Abstract
This chapter introduces methods for using an individual-level multimodal approach for studying the learning experience within the context of vocational education. There has recently been increased interest in recording physiological signals in pedagogical contexts. The current research literature on multimodal studies of adult learning experience is scarce and has been primarily applied and developed in studies of a preliminary nature and with varying combinations of modalities. Learning experience is a complex phenomenon which cannot be fully captured via a single-data modality. However, based on the reviewed literature, there is still a lack of larger datasets and strong empirical evidence to enable a comprehensive understanding of experiential learning as a phenomenon.
In addition to self-reported learning experiences, there is a need for theoretical development and a more holistic empirical approach that includes physiological and neurophysiological aspects involved in learning situation. We present a case example of simulation-based learning (SBL) of forestry skills, in which the modalities applied to explore the learning experience were video recordings, stimulated recall interviews, questionnaires, electrocardiography (ECG), and electroencephalography (EEG). Our example presents how multimodal research design can be used to study learning experience, by combining measurements of the human nervous system with subjective and observational data. It is too early to evaluate the practical impact of multimodal research for the field of adult education. Successful application of multimodal methods requires interaction across disciplines, harmonizing of conceptual frameworks and goals, as well as bringing together complementary, discipline-specific expertise to guarantee valid application of research methods. Opening of the disciplinary boundaries both at theoretical and methodological domains enables to increase discussion between researchers from different disciplines.
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References
Aagaard-Hansen, J. (2007). The challenges of cross-disciplinary research. Social Epistemology, 21(4), 425–438. https://doi.org/10.1080/02691720701746540
Aguayo, C., Dañobeitia, C., Cochrane, T., Aiello, S., Cook, S., & Cuevas, A. (2018). Embodied reports in paramedicine mixed reality learning. Research in Learning Technology, 26, 1–17. https://doi.org/10.25304/rlt.v26.2150
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. https://doi.org/10.1038/s41598-018-21518-3
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. https://doi.org/10.1111/j.1469-5812.2010.00705.x
Antonietti, A., Colombo, B., & Di Nuzzo, C. (2015). Metacognition in self-regulated multimedia learning: Integrating behavioural, psychophysiological and introspective measures. Learning, Media and Technology, 40(2), 187–209. https://doi.org/10.1080/17439884.2014.933112
Azevedo, R., & Aleven, V. (2013). Metacognition and learning technologies: An overview of current interdisciplinary research. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 1–16). Springer. https://doi.org/10.1007/978-1-4419-5546-3_1
Bahreini, K., Nadolski, R., & Westera, W. (2016). Towards multimodal emotion recognition in e-learning environments. Interactive Learning Environments, 24(3), 590–605. https://doi.org/10.1080/10494820.2014.908927
Baldwin, C., Persing, J., & Magnuson, D. (2004). The role of theory, research, and evaluation in adventure education. The Journal of Experimental Education, 26(3), 167–183. https://doi.org/10.1177/2F105382590402600307
Basit, T. (2003). Manual or electronic? The role of coding in qualitative data analysis. Educational Research, 45(2), 143–154. https://doi.org/10.1080/0013188032000133548
Bearman, M., Greenhill, J., & Nestel, D. (2019). The power of simulation: A large-scale narrative analysis of learners’ experiences. Medical Education, 53(4), 369–379. https://doi.org/10.1111/medu.13747
Berntson, G. G., Thomas Bigger, J., Jr., Eckberg, D. L., Grossman, P., Kaufmann, P. G., Malik, M., Nagaraja, H. N., Porges, S. W., Saul, J. P., Stone, P. H., & Van Der Molen, M. W. (1997). Heart rate variability: Origins, methods, and interpretive caveats. Psychophysiology, 34(6), 623–648. https://doi.org/10.1111/j.1469-8986.1997.tb02140.x
Bhoja, R., Guttman, O. T., Fox, A. A., Melikman, E., Kosemund, M., & Gingrich, K. J. (2020). Psychophysiological stress indicators of heart rate variability and electrodermal activity with application in healthcare simulation research. Simulation in Healthcare, 15(1), 39–45. https://doi.org/10.1097/SIH.0000000000000402
Birt, J., Stromberga, Z., Cowling, M., & Moro, C. (2018). Mobile mixed reality for experiential learning and simulation in medical and health sciences education. Information, 9(2), 31. https://doi.org/10.3390/info9020031
Black, A. E., & Deci, E. L. (2000). The effects of instructors’ autonomy support and students’ autonomous motivation on learning organic chemistry: A self-determination theory perspective. Science Education, 84(6), 740–756. https://doi.org/10.1002/1098-237X(200011)84:6<740::AID-SCE4>3.0.CO;2-3
Bore, A., & Wright, N. (2009). The wicked and complex in education: Developing a transdisciplinary perspective for policy formulation, implementation and professional practice. Journal of Education for Teaching, 35(3), 241–256. https://doi.org/10.1080/02607470903091286
Brever, J., & Hunter, A. (1989). Foundations of multimethod research: Synthesizing styles. Sage. https://doi.org/10.1080/02607470903091286
Calderhead, J. (1981). Stimulated recall: A method for research on teaching. British Journal of Educational Psychology, 51(2), 211–217. https://doi.org/10.1111/j.2044-8279.1981.tb02474.x
Calvo, R. A., & D’Mello, S. K. (Eds.). (2011). New perspectives on affect and learning technologies (Vol. 3). Springer.
Clapper, T. C. (2014). Beyond Knowles: What those conducting simulation need to know about adult learning theory. Clinical Simulation in Nursing, 6(1), e7–e14. https://doi.org/10.1016/j.ecns.2009.07.003
Cowley, B., Ravaja, N., & Heikura, T. (2013). Cardiovascular physiology predicts learning effects in a serious game activity. Computers & Education, 60(1), 299–309. https://doi.org/10.1016/j.compedu.2012.07.014
Daley, B., Martin, L. G., & Roessger, K. M. (2018). A call for methodological plurality: Reconsidering research approaches in adult education. Adult Education Quarterly, 68(2), 157–169. https://doi.org/10.1177/0741713618760560
Damasio, A. (1999). The feeling of what happens: Body and emotion in the making of consciousness. Mariner.
Derry, S. J., Pea, R. D., Barron, B., Engle, R. A., Erickson, F., Goldman, R., Hall, R., Koschmann, T., Lemke, J. L., Sherin, M. G., & Sherin, B. L. (2010). Conducting video research in the learning sciences: Guidance on selection, analysis, technology, and ethics. Journal of the Learning Sciences, 19(1), 3–53. https://doi.org/10.1080/10508400903452884
Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Michalareas, G., Van Bavel, J. J., Ding, M., & Poeppel, D. (2017). Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom. Current Biology, 27(9), 1375–1380. https://10.1016/j.cub.2017.04.002.
Dindar, M., Malmberg, J., Järvelä, S., Haataja, E., & Kirschner, P. A. (2020). Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning. Education and Information Technologies, 25, 1785–1802. https://doi.org/10.1007/s10639-019-10059-5
Dirkx, J. M. (2008). The meaning and role of emotions in adult learning. New Directions for Adult and Continuing Education, 2008(120), 7–18. https://doi.org/10.1002/ace.311
Eteläpelto, A., Kykyri, V.-L., Penttonen, M., Hökkä, P., Paloniemi, S., Vähäsantanen, K., Eteläpelto, T., & Lappalainen, V. (2018). A multi-componential methodology for exploring emotions in learning: Using self-reports, behaviour registration, and physiological indicators as complementary data. Frontline Learning Research, 6(3), 6–36. https://doi.org/10.14786/flr.v6i3.379
Evans, D. E., & Rothbart, M. K. (2007). Developing a model for adult temperament. Journal of Research in Personality, 41(4), 868–888. https://doi.org/10.1016/j.jrp.2006.11.002
Fenwick, T. (2006). Inside out of experiential learning: Fluid bodies, co-emergent minds. In R. Edwards, J. Gallacher, & S. Whittaker (Eds.), Learning outside the academy. International research perspectives on lifelong learning (pp. 42–55). Routledge.
Firstbeat Technologies Ltd. (2014). Stress and recovery analysis method based on 24-hour heart rate variability. https://assets.firstbeat.com/firstbeat/uploads/2015/11/Stress-and-recovery_white-paper_20145.pdf
Fortenbacher, A., & Yun, H. (2020). Can sensors effectively support learning? In N. Pickwart & S. Liu (Eds.), Artificial intelligence supported educational technologies: Advances in analytics for learning and teaching (pp. 93–114). Springer. https://doi.org/10.1007/978-3-030-41099-5_6
Fromm, J., Radianti, J., Wehking, C., Stieglitz, S., Majchrzak, T. A., & vom Brocke, J. (2021). More than experience? On the unique opportunities of virtual reality to afford a holistic experiential learning cycle. The Internet and Higher Education. https://doi.org/10.1016/j.iheduc.2021.100804
Giannakos, M. N., Sharma, K., Pappas, I. O., Kostakos, V., & Velloso, E. (2019). Multimodal data as a means to understand the learning experience. International Journal of Information Management, 48, 108–119. https://doi.org/10.1016/j.ijinfomgt.2019.02.003
Girzadas, D. V., Jr., Delis, S., Bose, S., Hall, J., Rzechula, K., & Kulsta, E. B. (2009). Measures of stress and learning seem to be equally affected among all roles in a simulation scenario. Simulation in Healthcare, 4(3), 149–154. https://doi.org/10.1097/SIH.0b013e3181abe9f2
Gorard, S. (2002). Can we overcome the methodological schism? Four models for combining qualitative and quantitative evidence. Research Papers in Education Policy and Practice, 17(4), 345–361. https://doi.org/10.1080/0267152022000031405
Gorard, S., & Taylor, C. (2004). Combining methods in educational and social research. McGraw-Hill.
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
Hagen, M., & Park, S. (2016). We knew it all along! Using cognitive science to explain how andragogy works. European Journal of Training and Development, 40(3), 171–190.
Hammond, C. (2005). The wider benefits of adult learning: An illustration of the advantages of multi-method research. International Journal of Social Research Methodology, 8(3), 239–255. https://doi.org/10.1080/13645570500155037
Hannaford, C. (1995). Smart moves: Why learning is not all in your head. Great Ocean Publishers.
Hardy, M., Wiebe, E. N., Grafsgaard, J. F., Boyer, K. E., & Lester, J. C. (2013). Physiological responses to events during training: Use of skin conductance to inform future adaptive learning systems. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57(1), 2101–2105. https://doi.org/10.1177/1541931213571468
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
Hemingway, A., Carter, S., Callaway, A., Kavanagh, E., & Ellis, S. (2019). An exploration of the mechanism of action of an equine-assisted intervention. Animals, 9(6), 303. https://doi.org/10.3390/ani9060303
Husebø, S. E., O’Regan, S., & Nestel, D. (2015). Reflective practice and its role in simulation. Clinical Simulation in Nursing, 11(8), 368–375. https://doi.org/10.1016/j.ecns.2015.04.005
Illeris, K. (2018). An overview of the history of learning theory. European Journal of Education, 53(1), 86–101. https://doi.org/10.1111/ejed.12265
Jarvis, P. (2005a). Towards a philosophy of human learning: An existentialist perspective. In P. Jarvis & S. Parker (Eds.), Human learning: An holistic approach (pp. 1–15). Routledge.
Jarvis, P. (2005b). Towards a comprehensive theory of human learning (1st ed.). Routledge. https://doi.org/10.4324/9780203001677
Jarvis, P. (2006). Towards a comprehensive theory of human learning. Routledge.
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. https://doi.org/10.3102/0013189X033007014
Jordi, R. (2010). Reframing the concept of reflection: Consciousness, experiential learning, and reflective learning practices. Adult Education Quarterly, 61(2), 181–197. https://doi.org/10.1177/0741713610380439
Judd, B. K., Alison, J. A., Waters, D., & Gordon, C. J. (2016). Comparison of psychophysiological stress in physiotherapy students undertaking simulation and hospital-based clinical education. Simulation in Healthcare, 11(4), 271–277. https://doi.org/10.1097/SIH.0000000000000155
Kagan, N., Krathwohl, D. R., & Miller, R. (1963). Stimulated recall in therapy using video tape: A case study. Journal of Counseling Psychology, 10(3), 237–243. https://doi.org/10.1037/h0045497
Kocialkowski, C., Rybinski, B., Hainsworth, L., & Yang, D. (2020). Simulation learning: Effectiveness and stressfulness in medical student teaching. MedEdPublish, 9(1). https://doi.org/10.15694/mep.2020.000143.1
Kolb, A., & Kolb, D. A. (2017). The experiential educator: Principles and practices of experiential learning. EBLS Press.
Larmuseau, C., Vanneste, P., Cornelis, J., Desmet, P., & Depaepe, F. (2019). Combining physiological data and subjective measurements to investigate cognitive load during complex learning. Frontline Learning Research, 7(2), 57–74. https://doi.org/10.14786/flr.v7i2.403
Lateef, F. (2010). Simulation-based learning: Just like the real thing. Journal of Emergencies, Trauma and Shock, 3(4), 348–352. https://doi.org/10.4103/0974-2700.70743
LeBlanc, V. R. (2019). The relationship between emotions and learning in simulation-based education. Simulation in Healthcare, 14(3), 137–139. https://doi.org/10.1097/SIH.0000000000000379
Lim, D. H., Chai, D. S., Park, S., & Doo, M. Y. (2019). Neuroscientism, the neuroscience of learning: An integrative review and implications for learning and development in the workplace. European Journal of Training and Development, 43(7/8), 619–642. https://doi.org/10.1108/EJTD-03-2019-0033
Lumma, A. L., & Weger, U. (2021). Looking from within: Comparing first-person approaches to studying experience. Current Psychology. https://doi.org/10.1007/s12144-021-02277-3
Lyle, J. (2003). Stimulated recall: A report on its use in naturalistic research. British Educational Research Journal, 29(6), 861–878. https://doi.org/10.1080/0141192032000137349
Malinen, A. (2000). Towards the essence of adult experiential learning: A reading of the theories of Knowles, Kolb, Mezirow, Revans and Schön. University of Jyväskylä.
Marín-Morales, J., Higuera-Trujillo, J. L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E. P., … Valenza, G. (2018). Affective computing in virtual reality: Emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports, 8(1), 1–15. https://doi.org/10.1038/s41598-018-32063-4
Morris, T. H. (2020). Experiential learning – A systematic review and revision of Kolb’s model. Interactive Learning Environments, 28(8), 1064–1077. https://doi.org/10.1080/10494820.2019.1570279
Mudrick, N. V., Azevedo, R., & Taub, M. (2019). Integrating metacognitive judgments and eye movements using sequential pattern mining to understand processes underlying multimedia learning. Computers in Human Behavior, 96, 223–234. https://doi.org/10.1016/j.chb.2018.06.028
Papastamatis, A., & Panitsides, E. A. (2014). Transformative learning: Advocating for a holistic approach. Review of European Studies, 6(4), 74–81. https://doi.org/10.5539/res.v6n4p74
Patton, M. Q. (2002). Two decades of developments in qualitative inquiry: A personal, experiential perspective. Qualitative Social Work, 1(3), 261–283.
Pekrun, R., & Schutz, P. A. (2007). Where do we go from here? Implications and future directions for inquiry on emotions in education. In P. A. Schutz & R. Pekrun (Eds.), Emotion in education: Educational psychology (pp. 313–331). Academic. https://doi.org/10.1016/B978-012372545-5/50019-8
Picard, R. W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, 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
Poore, J. A., Cullen, D. L., & Schaar, G. L. (2014). Simulation-based interprofessional education guided by Kolb’s experiential learning theory. Clinical Simulation in Nursing, 10(5), e241–e247. https://doi.org/10.1016/j.ecns.2014.01.004
Quintana, D., Guastella, A., Outhred, T., Hicki, I., & Kempf, A. (2012). Heart rate variability is associated with emotion recognition: Direct evidence for a relationship between the autonomic nervous system and social cognition. International Journal of Psychophysiology, 86(2), 168–172. https://doi.org/10.1016/j.ijpsycho.2012.08.012
Rogers, T., Andler, C., O’Brien, B., & van Schaik, S. (2019). Self-reported emotions in simulation-based learning: Active participants vs. observers. Simulation in Healthcare, 14(3), 140–145. https://doi.org/10.1097/SIH.0000000000000354
Rosenfield, P. L. (1992). The potential of transdisciplinary research for sustaining and extending linkages between the health and social sciences. Social Science & Medicine, 35(11), 1343–1357. https://doi.org/10.1016/0277-9536(92)90038-R
Rowley, J. (2012). Conducting research interviews. Management Research Review, 35(3/4), 260–271. https://doi.org/10.1108/01409171211210154
Salmi, E., Määttä, S., Vehkakoski, T., Aunola, K., Kairaluoma, L., & Pirttimaa, R. (2020). Oppimisvaikeuksien, motivaation ja oppijaminäkäsityksen merkitys ammatillisista opinnoista valmistumisessa. [The importance of learning difficulties, motivation and the student's self-perception in graduating from vocational studies.]. Oppimisen ja oppimisvaikeuksien erityislehti: NMI-bulletin, 30(3), 50–66. https://jyx.jyu.fi/handle/123456789/72613
Schaaff, K., Müller, L., Kirst, M., & Heuer, S. (2012). Affect–A modular framework for online affect recognition and biofeedback applications. In 7th European conference on technology enhanced learning (ECTEL 2012), MATEL workshop, Saarbrücken, Germany.
Schenck, J., & Cruickshank, J. (2015). Evolving Kolb: Experiential education in the age of neuroscience. The Journal of Experimental Education, 38(1), 73–95. https://doi.org/10.1177/1053825914547153
Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 1–17. https://doi.org/10.3389/fpubh.2017.00258
Shen, L., Wang, M., & Shen, R. (2009). Affective e-learning: Using “emotional” data to improve learning in pervasive learning environment. Journal of Educational Technology & Society, 12(2), 176–189. https://www.jstor.org/stable/jeductechsoci.12.2.176
Silvennoinen, M., Vesisenaho, M., Manu, M., Kullberg, T., Malinen, A., & Parviainen, T. (2020). Methodology development in adult learning research: Combining physiological reactions and learning experiences in simulation-based learning environments. In L. G. Chova, A. L. Martínez, & I. C. Torres (Eds.), EDULEARN20 proceedings. 12th international conference on education and new learning technologies (pp. 5037–5046). IATED. EDULEARN Proceedings. https://doi.org/10.21125/edulearn.2020.1316
Spangler, G., Pekrun, R., Kramer, K., & Hofmann, H. (2002). Students’ emotions, physiological reactions, and coping in academic exams. Anxiety, Stress & Coping, 15(4), 413–432. https://doi.org/10.1080/1061580021000056555
Stadler, M., Graesser, A., & Fischer, F. (2021). Editorial: Transdisciplinary research on learning and teaching: Chances and challenges. Frontiers in Psychology, 12, 696219. https://doi.org/10.3389/fpsyg.2021.696219
Staus, N. L., & Falk, J. H. (2017). The role of emotion in informal science learning: Testing an exploratory model. Mind, Brain, and Education, 11(2), 45–53. https://doi.org/10.1111/mbe.12139
Stepanova, E. R., Quesnel, D., & Riecke, B. E. (2019). Space—A virtual frontier: How to design and evaluate a virtual reality experience of the overview effect. Frontiers in Digital Humanities, 6(7). https://doi.org/10.3389/fdigh.2019.00007
Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV – Heart rate variability analysis software. Computer Methods and Programs in Biomedicine, 113, 210–220. https://doi.org/10.1016/j.cmpb.2013.07.024
van Atteveldt, N., van Kesteren, M. T. R., Braams, B., & Krabbendam, L. (2018). Neuroimaging of learning and development: Improving ecological validity. Frontline Learning Research, 6(3), 186–203. https://doi.org/10.14786/flr.v6i3.366
Varela, F. J., & Shear, J. (1999). First-person methodologies: What, why, how. Journal of Consciousness Studies, 6(2/3), 1–14.
Vesisenaho, M., Juntunen, M., Häkkinen, P., Pöysä-Tarhonen, J., Fagerlund, J., Miakush, I., & Parviainen, T. (2019). Virtual reality in education: Focus on the role of emotions and physiological reactivity. Journal of Virtual Worlds Research, 12(1), 1–15. https://doi.org/10.4101/jvwr.v12i1.7329
Wang, C., & Cesar, P. (2015). Physiological measurement on students’ engagement in a distributed learning environment. In Proceedings of the 2nd international conference on physiological computing systems (PhyCS 2015) (pp. 149–156). SCITEPRESS – Science and Technology Publications, Lda, Setubal, PRT. https://doi.org/10.5220/0005229101490156
Wilhelm, F. H., & Grossman, P. (2010). Emotions beyond the laboratory: Theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment. Biological Psychology, 84(3), 552–569. https://doi.org/10.1016/j.biopsycho.2010.01.017
Wilhelm, F. H., Grossman, P., & Müller, M. I. (2012). Bridging the gap between the laboratory and the real world: Integrative ambulatory psychophysiology. In M. Mehl & T. Conner (Eds.), Handbook of research methods for studying daily life (pp. 210–234). Guilford.
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: Recognising and responding to student affect. International Journal of Learning Technology, 4(3/4), 129–164.
Yang, B. (2006). A holistic conceptualization of adult learning and its critiques of selected concepts and theories. In Proceedings of the 47th annual adult education research conference (pp. 482–488). University of Minnesota. https://newprairiepress.org/aerc/2006/papers/79
Zembylas, M. (2007). Theory and methodology in researching emotions in education. International Journal of Research & Method in Education, 30(1), 57–72. https://doi.org/10.1080/17437270701207785
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Silvennoinen, M., Parviainen, T., Malinen, A., Karjalainen, S., Manu, M., Vesisenaho, M. (2022). Combining Physiological and Experiential Measures to Study the Adult Learning Experience. In: Goller, M., Kyndt, E., Paloniemi, S., Damşa, C. (eds) Methods for Researching Professional Learning and Development. Professional and Practice-based Learning, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-031-08518-5_7
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