What multimodal data can tell us about the students’ regulation of their learning process?

Learning and Instruction

Volumn 72, Issue , 2021, Pages

  Järvelä, Sanna ^a   Malmberg, Jonna ^a   Haataja, Eetu ^a   Sobocinski, Marta ^a   Kirschner, Paul A ^a,b  

^a UNIVERSITY OF OULU   (Finland)

^b OPEN UNIVERSITY OF THE NETHERLANDS   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85065164451     PISSN: 09594752     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.learninstruc.2019.04.004     Document Type: Article

References (68)

1. Ahonen, L., Cowley, B., Torniainen, J., Ukkonen, A., Vihavainen, A., Puolamäki, K., Cognitive collaboration found in cardiac Physiology: Study in classroom environment. PLoS One 11:7 (2016), 1–16 https://doi.org/10.1371/journal.pone.0159178.
2. Antonietti, A., Colombo, B., Di Nuzzo, C., Metacognition in self-regulated multimedia learning: Integrating behavioural, psychophysiological and introspective measures. 2014, Learning, Media and Technology, 1–23 http://doi.org/10.1080/17439884.2014.933112 April 2015.
3. Azevedo, R., Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist 50:1 (2015), 84–94 http://doi.org/10.1080/00461520.2015.1004069.
4. Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., et al. Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. Azevedo, R., Aleven, V., (eds.) International handbook of metacognition and learning technologies, 2013, Springer, New York, 427–449 http://doi.org/10.1007/978-1-4419-5546-3_28.
5. Baker, R., Siemens, G., Educational data mining and learning analytics. Sawyer, K., (eds.) Cambridge handbook of the learning sciences, 2nd ed., 2014, Cambridge University Press, Cambridge, MA, 253–274.
6. Bakhtiar, A., Webster, E.A., Hadwin, A.F., Regulation and socio-emotional interactions in a positive and a negative group climate. Metacognition and Learning 13:1 (2018), 57–90.
7. Bannert, M., Reimann, P., Sonnenberg, C., Process mining techniques for analysing patterns and strategies in students' self-regulated learning. Metacognition and Learning 9:2 (2014), 161–185 http://doi.org/10.1007/s11409-013-9107-6.
8. Ben-Shakhar, G., Standardization within individuals: A simple method to neutralize individual differences in skin conductance. Psychophysiology 22:3 (1985), 292–299 https://doi.org/10.1111/j.1469-8986.1985.tb01603.x.
9. Blikstein, P., Gomes, J.S., Akiba, H.T., Schneider, B., The effect of highly scaffolded versus general instruction on students' exploratory behavior and arousal. Technology, knowledge and learning, 2016 http://doi.org/10.1007/s10758-016-9291-y.
10. Blikstein, P., Worsley, M., Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics 3:2 (2016), 220–238 https://doi.org/10.18608/jla.2016.32.11.
11. Boucsein, W., Electrodermal activity. 2012, Springer US, Boston, MA http://doi.org/10.1007/978-1-4614-1126-0.
12. Chanel, G., Mühl, C., Connecting brains and bodies: Applying physiological computing to support social interaction. Interacting with Computers 27:5 (2015), 534–550 http://doi.org/10.1093/iwc/iwv013.
13. Cranford, K.N., Tiettmeyer, J.M., Chuprinko, B.C., Jordan, S., Grove, N.P., Measuring load on working memory: The use of heart rate as a means of measuring chemistry students' cognitive load. Journal of Chemical Education 91:5 (2014), 641–647 http://doi.org/10.1021/ed400576n.
14. D'Mello, S.K., Duckworth, A., Dieterle, E., Advanced, analytic, automated (AAA) measurement of engagement during learning. Educational Psychologist 52:2 (2017), 104–123, 10.1080/00461520.2017.1281747.
15. D'Mello, S., Graesser, A., The half-life of cognitive-affective states during complex learning. Cognition & Emotion 25:7 (2011), 1299–1308.
16. Dawson, M.E., Schell, A.M., Filion, D.L., The electrodermal system. Cacioppo, J.T., Tassinary, L.G., Berntson, G.G., (eds.) Handbook of psychophysiology, 2017, Cambridge University Press, Cambridge, 217–243 https://doi.org/10.1017/9781107415782.010.
17. D'Mello, S., A selective meta-analysis on the relative incidence of discrete affective states during learning with technology. Journal of Educational Psychology 105:4 (2013), 1082–1099 http://doi.org/10.1037/a0032674.
18. Fairclough, S.H., Venables, L., Tattersall, A., The influence of task demand and learning on the psychophysiological response. International Journal of Psychophysiology 56:2 (2005), 171–184 http://doi.org/10.1016/j.ijpsycho.2004.11.003.
19. Fritz, T., Begel, A., Müller, S.C., Yigit-Elliott, S., Züger, M., Using psycho-physiological measures to assess task difficulty in software development. Proceedings of the 36th international conference on software engineering - ICSE 2014, 2014, ACM Press, New York, New York, USA, 402–413 http://doi.org/10.1145/2568225.2568266.
20. Gašević, D., Dawson, S., Siemens, G., Let's not forget: Learning analytics are about learning TechTrends 59:1 (2015), 64–71, 10.1007/s11528-014-0822-x.
21. Hadwin, A., Järvelä, S., Social aspects of self-regulated learning: Where social and self meet in the strategic regulation of learning. Teachers College Record 113:2 (2011), 235–239.
22. Haapalainen, E., Kim, S., Forlizzi, J.F., Dey, A.K., Psycho-physiological measures for assessing cognitive load. Proceedings of the 12th ACM International Conference on Ubiquitous Computing, 2010, 301–310 http://doi.org/10.1145/1864349.1864395.
23. Hadwin, A.F., Järvelä, S., Miller, M., Self-regulation, co-regulation and shared regulation in collaborative learning environments (pp. 83-106). Schunk, D., Greene, J., (eds.) Handbook of self-regulation of learning and performance, 2rd ed., 2018, Routledge, New York, NY.
24. Hadwin, A.F., Järvelä, S., Miller, M., Self-regulation, co-regulation and shared regulation in collaborative learning environments (pp. 83-106). Schunk, D., Greene, J., (eds.) Handbook of self-regulation of learning and performance, 2nd ed., 2017, Routledge, New York, NY.
25. Harley, J.M., Bouchet, F., Hussain, M.S., Azevedo, R., Calvo, R., A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior 48 (2015), 615–625 http://doi.org/10.1016/j.chb.2015.02.013.
26. Henriques, R., Paiva, A., Antunes, C., On the need of new methods to mine electrodermal activity in emotion-centered studies. Cao, L., Zeng, Y., Symeonidis, A.L., Gorodetsky, V.I., Yu, P.S., Singh, M.P., (eds.) Agents and data mining interaction, Vol. 7607, 2013, Springer, Berlin, 203–215 http://doi.org/10.1007/978-3-642-36288-0_18.
27. Järvelä, S., Hadwin, A.F., New frontiers: Regulating learning in CSCL. Educational Psychologist 48:1 (2013), 25–39 http://doi.org/10.1080/00461520.2012.748006.
28. Järvenoja, H., Järvelä, S., Malmberg, J., Understanding regulated learning in situative and contextual frameworks. Educational Psychologist 50:3 (2015), 204–219, 10.1080/00461520.2015.1075400.
29. Järvelä, S., Järvenoja, H., Malmberg, J., Isohätälä, J., Sobocinski, M., How do types of interaction and phases of self-regulated learning set a stage for collaborative engagement?. Learning and Instruction 43 (2016), 39–51, 10.1016/j.learninstruc.2016.01.005.
30. Järvelä, S., Malmberg, J., Koivuniemi, M., Recognizing socially shared regulation by using the temporal sequences of online chat and logs in CSCL. Learning and Instruction 42 (2016), 1–11, 10.1016/j.learninstruc.2015.10.006.
31. Järvenoja, H., Järvelä, S., Malmberg, J., Understanding the process of motivational, emotional and cognitive regulation in learning situations. Educational Psychologist 50:3 (2015), 204–219.
32. Kaplan, H.B., Physiological correlates (GSR) of affect in small groups. Journal of Psychosomatic Research 11:2 (1967), 173–179.
33. Keskinarkaus, A., Huttunen, S., Siipo, A., Holappa, J., Laszlo, M., Juuso, I., et al. MORE – a multimodal observation and analysis system for social interaction research. Multimedia Tools and Applications, 2016, 1–25 http://doi.org/10.1007/s11042-015-2574-9.
34. Kreibig, S.D., Autonomic nervous system activity in emotion: A review. Biological Psychology 84 (2010), 14–41 http://doi.org/10.1016/j.biopsycho.2010.03.010.
35. Kreibig, S.D., Gendolla, G.H.E., Scherer, K.R., Goal relevance and goal conduciveness appraisals lead to differential autonomic reactivity in emotional responding to performance feedback. Biological Psychology 91:3 (2012), 365–375 http://doi.org/10.1016/j.biopsycho.2012.08.007.
36. Malmberg, J., Järvelä, S., Holappa, J., Haataja, E., Huang, X., Siipo, A., Going beyond what is visible: What multichannel data can reveal about interaction in the context of collaborative learning?. Computers in Human Behavior 96 (2019), 235–245 https://doi.org/10.1016/j.chb.2018.06.030.
37. Malmberg, J., Järvelä, S., Järvenoja, H., Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology 49 (2017), 160–174 https://doi.org/10.1016/j.cedpsych.2017.01.009.
38. Malmberg, J., Järvelä, S., Järvenoja, H., Panadero, E., Promoting socially shared regulation of learning in CSCL: Progress of socially shared regulation among high-and low-performing groups. Computers in Human Behavior 52 (2015), 562–572 https://doi.org/10.1016/j.chb.2015.03.082.
39. McCardle, L., Hadwin, A.F., Using multiple, contextualized data sources to measure learners' perceptions of their self-regulated learning. Metacognition and Learning 10:1 (2015), 43–75.
40. Miyake, N., Kirschner, P.A., The social and interactive dimensions of collaborative learning. Sawyer, K.R., (eds.) The cambridge handbook of the learning sciences, 2014, Cambridge University Press, New York, 418–438.
41. Mønster, D., Håkonsson, D.D., Eskildsen, J.K., Wallot, S., Physiological evidence of interpersonal dynamics in a cooperative production task. Physiology & Behavior 156 (2016), 24–34 https://doi.org/10.1016/j.physbeh.2016.01.004.
42. Morris, R., Hadwin, A.F., Gress, C.L.Z., Miller, M., Fior, M., Church, H., et al. Designing roles, scripts, and prompts to support CSCL in gStudy. Computers in Human Behavior 26:5 (2010), 815–824 http://doi.org/10.1016/j.chb.2008.12.001.
43. Noroozi, O., Alikhani, I., Järvelä, S., Kirschner, P.A., Juuso, I., Seppänen, T., Multimodal data to design visual learning analytics for understanding regulation of learning. Computers in Human Behavior, 2018, 10.1016/j.chb.2018.12.019.
44. Palumbo, R.V., Marraccini, M.E., Weyandt, L.L., Wilder-Smith, O., McGee, H.A., Liu, S., et al. Interpersonal autonomic physiology: A systematic review of the literature. Personality and social psychology review, 2016 http://doi.org/10.1177/1088868316628405.
45. Patton, M.Q., Qualitative evaluation and research methods. 2nd ed., 1990, Sage, Newbury Park, CA.
46. Pecchinenda, A., Smith, C.A., The affective significance of skin conductance activity during a difficult problem-solving task. Cognition & Emotion 10:5 (1996), 481–504 http://doi.org/10.1080/026999396380123.
47. Pijeira-Díaz, H.J., Drachsler, H., Järvelä, S., Kirschner, P.A., Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. Proceedings of the Sixth International Conference on Learning Analytics and Knowledge. ACM, 2016, 10.1145/1235.
48. Reimann, P., Markauskaite, L., Bannert, M., e-Research and learning theory: What do sequence and process mining methods contribute?. British Journal of Educational Technology 45:3 (2014), 528–540 http://doi.org/10.1111/bjet.12146.
49. Sobocinski, M., Malmberg, J., Järvelä, S., Exploring temporal sequences of regulatory phases and associated interactions in low-and high-challenge collaborative learning sessions. Metacognition and Learning 12:2 (2017), 275–294 https://doi.org/10.1007/s11409-016-9167-5.
50. Strijbos, J.W., Fischer, F., Methodological challenges for collaborative learning research. Learning and Instruction 17:4 (2007), 389–393.
51. Su, Y., Li, Y., Hu, H., Rosé, C.P., Exploring college English language learners' self and social regulation of learning during wiki-supported collaborative reading activities. International Journal of Computer-Supported Collaborative Learning, 1–26, 2018 https://doi.org/10.1007/s11412-018-9269-y.
52. Tomaka, J., Blascovich, J., Kelsey, R.M., Leitten, C.L., Subjective, physiological, and behavioral effects of threat and challenge appraisal. Journal of Personality and Social Psychology 65:2 (1993), 248–260.
53. Turner, J.R., Individual differences in heart rate response during behavioral challenge. Psychophysiology 26:5 (1989), 497–505 https://doi.org/10.1111/j.1469-8986.1989.tb00701.x.
54. Volet, S., Summers, M., Thurman, J., High-level co-regulation in collaborative learning: How does it emerge and how is it sustained?. Learning and Instruction 19:2 (2009), 128–143.
55. Wilson, G.F., An Analysis of mental workload in pilots during flight using multiple psychophysiological measures. The International Journal of Aviation Psychology 12:1 (2002), 3–18 http://doi.org/10.1207/S15327108IJAP1201_2.
56. Winne, P.H., Comments on motivation in real-life, dynamic, and interactive learning environments. European Psychologist 9:4 (2004), 257–263 http://doi.org/10.1027/1016-9040.9.4.257.
57. Winne, P.H., Improving measurements of self-regulated learning. Educational Psychologist 45:4 (2010), 267–276 http://doi.org/10.1080/00461520.2010.517150.
58. nd ed., 2017, Routledge, New York, NY, 36–48.
59. Winne, P.H., Hadwin, A.F., Studying as self-regulated learning. Hacker, D.J., Dunlosky, J., Graesser, A.C., (eds.) Metacognition in educational theory and practice, 1998, Lawrence Erlbaum, Mahwah, 277–304.
60. Winne, P.H., Hadwin, A.F., The weave of motivation and self-regulated learning. Schunk, D.H., Zimmerman, B.J., (eds.) Motivation and self-regulated learning: Theory, research, and applications, 2008, 297–314 2.
61. Winne, P.H., Nesbit, J.C., Supporting self-regulated learning with cognitive tools. Hacker, D.J., Dunlosky, J., Graesser, A.C., (eds.) Handbook of metacognition in education, 2009, Routledge, New York, 259–277 http://search.ebscohost.com/login.aspx?direct=true&db=psyh&AN=2010-06038-014&lang=it&site=ehost-live Retrieved from.
62. Wise, A.F., Shaffer, D.W., Why theory matters more than ever in the age of big data. Journal of Learning Analytics 2:2 (2015), 5–13 http://dx.doi.org/10.18608/jla.2015.22.2.
63. Worsley, M., Blikstein, P., Leveraging multimodal learning analytics to differentiate student learning strategies. Proceedings of the fifth international conference on learning analytics and knowledge - LAK ’15, 2015, ACM Press, New York, New York, USA, 360–367 http://doi.org/10.1145/2723576.2723624.
64. Zimmerman, B., A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 1989, 329, 10.1037//0022-0663.81.3.329.
65. Zimmerman, B.J., Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal 45:1 (2008), 166–183 http://doi.org/10.3102/0002831207312909.
66. Zimmerman, B., Motivational sources and outcomes of self-regulated learning and performance. Zimmerman, B., Schunk, D., (eds.) Handbook of Self-regulation of learning and performance, 2011, Routledge, New York, 49–64.
67. Zimmerman, B.J., Schunk, D.H., Self-regulated learning and academic achievement: Theory, research, and practice. 1989, Springer-Verlag Publishing, New York, NY, US.
68. Zimmerman, B.J., Schunk, D.H., Self-regulated learning and performance: An Introduction and an overview. Zimmerman, B.J., Schunk, D.H., (eds.) Handbook of self-regulation of learning and performance, 2011, Routledge, New York, 1–12.