the future of generative AI

Generative AI took the world by storm in 2023. Its future—and ours—will be shaped by what we do next.

Google DeepMind’s new AI system can solve complex geometry problems

Its performance matches the smartest high school mathematicians and is much stronger than the previous state-of-the-art system.

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    portable personal computers (PCs) [24-26]. This paper tries to analyze the energy efficiency of executing scientific-technical prob-lems on personal computers, and compares it with that obtained on servers and large computers. The aim is to show how shifting the execution of scientific-technical appli-cations to smaller computers, such as ...

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    This paper tries to analyze the energy efficiency of executing scientific-technical problems on personal computers, and compares it with that obtained on servers and large computers. The aim is to show how shifting the execution of scientific-technical applications to smaller computers, such as PCs, can contribute to the overall reduction of ...

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  11. The Personal Computer as a Research Tool in Consumer ...

    For the professional marketer of the 1980's and 90's, the personal computer will be increasingly important tool of the trade. Relevant applications will include: 1) local statistical analysis of data, 2) interfacing with a mainframe for more elaborate statistical procedures or larger samples, and 3) accessing national data bases in bibliographic research and/or the collection of ...

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  13. A comprehensive review study of cyber-attacks and cyber security

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  16. The History, Development, and Importance of Personal Computers

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    Part 1: Orientation to Small Group Systems Chapter 1: Small Groups as the Heart of Society Chapter 2: Groups as Structured Open Systems Part 2: Foundations of Small Group Communication Chapter 3: Communication Principles for Group Members... more. Download. by Gloria Galanes. Computer Science.

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    View sample computer research paper. Browse other research paper examples and check the list of history research paper topics for more inspiration. If you nee ... Before any of the big information technology companies offered personal computers to the public, hobbyists were building their own from kits, notably the Altair first announced in the ...

  20. Personal Computers and Protection of Privacy Research Paper

    Protection of privacy plays an important role in the maintenance of freedom of property, speech, and press supported by the First and Fourth Amendments. Since nowadays personal computers contain large volumes of private information, the unwarranted search and seizure of the technology may violate the basic right for privacy without which the ...

  21. Why to Choose Mac Over Windows Personal Computer Research Paper

    Why to Choose Mac Over Windows Personal Computer Research Paper Exclusively available on IvyPanda Table of Contents Purpose The purpose of this paper is to highlight why a consumer should select a MAC operating system over the Windows PC.

  22. How to use Google's genAI-powered note-taking app

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  23. [2402.05929] An Interactive Agent Foundation Model

    The development of artificial intelligence systems is transitioning from creating static, task-specific models to dynamic, agent-based systems capable of performing well in a wide range of applications. We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents across a wide range of domains, datasets, and tasks. Our training ...

  24. Personal Computer Research Paper

    Personal Computer Research Paper Decent Essays 583 Words 2 Pages Open Document History of the Personal Computer 1. Introduction and thesis statement The modern day society is the result of countless processes of change and evolution, among the more notable of them being the evolution of Information Technology.

  25. Personal Computer Research Papers Samples For Students

    Personal Computer Research Papers Samples For Students 17 samples of this type WowEssays.com paper writer service proudly presents to you a free catalog of Personal Computer Research Papers meant to help struggling students tackle their writing challenges.

  26. OpenAI's Sora video-generating model can render video games, too

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  27. Influence of computers in students' academic achievement

    In this way, we identify the factors that lead to better academic achievement, helping schools and parents use them as a strategic advantage. , it investigates the moderation effect of family size and computer self-efficacy and the mediation effect of computer use between the factors identified and AA. , to understand how the COVID-19 pandemic i...

  28. Video generation models as world simulators

    We explore large-scale training of generative models on video data. Specifically, we train text-conditional diffusion models jointly on videos and images of variable durations, resolutions and aspect ratios. We leverage a transformer architecture that operates on spacetime patches of video and image latent codes. Our largest model, Sora, is capable of generating a minute of high fidelity video.

  29. Google's Gemini is now in everything. Here's how you can try it out

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