Abstract
This chapter considers the value of engagement in eLearning environments where engagement is, of course, not the end goal of the interaction. Rather, engagement mediates learners’ short- and long-term goals and the formal and self-evaluative outcomes that indicate progress toward those goals. The chapter uses theories of learning to elaborate models of engagement “as a necessary pre-condition to learning” that inform the design of eLearning environments. The full model is then illustrated through two case studies derived from an experiment and a massive open online course. These reinforce the theoretically derived characteristics of engaging eLearning environments within these very different settings.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abramovich, S., Schunn, C., Higashi, R.: Are badges useful in education? It depends upon the type of badge and expertise of learner. Educ. Technol. Res. Dev. 61 (2), 217–232 (2013). doi:10.1007/s11423-013-9289-2
Admiraal, W., Huizenga, J., Akkerman, S., Dam, G.: The concept of flow in collaborative game-based learning. Comput. Hum. Behav. 27 (3), 1185–1194 (2011). doi:10.1016/j.chb.2010.12.013
Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Engaging with massive online courses. Paper presented at the WWW ’14, Seoul (2014)
Appleton, J.J., Lawrenz, F.: Student and teacher perspectives across mathematics and science classrooms: the importance of engaging contexts. Sch. Sci. Math. 111 (4), 143–155 (2011). doi:10.1111/j.1949-8594.2011.00072.x
Appleton, J.J., Christenson, S.L., Kim, D., Reschly, A.L.: Measuring cognitive and psychological engagement: validation of the student engagement instrument. J. Sch. Psychol. 44 (5), 427–445 (2006)
Baker, R., Yacef, K.: The state of educational data mining in 2009: a review and future visions. J. Educ. Data Min. 1 (1), 3–17 (2009)
Baker, R.S.J.d., D’Mello, S.K., Rodrigo, M.M.T., Graesser, A.C.: Better to be frustrated than bored: the incidence, persistence, and impact of learners’ cognitive-affective states during interactions with three different computer-based learning environments. Int. J. Hum. Comput. Stud. 68 (4), 223–241 (2010). doi:10.1016/j.ijhcs.2009.12.003
Bandura, A.: Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall, Englewood Cliffs (1986)
Bauer, D.J., Shanahan, M.J.: Modeling complex interactions: person-centered and variable-centered approaches. Mod. Context Eff. Longitud Stud. 42 (4), 255–283 (2007)
Bempechat, J., Shernoff, D.: Parental influences on achievement motivation and student engagement. In: Christenson, S.L., Reschly, A.L., Wylie, C. (eds.) Handbook of Research on Student Engagement, pp. 315–342. Springer, New York (2012)
Bienkowski, M., Feng, M., Means, B.: Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. U.S. Department of Education, Office of Educational Technology, Washington, DC (2012)
Boyle, E., Connolly, T.M., Hainey, T.: The role of psychology in understanding the impact of computer games. Entertain. Comput. 2 (2), 69–74 (2011). doi:10.1016/j.entcom.2010.12.002
Charleer, S., Klerkx, J., Duval, E.: Learning dashboards. J. Learn. Anal. 1 (3), 199–202 (2014)
Clow, D.: MOOCs and the funnel of participation. Paper presented at the Third International Conference on Learning Analytics (2013)
Csikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Harper-Row, New York (1990)
Dabbagh, N., Kitsantas, A.: Personal learning environments, social media, and self-regulated learning: a natural formula for connecting formal and informal learning. Internet High. Educ. 15 (1), 3–8 (2012). doi:http://dx.doi.org/10.1016/j.iheduc.2011.06.002
de Kort, Y., Ijsselsteijn, W., Poels, K.: Digital games as social presence technology: development of the social presence in gaming questionnaire (SPGQ). Paper presented at the presence (2007)
Deater-Deckard, K., Chang, M., Evans, M.A.: Engagement states and learning from educational games. New Dir. Child Adolesc. Dev. 139, 21–30 (2013). doi:10.1002/cad.20028
DeBoer, J., Stump, G., Seaton, D., Breslow, L.: Diversity in MOOC students’ backgrounds and behaviors in relationship to performance in 6.002x. In: Proceedings of the Sixth Learning International Networks Consortium Conference. http://tll.mit.edu/sites/default/files/library/LINC’13.pdf (2013). Cited 15 Feb 2015
DeBoer, J., Ho, A.D., Stump, G.S., Breslow, L.: Changing “course”: reconceptualizing educational variables for massive open online courses. Educ. Res. 43 (2), 74–84 (2014). doi:10.3102/0013189x14523038
Deci, E.L., Ryan, R.M.: Intrinsic Motivation and Self-determination in Human Behavior. Plenum, New York (1985)
Dede, C.: Immersive interfaces for engagement and learning. Science 323 (66), 66–69 (2009). doi:10.1126/science.1167311
Delgado, A.R., Picking, R., Grout, V.: Remote-controlled home automation systems with different network technologies. In: Proceedings of the 6th International Network Conference, pp. 357–366 (2006)
Eccles, J.S., Wigfield, A.: Motivational beliefs, values, and goals. Annu. Rev. Psychol. 53, 109–132 (2002)
Fairclough, S.H.: Fundamentals of physiological computing. Interact. Comput. 21 (1–2), 133–145 (2009). doi:10.1016/j.intcom.2008.10.011
Fredricks, J.A., Blumenfeld, P.C., Paris, A.H.: School engagement: potential of the concept, state of the evidence. Rev. Educ. Res. 74 (1), 59–109 (2004)
Fredricks, J.A., McColskey, W., Meli, J., Montrosse, B., Mordica, J., Mooney, K.: Measuring Student Engagement in Upper Elementary Through High School: A Description of 21 Instruments. SERVE Center, Greensboro (2011)
Gosling, S., Augustine, A., Vazire, S., Holtzman, N., Gaddis, S.: Manifestations of personality in online social networks: self-reported Facebook-related behaviors and observable profile information. Cyberpsychol. Behav. Soc. Netw. 14 (9), 483–488 (2011). doi:10.1089/cyber.2010.0087
Grafsgaard, J.F., Wiggins, J.B., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Predicting learning and affect from multimodal data streams in task-oriented tutorial dialogue. Paper presented at the EDM2014 (2014)
Hardy, M., Wiebe, E.N., Grafsgaard, J.F., Boyer, K.E., Lester, J.C.: Physiological responses to events during training: use of skin conductance to design adaptive learning systems. Paper presented at the Human Factors and Ergonomic Society 57th Annual Meeting (2013)
Hassenzahl, M., Diefenbach, S., Göritz, A.: Needs, affect, and interactive products—facets of user experience. Interact. Comput. 22 (5), 353–362 (2010). doi:10.1016/j.intcom.2010.04.002
Hazari, S., North, A., Moreland, D.: Investigating pedagogical value of wiki technology. J. Inf. Syst. Educ. 20 (2), 187–198 (2009)
Hollands, F.M., Tirthali, D.: MOOCs: expectations and reality. Report from Center or Benefit-Cost Studies of Education: Center for Benefit-Cost Studies of Education, Teachers College, Columbia University (2014)
Honey, M.A., Hilton, M. (eds.): Learning Science: Computer Games, Simulations, and Education. Committee on Science Learning: Computer Games, Simulations, and Education. National Research Council, Washington, DC (2011)
Jimerson, S.R., Campos, E., Greif, J.L.: Toward an understanding of definitions and measures of school engagement and related terms. Calif. Sch. Psychol. 8, 7–27 (2003)
Kizilcec, R.F., Piech, C., Schneider, E.: Deconstructing disengagement: analyzing learner subpopulations in massive open online courses categories and subject descriptors. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, pp. 170–179 (2013)
Kleiman, B.G.M., Wolf, M.A., Frye, D.: The Digital Learning Transition MOOC for Educators: Exploring a Scalable Approach to Professional Development, pp. 1–8. Friday Institute, Raleigh (2013)
Kunkle, D., Cooperman, G.: Twenty-six moves suffice for Rubik’s cube. In: Proceedings of the 2007 International Symposium on Symbolic and Algebraic Computation, pp. 235–242 (2007)
Laurel, B. (ed.): The Art of Human-Computer Interface Design. Addison-Wesley, Reading (1990)
Laurel, B.: Computers as Theatre. Addison-Wesley, Reading (1993)
Lee, V., Drake, J.: Digital physical activity data collection and use by endurance runners and distance cyclists. Technol. Knowl. Learn. 18 (1–2), 39–63 (2013). doi:10.1007/s10758-013-9203-3
Lin, Q.: Student views of hybrid learning: a one-year exploratory study. J. Comput. Teach. Educ. 25 (2), 57–66 (2009)
Macy, M.W.: Learning theory and the logic of critical mass. Am. Sociol. Rev. 55 (6), 809–826 (1990). doi:10.2307/2095747
Malone, T.W, Lepper, M.R.: Making learning fun: a taxonomy of intrinsic motivations for learning. In: Snow, R.E., Farr, M.J. (eds.) Aptitude Learning and Instruction, vol. 3, pp. 223–253. Erlbaum, Mahwah (1987)
Mayer, R.E.: Multimedia Learning. Cambridge University Press, Cambridge (2001)
Mayer, R.E.: Elements of a science of e-learning. J. Educ. Comput. Res. 29 (3), 297–313 (2003)
Mayer, R.E., Johnson, C.: Adding instructional features that promote learning in a game-like environment. J. Educ. Comput. Res. 42 (3), 241–265 (2010)
O’Brien, H.: The influence of hedonic and utilitarian motivations on user engagement: the case of online shopping experiences. Interact. Comput. 22 (5), 344–352 (2010). doi:http://dx.doi.org/10.1016/j.intcom.2010.04.001
O’Brien, H., Toms, E.G.: What is user engagement? A conceptual framework for defining user engagement with technology. J. Am. Soc. Inf. Sci. Technol. 59 (6), 938–955 (2008)
O’Brien, H., Toms, E.G.: The development and evaluation of a survey to measure user engagement. J. Am. Soc. Inf. Sci. Technol. 61 (1), 50–69 (2010). doi:10.1002/asi.21229
O’Brien, H., Toms, E.G.: Examining the generalizability of the user engagement scale (UES) in exploratory search. Inf. Process. Manag. 49 (5), 1092–1107 (2012). doi:10.1016/j.ipm.2012.08.005
Paas, F., Sweller, J.: An evolutionary upgrade of cognitive load theory: using the human motor system and collaboration to support the learning of complex cognitive tasks. Educ. Psychol. Rev. 24 (1), 27–45 (2012). doi:10.1007/s10648-011-9179-2
Paas, F., Renkl, A., Sweller, J.: Cognitive load theory and instructional design: recent developments. Educ. Psychol. 38 (1), 1–4 (2003)
Picard, R.W.: Affective computing: from laughter to IEEE. IEEE Trans. Affect. Comput. 1 (1), 11–17 (2010). doi:10.1109/t-affc.2010.10
Pintrich, P.R., Schunk, D.H.: Motivation in Education: Theory, Research, and Applications. Prentice Hall, Englewood Cliffs (1996)
Poirier, J., Cobb, K.N.: Social influence as a driver of engagement in a web-based health intervention. J. Med. Internet Res. 14 (1), e36 (2012)
Przybylski, A.K., Rigby, C.S., Ryan, R.M.: A motivational model of video game engagement. Rev. Gen. Psychol. 14 (2), 154–166 (2010). doi:10.1037/a0019440
Ramesh, A., Goldwasser, D., Huang, B., Daum, H., Getoor, L.: Modeling learner engagement in MOOCs using probabilistic soft logic. In: NIPS Workshop on Data Driven Education, pp. 1–7 (2013)
Reasons, S., Valadares, K., Slavkin, M.: Questioning the hybrid model: student outcomes in different course formats. J. Asynch. Learn Netw. 9 (1), 83–94 (2005)
Rosé, C., Wang, Y.-C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F.: Analyzing collaborative learning processes automatically: exploiting the advances of computational linguistics in computer-supported collaborative learning. Int. J. Comput. Support. Collab. Learn. 3 (3), 237–271 (2008)
Ryan, R.M., Deci, E.L.: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55 (1), 68–78 (2000)
Sabourin, J.L., Rowe, J.P., Mott, B.W., Lester, J.C.: Considering alternate futures to classify off-task behavior as emotion self-regulation: a supervised learning approach. J. Educ. Data Min. 5 (1), 9–38 (2013)
Scardamalia, M., Bereiter, C.: Knowledge building: theory, pedagogy, and technology. In: Sawyer, R.K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 97–118. Cambridge University Press, Cambridge (2006)
Schnotz, W., Kürschner, C.: A reconsideration of cognitive load theory. Educ. Psychol. Rev. 19, 469–508 (2007)
Schwamborn, A., Thillmann, H., Opfermann, M., Leutner, D.: Cognitive load and instructionally supported learning with provided and learner-generated visualizations. Comput. Hum. Behav. 27 (1), 89–93 (2011). doi:10.1016/j.chb.2010.05.028
Shane, J., Heckhausen, J., Lessard, J., Chen, C.S., Greenberger, E.: Career-related goal pursuit among post-high school youth: relations between personal control beliefs and control strivings. Motiv. Emot. 36 (2), 159–169 (2012). doi:10.1007/s11031-011-9245-6
Sharek, D.: The influence of flow in the measure of engagement. M.S. Master’s thesis, North Carolina State University. http://catalog.lib.ncsu.edu/record/NCSU2257724 (2010). Cited 15 Feb 2015
Sharek, D.: GridBlocker (Version 1.0) [Computer Game]. North Carolina State University, Raleigh (2011)
Sharek, D.: Investigating real-time predictors of engagement: implications for adaptive video games and online training. Ph.D. dissertation, North Carolina State University. http://catalog.lib.ncsu.edu/record/NCSU2700850 (2012). Cited 15 Feb 2015
Sheridan, T.B., Parasuraman, R.: Human-automation interaction. Rev. Hum. Factors Ergon. 1 (1), 89–129 (2005). doi:10.1518/155723405783703082
Shernoff, D.F., Abdi, B., Anderson, B., Csikszentmihalyi, M.: Flow in schools revisited: cultivating engaged learners and optimal learning environments. In: Furlong, M.J., Gilman, R., Huebner, E.S. (eds.) Handbook of Positive Psychology in Schools, 2nd edn. Taylor and Francis, Florence (2014)
Sherry, J.L.: Flow and media enjoyment. Commun. Theory 14 (4), 328–347 (2004). doi:10.1111/j.1468-2885.2004.tb00318.x
Shuell, T.J.: The role of the student in the learning from instruction. Contemp. Educ. Psychol. 13, 276–295 (1988)
Smetana, L.K., Bell, R.L.: Computer simulations to support science instruction and learning: a critical review of the literature. Int. J. Sci. Educ. 34 (9), 1337–1370 (2011). doi:10.1080/09500693.2011.605182
Sweller, J., Merrienboer, J., Paas, F.: Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251–296 (1998)
Tops, M., Boksem, M., Wester, A.E., Lorist, M.M., Meijman, T.F.: Task engagement and the relationships between the error-related negativity, agreeableness, behavioral shame proneness and cortisol. Psychoneuroendocrinology 31 (7), 847–858 (2006). doi:10.1016/j.psyneuen.2006.04.001
Vorderer, P., Klimmt, C., Ritterfeld, U.: Enjoyment: at the heart of media entertainment. Commun. Theory 14 (4), 388–408 (2004). doi:10.1111/j.1468-2885.2004.tb00321.x
Wickens, C.D.: Multiple resources and performance prediction. Theor. Issues Ergon. Sci. 3 (2), 159–177 (2002)
Wickens, C.D., Hollands, J.G.: Engineering Psychology and Human Performance, 3rd edn. Prentice-Hall, Upper Saddle River (2000)
Wiebe, E.N., Annetta, L.A.: Influences on visual attentional distribution in multimedia instruction. J. Educ. Multimed. Hypermedia 17 (2), 259–277 (2008)
Wiebe, E.N., Lamb, A., Hardy, M., Sharek, D.: Measuring engagement in video game-based environments: investigation of the user engagement scale. Comput. Hum. Behav. 32 (3), 123–132 (2014). doi:10.1016/j.chb.2013.12.001
Wigfield, A., Tonks, S., Klauda, S.L.: Expectancy-value theory. In: Wentzel, K., Miele, D. (eds.) Handbook of Motivation at School, pp. 55–75. Routledge, London (2009)
Wilkowski, J., Deutsch, A., Russell, D.: Student skill and goal achievement in the mapping with Google MOOC. In: Proceedings of the First ACM Conference on Learning @ Scale Conference, pp. 3–10. http://dl.acm.org/citation.cfm?id=2566240 (2014). Cited 15 Feb 2015
Winne, P.H., Baker, R.S.J.d.: The potentials of educational data mining for researching metacognition, motivation, and self-regulated learning. J. Educ. Data Min. 5 (1), 1–8 (2013)
Wise, A.F., Speer, J., Marbouti, F., Hsiao, Y.-T.: Broadening the notion of participation in online discussions: examining patterns in learners’ online listening behaviors. Instr. Sci. 41 (2), 323–343 (2012). doi:10.1007/s11251-012-9230-9
Wrzesniewski, A., Schwartz, B.: The secret of effective motivation. New York Times. http://nyti.ms/1qG0jiO7/14 (2014). Cited 15 Feb 2015
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Wiebe, E., Sharek, D. (2016). eLearning. In: O'Brien, H., Cairns, P. (eds) Why Engagement Matters. Springer, Cham. https://doi.org/10.1007/978-3-319-27446-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-27446-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27444-7
Online ISBN: 978-3-319-27446-1
eBook Packages: Computer ScienceComputer Science (R0)