Skip to main content
SpringerLink
Log in

The Feasibility and Interest of Monitoring the Cognitive and Affective States of Groups of Co-learners in Real Time as They Learn

  • Chapter
  • First Online:
Research on e-Learning and ICT in Education

  • 1383 Accesses

Abstract

The benefits of collaborative learning are based on the capacity of co-learners to perform better and to learn more despite the increased complexity of this setting over individual learning. These benefits are not always present, and may depend on co-learners’ collaboration skills. Collaboration skills are complex, as they target the dynamic alignment between the individual and joint actions, as well as cognitive and affective states of co-learners with the requirements of a learning task. This chapter emphasizes the pivotal role of co-learners’ monitoring and regulation in attaining and maintaining a coordination of efforts that is conducive to learning. This perspective highlights the hypothesis that the scarcity of the information that the co-learners have access to during natural interaction leads to suboptimal learning interactions that may not always outweigh the increased complexity of collaborative learning. Methodologies from neuroscience can provide pertinent information during or after a learning interaction to empower co-learners.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Anderson, J. R. (2002). Spanning seven orders of magnitude: A challenge for cognitive modeling. Cognitive Science, 26, 85–112.

    Article  Google Scholar 

  • Anderson, J. R., Fincham, J. M., Schneider, D. W., & Yang, J. (2012). Using brain imaging to track problem-solving in a complex state space. NeuroImage, 60, 633–643.

    Article  Google Scholar 

  • Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Ansari, D., Coch, D., & De Smedt, B. (2011). Connecting education and cognitive neuroscience: Where will the journey take us? Educational Philosophy and Theory, 43(1), 37–42.

    Article  Google Scholar 

  • Antonenko, P., Paas, F., Garbner, R., & van Gog, T. (2010). Using electroencephalography to measure cognitive load. Educational Psychology Review, 22, 425–438.

    Article  Google Scholar 

  • Astolfi, L., Cincotti, F., Mattia, D., De Vico Fallani, F., Vecciato, G., Salinari, S., et al. (2010). Time-varying cortical connectivity estimation from noninvasive, high-resolution EEG recordings. Journal of Psychophysiology, 24(2), 83–90.

    Article  Google Scholar 

  • Astolfi, L., Toppi, J., De Vico Fallani, F., Vecchiato, G., Salinari, S., Mattia, D., et al. (2010). Neuroelectrical hyperscanning measures simultaneous brain activity in humans. Brain Topography, 23(3), 243–256.

    Article  Google Scholar 

  • Azevedo, R., Moos, D. C., Johnson, A. M., & Chauncey, A. D. (2010). Measuring cognitive and metacognitive regulatory processes during hypermedia learning: Issues and challenges. Educational Psychologist, 45(4), 210–223.

    Article  Google Scholar 

  • Babiloni, C., Vecchio, F., Infarinato, F., Buffo, P., Marzano, N., Spada, D., et al. (2011). Simultaneous recording of electroencephalographic data in musicians playing in ensemble. Cortex, 47(9), 1082–1090.

    Article  Google Scholar 

  • Babiloni, F., Cincotti, F., Mattia, D., De Vico Fallani, F., Tocci, A., Bianchi, L., et al. (2007). High resolution EEG hyperscanning during a card game. In Conference Proceedings of the IEEE Engineering in Medicine and Biology Society (pp. 4957–4960).

    Google Scholar 

  • Bakeman, R., & Quera, V. (2011). Sequential analysis and observational methods for the behavioral sciences. New York: Cambridge University Press.

    Book  Google Scholar 

  • 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, 223–241.

    Article  Google Scholar 

  • Belenky, D., Ringenber, M., Olsen, J., Aleven, V., & Rummel, N. (2014). Using dual eye-tracking to evaluate students’ collaboration with an intelligent tutoring system for elementary-level fractions. In Proceedings of the 36th Annual Meeting of the Cognitive Science Society (CogSci 2014) (pp. 176–181).

    Google Scholar 

  • Berka, C., Levendowski, D. J., Cvetinovic, M. M., Petrovic, M. M., Davis, G., Lumicao, M. N., et al. (2004). Real-time analysis of EEG indexes of alertness, cognition and memory acquired with a wireless EEG headset. International Journal of Human-Computer Interaction, 17(2), 151–170.

    Article  Google Scholar 

  • Blumen, H. M., Young, K. E., & Rajaram, S. (2014). Optimizing group collaboration to improve later retention. Journal of Applied Research in Memory and Cognition, 3, 244–251.

    Article  Google Scholar 

  • Bouras, C., Triglianos, V., & Tsiatsos, T. (2014). Implementing advanced characteristics of X3D collaborative virtual environments for supporting e-learning: The case of EVE platform. International Journal of Distance Education Technologies, 12(1), 13–37.

    Article  Google Scholar 

  • Bouyias, Y., & Demetriadis, S. (2012). Peer-monitoring vs. micro-script fading for enhancing knowledge acquisition when learning in computer-supported argumentation environments. Computers & Education, 59, 236–249.

    Article  Google Scholar 

  • Boyer, K. E., Phillips, R., Ingram, A., Young Ha, E., Wallis, M., Vouk, M., et al. (2011). Investigating the relationship between dialogue structure and tutoring effectiveness: A hidden Markov modeling approach. International Journal of Artificial Intelligence in Education, 21, 65–81.

    Google Scholar 

  • Bratitsis, T., & Demetriadis, S. (2013). Research approaches in computer-supported collaborative learning. International Journal of e-Collaboration, 9(1), 1–8.

    Article  Google Scholar 

  • Burgess, A. P. (2013). On the interpretation of synchronization in EEG hyperscanning studies: A cautionary note. Frontiers in Human Neuroscience, 7, 1–17.

    Google Scholar 

  • Byrnes, J. P. (2012). How neuroscience contributes to our understanding of learning and development in typically developing and special-needs students. In K. R. Harris, S. Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, & J. Sweller (Eds.), APA educational psychology handbook, Vol. 1: Theories, constructs, and critical issues (pp. 561–595). Washington, DC: American Psychological Association.

    Google Scholar 

  • Charland, P., Léger, P. M., Sénécal, S., Courtemanche, F., Mercier, J., Skelling, Y., et al. (2015). Assessing the multiple dimensions of engagement to characterize learning: A neurophysiological perspective. Journal of Visualized Experiments, e52627.

    Google Scholar 

  • Clara, M., & Mauri, T. (2010). Toward a dialectic relation between the results in CSCL: Three critical methodological aspects of content analysis schemes. Computer Supported Collaborative Learning, 5, 117–136. https://doi.org/10.1007/s11412-009-9078-4

    Article  Google Scholar 

  • Clark, A. (2013a). Expecting the world: Perception, prediction, and the origins of human knowledge. Journal of Philosophy, 110(9), 469–496.

    Article  Google Scholar 

  • Clark, A. (2013b). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–253.

    Article  Google Scholar 

  • Colace, F., Casaburi, L., De Santo, M., & Greco, L. (2015). Sentiment detection in social networks and in collaborative learning environments. Computers in Human Behavior, 51, 1061–1067.

    Article  Google Scholar 

  • Coltheart, M., & McArthur, G. (2012). Neuroscience, education and educational efficacy research. In S. Della Sala & M. Anderson (Eds.), Neuroscience in education: The good, the bad, and the ugly. New York: Oxford University Press.

    Google Scholar 

  • Csíkszentmihályi, M. (1998). Finding flow: The psychology of engagement with everyday life. New York: Basic Books.

    Google Scholar 

  • Curilem, S. G., Barbosa, A. R., & de Azevedo, F. M. (2007). Intelligent tutoring systems: Formalization as automata and interface design using neural networks. Computers & Education, 49, 545–561.

    Article  Google Scholar 

  • De Bruin, A. B. H. (2012). Improving self-monitoring and self-regulation: From cognitive psychology to the classroom. Learning and Instruction, 22, 245–252.

    Article  Google Scholar 

  • Di Paolo, E., & De Jaegher, H. (2012). The interactive brain hypothesis. Frontiers in Human Neuroscience, 6, 1–16.

    Article  Google Scholar 

  • Dunlosky, J., & Rawson, K. A. (2012). Overconfidence produces underachievement: Inaccurate self-evaluations undermine students’ learning and retention. Learning and Instruction, 22, 271–280.

    Article  Google Scholar 

  • Eckstein, M. P., Das, K., Pham, B. T., Peterson, M. F., Abbey, C. K., Sy, J. L., et al. (2012). Neural decoding of collective wisdom with multi-brain computing. NeuroImage, 59, 94–108.

    Article  Google Scholar 

  • Efklides, A. (2012). Commentary: How readily can findings from basic cognitive psychology research be applied in the classroom? Learning and Instruction, 22, 290–295.

    Article  Google Scholar 

  • Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.

    Article  Google Scholar 

  • Fessakis, G., Dimitracopoulou, A., & Palaiodimos, A. (2013). Graphical interaction analysis impact on groups collaborating through blogs. Educational Technology & Society, 16(1), 243–253.

    Google Scholar 

  • Fisher, B., Kollar, I., Stegman, K., & Wecker, C. (2013). Toward a script theory of guidance in computer-supported collaborative learning. Educational Psychologist, 48(1), 56–66.

    Article  Google Scholar 

  • Forbes-Riley, K., Rotaru, M., & Litman, D. J. (2008). The relative impact of student affect on performance models in a spoken dialogue tutoring system. User Modeling and User-adapted Interaction, 18, 11–43.

    Article  Google Scholar 

  • Foutsitzis, C. G., & Demetriadis, S. N. (2013). Scripted collaboration to leverage the impact of algorithm visualization tools in online learning: Results from two small scale studies. International Journal of e-Collaboration, 9(1), 42–56.

    Article  Google Scholar 

  • Freeman, F. G., Mikulka, P. J., Scerbo, M. W., & Scott, L. (2004). An evaluation of an adaptive automation system using a cognitive vigilance task. Biological Psychology, 67, 283–297.

    Article  Google Scholar 

  • Fulmer, S.M. & Frijters, J.C. (2009). A Review of Self-Report and Alternative Approaches in the Measurement of Student Motivation. Educational psychological review, 21, 219-246.

    Article  Google Scholar 

  • Furasoli, R., Konvalinka, I., & Wallot, S. (2014). Analyzing social interactions: The promises and challenges of using cross recurrence quantification analysis. In N. Marwan, M. Riley, A. Giuliani, & C. L. Webber, Jr. (Eds.), Translational Recurrences, Springer Proceedings in Mathematics & Statistics (Vol. 103, pp. 137–155).

    Google Scholar 

  • Gadgil, S., & Nokes-Malach, T. J. (2012). Overcoming collaborative inhibition through error correction: A classroom experiment. Applied Cognitive Psychology, 26, 410–420.

    Article  Google Scholar 

  • Galan, F. C., & Beal, C. R. (2012). EEG estimates of engagement and cognitive workload predict math problem solving outcomes. In User modeling, adaptation, and personalization. Lecture notes in computer science (Vol. 7379, pp. 51–62).

    Google Scholar 

  • Gevins, A., Chan, C. S., & Sam-Vargas, L. (2012). Toward measuring brain function on groups of people in the real world. PLoS One, 7(9), 1–9.

    Article  Google Scholar 

  • Goel, L., Johnson, N. A., Junglas, I., & Ives, B. (2013). How cues of what can be done in a virtual world influence learning: An affordance perspective. Information & Management, 50, 197–206.

    Article  Google Scholar 

  • Gomez, P., Zimmermann, P. G., Schär, S. G., & Danuser, B. (2009). Valence lasts longer than arousal: Persistence of induced moods as assessed by psychophysiological measures. Journal of Psychophysiology, 23(1), 7–17.

    Article  Google Scholar 

  • Goswami, U. (2011). Educational neuroscience: Developmental mechanisms: Toward a conceptual framework. NeuroImage, 57, 651–658.

    Article  Google Scholar 

  • Grabner, R. H., & De Smelt, B. (2012). Oscillatory EEG correlates of arithmetic strategies: A training study. Frontiers in Psychology, 3, 1–11.

    Article  Google Scholar 

  • Haythornwaite, C., de Laat, M., & Dawson, S. (2013). Introduction to the special issue on learning analytics. American Behavioral Scientist, 57(10), 1371–1379.

    Article  Google Scholar 

  • Howard-Jones, P. A. (2011). A multiperspective approach to neuroeducational research. Educational Philosophy and Theory, 43(1), 24–30.

    Article  Google Scholar 

  • Hruby, G. G. (2012). Three requirements for justifying an educational neuroscience. British Journal of Educational Psychology, 82, 1–23.

    Article  Google Scholar 

  • Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 22, 379–393.

    Article  Google Scholar 

  • Immordino-Yang, M. H. (2011). Implications of affective and social neuroscience for educational theory. Educational Philosophy and Theory, 43(1), 98–103.

    Article  Google Scholar 

  • Janssen, J., Erksen, G., Kirschner, P. A., & Kanselaar, G. (2012). Task-related and social regulation during online collaborative learning. Metacognition & Learning, 7, 25–43.

    Article  Google Scholar 

  • Järvelä, S., & Hadwin, A. F. (2013). New frontiers: Regulating learning in CSCL. Educational Psychologist, 48(1), 25–39.

    Article  Google Scholar 

  • Järvelä, S., Kisrchner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., et al. (2015). Enhancing socially shared regulation in collaborative learning groups: Designing for CSCL regulation tools. Educational Technology Research Development, 63, 125–142.

    Article  Google Scholar 

  • Jung, I., Kudo, M., & Choi, S. K. (2012). Stress in Japanese learners engaged in online collaborative learning in English. British Journal of Educational Technology, 43(6), 1016–1029.

    Article  Google Scholar 

  • Kapur, M. (2011). Temporality matters: Advancing a method for analyzing problem-solving processes in a computer-supported collaborative environment. Computer-Supported Collaborative Learning, 6, 39–56.

    Article  Google Scholar 

  • Karakostas, A., & Demetriadis, S. (2014). Adaptive vs. fixed domain support in the context of scripted collaborative learning. Educational Technology & Society, 17(1), 206–217.

    Google Scholar 

  • Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: Can it explain differences in students’ conceptual understanding? Metacognition & Learning, 9, 287–307.

    Article  Google Scholar 

  • Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25, 615–624.

    Article  Google Scholar 

  • Kirschner, P. A., & Erkens, G. (2013). Toward a framework for CSCL research. Educational Psychologist, 48(1), 1–8.

    Article  Google Scholar 

  • Kirschner, P. A., Kreijns, K., & Fransen, P. J. (2014). Awareness of cognitive and social behaviour in a CSCL environment. Journal of Computer Assisted Learning, 31, 59–77.

    Article  Google Scholar 

  • Koike, T., Tanabe, H. C., & Sadato, N. (2015). Hyperscanning neuroimaging technique to reveal the “two-in-one” system in social interactions. Neuroscience Research, 90, 25–32.

    Article  Google Scholar 

  • Konvalinka, I., Bauer, M., Stahlhut, C., Hansen, L. K., Roepstorff, A., & Frith, C. D. (2014). Frontal alpha oscillations distinguish leaders from followers: Multivariate decoding of mutually interacting brains. NeuroImage, 94, 79–88.

    Article  Google Scholar 

  • Konvalinka, I., & Roepstorff, A. (2012). The two-brain approach: How can mutually interacting brains teach us something about social interaction? Frontiers in Human Neuroscience, 6, 1–10.

    Article  Google Scholar 

  • Konvalinka, I., Xygalatas, D., Bulbulia, J., Schjodt, U., Jegindo, E.-M., Wallot, S., et al. (2011). Synchronized arousal between performers and related spectators in a fire-walking ritual. Proceedings of the National Academy of Sciences, 108(20), 8514–8519.

    Article  Google Scholar 

  • Koriat, A. (2012). The relationships between monitoring, regulation and performance. Learning and Instruction, 22, 296–298.

    Article  Google Scholar 

  • Kreibig, S. D. (2010). Autonomic nervous system activity in emotion: A review. Biological Psychology, 84, 394–421.

    Article  Google Scholar 

  • Lachat, F., Hugueville, L., Lemaréchal, J. D., Conty, L., & George, N. (2012). Oscillatory brain correlates of live joint attention: A dual-EEG study. Frontiers in Human Neuroscience, 6, 1–10.

    Article  Google Scholar 

  • Lai, M. L., Tsai, M. J., Yang, F. Y., Hsu, C. Y., Liu, T. C., Lee, S. W. Y., et al. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115.

    Article  Google Scholar 

  • Lajoie, S., Lee, L., Bassiri, M., Cruz-Panesso, I., Kazemitabar, M., Poitras, E., et al. (2015). The role of regulation in medical student learning in small groups: Regulating oneself and others’ learning and emotions. Journal of Computer and Human Behavior, 52, 601–616.

    Article  Google Scholar 

  • Lee, A., O’Donnell, A. M., & Rogat, T. K. (2015). Exploration of the cognitive regulatory sub-processes employed by groups characterized by socially shared and other-regulation in a CSCL context. Computers in Human Behavior, 52, 617–627.

    Article  Google Scholar 

  • Lu, J., & Law, N. W. Y. (2012). Understanding collaborative learning behavior from Moodle log data. Interactive Learning Environments, 20(5), 451–466.

    Article  Google Scholar 

  • Martinez-Maldonado, R., Dimitriadis, Y., Martinez-Monés, A., Kay, J., & Yacef, K. (2014). Capturing and analyzing verbal and physical collaborative learning interactions at an enriched interactive tabletop. Computer-Supported Collaborative Learning, 8, 455–485.

    Article  Google Scholar 

  • Mattout, J. (2012). Brain-computer interfaces: A neuroscience paradigm of social interaction? A matter of perspective. Frontiers in Human Neuroscience, 6, 1–10.

    Article  Google Scholar 

  • Mazzoni, E. (2014). The Cliques Participation Index (CPI) as an indicator of creativity in online collaborative groups. Journal of Cognitive Education and Psychology, 13(1), 32–52.

    Article  Google Scholar 

  • Müller, V., & Lindenberger, U. (2011). Cardiac and respiratory patterns synchronize between persons during choir singing. PLoS One, 6(9), 1–15.

    Article  Google Scholar 

  • Newell, A. (1990). Unified theories of cognition. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Noroozi, O., Biermans, H. J. A., Weinberger, A., Mulder, M., & Chizari, M. (2013a). Scripting for construction of a transactive memory system in multidisciplinary CSCL environments. Learning and Instruction, 25, 1–12.

    Article  Google Scholar 

  • Noroozi, O., Biermans, H. J. A., Weinberger, A., Mulder, M., & Chizari, M. (2013b). Facilitating argumentative knowledge construction through a transactive discussion script in CSCL. Computers & Education, 61, 59–76.

    Article  Google Scholar 

  • Palomo-Duarte, M., Dodero, J. M., Medina-Bulo, I., Rodríguez-Posada, E. J., & Ruiz-Rube, I. (2014). Assessment of collaborative learning experiences by graphical analysis of wiki contributions. Interactive Learning Environments, 22(4), 444–466.

    Article  Google Scholar 

  • Panadero, E., & Järvelä, S. (2015). Socially shared regulation of learning: A review. European Psychologist, 20, 190. https://doi.org/10.1027/1016-9040/a000226

    Article  Google Scholar 

  • Papadopoulos, P. M., Demetriadis, D. N., & Weinbergert, A. (2013). ‘Make it explicit!’: Improving collaboration through increase of script coercion. Journal of Computer Assisted Learning, 29, 383–398.

    Article  Google Scholar 

  • Parasuraman, R. (2012). Neuroergonomics: The brain in action and at work. NeuroImage, 59, 1–3.

    Article  Google Scholar 

  • Patten, K. E. (2011). The somatic appraisal model of affect: Paradigm for educational neuroscience and neuropedagogy. Educational Philosophy and Theory, 43(1), 87–97.

    Article  Google Scholar 

  • Pekrun, R. (2010). Academic emotions. In T. Urdan (Ed.), APA educational psychology handbook (Vol. 2). Washington, DC: American Psychological Association.

    Google Scholar 

  • Poole, A., & Ball, L. J. (2005). Eye tracking in human-computer interaction and usability research: Current status and future. In C. Ghaoui (Ed.), Encyclopedia of human-computer interaction. Pennsylvania: Idea Group.

    Google Scholar 

  • Pope, A. T., Bogart, E. H., & Bartolome, D. S. (1996). Biocybernetic system evaluates indices of operator engagement in automated task. Biological Psychology, 40, 187–195.

    Article  Google Scholar 

  • Popov, V., Biemans, H. J. A., Brinkman, D., Kuznetsov, A. N., & Mulder, M. (2013). Facilitation of computer-supported collaborative learning in mixed- versus same-culture dyads: Does a collaboration script help? Internet and Higher Education, 19, 36–48.

    Article  Google Scholar 

  • Popov, V., Biemans, H. J. A., Brinkman, D., Kuznetsov, A. N., & Mulder, M. (2014). Use of an interculturally enriched collaboration script in computer-supported collaborative learning in higher education. Technology, Pedagogy and Education, 23(3), 349–374.

    Article  Google Scholar 

  • Popov, V., Noroozi, O., Barrett, J. B., Biemans, H. J. A., Teasley, S. D., Slof, B., et al. (2014). Perceptions and experiences of, and outcomes for, university students in culturally diversified dyads in a computer-supported collaborative learning environment. Computers in Human Behavior, 32, 186–200.

    Article  Google Scholar 

  • Poythress, M., Russell, C., Siegel, S., Tremoulet, P. D., Craven, P., Berka, C., et al. (2006). Correlation between expected workload and EEG indices of cognitive workload and task engagement. Research report.

    Google Scholar 

  • Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. Computer-Supported Collaborative Learning, 4, 239–257.

    Article  Google Scholar 

  • Remesal, A., & Colomina, R. (2013). Social presence and online collaborative small group work: A socioconstructivist account. Computers & Education, 60, 357–367.

    Article  Google Scholar 

  • Riganello, F., Garbarino, S., & Sannita, W. G. (2012). Heart rate variability, homeostasis, and brain function: A tutorial and review of application. Journal of Psychophysiology, 26(4), 178–203.

    Article  Google Scholar 

  • Robinson, K. (2013). The interrelationship of emotion and cognition when students undertake collaborative group work online: An interdisciplinary approach. Computers & Education, 62, 298–307.

    Article  Google Scholar 

  • Saab, N. (2012). Team regulation, regulation of social activities or co-regulation: Different labels for effective regulation of learning in CSCL. Metacognition and Learning, 7, 1–6.

    Article  Google Scholar 

  • Sanger, J., Muller, V., & Lindenberger, U. (2012). Intra- and interbrain synchronization and network properties when playing guitar in duets. Frontiers in Human Neuroscience, 6, 312.

    Article  Google Scholar 

  • Schneider, B., & Pea, R. (2013). Real-time mutual gaze perception enhances collaborative learning and collaboration quality. Computer-Supported Collaborative Learning, 8, 375–397.

    Article  Google Scholar 

  • Schneider, B., & Pea, R. (2014). Toward collaboration sensing. International Journal of Computer-Supported Collaborative Learning, 9, 371–395.

    Article  Google Scholar 

  • Sedda, A., Manfredi, V., Bottini, G., Cristani, M., & Murino, V. (2012). Automatic human interaction understanding: Lessons from a multidisciplinary approach. Frontiers in Human Neuroscience, 6, 1–3.

    Article  Google Scholar 

  • Sobreira, P., & Tchnikine, P. (2012). A model for flexibly editing CSCL scripts. Computer-Supported Collaborative Learning, 7, 567–592.

    Article  Google Scholar 

  • Stamper, J., Barnes, T., & Croy, M. (2011). Enhancing the automatic generation of hints with expert seeding. International Journal of Artificial Intelligence in Education, 21, 153–167. https://doi.org/10.3233/JAI-2011-021

  • Stein, Z., & Fischer, K. W. (2011). Directions for mind, brain, and education: Methods, models, and morality. Educational Philosophy and Theory, 43(1), 56–66.

    Article  Google Scholar 

  • Stevens, R. H., Galloway, T., & Berka, C. (2007). EEG-related changes in cognitive workload, engagement and distraction as students acquire problem solving skills. In C. Conati, K. McCoy, & G. Paliouras (Eds.), UM 2007, LNAI (Vol. 4511, pp. 197–206).

    Google Scholar 

  • Stevens, R. H., Galloway, T. L., Wang, P., & Berka, C. (2012). Cognitive neurophysiologic synchronies: What can they contribute to the study of teamwork? Human Factors, 54, 489–502.

    Article  Google Scholar 

  • Stikic, M., Berka, C., Levendowski, D. J., Rubio, R. F., Tan, V., Korszen, S., et al. (2014). Modeling temporal sequences of cognitive state changes based on a combination of EEG engagement EEG workload and heart rate metrics. Frontiers in Neuroscience, 8, 342.

    Article  Google Scholar 

  • Strain, A. C., Azevedo, R., & D’Mello, S. K. (2013). Using a false biofeedback methodology to explore relationships between learners’ affect, metacognition, and performance. Contemporary Educational Psychology, 38, 22–39.

    Article  Google Scholar 

  • Sun, R. (2006). Prolegomena to integrating cognitive modeling and social simulation. In R. Sun (Ed.), Cognition and multi-agent interaction. New York: Cambridge University Press.

    Google Scholar 

  • Tommerdahl, J. (2010). A model for bridging the gap between neuroscience and education. Oxford Review of Education, 36(1), 97–109.

    Article  Google Scholar 

  • Turner, D. A. (2012). Education and neuroscience. Contemporary Social Science, 7(2), 167–179.

    Article  Google Scholar 

  • van Hemmen, J. L., & Sejnowski, T. J. (2006). 23 problems in systems neuroscience. New York: Oxford University Press.

    Book  Google Scholar 

  • Van Schaik, P., Martin, S., & Vallance, M. (2012). Measuring flow experience in an immersive virtual environment for collaborative learning. Journal of Computer Assisted Learning, 28(4), 350–365.

    Article  Google Scholar 

  • Vasiliou, C., Ioannou, A., & Zaphiris, P. (2014). Understanding collaborative learning activities in an information ecology: A distributed cognition account. Computers in Human Behavior, 41, 544–553.

    Article  Google Scholar 

  • Volet, S., Vauras, M., & Salonen, P. (2009). Self- and social regulation in learning contexts: An integrative perspective. Educational Psychologist, 44(4), 215–226.

    Article  Google Scholar 

  • Wang, H.-Y., Duh, H. B.-L., Li, N., Lin, T.-J., & Tsai, C.-C. (2014). An investigation of university students’ collaborative inquiry learning behaviors in an augmented reality simulation and a traditional simulation. Journal of Science Education & Technology, 23, 682–691.

    Article  Google Scholar 

  • Wing, A. M., Endo, S., Bradbury, A., & Vorberg, D. (2014). Optimal feedback correction in string quartet synchronization. Journal of the Royal Society, Interface, 11.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. NeuroLab, Department of Special Education, University of Quebec in Montreal (UQAM), Montreal, QC, Canada

    Julien Mercier

Authors
  1. Julien Mercier
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julien Mercier .

Editor information

Editors and Affiliations

  1. Department of Primary Education, University of Ioannina, Ioannina, Greece

    Tassos Anastasios Mikropoulos

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Mercier, J. (2018). The Feasibility and Interest of Monitoring the Cognitive and Affective States of Groups of Co-learners in Real Time as They Learn. In: Mikropoulos, T. (eds) Research on e-Learning and ICT in Education. Springer, Cham. https://doi.org/10.1007/978-3-319-95059-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95059-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95058-7

  • Online ISBN: 978-3-319-95059-4

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation