PRE2017 3 Group8 SOTA

From Control Systems Technology Group

Jump to: navigation, search

Main wiki page

Contents

Flow theory and education

Karen Wesson & Ilona Boniwell (2007) describe flow in the following way: Being ‘in flow’ or ‘in the zone’ enables individuals to focus on tasks more fully and to maximise performance. They describe conditions that should be met to get people in flow as well as ways to meet these in the context of coaching. Their list of conditions is as follows.

  1. Having clear goals
  2. Balancing challenge and skill
  3. Importance placed on doing well in an activity
  4. maintaining goal congruence
  5. Receiving clear and immediate feedback
  6. Increasing autonomy
  7. Increasing absorption

Sheehan & Katz (2012) apply flow theory to physical education. They mention Csikszentmihalyi’s (1975) eight elements which they describe as follows:

  1. Balance between the difficulty of an activity and an individual’s proficiency. Is there an achievable perceived challenge?
  2. Apparent goals. Is there a clear objective that distinguishes pertinent from immaterial information?
  3. Immediate feedback. Is there personalized feedback being received in a timely manner?
  4. The harmony of action and awareness. Is there awareness of what’s happening without thinking about the need for this awareness?
  5. Focused concentration. Is the person able to concentrate on a limited stimulus field?
  6. Decreased self- consciousness. Is there awareness of internal processes and less emphasis on one’s self-image (while maintaining a sense of their physical reality)?
  7. Perception of Control. Is the person capable of adequately achieving the prescribed task and less concerned about perfection?
  8. Decreased awareness of time. Is there a feeling that the importance of time is diminished (losing track of time)

Rodríguez-Ardura and Meseguer-Artola (2017) analysed the way flow relates to other constructs such as challenge and Control in the context of e-learning, this is done using a questionnaire distributed among the students of an established pure-online University. This analysis could teach us both what is needed to achieve flow as well as what the benefits to achieving flow are.

Ghan & Deshpande (1994) propose a model to examine the optimal flow in human-computer interaction, this could tell us which aspects are more important for reaching optimal flow. This paper also studies the impact of task scope which is the motivating potential of a job. It is noteworthy that this paper is from a time where human-computer interaction was relatively new.

Education by human teachers

A large-scale initiative to develop more efficient teaching methods in the USA called Follow Through resulted in the development of the Direct Instruction (DI) teaching model, as well as a monitoring method called Precision Teaching (PT) (Moran & Malott, 2004).

DI aims to improve learning outcomes by increasing clarity in the learning process (Kider & Carnine, 1991). On top of that, DI’s strongest focus is on repetition and continuous practice. At the beginning of the program, students are tested to determine their current skill level and are then placed in groups of students with the same skill level. Only 10% of each lesson is new material, the rest is repetition and practice of previous study material (National Institute for Direct Instruction (2018).

In PT, the teacher has more of a guiding role and the students reflect on themselves more (Lindsley, 1992). The learning curve is monitored using a so-called Standard Celeration Chart (SCC), which allows comparison of the learning curve of different students for the same task, or comparison of different tasks for the same student. This is thanks to the standard format for the SCC, in addition to the logarithmic scale which allows many numbers to be compared in their own order of magnitude (FHSS Information Architect, 2017). Both methods have given promising results, also on long-term after following regular teaching for several years (Becker & Gersten, 1982)(Gersten, Keating & Becker, 1988). The combination of the two has also been shown to have a positive effect on the learning process (Binder & Watkins, 1990).

Learning Styles

Learning styles aim to account for different ways of learning that individuals employ. According to these theories, people can be classified according to the way in which they learn, their ‘learning style’. Different learning style models have been identified over the years, Coffield, Moseley, Hall & Ecclestone (2004) identified a total of 71 different learning style models, and determined five families of learning styles: Learning styles that are constitutionally based. Learning styles that reflect cognitive structure. Learning styles as a component of personality types. Learning styles as flexible learning preferences. Models that move on from learning styles towards learning approaches, strategies and orientations.

Moving from top to bottom along this list we start with theories that believe that learning styles are fixed and move towards models that are based on dynamic learning styles based that take into account personal and environmental factors.

While much research has been done regarding learning styles, and many schools implement them, much of the research has not lead to conclusive results. There is also a considerable amount of criticism of applying learning styles in education. Many researchers have found that there is a lack of evidence for the effectiveness of learning style models (Lilienfeld, Kynn, Ruscio & Beyerstein, 2011)(Rohrer & Pashler, 2012), or that their effectiveness is a self-fulfilling prophecy (Gurung & Prieto, 2009). Glenn (2009) states that instead of adapting the style to the students, the style should be matched with the content. He states that some concepts are best learned through hands-on work, while others are best taught through lectures or discussions.

Future of E-learning

Among teachers, it is expected that cost will have the largest impact on education. Furthermore, teachers regard student retention to be the biggest problem in online courses as opposed to face-to- face teaching. (Allen, 2015) Lower computer skills are also found to relate to lower learning outcomes, although the impact of this problem is decreasing as more and more children have access to and thus gain experience using a computer or computerised device. (Welsh, 2003) Students find ease of access and use to be the most important beneficial aspect of e-learning, and teachers find the gathering of information on the students’ data and progress most useful. Both want it to run smoothly and stably. (Pollock, 2018) The necessity to solve this latter problem is also backed by research that finds that it is detrimental to the effectiveness of the e-learning course (Welsh, 2003).

Another big problem in e-learning reception is that it often does not support peer-to-peer networking. (Wang, 2010 & Welsh, 2003) The e-learning system is often difficult to integrate and requires thorough planning and discussion between all parties involved before it can be successfully integrated. If this is not done carefully, the e-learning system can actually have adverse effects. (Delgado-Almonte, 2010) This also leads to the infrastructure and ease of use, as well as the ability to change the system if needed to be very important (Welsh, 2003).

E-learning should also provide learners with an incentive to complete the course. However, at the same time, the course should allow learners to quickly skip through the information in a course in order to find and (re-)learn only one particular aspect of the course. Lastly, the ease of e-learning may get management under the impression that e-learning is all that is required for a student to learn the subject matter. E-learning, however, should not completely replace all studying, but always be backed by face-to-face lessons (Welsh, 2003).

Gamification

Deterding et al. (2011) aim to investigate the origins of gamification and how it relates to serious games, pervasive games, alternate reality games and playful design. The paper suggests that gamified applications provide insight into new gameful phenomena that complement playful phenomena. The definition of gamification that is agreed upon states that gamification entails the use of game design elements in non-game contexts. Gamification is a new term for an older phenomenon, several precursors and parallels exist. Already in the early 1980’s (Deterding et al., 2011, p.2) research was performed in HCI to redress routine work using game elements.

Hamari et al. (2014) performed a literature study of peer-reviewed empirical studies on gamification. Their aim was to create a framework for examining the effects of gamification using definitions of gamification and motivational affordances. The paper gives insight into the experiments performed in the peer-reviewed studies. Hamari and Huotari stress that gamification should invoke the same psychological experiences that games invoke. Deterding, on the other hand, argues that affordances in gamified systems should be the same ones that are used in games. The studies that were included in the literature review used any of the following motivational affordances: points, leaderboards, achievements/badges, levels, story/theme, clear goals, feedback, rewards, progress, and challenge. The majority of studies focused on education/learning, intra-organizational systems, and work. But there were also studies on commerce, health/exercise, sharing, sustainable consumption, innovation/ideation, and data gathering. The paper concludes that gamification does appear to work, but that there are caveats. Quantitative research concluded that positive effects only existed in part of the considered relationships between gamification and the studied outcomes. Qualitative research showed that there may be underlying confounding factors that influence the effectiveness of gamification. The authors also state that more rigorous methodologies ought to be used in further research. The suggestions they give may be of use for our project in 0LAUK0.

In Deterding (2012), various views on gamification are presented by people involved in industries were gamification is relevant. Judd Antin, a social psychologist in the Internet Experiences research group at Yahoo! Research, remarks that gamification is a positive trend in that it signals a shift away from pecuniary and instrumental rewards. When done right, gamification can make use of powerful social psychological processes, such as self-efficacy, group identification and social approval to aid long-term performance. Unfortunately many modern applications of gamification lack the ability to account for differences in individuals and contexts. Elizabeth Lawley, professor of interactive games and media and founder and director of the Lab for Social Computing at Rochester Institute of Technology argues as well that modern applications of gamification reduce well-designed games to their simplest components. These implementations may fail to engage players, but they might also damage existing interest or engagement with the service or product. She worked on “Just Press Play”, an achievement system for students in interactive games and media at the Rochester Institute of Technology. This system may be relevant for our study for 0LAUK0. Rajat Paharia, founder and chief product officer of Bunchball, describes in his section how his company designs gamified systems. He stresses the importance of context, and that for gamification to work, the goal that is gamified needs to have a core intrinsic value.

Lawley (n.d.) reflects on issues that the first version of “Just Press Play” suffered from (see also Deterding, 2012). Just Press Play is a gamified system designed at Rochester Institute of Technology, meant to help new students find their way around campus and to get them out of their comfort zone to partake in the university’s activities. Just Press Play is an achievement system, the original version used achievements based on internal system triggers (e.g. completing the tutorial), administratively assigned achievements (e.g. a certain percentage of the class manages to finish a difficult course), user-submitted content (e.g. photos of things around the campus), collectible cards with a special code on it, and RFID keychains that can be used to receive credit for attending events. Due to technical issues, the collectable cards and RFID keychains did not work out properly. In the second version of Just Press Play, RFID tags were replaced with QR code stickers that students can place on (for instance) their campus card or phone, which they can scan at events. Collectable cards are printed offsite and distributed to students after they unlock an achievement. Since the original cards were very popular, there are plans to make a card game using these cards. Privacy aspects and stability of the system was improved, and the categorization of the achievements was modified, as such, there are now achievement quadrants (create, learn, socialize, explore).

Nicholson (2015) describes six concepts (Reflection, Exposition, Choice, Information, Play, and Engagement) to help designers implement gamification in a meaningful way. Gamification can help users find personal connections, thereby motivating engagement. Nicholson argues that reward-based gamification (akin to operant conditioning) can lead to short-term improvements, but other game-based elements should be used to facilitate long-term change. He also argues that gamification should not be permanent. Reward-based gamification can be used to ease a user into a certain task, meaningful gamification can be used to strengthen the behaviour, but eventually, the user will get bored of the gamified system. As such gamification should be designed to ease the user into the real world context of the task.

In Huang et al. (2013), gamification in education is discussed. It is stated that gamification is a specific application of “nudging”. A five-step process is discussed that can help in making a gamified system:

  1. Understanding the target audience and context
  2. Defining learning objectives
  3. Structuring the experience
  4. Identifying resources
  5. Applying gamification elements

In understanding the target audience and context it is also important to take into account the length of the learning program, where the program is conducted (classroom/at home) if students work in groups (and how large these groups are). There are several common pain points in education that need to be considered:

  • Focus: younger students are more easily distracted.
  • Skills: students may be demotivated to try when the task is too difficult, the student lacks the skills or knowledge required to complete the task.
  • Physical, mental and emotional factors: fatigue, hunger, or emotions are factors that can affect a student’s learning abilities or other pain points.
  • Motivation: young adults and adolescents commonly lack motivation.
  • Pride: adults may believe they already know what is being taught, they may also choose to study material that is well above their skill/knowledge level. This issue may also occur when the instructor is younger than the students.
  • Learning environment and nature of the course: this pain point consists of properties of the course, such as class size and structure of the program.

The paper discusses in an example how a math class can be structured such that gamification could be applied to it, this is very relevant regarding our project in 0LAUK0. Furthermore, a distinction is made between push and complete, where complete entails understanding the concepts in each stage, and push entails the motivation to go to the next stage. Lastly, Huang et al. (2013) also categorize game mechanics in self-elements and social elements:

Examples of game mechanics, from Huang et al. (2013, p. 14)
Self-elements (complete stage) Social elements (push stage)
Points Leaderboards
Levels Virtual Goods
Trophies/badges Interactive cooperation
Virtual goods Storyline
Storyline -
Time restrictions -
Aesthetics -

Arnold (2014) discusses in his paper among other things Bartle’s four basic categories of gamer, and how these categories are (mis)used in gamification. When making a gamified system it is important to notice that not all gamer categories like the same game elements (e.g. socializers do not care for leaderboards).

  • Socializers: more interested in having relations with the other players than playing the game.
  • Achievers: competitive and enjoy beating challenges.
  • Killers: provoke and cause drama in the scope of the virtual world.
  • Explorers: like to explore the geography of the world as well as the mechanics of the game.

Online learning systems

Seven Principles For Good Practice in Undergraduate Education

[Abstract] The Seven Principles for Good Practice in Undergraduate Education grew out of a review of 50 years of research on the way teachers teach and students learn (Chickering and Gamson, 1987, p. 1) and a conference that brought together a distinguished group of researchers and commentators on higher education. The primary goal of the Principles’ authors was to identify practices, policies, and institutional conditions that would result in a powerful and enduring undergraduate education. (Sorcinelli, 1991, p. 13)

In Chickering et al. (1987), Seven Principles of Good Practice in education are laid out. These practices help students learn more effectively. These are:

  1. Contact between student and faculty. “Faculty concern helps students get through rough times and keep on working. Knowing a few faculty members well enhances students’ intellectual commitment and encourages them to think about their own values and future plans.” Discussion groups are a valuable tool for this.
  2. Cooperation among students. “Working with others often increases involvement in learning.” This can be accomplished using peer tutoring, group work - possibly in a project setting - or seminars
  3. Active learning. Active learning is a method in which the student learns by working with the course material. “Students do not learn much just by sitting in classes listening to teachers, memorizing prepackaged assignments, and spitting out answers.” Examples here are exercises, discussions, (team) projects, peer critiques and internships.
  4. Good feedback. Assess what the student knows, and more importantly what he doesn’t know. Give timely feedback, so that the student can incorporate it. The feedback needs to be frequent. Students should also learn to assess themselves.
  5. Time management. “Time plus energy equals learning. There is no substitute for time on task. Learning to use one’s time well is critical for students and professionals alike.” Make sure students spend time on a task and that students use their time efficiently. Tools: Mastery learning, contract learning, computer-assisted instruction.
  6. High Expectations. “Expecting students to perform well becomes a self-fulfilling prophecy when teachers and institutions hold high expectations of themselves and make extra efforts.” Communicate the expectations, create programs out of the curriculum.
  7. Diverse Talents and Ways of Learning. “Students need the opportunity to show their talents and learn in ways that work for them.” Develop multiple ways for students to learn and work.

Principles for Good Practice in Undergraduate Education: Effective Online Course Design to Assist Students’ Success

[Abstract] The purpose of this study was to apply the Seven Principles for Good Practice in Undergraduate Education (Chickering & Gamson, 1991) to online course design to enhance students ’ success in an online course. A survey was created to determine students’ perception of strategies and skills they perceived as important to complete an online course. The survey was created based on behavioral learning, cognitive learning, and social learning frameworks. The responses of the 179 students in this study in an undergraduate Computer Applications in Business course at a large southeastern university were categorized by the Seven Principles . Results of the survey showed the course design strategies and what students valued matched well with the Seven Principles Implications of the study provide evidence that good course design embed s the seven principles to ensure students are successful in the online learning environment. (Crews et al., 2015)

Online design which takes into account these seven principles can be perceived as being a good system by the students using it. The literature review is useful: Disadvantages of online course design as noted by Clark (2003):

  • discussions that are not connected in time and seem disjointed;
  • lack of clear guidelines for participation;
  • lack of engagement in an asynchronous environment;
  • difficulty in collaborative online projects; and
  • lack of communication with the instructor and other students.

These points should be taken into account when designing an online learning system. Salmon (2002) and Huang (2002) say online systems should focus on:

  • access
  • motivation
  • socialization
  • information exchange
  • knowledge construction
  • interactive learning
  • collaborative learning
  • facilitating learning
  • authentic learning
  • student-centered learning

Implementing the Seven Principles: Technology as Lever

[Abstract] In March 1987, the AAHE Bulletin first published “Seven Principles for Good Practice in Undergraduate Education.” With support from Lilly Endowment, that document was followed by a Seven Principles Faculty Inventory and an Institutional Inventory (Johnson Foundation, 1989) and by a Student Inventory (1990). The Principles, created by Art Chickering and Zelda Gamson with help from higher education colleagues, AAHE, and the Education Commission of the States, with support from the Johnson Foundation, distilled findings from decades of research on the undergraduate experience. Since the Seven Principles of Good Practice were created in 1987, new communication and information technologies have become major resources for teaching and learning in higher education. If the power of the new technologies is to be fully realized, they should be employed in ways consistent with the Seven Principles. Such technologies are tools with multiple capabilities; it is misleading to make assertions like Microcomputers will empower students because that is only one way in which computers might be used. Any given instructional strategy can be supported by a number of contrasting technologies (old and new), just as any given technology might support different instructional strategies. But for any given instructional strategy, some technologies are better than others: Better to turn a screw with a screwdriver than a hammer a dime may also do the trick, but a screwdriver is usually better. This essay, then, describes some of the most cost-effective and appropriate ways to use computers, video, and telecommunications technologies to advance the Seven Principles. (Chickering et al., 1996)

  • Good Practice Encourages Contacts Between Students and Faculty. Technology can be very useful here. Digital questions can graded quicker than for example physical homework, students that are shy or otherwise not able to communicate with the teacher face to face can more easily and safely do so by online communication. language barriers are not as high when people have more time to interpret the questions.
  • Cooperation. Same story here, communication between students is improved.
  • Active learning. The internet gives a big opportunity for researching into topics. computer software can be used to encourage active learning, through software based homework. Simulation can be done of what is not feasible or otherwise more cumbersome in real life. An example of this is physics simulations.
  • Feedback: “Computers also have a growing role in recording and analyzing personal and professional performances. Teachers can use technology to provide critical observations for an apprentice; for example, video to help a novice teacher, actor, or athlete critique his or her own performance.” Next to this, computers can be used to store past performances and later be used by teachers to evaluate growth.
  • Time on task: working from home can save student’s time otherwise spent commuting. technology can be used to document time on task and possibly communicate this back to students.
  • High Expectations: “Many faculty report that students feel stimulated by knowing their finished work will be “published” on the World Wide Web. With technology, criteria for evaluating products and performances can be more clearly articulated by the teacher or generated collaboratively with students. General criteria can be illustrated with samples of excellent, average, mediocre, and faulty performance. These samples can be shared and modified easily. They provide a basis for peer evaluation, so learning teams can help everyone succeed. ”
  • Diverse talents and ways of learning. Give students who can handle it freedom. Give those who can’t extra attention. Students with similar learning styles, or who need each other for learning can be brought together.

The article also mentions that simply using technology is not enough. It must be in line with the seven principles. Technology should motivate the student, i.e. with materials that are problem-oriented, relevant to real-world problems or interactive.

Computer-Supported Collaborative Learning in Higher Education: An Introduction

[abstract] The rapidly increasing use of computers in education, and in particular the migration of many university courses to web-based delivery, has caused a resurgence of interest among educators in non-traditional methods of course design and delivery. This chapter provides an introduction to the field of computer-supported collaborative learning (CSCL). First, some of the major benefits are listed. Then, some of the common problems are described, and solutions are either given or pointed to in the literature. Finally, pointers are given to some of the more recent research in this area. (Roberts, 2015)

References

  1. Allen, I. E., & Seaman, J. (2015). Grade Level: Tracking Online Education in the United States. Babson Survey Research Group.
  2. Arnold, B. J. (2014). Gamification in education. ASBBS Proceedings, 21(1), 32. Retrieved from https://search.proquest.com/docview/1519057772?pq-origsite=gscholar
  3. Becker, W.C., & Gersten, R. (1982). A Follow-up of Follow Through: The Later Effects of the Direct Instruction Model on Children in Fifth and Sixth Grades. American Educational Research Journal, 19(1), 75-92.
  4. Binder, C., & Watkins, C. L. (1990). Precision teaching and direct instruction: Measurably superior instructional technology in schools. Performance Improvement Quarterly, 3(4), 74-96.
  5. Chickering, A. W., & Gamson, Z. F. (1987, March). Seven principles for good practice in undergraduate education. American Association for Higher Education Bulletin, 39(7), 3–6. Retrieved from http://www.lonestar.edu/multimedia/SevenPrinciples.pdf
  6. Chickering, A.W.,, Arthur & Ehrmann, Stephen. (1996). Implementing the Seven Principles: Technology as Lever. American Association for Higher Education Bulletin. 49. 3-6.
  7. Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post-16 learning: a systematic and critical review. LSRC reference, Learning & Skills Research Centre.
  8. Crews, T. B., Wilkinson, K., & Neill, J. K. (2015). Principles for good practice in undergraduate education: Effective online course design to assist students' success. Journal of Online Learning and Teaching, 11(1), 87. Retrieved from http://virtualchalkdust.com/wp-content/uploads/2016/02/Crews_0315.pdf
  9. Delgado-Almonte, M., Andreu, H. B., & Pedraja-Rejas, L. (2010). Information technologies in higher education: Lessons learned in industrial engineering. Educational Technology & Society, 13(4), 140-154.
  10. Deterding, S. (2012). Gamification: designing for motivation. interactions, 19(4), 14-17. Retrieved from https://www.researchgate.net/profile/Sebastian_Deterding/publication/244486331_Gamification_Designing_for_motivation/links/0a85e53a049814673c000000.pdf
  11. Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011, September). From game design elements to gamefulness: defining gamification. In Proceedings of the 15th international academic MindTrek conference: Envisioning future media environments (pp. 9-15). ACM. Retrieved from https://dl.acm.org/citation.cfm?id=2181040
  12. FHSS Information Architect, (2017, July) Precision Teaching: Concept Definition and Guiding Principles, Retrieved from https://psych.athabascau.ca/open/lindsley/concept.php
  13. Gersten, R., Keating, T., & Becker, W. (1988). The continued impact of the Direct Instruction model: Longitudinal studies of Follow Through students. Education and Treatment of Children, 318-327.
  14. Ghani, J. A., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human—computer interaction. The Journal of psychology, 128(4), 381-391.
  15. Glenn, D. (2009, December 15). Matching Teaching Style to Learning Style May Not Help Students. Retrieved February 21, 2018, from https://www.chronicle.com/article/Matching-Teaching-Style-to/49497
  16. Gurung, R.A.R, & Prieto, L.R. (2009). Learning styles as self-fulfilling prophecies. In Getting Culture: Incorporating Diversity Across the Curriculum (pp. 45-81). Stylus.
  17. Hamari, J., Koivisto, J., & Sarsa, H. (2014, January). Does gamification work?--a literature review of empirical studies on gamification. In System Sciences (HICSS), 2014 47th Hawaii International Conference on (pp. 3025-3034). IEEE. Retrieved from http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6758978
  18. Huang, W. H. Y., Soman, D. (2013, December). A Practitioner’s Guide To Gamification Of Education. Retrieved from https://inside.rotman.utoronto.ca/behaviouraleconomicsinaction/files/2013/09/GuideGamificationEducationDec2013.pdf
  19. Kinder, D., & Carnine, D. (1991). Direct Instruction: What It Is and What It Is Becoming. Journal of Behavioral Education, 1(2), 193-213.
  20. Lawley, E. L., Phelps, A. (n.d.). “You Know You’re Going to Fail, Right?”: Learning From Design Flaws in Just Press Play at RIT. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.707.2212&rep=rep1&type=pdf
  21. Lilienfeld, S.O., Lynn, S.J., Ruscio, J., & Beyerstein, B.L. (2011). Myth #18 Students Learn Best When Teaching Styles Are Matched to Their Learning Styles. In 50 Great Myths of Popular Psychology (pp. 92-99). Wiley-Blackwell.
  22. Lindsley, O. R. (1992). Precision teaching: Discoveries and effects. Journal of Applied Behavior Analysis, 25(1), 51.
  23. Moran, D.J. & Malott, R.W. (2004), Evidence-Based Educational Methods. Academic Press
  24. National Institute for Direct Instruction, (2018, Februari 16), Basic Philosophy of Direct Instruction (DI), https://www.nifdi.org/what-is-di/basic-philosophy
  25. Nicholson, S. (2015). A recipe for meaningful gamification. In Gamification in education and business (pp. 1-20). Springer, Cham. Retrieved from http://scottnicholson.com/pubs/recipepreprint.pdf [pdf is preprint version with custom citation]
  26. Pollock, B., & Al-Bataineh, A. (2018). BENEFITS OF CURRENT EDUCATIONAL TECHNOLOGY: A COMPARISON OF STUDENT AND TEACHER PREPARATIONS IN A RURAL ILLINOIS SCHOOL DISTRICT. The Online Journal of Distance Education and e-Learning, 6(1), 17.
  27. Roberts, Tim. (2005). Computer-Supported Collaborative Learning in Higher Education: An Introduction. Computer-supported Collaborative Learning in Higher Education. 16. . 10.4018/978-1-59140-408-8.ch001.
  28. Rodríguez-Ardura, I. and Meseguer-Artola, A. (2017), Flow in e-learning: What drives it and why it matters. Br J Educ Technol, 48: 899–915. doi:10.1111/bjet.12480
  29. Rohrer, D., & Pashler, H. (2012). Learning styles: Where’s the evidence? Medical Education, 46, 34-35
  30. Sorcinelli, M. D. (1991), Research findings on the seven principles. New Directions for Teaching and Learning, 1991: 13–25. doi: 10.1002/tl.37219914704
  31. Sheehan, D. P., & Katz, L. (2012). The practical and theoretical implications of flow theory and intrinsic motivation in designing and implementing exergaming in the school environment. Loading... The Journal of the Canadian Game Studies Association, 6(9).
  32. Wang, M., Ran, W., Liao, J., & Yang, S. J. (2010). A performance-oriented approach to e-learning in the workplace. Journal of Educational Technology & Society, 13(4), 167.
  33. Welsh, E. T., Wanberg, C. R., Brown, K. G., & Simmering, M. J. (2003). E‐learning: emerging uses, empirical results and future directions. international Journal of Training and Development, 7(4), 245-258.
  34. Wesson, K., & Boniwell, I. (2007). Flow theory–its application to coaching psychology. International Coaching Psychology Review, 2(1), 33-43.
Personal tools