Learner Engagement in Blended Learning Environments: A Conceptual Framework





learner engagement, cognitive engagement, emotional engagement, blended learning, hybrid learning, theory, conceptual framework


Learner engagement correlates with important educational outcomes, including academic achievement and satisfaction.  Although research is already exploring learner engagement in blended contexts, no theoretical framework guides inquiry or practice, and little consistency or specificity exists in engagement definitions and operationalizations.  Developing definitions, models, and measures of the factors that indicate learner engagement is important to establishing whether changes in instructional methods (facilitators) result in improved engagement (measured via indicators).  This article reviews the existing literature on learner engagement and identifies constructs most relevant to learning in general and blended learning in particular.  We present a possible conceptual framework for engagement that includes cognitive and emotional indicators, offering examples of research measuring these engagement indicators in technology-mediated learning contexts.  Finally, we suggest future studies to test the framework, which we believe can support advances in blended learning engagement research that is increasingly real-time, minimally intrusive, and maximally generalizable across subject matter contexts.

Author Biographies

Lisa R. Halverson, Brigham Young University

Lisa R. Halverson completed her Ph.D. in Instructional Psychology & Technology from Brigham Young University.  In addition to researching blended learning engagement, she has published on high-impact publications in blended learning research.  She adjuncts for BYU and for George Mason’s Blended and Online Learning in Schools Program.

Charles R. Graham, Brigham Young University

Charles R. Graham is a Professor at Brigham Young University who studies technology-mediated teaching and learning, with a focus on the design and evaluation of blended and online learning environments.  His current research publications can be found online at: https://sites.google.com/site/charlesrgraham/.


Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665–694. Retrieved from http://www.jstor.org/stable/3250951

Ainley, M. (2012). Students’ interest and engagement in classroom activities. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 283–302). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

Appleton, J. J. (2012). Systems consultation: Developing the assessment-to-intervention link with the Student Engagement Instrument. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 725–741). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369–386. https://doi.org/10.1002/pits.20303

Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology, 44(5), 427–445. https://doi.org/10.1016/j.jsp.2006.04.002

Arnone, M. P., Small, R. V., Chauncey, S. A., & McKenna, H. P. (2011). Curiosity, interest and engagement in technology-pervasive learning environments: A new research agenda. Educational Technology Research and Development, 59(2), 181–198. https://doi.org/10.1007/s11423-011-9190-9

Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion sensors go to school. In V. Dimitrova, R. Mizoguchi, B. Du Boulay, & A. C. Graesser (Eds.), Artificial intelligence in education (pp. 17–24). Amsterdam, NL: IOS Press.

Aspden, L., & Helm, P. (2004). Making the connection in a blended learning environment. Educational Media International, 41(3), 245–252. https://doi.org/10.1080/09523980410001680851

Astin, A. W. (1984). Student involvement: A developmental theory for higher education. Journal of College Student Personnel, 40(5), 518–529. Retrieved from https://www.middlesex.mass.edu/tutoringservices/downloads/astininv.pdf

Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89-195). New York, NY: Academic Press.

Azevedo, R. (2015, March). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50, 84-94. https://doi.org/10.1080/00461520.2015.1004069

Azevedo, R. & Bernard, R. M. (1995). A meta-analysis of the effects of feedback in computer-based instruction. Journal of Educational Computing Research, 13(2), 111-127.

Baker, R. S. J. d., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). 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. International Journal of Human-Computer Studies, 68(4), 223–241. https://doi.org/10.1016/j.ijhcs.2009.12.003

Bangert-Drowns, R. L., & Pyke, C. (2001). A taxonomy of student engagement with educational software: An exploration of literate thinking with electronic text. Journal of Educational Computing Research, 24(3), 213–234. https://doi.org/10.2190/0CKM-FKTR-0CPF-JLGR

Beck, J. E. (2004). Using response times to model student disengagement. Paper presented at the ITS2004 Workshop on Social and Emotional Intelligence in Learning Environments, Maceió, Alagoas, BR.

Bergland, C. (2017, May 19). Face-to-face connectedness, oxytocin, and your vague nerve [Blog post]. Retrieved from https://www.psychologytoday.com/blog/the-athletes-way/201705/face-face-connectedness-oxytocin-and-your-vagus-nerve

Berlyne, D. E. (1978). Curiosity and learning. Motivation and Emotion, 2(2), 97–175. Retrieved from http://link.springer.com/article/10.1007/BF00993037

Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, DC: SRI International. Retrieved from http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf

Bollinger, D. U. & Inan, F. A. (2012). Development and validation of the Online Student Connectedness Survey (OSCS). International Review of Research in Open & Distance Learning, 13(2), 41-65. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1171/2206

Burkhardt, H., & Schoenteld, A. H. (2003). Improving educational research: Toward a more useful, more influential, and better-funded enterprise. Educational Researcher, 32(9), 3-14. https://doi.org/l0.3102/0013189X032009003

Calvo, R. A., & Mello, S. D. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5520655

Campanelli, P. (2008). Testing survey questions. In E. D. de Leeuw, J. J. Hox, & D. A. Dillman (Eds.), International handbook of survey methodology (pp. 176-200). New York, NY: Lawrence Erlbaum Associates.

Carr, S. (2000, February 11). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 46. Retrieved from http://chronicle.com.erl.lib.byu.edu/article/As-Distance-Education-Comes-of/14334/

Chapman, C., Laird, J., & Kewalramani, A. (2011). Trends in high school dropout and completion rates in the United States: 1972–2008. Population. Washington, DC.: National Center for Educational Statistics.

Christenson, S. L., Reschly, A. L., & Wylie, C. (2012). Handbook of research on student engagement. New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

Coates, H. (2006). Student engagement in campus-based and online education: University connections. New York, NY: Routledge.

Coates, H. (2007). A model of online and general campus-based student engagement. Assessment & Evaluation in Higher Education, 32(2), 121–141. https://doi.org/10.1080/02602930600801878

Cocea, M., & Weibelzahl, S. (2011). Disengagement detection in online learning: Validation studies and perspectives. IEEE Transactions on Learning Technologies, 4(2), 114–124. https://doi.org/10.1109/TLT.2010.14

Conrad, D. L. (2010). Engagement, excitement, anxiety, and fear: Learners’ experiences of starting an online course. The American Journal of Distance Education, 16(4), 205–226. https://doi.org/10.1207/S15389286AJDE1604

Conrad, D. L., & Kanuka, H. (1999). Learning in safety and comfort: Towards managing online learning transactions. Paper presented at the Association for Advancement of Computers in Education Conference, Seattle, WA. https://doi.org/10.1207/s15327752jpa8502

Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy and relatedness: A motivational analysis of self-system processes. In M. Gunnar & L.A. Sroufe (Eds.), Minnesota Symposium on Child Psychology: Vol. 23. Self processes in development (pp. 43-77). Chicago, IL: University of Chicago Press.

Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7). Retrieved from http://pareonline.net/pdf/v10n7.pdf

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York, NY: Harper-Perennial.

Davis, M. H., & McPartland, J. M. (2012). High school reform and student engagement. In S. L. Christenson, A. L., Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 515-539). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

Dennerlein, J., Becker, T., Johnson, P., Reynolds, C., Picard, R.W. (2003). Frustrating computer users increases exposure to physical factors. Paper presented at the Proceedings of the IEA, Seoul, Korea. Retrieved from http://www.researchgate.net/publication/2832780_Frustrating_Computers_Users_Increases/file/60b7d528f8535cac39.pdf

Dewey, J. (1910). How we think. Boston, MA: D. C. Heath.

Diaz, D. P. (2002). Online drop rates revisited. The Technology Source. Retrieved from http://technologysource.org/article/online_drop_rates_revisited/

Dillman, D. A., Smith, J. D., & Christian, L. M. (2009). Internet, mail, and mixed-mode surveys: The tailored design method (3rd ed.). Hoboken, NJ: John Wiley & Sons.

D’Mello, S. (2013). A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology, 105(4), 1082–1099. https://doi.org/10.1037/a0032674

D’Mello, S., & Graesser, A. (2011). The half-life of cognitive-affective states during complex learning. Cognition & Emotion, 25, 1299–1308.

D’Mello, S. K., & Graesser, A. (2012). AutoTutor and Affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2(4), 1–39. https://doi.org/10.1145/2395123.2395128

D’Mello, S. K., Jackson, G. T., Craig, S. D., Morgan, B., Chipman, P., White, H., Person, N., Kort, B., el Kaliouby, R., Picard, R., & Graesser, A. C. (2008). AutoTutor detects and responds to learners affective and cognitive states. Workshop on Emotional and Cognitive issues in ITS held in conjunction with Ninth International Conference on Intelligent Tutoring Systems. Retrieved from

D’Mello, S. K., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170. https://doi.org/10.1016/j.learninstruc.2012.05.003

D’Mello, S. K., Lehman, B. A., & Person, N. (2010). Monitoring affect states during effortful problem solving activities. International Journal of Artificial Intelligence in Education, 20(4), 361-389.

Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4), 169-200.

Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., & Rodríguez-Ardura, I. (2014). Modelling students’ flow experiences in an online learning environment. Computers and Education, 71, 111–123. https://doi.org/10.1016/j.compedu.2013.09.012

Fabrigar, L.R., & Wegener, D. T. (2012). Exploratory factor analysis. New York, NY: Oxford University Press.

Farragher, P., & Yore, L. D. (1997). The effects of embedded monitoring and regulating devices on the achievement of high school students learning science from text. School Science and Mathematics, 97(2), 87–95.

Filak, V. F., & Sheldon, K. M. (2008). Teacher support, student motivation, student need satisfaction, and college teacher course evaluations: Testing a sequential path model. Educational Psychology, 28(6), 711–724. https://doi.org/10.1080/01443410802337794

Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59(2), 117–142. Retrieved from http://rer.sagepub.com/content/59/2/117.short

Fleeson, W. (2007). Using experience sampling and multilevel modeling to study person-situation interactionist approaches to positive psychology. In A. D. Ong & M. H. M. Van Dulmen (Eds.), Oxford handbook of methods in positive psychology (pp. 487-500). New York, NY: Oxford University Press.

Floyd, F. J., & Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7(3), 286–299. https://doi.org/10.1037//1040-3590.7.3.286

Frazee, R. V. (2003). Using relevance to facilitate online participation in a hybrid course. Educause Quarterly, 4, 67–69.

Fredricks, J. A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In What do children need to flourish? Conceptualizing and measuring indicators of positive development (pp. 419–446). https://doi.org/10.1207/s15327752jpa8502

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. Retrieved from http://rer.sagepub.com/content/74/1/59.short

Fredricks, J., McCo!skey, W., Meli, J., Mordica, J., Montrosse, B., & Mooney, K. (2011). Measuring student engagement in upper elementary through high school: A description of 21 instruments (Issues & Answers Report, REL 2011-No. 098). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southeast. Retrieved from http://www.talkgroups-mentors.org/pdfs/research/2011 Student Engagement UNC.pdf

Fredrickson, B. L. (1998). What good are positive emotions? Review of General Psychology 2(3), 300–319. https://doi.org/10.1037/1089-2680.2.3.300

Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218–226. https://doi.org/10.1037/0003-066X.56.3.218

Furlong, M. J., Whipple, A. D., St. Jean, G., Simental, J., Soliz, A., & Punthuna, S. (2003). Multiple contexts of school engagement: Moving toward a unifying framework for educational research and practice. The California School Psychologist, 8, 99–113.

Gable, R. K. (1986). Instrument development in the affective domain. Boston, MA: Kluwer-Nijhoff Pub.

Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7–23. https://doi.org/10.1080/08923640109527071

Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7, 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001

Gedik, N., Kiraz, E., & Ozden, Y. (2012). The optimum blend: Affordances and challenges of blended learning for students. Turkish Online Journal of Qualitative Inquiry, 3(3), 102–117.

Gettinger, M. & Walte, M. J. (2012). Classroom strategies to enhance academic engaged time. In S. L. Christenson, A. L., Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 653-673). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

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. https://doi.org/10.1080/00223980.1994.9712742

Gobert, J. D., Baker, R. S., & Wixon, M. B. (2015). Operationalizing and detecting disengagement within online science microworlds. Educational Psychologist, 50(1), 43–57. http://doi.org/10.1080/00461520.2014.999919

Graesser, A. C., Lu, S., Olde, B. a, Cooper-Pye, E., & Whitten, S. (2005). Question asking and eye tracking during cognitive disequilibrium: comprehending illustrated texts on devices when the devices break down. Memory & Cognition, 33(7), 1235–1247. https://doi.org/10.3758/BF03193225

Graesser, A., & D’Mello, S. K. (2011). Theoretical perspectives on affect and deep learning. In R. A. Calvo & S. K. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 11–21). New York, NY: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-1-4419-9625-1_2

Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14–30. https://doi.org/10.1080/00461520.2014.989230

Handelsman, M. M., Briggs, W. L., Sullivan, N., & Towler, A. (2005). A measure of college student course engagement. The Journal of Education, 98(3), 184–191. Retrieved from http://www.tandfonline.com/doi/abs/10.3200/JOER.98.3.184-192

Harzing, A. W. (2017). Publish or perish (Version 5) [Computer software]. Retrieved from http://www.harzing.com/pop.htm

Hazlett, R. L., & Benedek, J. (2007). Measuring emotional valence to understand the user’s experience of software. International Journal of Human-Computer Studies, 65(4), 306–314. https://doi.org/10.1016/j.ijhcs.2006.11.005

Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007).

Experience sampling method: Measuring the quality of everyday life. Thousand Oaks, CA: Sage.

Hidi, S. (1990). Interest and its contribution as a mental resource for learning. Review of Educational Research, 60(4), 549–571. https://doi.org/10.3102/00346543060004549

Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.

Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60(3), 50–68.

Horn, M., & Staker, H. (2015). Blended: Using disruptive innovation to improve schools. San Francisco: Jossey-Bass.

Hu, S., & Kuh, G. D. (2002). Being (dis)engaged in educationally purposeful activities: The influences of student and institutional characteristics. Research in Higher Education, 43(5), 555-575.

Hughes, J. N., Luo, W., Kwok, O.-M., & Loyd, L. K. (2008). Teacher-student support, effortful engagement, and achievement: A 3-year longitudinal study. Journal of Educational Psychology, 100(1), 1–14. https://doi.org/10.1037/0022-0663.100.1.1

Hussain, M. S., AlZoubi, O., Calvo, R. A.,& D’Mello, S. K. (2011). Affect detection from multichannel physiology during learning. In S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education (pp. 131–138). New York, NY: Springer.

James, W. (1890/1950). The principles of psychology. New York, NY: Dover.

Janosz, M. (2012). Part IV commentary: Outcomes of engagement and engagement as an outcome: Some consensus, divergences, and unanswered questions. In S. L. Christenson, A. L., Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 695-703). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

Jayanti, R. K., McManamon, M. K., & Whipple, T. W. (2004). The effects of aging on brand attitude measurement. Journal of Consumer Marketing, 21(4), 264–273.

Jimerson, S., Campos, E., & Greif, J. (2003). Toward an understanding of definitions and measures of school engagement and related terms. The California School Psychologist, 8, 7–27. Retrieved from http://casel.org/wp-content/uploads/CSP2003volume_81.pdf#page=9

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

Keller, J. M. (1987). Development and use of the ARCS model of motivational design. Journal of Instructional Development, 10(3), 2–10.

Keller, J. M. (2008). First principles of motivation to learn and e3-learning. Distance Education, 29(2), 175–185. https://doi.org/10.1080/01587910802154970

Kelley, T. L. (1927). Interpretation of educational measurements. Yonkers, NY: World Book.

Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: Analyzing learner subpopulations in massive open online courses. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 170–179). Leuven, Belgium: ACM.

Klem, A. M., & Connell, J. P. (2004). Relationships matter: Linking teacher support to student engagement and achievement. Journal of School Health, 74(7), 262–73. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15493703

Kong, S. C. (2011). An evaluation study of the use of a cognitive tool in a one-to-one classroom for promoting classroom-based dialogic interaction. Computers & Education, 57(3), 1851–1864. https://doi.org/10.1016/j.compedu.2011.04.008

Kort, B., Reilly, R., & Picard, R. W. (2001). An affective model of interplay between emotions and learning: Reengineering educational pedagogy—building a learning companion. Proceedings of IEEE International Conference on Advanced Learning Technologies (pp.43-46). Madison, WI.

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

Kuh, G. D. (2009, Spring). The national survey of student engagement: Conceptual and empirical foundations. New Directions for Institutional Research, 141, 5–20. https://doi.org/10.1002/ir

Kuh, G. D., Cruce, T. M., Shoup, R., Kinzie, J., Gonyea, R. M., & Gonyea, M. (2008). Unmasking the effects of student engagement on first-year college grades and persistence. The Journal of Higher Education, 79(5), 540–563.

Ladd, G. W., & Dinella, L. M. (2009). Continuity and change in early school engagement: Predictive of children’s achievement trajectories from first to eighth grade? Journal of Educational Psychology, 101(1), 190–206. https://doi.org/10.1037/a0013153

Lehman, B., D’Mello, S., & Graesser, A. (2012). Confusion and complex learning during interactions with computer learning environments. The Internet and Higher Education, 15(3), 184–194. https://doi.org/10.1016/j.iheduc.2012.01.002

Lehman, B., D’Mello, S., & Person, N. (2008). All alone with your emotions: An analysis of student emotions during effortful problem solving activities. Paper presented at the Workshop on Emotional and Cognitive issues in ITS at the Ninth International Conference on Intelligent Tutoring Systems. https://doi.org/10.1207/s15327752jpa8502

Lepper, M. R., Woolverton, M., Mumme, D. L., & Gurtner, J-L. (1993). Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. In S. P. Lajoie & S. J. Derry (Eds.), Computers as cognitive tools. Technology in education (pp. 75-105). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

Lewin, K. (1951). Field theory in social science. New York, NY: Harper & Brothers Publishers.

Locke, L. F., Spirduso, W. W., & Silverman, S. J. (2014). Proposals that work. Sage.

Loorbach, N., Peters, O., Karreman, J., & Steehouder, M. (2015). Validation of the Instructional Materials Motivation Survey (IMMS) in a self-directed instructional setting aimed at working with technology. British Journal of Educational Technology, 46(1), 204-218. https://doi.org/10.1111/bjet.12138

Lopatovska, I., & Arapakis, I. (2011). Theories, methods and current research on emotions in library and information science, information retrieval and human-computer interaction. Information Processing and Management, 47(4), 575–592. https://doi.org/10.1016/j.ipm.2010.09.001

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008

Martin, A. J. (2007). Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77(2), 413–440. https://doi.org/10.1348/000709906X118036

McCormick, A. C., Gonyea, R. M., & Kinzie, J. (2013, May/June). Refreshing engagement: NSSE at 13. Change: The Magazine of Higher Learning, 45(3), 6–15.

Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The Effectiveness of Online and Blended Learning: A Meta-Analysis of the Empirical Literature.pdf. Teachers College Record, 115(3). Retrieved from http://www.tcrecord.org/library/content.asp?contentid=16882

Mehl, M. R., & Conner, T. S. (2012). Handbook of research methods for studying daily life. New York, NY: The Guilford Press.

Meyer, K. A. (2014). Student engagement in online learning: What works and why. ASHE Higher Education Report, 40(6), 1–114. https://doi.org/10.1002/aehe.20018

Miller, B. W. (2015, March). Using reading times and eye-movements to measure cognitive engagement. Educational Psychologist, 50, 31–42. https://doi.org/10.1080/00461520.2015.1004068

Miller, R. B., Greene, B. A., Montalvo, G. P., Ravindran, B., & Nichols, J. D. (1996). Engagement in academic work: The role of learning goals, future consequences, pleasing others, and perceived ability. Contemporary Educational Psychology, 21, 388–422.

Milligan, C., Littlejohn, A., & Margaryan, A. (2013). Patterns of engagement in connectivist MOOCs. MERLOT Journal of Online Learning and Teaching, 9(2), 149–159.

Milne, C., & Otieno, T. (2007). Understanding engagement: Science demonstrations and emotional energy. Science Education, 91, 523–553. https://doi.org/10.1002/sce

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054.

Moskal, P., Dziuban, C., & Hartman, J. (2012). Blended learning: A dangerous idea? The Internet and Higher Education, 18, 15-23. https://doi.org/10.1016/j.iheduc.2012.12.001

Nelson Laird, T. F., & Kuh, G. F. (2005). Student experiences with information technology and their relationship to other aspects of student engagement. Research in Higher Education, 46(2), 211–33.

Norberg, A., Dziuban, C. D., & Moskal, P. D. (2011). A time-based blended learning model. On the Horizon, 19(3), 207-216. https://doi.org/10.1108/10748121111163913

NSSE. (2014, January). From benchmarks to engagement indicators and high-impact practices. Bloomington, IN: National Survey of Student Engagement. Retrieved from http://nsse.iub.edu/pdf/Benchmarks%20to%20Indicators.pdf

Nystrand, M., & Gamoran, A. (1991). Instructional discourse, student engagement, and literature achievement. Research in the Teaching of English, 25(3), 261–290. Retrieved from http://www.jstor.org/stable/10.2307/40171413

O’Brien, H. L., & Toms, E. G. (2008). What is user engagement? A conceptual framework for defining user engagement with technology. Journal of the American Society for Information Science and Technology, 59(6), 938–955. https://doi.org/10.1002/asi

O’Neill, T. A., Deacon, A., Larson, N., Hoffart, G., Brennan, R., Eggermont, M., & Rosehart, W. (2015). Life-long learning, conscientious disposition, and longitudinal measures of academic engagement in engineering design teamwork. Learning and Individual Differences, 39, 124–131. https://doi.org/10.1016/j.lindif.2015.03.022

Pajares, F. (1996). Self-efficacy beliefs and mathematical problem-solving of gifted students. Contemporary Educational Psychology, 21, 325–344. https://doi.org/S0361476X96900259 [pii]

Parks, A. C., Schueller, S., & Tasimi, A. (2013). Increasing happiness in the general population: Empirically supported self-help? In I. Boniwell & S. David (Eds.), Oxford handbook of happiness. Oxford, UK: Oxford University Press.

Patrick, B. C., Skinner, E. A., & Connell, J. P. (1993). What motivates children’s behavior and emotion? Joint effects of perceived control and autonomy in the academic domain. Journal of Personality and Social Psychology, 65(4), 781–791. https://doi.org/10.1037//0022-3514.65.4.781

Paul, A. M. (2014, September 17). Computer tutors that can read students’ emotions. Retrieved from http://hechingerreport.org/content/computer-tutors-can-read-students-emotions_17371/

Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In R. A. Calvo & S. K. D’Mello (Eds.), New perspectives on affect and learning technologies (pp. 23–39). New York, NY: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-1-4419-9625-1_3

Pekrun, R., & Linnenbrink-Garcia, L. (2012). Academic emotions and student engagement. In n S. L. Christenson, A. L., Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 259–282). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., … Strohecker, C. (2004). Affective learning—A manifesto. BT Technology Journal, 22(4), 253–269. https://doi.org/10.1023/B:BTTJ.0000047603.37042.33

Picciano, A. G. (2009). Blending with purpose: The multimodal model. Journal of Asynchronous Learning Networks, 13(1), 7-18.

Picciano, A. G. (2014). Big data and learning analytics in blended learning environments: Benefits and concerns. International Journal of Interactive Multimedia and Artificial Intelligence, 2(7), 35–43. http://doi.org/10.9781/ijimai.2014.275

Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459–470. https://doi.org/10.1016/S0883-0355(99)00015-4

Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037//0022-0663.82.1.33

Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor, MI: The University of Michigan.

Pour, P. A., Hussein, S., AlZoubi, O., D’Mello, S., & Calvo, R. (2010). The impact of system feedback on learners’ affective and physiological states. In J. Kay & V. Aleven (Eds.), Proceedings of 10th International Conference on Intelligent Tutoring Systems (pp. 264–273). Berlin, Germany: Springer-Verlag.

Ramesh, A., Goldwasser, D., Huang, B., Daum, H., & Getoor, L. (2013). Modeling learner engagement in MOOCs using probabilistic soft logic. NIPS Workshop on Data Driven Education.

Redcay, E., Dodell-Feder, D., Pearrow, M. J., Mavros, P. L., Kleiner, M., Gabrieli, J. D. E., & Saxe, R. (2010). Live face-to-face interaction during fMRI: A new tool for social cognitive neuroscience. NeuroImage, 50(4), 1639–1647. https://doi.org/10.1016/j.neuroimage.2010.01.052

Redmond, P., Heffernan, A., Abawi, L., Brown, A., & Henderson, R. (2018). An Online Engagement Framework for Higher Education. Online Learning, 22(1). http://dx.doi.org/10.24059/olj.v22i1.1175

Reeve, J. (2012). A self-determination theory perspective on student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 149–172). New York, NY: Springer.

Reeve, J. (2013). How students create motivationally supportive learning environments for themselves: The concept of agentic engagement. Journal of Educational Psychology, 105(3), 579–595. https://doi.org/10.1037/a0032690

Reeve, J., & Tseng, C.-M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257–267. https://doi.org/10.1016/j.cedpsych.2011.05.002

Reio, T. G., Petrosko, J. M., Wiswell, A. K., & Thongsukmag, J. (2006). The measurement and conceptualization of curiosity. The Journal of Genetic Psychology, 167(2), 117–35. https://doi.org/10.3200/GNTP.167.2.117-135

Renninger, K. A., & Bachrach, J. E. (2015, March). Studying triggers for interest and engagement using observational methods. Educational Psychologist, 50, 58–69. https://doi.org/10.1080/00461520.2014.999920

Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 3-19). New York, NY: Springer.

Ritzhaupt, A. D., Sessums, C., & Johnson, M. (2012). Where should educational technologists publish their research? An examination of peer-reviewed journals within the field of educational technology and factors influencing publication choice. Educational Technology, 52(6), 47 – 56.

Rowe, J. P., Shores, L. R., Mott, B. W., & Lester, J. C. (2011). Integrating learning, problem solving, and engagement in narrative-centered learning environments. International Journal of Artificial Intelligence in Education, 21(2), 115–133. https://doi.org/10.3233/JAI-2011-019

Rumberger, R. W., & Rotermun, S. (2012). The relationship between engagement and high school dropout. In S. L. Christenson, A. L., Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 491-513). New York, NY: Springer.

Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172. https://doi.org/10.1037/0033-295X.110.1.145

Sabourin, J., Mott, B., & Lester, J. (2011). Modeling learner affect with

theoretically grounded dynamic bayesian networks. In S. D’Mello, A. Graesser, B. Schuller, & J. Martin (Eds.), Proceedings of the Fourth International Conference on Affective Computing and Intelligent Interaction (pp. 286–295). Berlin, Germany: Springer-Verlag. https://doi.org/10.1007/978-3-642-24600-5_32

Schraw, G. (2010). Measuring self-regulation in computer-based learning environments. Educational Psychologist, 45(4), 258–266. https://doi.org/10.1080/00461520.2010.515936

Schunk, D. H., & Mullen, C. A. (2012). Self-efficacy as an engaged learner. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 219-235). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-2018-7

Segura, S. L., & Gonzalez-Roma, V. (2003). How do respondents construe ambiguous response formats of affect items? Journal of Personality and Social Psychology, 85(5), 956-968.

Seligman, M. E. P., Ernst, R. M., Gillham, J., Reivich, K., & Linkins, M. (2009). Positive education: Positive psychology and classroom interventions. Oxford Review of Education, 35(3), 293–311. https://doi.org/10.1080/03054980902934563

Senko, C., & Miles, K. M. (2008). Pursuing their own learning agenda: How mastery-oriented students jeopardize their class performance. Contemporary Educational Psychology, 33, 561-583.

Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721–1731. https://doi.org/10.1016/j.compedu.2010.07.017

Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153-189.

Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement. Educational Psychologist, 50(1), 1–13. https://doi.org/10.1080/00461520.2014.1002924

Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year. Journal of Educational Psychology, 85(4), 571–581.

Skinner, E., Furrer, C., Marchand, G., & Kindermann, T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology, 100(4), 765–781. https://doi.org/10.1037/a0012840

Skinner, E. A., Kindermann, T. A., Connell, J. P., & Wellborn, J. G. (2009). Engagement and disaffection as organizational constructs in the dynamics of motivational development. In K. Wentzel & A. Wigfield (Eds.), Handbook of motivation at school (pp. 223–246). Malwah, NJ: Erlbaum.

Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on engagement and disaffection: Conceptualization and assessment of children’s behavioral and emotional participation in academic activities in the classroom. Educational and Psychological Measurement, 69(3), 493–525. https://doi.org/10.1177/0013164408323233

Skinner, E. A. & Pitzer, J. (2012). Developmental dynamics of student engagement, coping, and everyday resilience. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 21-44). New York, NY: Springer.

Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, “translations” and boundary objects: Amateurs and professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39. Social Studies of Science, 19(3), 387-420. https://doi.org/10.1177/030631289019003001

Stein, N. L., & Levine, L. J. (1991). Making sense out of emotion: The representation and use of goal-structured knowledge. In Memories, thoughts, and emotions: Essays in honors of George Mandler (pp. 295–322).

Sun, J. C.-Y. (2013). Influence of polling technologies on student engagement: An analysis of student motivation, academic performance, and brainwave data. Computers & Education, 72, 80-89. https://doi.org/10.1016/j.compedu.2013.10.010

Sun, J. C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204. https://doi.org/10.1111/j.1467-8535.2010.01157.x

Tan, L., Sun, X., & Khoo, S. T. (2014). Can engagement be compared? Measuring academic engagement for comparison. In International Conference on Educational Data Mining (pp. 213–216). Retrieved from http://educationaldatamining.org/EDM2014/uploads/procs2014/short papers/213_EDM-2014-Short.pdf

Tellegen, A., & Atkinson, G. (1974). Openness to absorbing and self-altering experiences (“absorption”), a trait related to hypnotic susceptibility. Journal of Abnormal Psychology, 83(3), 268–277. https://doi.org/10.1037/h0036681

Tempelaar, D. T., Niculescu, A., Rienties, B., Gijselaers, W. H., & Giesbers, B. (2012). How achievement emotions impact students’ decisions for online learning, and what precedes those emotions. The Internet and Higher Education, 15(3), 161–169. https://doi.org/10.1016/j.iheduc.2011.10.003

Thorndike, E. L. (1913). An introduction to the theory of mental and social measurements (2nd ed.). New York, NY: Teachers College, Columbia University.

Toyama, T., Sonntag, D., Orlosky, J., & Kiyokawa, K. (2015). Attention engagement and cognitive state analysis for augmented reality text display functions. In IUI 2015 (pp. 322–332). Atlanta, GA: ACM Press. https://doi.org/10.1145/2678025.2701384

Wang, Z., Bergin, C., & Bergin, D. A. (2014). Measuring engagement in fourth to twelfth grade classrooms: The Classroom Engagement Inventory. School Psychology Quarterly, 29(4), 517–535. https://doi.org/10.1037/spq0000050

Wang, M. T., Chow, A., Hofkens, T., & Salmela-Aro, K. (2015). The trajectories of student emotional engagement and school burnout with academic and psychological development: findings from Finish adolescents. Learning and Instruction, 36, 57-65. http://dx.doi.org/10.1016/j.learninstruc.2014.11.004.

Wang, M. T., & Degol, J. (2014). Staying engaged: knowledge and research needs in student engagement. Child Development Perspectives, 8(3), 137-143.

Wang, M.-T., & Eccles, J. S. (2013). School context, achievement motivation, and academic engagement: A longitudinal study of school engagement using a multidimensional perspective. Learning and Instruction, 28, 12–23. https://doi.org/10.1016/j.learninstruc.2013.04.002

Watson, D., & Clark, L. A. (1994). Emotions, moods, traits and temperaments: Conceptual distinctions and empirical findings. In P. Ekman & R. J. Davidson (Eds.), The nature of emotion: Fundamental questions (pp. 89–93). New York, NY: Oxford University Press.

Winne, P. H., & Baker, R. S. J. d. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1), 1–8.

Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225-240.

Yazzie-Mintz, E. (2010). Charting the path from engagement to achievement: A report on the 2009 High School Survey of Student Engagement. Bloomington, IN: Center for Evaluation & Education Policy.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/0.1207/s15430421tip4102_2






Student Issues, Pedagogy, Tools, and Support