Continuously analysing fine-grained student behaviours in an online collaborative learning environment

References

• Arguedas, M., Daradoumis, A., & Xhafa Xhafa, F. (2016). Analyzing how emotion awareness influences students’ motivation, engagement, self-regulation and learning outcome. Educational Technology and Society, 19(2), 87–103.
   Web of Science ®Google Scholar
• Barbalet, J. (1998). Emotion, social theory and social structure. Cambridge University Press.
   Google Scholar
• Becker, B. (2013). Learning analytics: Insights into the natural learning behavior of our students. Behavioral & Social Sciences Librarian, 32(1), 63–67. https://doi.org/10.1080/01639269.2013.751804
   Google Scholar
• Bell, K. R. (2014). Online 3.0 – The rise of the gamer educator the potential role of gamification in online education [EdD Thesis]. University of Pennsylvania.
   Google Scholar
• Berliner, D. C. (1990). What’s all the fuss about instructional time. In The nature of time in schools: Theoretical concepts, practitioner perceptions (pp. 3–35). Teachers College Press.
   Google Scholar
• Blikstein, P. (2013). Multimodal learning analytics. In D. Suthers, & K. Verbert (Eds.), Proceedings of the third International Conference on learning analytics and Knowledge (LAK ‘13) (pp. 102–106). ACM.
   Google Scholar
• Brawner, K. W., & Goldberg, B. S. (2012, June). Real-time monitoring of ECG and GSR signals during computer-based training. In International Conference on intelligent tutoring systems (pp. 72–77). Springer.
   Google Scholar
• Brusilovsky, P., Wade, V., & Conlan, O. (2007). From learning objects to adaptive content services for e-learning. In Architecture solutions for e-learning systems (pp. 243–261). Idea Group Inc., Editors.
   Google Scholar
• Bulger, M. E., Mayer, R. E., Almeroth, K. C., & Blau, S. D. (2008). Measuring learner engagement in computer-equipped college classrooms. Journal of Educational Multimedia and Hypermedia, 17(2), 129–143.
   Google Scholar
• Cardona, T. L. (2013). The effects of service-learning on student classroom engagement: A mixed-method study of the effects of service-learning on student engagement in eighth grade science classrooms [Doctoral dissertation]. Missouri Baptist University.
   Google Scholar
• Chen, C. H., & Chiu, C. H. (2016). Employing intergroup competition in multitouch design-based learning to foster student engagement, learning achievement, and creativity. Computers & Education, 103, 99–113. https://doi.org/10.1016/j.compedu.2016.09.007
   Web of Science ®Google Scholar
• Chen, C. H., Wang, K. C., & Lin, Y. H. (2015). The comparison of solitary and collaborative modes of game-based learning on students’ science learning and motivation. Journal of Educational Technology & Society, 18(2), 237–248.
   Web of Science ®Google Scholar
• Creswell, J. W. (2008). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson/Merrill Education.
   Google Scholar
• Cristobal, E., Flavian, C., & Guinaliu, M. (2007). Perceived e-service quality (PeSQ). Managing Service Quality: An International Journal, 17(3), 317–340. https://doi.org/10.1108/09604520710744326
   Google Scholar
• Dawson, S. (2010). ‘‘Seeing’ the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736–752. https://doi.org/10.1111/j.1467-8535.2009.00970.x
   Web of Science ®Google Scholar
• Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum Press.
   Google Scholar
• Dewey, J. (1913). Interest and effort in education. Riverside Press.
   Google Scholar
• Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 1–19). Elsevier.
   Google Scholar
• Ellis, R. A., Han, F., & Pardo, A. (2017). Improving learning analytics—combining observational and self-report data on student learning. Journal of Educational Technology and Society, 20(3), 158–169.
   Google Scholar
• Fane, J., MacDougall, C., Jovanovic, J., Redmond, G., & Gibbs, L. (2018). Exploring the use of emoji as a visual research method for eliciting young children’s voices in childhood research. Early Child Development and Care, 188(3), 359–374. https://doi.org/10.1080/03004430.2016.1219730
   Web of Science ®Google Scholar
• Frey, B. B.2018). The SAGE encyclopedia of educational research, measurement, and evaluation. Sage Publications.
   Google Scholar
• Gasevic, D., Jovanovic, J., Pardo, A., & Dawson, S. (2017). Detecting learning strategies with analytics: Links with self-reported measures and academic performance. Journal of Learning Analytics, 4(2), 113–128. https://doi.org/10.18608/jla.2017.42.10
   Google Scholar
• Hannula, M. S. (2006). Motivation in mathematics: Goals reflected in emotions. Educational Studies in Mathematics, 63(2), 165–178. https://doi.org/10.1007/s10649-005-9019-8
   Google Scholar
• Hartigan, J. (1975). Clustering algorithms. John Wiley & Sons.
   Google Scholar
• Hartigan, J., & Wong, M. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28(1), 100–108. https://doi.org/10.2307/2346830
   Google Scholar
• Hu, D. (2006). The effects of scaffolding on the performance of students in computer-based concept linking and retention of comprehension [Doctoral dissertation]. Virginia Polytechnic Institute and State University.
   Google Scholar
• 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. https://doi.org/10.1023/A:1020114231387
   Web of Science ®Google Scholar
• Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly, 24(4), 602–611. https://doi.org/10.2307/2392366
   Web of Science ®Google Scholar
• Johnson, D. W., & Johnson, R. T. (1996). Cooperation and the use of technology. In Handbook of research for educational communications and technology: A project of the association for Educational communications and technology (pp. 1017–1044). MacMillan.
   Google Scholar
• Keller, J. M. (1987). Development and use of the ARCS model of instructional design. Journal of Instructional Development, 10(3), 2–10. https://doi.org/10.1007/BF02905780
   Google Scholar
• Kim, C., & Pekrun, R. (2014). Emotions and motivation in learning and performance. In Handbook of research on educational communications and technology (pp. 65–75). Springer.
   Google Scholar
• Laurillard, D. (2002). Rethinking university teaching: A conversational framework for the effective use of learning technologies. 2nd edition. Routledge.
   Google Scholar
• Liljedahl, P. G. (2005). Mathematical discovery andaffect: theeffectof AHA! experiences on undergraduate mathematics students. International Journal of Mathematical Education in Science and Technology, 36(2–3), 219–234. https://doi.org/10.1080/00207390412331316997
   Google Scholar
• Liu, T. Y., & Chu, Y. L. (2010). Using ubiquitous games in an English listening and speaking course: Impact on learning outcomes and motivation. Computers & Education, 55(2), 630–643. https://doi.org/10.1016/j.compedu.2010.02.023
   Web of Science ®Google Scholar
• Lockyer, L., Patterson, J., & Harper, B. (2001). Ict in higher education: Evaluating outcomes for health education. Journal of Computer Assisted Learning, 17(3), 275–283. https://doi.org/10.1046/j.0266-4909.2001.00182.x
   Web of Science ®Google Scholar
• McLeod, D. B. (1988). Affective issues in mathematical problem solving: Some theoretical considerations. Journal for Research in Mathematics Education, 19(2), 134–141. https://doi.org/10.2307/749407
   Web of Science ®Google Scholar
• McLeod, D. B. (1994). Research on affect and mathematics learning in the JRME: 1970 to the present. Journal for Research in Mathematics Education, 25(6), 637–647. https://doi.org/10.2307/749576
   Web of Science ®Google Scholar
• McMahon, B., & Portelli, J. P. (2004). Engagement for what? Beyond popular discourses of student engagement. Leadership and Policy in Schools, 3(1), 59–76. https://doi.org/10.1076/lpos.3.1.59.27841
   Google Scholar
• Meece, J. L., Blumenfeld, P. C., & Hoyle, R. H. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80(4), 514. https://doi.org/10.1037/0022-0663.80.4.514
   Web of Science ®Google Scholar
• Ocker, R. J. (2001). Collaborative learning environments: Exploring student attitudes and satisfaction in face-to-face and asynchronous computer conferencing settings. Journal of Interactive Learning Research, 12(4), 427–448.
   Google Scholar
• Peixoto, F., Mata, L., Monteiro, V., Sanches, C., & Pekrun, R. (2015). The achievement emotions questionnaire: Validation for pre-adolescent students. European Journal of Developmental Psychology, 12(4), 472–481. https://doi.org/10.1080/17405629.2015.1040757
   Web of Science ®Google Scholar
• Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9
   Web of Science ®Google Scholar
• Pekrun, R., Elliot, A. J., & Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance. Journal of Educational Psychology, 101(1), 115. https://doi.org/10.1037/a0013383
   Web of Science ®Google Scholar
• Pekrun, R., Goetz, T., Perry, R. P., Kramer, K., Hochstadt, M., & Molfenter, S. (2004). Beyond test anxiety: Development and validation of the test emotions questionnaire (TEQ). Anxiety, Stress & Coping, 17(3), 287–316. https://doi.org/10.1080/10615800412331303847
   Web of Science ®Google Scholar
• Pekrun, R., Goetz, T., Titz, W., & Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37(2), 91–105. https://doi.org/10.1207/S15326985EP3702_4
   Web of Science ®Google Scholar
• Penuel, W. R., & Spillane, J. P. (2014). Learning sciences and policy design and implementation: Key concepts and tools for collaborative engagement. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. 2nd edition (pp. 649–667). Cambridge University Press.
   Google Scholar
• Plass, J. L., O’Keefe, P. A., Homer, B. D., Case, J., Hayward, E. O., Stein, M., & Perlin, K. (2013). The impact of individual, competitive, and collaborative mathematics game play on learning, performance, and motivation. Journal of Educational Psychology, 105(4), 1050. https://doi.org/10.1037/a0032688
   Web of Science ®Google Scholar
• Resta, P., & Laferrière, T. (2007). Technology in support of collaborative learning. Educational Psychology Review, 19(1), 65–83. https://doi.org/10.1007/s10648-007-9042-7
   Web of Science ®Google Scholar
• Retnowati, E., Ayres, P., & Sweller, J. (2017). Can collaborative learning improve the effectiveness of worked examples in learning mathematics? Journal of Educational Psychology, 109(5), 666. https://doi.org/10.1037/edu0000167
   Web of Science ®Google Scholar
• Rienties, B., & Rivers, B. A. (2014). Measuring and understanding learner emotions: Evidence and prospects. Learning Analytics Review, no. 1, December 2014, ISSN: 2057–7494. http://www.laceproject.eu/learning-analytics-review/measuring-and-understanding-learner-emotions/
   Google Scholar
• Roschelle, J., Rafanan, K., Bhanot, R., Estrella, G., Penuel, B., Nussbaum, M., & Claro, S. (2010). Scaffolding group explanation and feedback with handheld technology: Impact on students’ mathematics learning. Educational Technology Research and Development, 58(4), 399–419. https://doi.org/10.1007/s11423-009-9142-9
   Web of Science ®Google Scholar
• Rothkrantz, L., Datcu, D., Chiriacescu, I., & Chitu, A. (2009, August). Assessment of the emotional states of students during e-learning. In Proceedings of the International Conference on e-Learning and Knowledge Society, Berlin, pp. 77–82.
   Google Scholar
• Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68. https://doi.org/10.1037/0003-066X.55.1.68
   PubMed Web of Science ®Google Scholar
• Siemens, G. (2010). What are learning analytics? Elearnspace, http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/
   Google Scholar
• Skinner, E. A., Kindermann, T. A., & Furrer, C. J. (2009). A motivational perspective on engagement and disaffection. Educational and Psychological Measurement, 69(3), 493–525. https://doi.org/10.1177/0013164408323233
   Web of Science ®Google Scholar
• Slavin, R. E. (1985). An introduction to cooperative learning research: Learning to cooperate, cooperating to learn. Springer.
   Google Scholar
• Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409–426). Cambridge University Press.
   Google Scholar
• Strong, R., Silver, H., & Robinson, A. (1995). Strengthening student engagement: What do students want (and what really motivates them)? Educational Leadership, 53(1), 8–12.
   Web of Science ®Google Scholar
• Villavicencio, F. T., & Bernardo, A. B. (2013). Positive academic emotions moderate the relationship between self-regulation and academic achievement. British Journal of Educational Psychology, 83(2), 329–340. https://doi.org/10.1111/j.2044-8279.2012.02064.x
   PubMed Web of Science ®Google Scholar
• Vujovic, M., Tassani, S., & Hernández-Leo, D. (2019, September). Motion capture as an instrument in multimodal collaborative learning analytics. In European Conference on Technology enhanced learning (pp. 604–608). Springer.
   Google Scholar
• Wang, H., Prendinger, H., & Igarashi, T. (2004). April). communicating emotions in online chat using physiological sensors and animated text. In Chi'04 extended abstracts on human factors in computing systems (pp. 1171–1174). ACM.
   Google Scholar
• Wellman, B., & Hampton, K. (1999). Living networked on and offline. Contemporary Sociology, 28(6), 648–654. https://doi.org/10.2307/2655535
   Web of Science ®Google Scholar
• Whicker, K. M., Bol, L., & Nunnery, J. A. (1997). Cooperative learning in the secondary mathematics classroom. The Journal of Educational Research, 91(1), 42–48. https://doi.org/10.1080/00220679709597519
   Web of Science ®Google Scholar
• Wise, A. F. (2014). Designing pedagogical interventions to support student use of learning analytics. In Proceedings of the fourth International Conference on learning analytics & knowledge (pp. 203–211). ACM.
   Google Scholar
• Zhou, M., & Winne, P. H. (2012). Modeling academic achievement by self-reported versus traced goal orientation. Learning and Instruction, 22(6), 413–419. doi:10.1016/j.learninstruc.2012.03.004
   Web of Science ®Google Scholar