Modeling the Complex Interplay Between Monitoring Events for Regulated Learning with Psychological Networks

• Ahonen, L., Cowley, B. U., Hellas, A., & Puolamäki, K. (2018). Biosignals reflect pair-dynamics in collaborative work: EDA and ECG study of pair-programming in a classroom environment. Scientific Reports, 8(1), 3138. https://doi.org/10.1038/s41598-018-21518-3

  Article  Google Scholar 

• Azevedo, R., & Gašević, D. (2019). Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges. Computers in Human Behavior, 96, 207–210.

  Article  Google Scholar 

• Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253–274). https://doi.org/10.1017/CBO9781139519526.016

  Chapter  Google Scholar 

• Beltz, A. M., Wright, A. G. C., Sprague, B. N., & Molenaar, P. C. M. (2016). Bridging the nomothetic and idiographic approaches to the analysis of clinical data. Assessment, 23(4), 447–458. https://doi.org/10.1177/1073191116648209

  Article  Google Scholar 

• Benedek, M., & Kaernbach, C. (2010a). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80–91. https://doi.org/10.1016/j.jneumeth.2010.04.028

  Article  Google Scholar 

• Benedek, M., & Kaernbach, C. (2010b). Decomposition of skin conductance by means of nonnegative deconvolution. Psychophysiology, 47(4), 647–658. https://doi.org/10.1111/j.1469-8986.2009.00972.x

  Article  Google Scholar 

• Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220–238. https://doi.org/10.18608/jla.2016.32.11

  Article  Google Scholar 

• Blikstein, P., Gomes, J. S., Akiba, H. T., & Schneider, B. (2017). The effect of highly scaffolded versus general instruction on students’ exploratory behavior and arousal. Technology, Knowledge and Learning, 22(1), 105–128. https://doi.org/10.1007/s10758-016-9291-y

  Article  Google Scholar 

• Borgatti, S. P., & Halgin, D. S. (2011). On network theory. Organization Science, 22(5), 1168–1181. https://doi.org/10.1287/orsc.1100.0641

  Article  Google Scholar 

• Borgatti, S. P., Mahra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895. https://doi.org/10.1126/science.1165821

  Article  Google Scholar 

• Boucsein, W. (2012). Electrodermal activity (2ndnd ed.). Springer. https://doi.org/10.1007/978-1-4614-1126-0.

• Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45(1), 16–28. https://doi.org/10.1037/0003-066X.45.1.16

  Article  Google Scholar 

• Critchley, H. D. (2002). Electrodermal responses: What happens in the brain. The Neuroscientist, 8(2), 132–142. https://doi.org/10.1177/107385840200800209

  Article  Google Scholar 

• Critchley, H. D., Eccles, J., & Garfinkel, S. N. (2013). Interaction between cognition, emotion, and the autonomic nervous system. Handbook of Clinical Neurology, 117, 59–77. https://doi.org/10.1016/B978-0-444-53491-0.00006-7

  Article  Google Scholar 

• Dawson, M. E., Schell, A. M., & Filion, D. L. (2017). The electrodermal system. In J. T. Cacioppo, L. G. Tassinary, & G. G. Berntson (Eds.), Handbook of psychophysiology (4th ed., pp. 217–243). Cambridge University Press. https://doi.org/10.1017/9781107415782.010

  Chapter  Google Scholar 

• Di Mitri, D., Schneider, J., Specht, M., & Drachsler, H. (2018). From signals to knowledge: A conceptual model for multimodal learning analytics. Journal of Computer Assisted Learning, 34(4), 338–349. https://doi.org/10.1111/jcal.12288

  Article  Google Scholar 

• Dindar, M., Järvelä, S., & Haataja, E. (2020). What does physiological synchrony reveal about metacognitive experiences and group performance? British Journal of Educational Technology, 51(5), 1577–1596. https://doi.org/10.1111/bjet.12981

  Article  Google Scholar 

• Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychologist, 46(1), 6–25. https://doi.org/10.1080/00461520.2011.538645

  Article  Google Scholar 

• Empatica Inc. (2018). Recommended tools for signal processing and data analysis. https://support.empatica.com/hc/en-us/articles/202872739-Recommended-tools-for-signal-processing-and-data-analysis

• Epskamp, S., Kruis, J., & Marsman, M. (2017). Estimating psychopathological networks: Be careful what you wish for. PLoS One, 12(6), e0179891. https://doi.org/10.1371/journal.pone.0179891

  Article  Google Scholar 

• Epskamp, S., Borsboom, D., & Fried, E. I. (2018a). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1

  Article  Google Scholar 

• Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A. M., Riese, H., & Cramer, A. O. J. (2018b). Personalized network modeling in psychopathology: The importance of contemporaneous and temporal connections. Clinical Psychological Science, 6(3), 416–427. https://doi.org/10.1177/2167702617744325

  Article  Google Scholar 

• Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018c). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453–480. https://doi.org/10.1080/00273171.2018.1454823

  Article  Google Scholar 

• Eysenck, M. W. (1976). Arousal, learning, and memory. Psychological Bulletin, 83(3), 389–404. https://doi.org/10.1037/0033-2909.83.3.389

  Article  Google Scholar 

• Féré, C. (1888). Note Sur les modifications de la résistance électrique sous l’influence des excitations sensorielles et des émotions [note on the modification of electric resistance under the influence of sensory excitement and emotions]. Comptes Rendus Société de Biologie, 5, 217–219.

  Google Scholar 

• Fried, E. I., van Borkulo, C. D., Cramer, A. O. J., Boschloo, L., Schoevers, R. A., & Borsboom, D. (2017). Mental disorders as networks of problems: A review of recent insights. Social Psychiatry and Psychiatric Epidemiology, 52(1), 1–10. https://doi.org/10.1007/s00127-016-1319-z

  Article  Google Scholar 

• Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. https://doi.org/10.1037/0003-066X.34.10.906

• Garbarino, M., Lai, M., Tognetti, S., Picard, R., & Bender, D. (2014). Empatica E3 - a wearable wireless multi-sensor device for real-time computerized biofeedback and data acquisition. In Proceedings of the 4th international conference on wireless Mobile communication and healthcare (pp. 39–42). https://doi.org/10.4108/icst.mobihealth.2014.257418

  Chapter  Google Scholar 

• Graesser, A. C., Hu, X., & Sottilare, R. A. (2018). Intelligent tutoring systems. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 246–255). Routledge.

  Chapter  Google Scholar 

• Haataja, E., Malmberg, J., & Järvelä, S. (2018). Monitoring in collaborative learning: Co-occurrence of observed behavior and physiological synchrony explored. Computers in Human Behavior, 87, 337–347. https://doi.org/10.1016/j.chb.2018.06.007

  Article  Google Scholar 

• Haataja, E., Malmberg, J., Dindar, M., & Järvelä, S. (2020). The pivotal role of monitoring for collaborative problem solving seen in interaction, performance, and interpersonal physiology. [Manuscript submitted for publication].

  Google Scholar 

• Hadwin, A. F., Järvelä, S., & Miller, M. (2017). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. Schunk & J. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 65–84). Routledge. https://doi.org/10.4324/9781315697048

  Chapter  Google Scholar 

• Hajcak, G., McDonald, N., & Simons, R. F. (2003). To err is autonomic: Error-related brain potentials, ANS activity, and post-error compensatory behavior. Psychophysiology, 40(6), 895–903. https://doi.org/10.1111/1469-8986.00107

  Article  Google Scholar 

• Harley, J. M., Bouchet, F., Hussain, M. S., Azevedo, R., & Calvo, R. (2015). A multi-componential analysis of emotions during complex learning with an intelligent multi-agent system. Computers in Human Behavior, 48, 615–625. https://doi.org/10.1016/j.chb.2015.02.013

  Article  Google Scholar 

• Järvelä, S., & Bannert, M. (2021). Temporal and adaptive processes of regulated learning - what can multimodal data tell? Learning and Instruction, 72, 101268. https://doi.org/10.1016/j.learninstruc.2019.101268

  Article  Google Scholar 

• Järvelä, S., Malmberg, J., Haataja, E., Sobocinski, M., & Kirschner, P. A. (2019). What multimodal data can tell us about the students’ regulation of their learning process? Learning and Instruction, 72, 101203. https://doi.org/10.1016/j.learninstruc.2019.04.004

  Article  Google Scholar 

• Järvelä, S., Gašević, D., Seppänen, T., Pechenizkiy, M., & Kirschner, P. A. (2020). Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning. British Journal of Educational Technology, 51(6), 2391–2406. https://doi.org/10.1111/bjet.12917

  Article  Google Scholar 

• Kreibig, S. D., Gendolla, G. H. E., & Scherer, K. R. (2012). Goal relevance and goal conduciveness appraisals lead to differential autonomic reactivity in emotional responding to performance feedback. Biological Psychology, 91(3), 365–375. https://doi.org/10.1016/j.biopsycho.2012.08.007

  Article  Google Scholar 

• Li, S., Du, H., Xing, W., Zheng, J., Chen, G., & Xie, C. (2020). Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A network approach. Computers and Education, 158, 103987. https://doi.org/10.1016/j.compedu.2020.103987

  Article  Google Scholar 

• Malmberg, J., Järvelä, S., & Järvenoja, H. (2017). Capturing temporal and sequential patterns of self, co-, and socially shared regulation in the context of collaborative learning. Contemporary Educational Psychology, 49, 160–174. https://doi.org/10.1016/j.cedpsych.2017.01.009

  Article  Google Scholar 

• Malmberg, J., Haataja, E., Seppänen, T., & Järvelä, S. (2019a). Are we together or not? The temporal interplay of monitoring, physiological arousal and physiological synchrony during a collaborative exam. International Journal of Computer-Supported Collaborative Learning, 14(4), 467–490. https://doi.org/10.1007/s11412-019-09311-4

  Article  Google Scholar 

• Malmberg, J., Järvelä, S., Holappa, J., Haataja, E., Huang, X., & Siipo, A. (2019b). Going beyond what is visible: What multichannel data can reveal about interaction in the context of collaborative learning? Computers in Human Behavior, 96, 235–245. https://doi.org/10.1016/j.chb.2018.06.030

  Article  Google Scholar 

• Malmberg, J., Fincham, O., Pijeira-Díaz, H. J., Järvelä, S., & Gašević, D. (2021). Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning. Journal of Computer Assisted Learning., 37, 861–874.

  Article  Google Scholar 

• Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x

  Article  Google Scholar 

• Molenaar, I., Horvers, A., & Baker, R. S. (2019). What can moment-by-moment learning curves tell about students’ self-regulated learning? Learning and Instruction, 101206. https://doi.org/10.1016/j.learninstruc.2019.05.003

• Mønster, D., Håkonsson, D. D., Eskildsen, J. K., & Wallot, S. (2016). Physiological evidence of interpersonal dynamics in a cooperative production task. Physiology & Behavior, 156, 24–34. https://doi.org/10.1016/j.physbeh.2016.01.004

  Article  Google Scholar 

• Morris, A. L., Cleary, A. M., & Still, M. L. (2008). The role of autonomic arousal in feelings of familiarity. Consciousness and Cognition, 17(4), 1378–1385. https://doi.org/10.1016/j.concog.2008.04.005

  Article  Google Scholar 

• Neumann, E., & Blanton, R. (1970). The early history of electrodermal research. Psychophysiology, 6(4), 453–475. https://doi.org/10.1111/j.1469-8986.1970.tb01755.x

  Article  Google Scholar 

• Noroozi, O., Pijeira-Díaz, H. J., Sobocinski, M., Dindar, M., Järvelä, S., & Kirschner, P. A. (2020). Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature review. Education and Information Technologies, 25(November), 5499–5547. https://doi.org/10.1007/s10639-020-10229-w

  Article  Google Scholar 

• Palumbo, R. V., Marraccini, M. E., Weyandt, L. L., Wilder-Smith, O., McGee, H. A., Liu, S., & Goodwin, M. S. (2017). Interpersonal autonomic physiology: A systematic review of the literature. Personality and Social Psychology Review, 21(2), 99–141. https://doi.org/10.1177/1088868316628405

  Article  Google Scholar 

• Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 1–28. https://doi.org/10.3389/fpsyg.2017.00422

  Article  Google Scholar 

• Pecchinenda, A., & Smith, C. A. (1996). The affective significance of skin conductance activity during a difficult problem-solving task. Cognition & Emotion, 10(5), 481–504. https://doi.org/10.1080/026999396380123

  Article  Google Scholar 

• Peeters, W., Saqr, M., & Viberg, O. (2020). Applying learning analytics to map students’ self-regulated learning tactics in an academic writing course. Proceedings of the 28th International Conference on Computers in Education, 1(September), 245–254.

  Google Scholar 

• Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2016). Investigating collaborative learning success with physiological coupling indices based on electrodermal activity. Proceedings of the Sixth International Conference on Learning Analytics and Knowledge, 64–73. https://doi.org/10.1145/2883851.2883897

• Pijeira-Díaz, H. J., Drachsler, H., Kirschner, P. A., & Järvelä, S. (2018). Profiling sympathetic arousal in a physics course: How active are students? Journal of Computer Assisted Learning, 34(4), 397–408. https://doi.org/10.1111/jcal.12271

  Article  Google Scholar 

• Pijeira-Díaz, H. J., Drachsler, H., Järvelä, S., & Kirschner, P. A. (2019). Sympathetic arousal commonalities and arousal contagion during collaborative learning: How attuned are triad members? Computers in Human Behavior, 92(March), 188–197. https://doi.org/10.1016/j.chb.2018.11.008

  Article  Google Scholar 

• Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic. https://doi.org/10.1016/B978-012109890-2/50043-3

  Chapter  Google Scholar 

• Raskin, D. C. (1973). Attention and arousal. In W. F. Prokasy & D. C. Raskin (Eds.), Electrodermal activity in psychological research (pp. 125–155). Academic.

  Chapter  Google Scholar 

• Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4(3), 239–257. https://doi.org/10.1007/s11412-009-9070-z

  Article  Google Scholar 

• Reimann, P. (2019). Methodological progress in the study of self-regulated learning enables theory advancement. Learning and Instruction, 72, 101269.

  Article  Google Scholar 

• Robbins, T. W. (1997). Arousal systems and attentional processes. Biological Psychology, 45(1–3), 57–71. https://doi.org/10.1016/S0301-0511(96)05222-2

  Article  Google Scholar 

• Roll, I., & Winne, P. H. (2015). Understanding, evaluating, and supporting self-regulated learning using learning analytics. Journal of Learning Analytics, 2, 7–12. https://doi.org/10.18608/jla.2015.21.2

  Article  Google Scholar 

• Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714

  Article  Google Scholar 

• Saqr, M., & Lopez-Pernas, S. (2021a). Idiographic Learning Analytics: A single student (N=1) approach using psychological networks. Companion Proceedings 11th International Conference on Learning Analytics & Knowledge (LAK21), April, 456–463.

  Google Scholar 

• Saqr, M., & Lopez-Pernas, S. (2021b). Idiographic learning analytics: A definition and a case study. In 2021 international conference on advanced learning technologies (ICALT) (pp. 163–165). https://doi.org/10.1109/ICALT52272.2021.00056

  Chapter  Google Scholar 

• Saqr, M., & Nouri, J. (2020). High resolution temporal network analysis to understand and improve collaborative learning. ACM International Conference Proceeding Series, 314–319. https://doi.org/10.1145/3375462.3375501

• Saqr, M., Nouri, J., & Jormanainen, I. (2019). A learning analytics study of the effect of group size on social dynamics and performance in online collaborative learning. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Ioannou, & J. Schneider (Eds.), Transforming learning with meaningful technologies. EC-TEL 2019. Lecture notes in computer science (Vol. 11722). Springer. https://doi.org/10.1007/978-3-030-29736-7_35

  Chapter  Google Scholar 

• Saqr, M., Viberg, O., & Peeters, W. (2021). Using psychological networks to reveal the interplay between foreign language students’ self-regulated learning tactics. STELLA2020, 1, 12.

  Google Scholar 

• Schachter, S., & Singer, J. (1962). Cognitive, social, and physiological determinants of emotional state. Psychological Review, 69(5), 379–399. https://doi.org/10.1037/h0046234

  Article  Google Scholar 

• Schneider, J., Börner, D., van Rosmalen, P., & Specht, M. (2015). Augmenting the senses: A review on sensor-based learning support. Sensors, 15(2), 4097–4133. https://doi.org/10.3390/s150204097

  Article  Google Scholar 

• Schneider, B., Dich, Y., & Radu, I. (2020). Unpacking the relationship between existing and new measures of physiological synchrony and collaborative learning: A mixed methods study. International Journal of Computer-Supported Collaborative Learning, 15(1), 89–113. https://doi.org/10.1007/s11412-020-09318-2

  Article  Google Scholar 

• Siew, C. S. Q., Wulff, D. U., Beckage, N. M., & Kenett, Y. N. (2019). Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics. Complexity, 2019, 2108423. https://doi.org/10.1155/2019/2108423

  Article  Google Scholar 

• Sobocinski, M., Järvelä, S., Malmberg, J., Dindar, M., Isosalo, A., & Noponen, K. (2020). How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning? Metacognition and Learning, 15, 99–127. https://doi.org/10.1007/s11409-020-09224-w

  Article  Google Scholar 

• Sterling, P. (2012). Allostasis: A model of predictive regulation. Physiology & Behavior, 106(1), 5–15. https://doi.org/10.1016/j.physbeh.2011.06.004

  Article  Google Scholar 

• Stuldreher, I. V., Thammasan, N., van Erp, J. B. F., & Brouwer, A. M. (2020). Physiological synchrony in EEG, electrodermal activity and heart rate detects attentionally relevant events in time. Frontiers in Neuroscience, 14, 575521. https://doi.org/10.3389/fnins.2020.575521

  Article  Google Scholar 

• Swan, M. (2012). Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. Journal of Sensor and Actuator Networks, 1(3), 217–253. https://doi.org/10.3390/jsan1030217

  Article  Google Scholar 

• Taub, M., Azevedo, R., Rajendran, R., Cloude, E. B., Biswas, G., & Price, M. J. (2019). How are students’ emotions related to the accuracy of cognitive and metacognitive processes during learning with an intelligent tutoring system? Learning and Instruction, 72. https://doi.org/10.1016/j.learninstruc.2019.04.001

• Verwey, W. B., & Veltman, H. A. (1996). Detecting short periods of elevated workload: A comparison of nine workload assessment techniques. Journal of Experimental Psychology: Applied, 2(3), 270–285. https://doi.org/10.1037/1076-898X.2.3.270

  Article  Google Scholar 

• Winne, P. H. (2017). Leveraging big data to help each learner and accelerate learning science. Teachers College Record, 119(3).

  Google Scholar 

• Winne, P. H. (2019). Paradigmatic dimensions of instrumentation and analytic methods in research on self-regulated learning. Computers in Human Behavior, 96, 285–289. https://doi.org/10.1016/j.chb.2019.03.026

  Article  Google Scholar 

• Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Routledge.

  Google Scholar 

• Winne, P. H., Nesbit, J. C., & Popowich, F. (2017). nStudy: A system for researching information problem solving. Technology, Knowledge and Learning, 22(3), 369–376. https://doi.org/10.1007/s10758-017-9327-y

  Article  Google Scholar 

• Wolters, C. A. (2003). Regulation of motivation: Evaluating an underemphasized aspect of self-regulated learning. Educational Psychologist, 38(4), 189–205. https://doi.org/10.1207/S15326985EP3804_1

  Article  Google Scholar 

Download references