Abstract
Students process qualitatively and quantitatively different information during the dynamic self-regulated learning (SRL) process, and thus they may experience varying cognitive load in different SRL behaviors. However, there is limited research on the role of cognitive load in SRL. This study examined students’ cognitive load in micro-level SRL behaviors and explored how cognitive load affected metacognitive judgments and SRL performance. In this study, thirty-four medical students solved two diagnostic tasks of varying complexity (i.e., simple and complex) in BioWorld, an intelligent tutoring system designed for promoting clinical reasoning skills. Think-aloud protocols (TAPs) were utilized to code students’ SRL activities, i.e., Orientation, Planning, Monitoring, Evaluation, and Self-reflection. Using the text mining techniques, we also extracted students’ linguistic features from TAPs to represent their cognitive load in each SRL behavior. The results demonstrated that students experienced significantly different cognitive load during different SRL behaviors as they solved the complex task. Moreover, students’ cognitive load during the Orientation and Evaluation behavior significantly increased as task complexity increased. Furthermore, the linear mixed-effects model (LMMs) indicated that students’ cognitive load in the Orientation and Monitoring behaviors negatively predicted confidence ratings but did not affect diagnostic performance. This study not only provides theoretical and methodological insights about cognitive load during SRL but inspires the design of intelligent tutoring systems regarding the effective self-regulation of cognitive load.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author.
References
Artino, A. R., Cleary, T. J., Dong, T., Hemmer, P. A., & Durning, S. J. (2014). Exploring clinical reasoning in novices: A self-regulated learning microanalytic assessment approach. Medical Education, 48(3), 280–291. https://doi.org/10.1111/medu.12303
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. https://doi.org/10.1016/j.chb.2019.03.025
Baddeley, A. (1992). Working memory. Science, 255(5044), 556–559. https://doi.org/10.1126/science.1736359
Baars, M., Wijnia, L., & Paas, F. (2017). The association between motivation, affect, and self-regulated learning when solving problems. Frontiers in Psychology, 8, 1346. https://doi.org/10.3389/fpsyg.2017.01346
Bates, D., Mächler, M., Bolker, B. M., & Walker, S. C. (2014). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), https://doi.org/10.18637/jss.v067.i01
Blissett, S., Sibbald, M., Kok, E., & van Merrienboer, J. (2018). Optimizing self-regulation of performance: Is mental effort a cue? Advances in Health Sciences Education, 23(5), 891–898. https://doi.org/10.1007/s10459-018-9838-x
Boekaerts, M., Maes, S., & Karoly, P. (2005). Self-regulation across domains of applied psychology: Is there an emerging consensus? Applied Psychology, 54(2), 149–154. https://doi.org/10.1111/j.1464-0597.2005.00201.x
Corno, L., & Mandinach, E. B. (2004). What we have learned about student engagement in the past twenty years. In D. M. McInerney, & Van S. Etten (Eds.), Research on sociocultural influences on motivation and learning (1 vol., pp. 297–326). Information Age Publishing, Inc
Creswell, J. D., Lam, S., Stanton, A. L., Taylor, S. E., Bower, J. E., & Sherman, D. K. (2007). Does self-affirmation, cognitive processing, or discovery of meaning explain cancer-related health benefits of expressive writing? Personality and Social Psychology Bulletin, 33(2), 238–250. https://doi.org/10.1177/0146167206294412
de Bruin, A. B., Roelle, J., Carpenter, S. K., & Baars, M. (2020). Synthesizing cognitive load and self-regulation theory: A theoretical framework and research agenda. Educational Psychology Review, 32(4), 903–915. https://doi.org/10.1007/s10648-020-09576-4
de Bruin, A. B., & van Merriënboer, J. J. (2017). Bridging cognitive load and self-regulated learning research: A complementary approach to contemporary issues in educational research. Learning and Instruction, 51, 1–9. https://doi.org/10.1016/j.learninstruc.2017.06.001
Dinsmore, D. L., & Zoellner, B. P. (2018). The relation between cognitive and metacognitive strategic processing during a science simulation. British Journal of Educational Psychology, 88(1), 95–117. https://doi.org/10.1111/bjep.12177
Durning, S. J., Dong, T., Artino, A. R., van der Vleuten, C., Holmboe, E., & Schuwirth, L. (2015). Dual processing theory and expertsʼ reasoning: exploring thinking on national multiple-choice questions. Perspectives on Medical Education, 4(4), 168–175. https://doi.org/10.1007/s40037-015-0196-6
Fraser, K. L., Ayres, P., & Sweller, J. (2015). Cognitive load theory for the design of medical simulations. Simulation in Healthcare, 10(5), 295–307
Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102(2), 211–245. https://doi.org/10.1037/0033-295x.102.2.211
Greene, J. A., & Azevedo, R. (2009). A macro-level analysis of SRL processes and their relations to the acquisition of a sophisticated mental model of a complex system. Contemporary Educational Psychology, 34(1), 18–29. https://doi.org/10.1016/j.cedpsych.2008.05.006
Haji, F. A., Rojas, D., Childs, R., de Ribaupierre, S., & Dubrowski, A. (2015). Measuring cognitive load: performance, mental effort and simulation task complexity. Medical education, 49(8), 815–827
Händel, M., de Bruin, A. B., & Dresel, M. (2020). Individual differences in local and global metacognitive judgments. Metacognition and Learning, 15(1), 51–75. https://doi.org/10.1007/s11409-020-09220-0
Hu, J., & Gao, X. A. (2017). Using think-aloud protocol in self-regulated reading research. Educational Research Review, 22, 181–193
Khawaja, M. A., Chen, F., & Marcus, N. (2014). Measuring cognitive load using linguistic features: Implications for usability evaluation and adaptive interaction design. International Journal of Human-Computer Interaction, 30(5), 343–368. https://doi.org/10.1080/10447318.2013.860579
Konopasky, A., Durning, S. J., Artino, A. R., Ramani, D., & Battista, A. (2020). The linguistic effects of context specificity: Exploring affect, cognitive processing, and agency in physicians’ think-aloud reflections. Diagnosis, 7(3), 273–280. https://doi.org/10.1515/dx-2019-0103
Koriat, A. (1997). Monitoring one’s own knowledge during study: A cue-utilization approach to judgments of learning. Journal of Experimental Psychology: General, 126(4), 349–370. https://doi.org/10.1037/0096-3445.126.4.349
Koriat, A. (2018). Agency attributions of mental effort during self-regulated learning. Memory and Cognition, 46(3), 370–383. https://doi.org/10.3758/S13421-017-0771-7
Kuiper, R. A. (2013). Integration of innovative clinical reasoning pedagogies into a baccalaureate nursing curriculum. Creative Nursing, 19(3), 128–139. https://doi.org/10.1891/1078-4535.19.3.128
Lajoie, S. P. (2009). Developing professional expertise with a cognitive apprenticeship model: Examples from Avionics and Medicine. In K. A. Ericsson (Ed.), Development of Professional Expertise: Toward Measurement of Expert Performance and Design of Optimal Learning Environments (pp. 61–83). Cambridge University Press.
Lajoie, S. P., & Lu, J. (2012). Supporting collaboration with technology: does shared cognition lead to co-regulation in medicine? Metacognition and Learning, 7(1), 45–62. https://doi.org/10.1007/s11409-011-9077-5
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52. https://doi.org/10.1207/S15326985EP3801_6
Moos, D. C., & Azevedo, R. (2008). Self-regulated learning with hypermedia: The role of prior domain knowledge. Contemporary Educational Psychology, 33(2), 270–298
Müller, C., Großmann-Hutter, B., Jameson, A., Rummer, R., & Wittig, F. (2001, July). Recognizing time pressure and cognitive load on the basis of speech: An experimental study. Internationl Conference on User Modeling, Sonthofen, BY, Germany. https://doi.org/10.1007/3-540-44566-8_3
Nelson, T. O., & Narens, L. (1994). Why investigate metacognition?. In J. Metcalfe, & A. P. Shimamura (Eds.), Metacognition: Knowing about Knowing (pp. 1–25). MIT Press. https://doi.org/10.7551/mitpress/4561.003.0003
Nourbakhsh, N., Wang, Y., Chen, F., & Calvo, R. A. (2012, November). Using galvanic skin response for cognitive load measurement in arithmetic and reading tasks. OzCHI’12: Proceedings Australian Computer-Human Interaction Conference, New York, NY, USA. https://doi.org/10.1145/2414536.2414602
Nückles, M., Roelle, J., Glogger-Frey, I., Waldeyer, J., & Renkl, A. (2020). The self-regulation-view in writing-to-learn: Using journal writing to optimize cognitive load in self-regulated learning. Educational Psychology Review, 32(4), 1089–1126. https://doi.org/10.1007/s10648-020-09541-1
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71. https://doi.org/10.1207/S15326985EP3801_8
Paas, F., Van Gog, T., & Sweller, J. (2010). Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives. Educational psychology review, 22(2), 115–121. https://doi.org/10.1007/s10648-010-9133-8
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422
Park, B., Korbach, A., & Brünken, R. (2015). Do learner characteristics moderate the seductive-details-effect? A cognitive-load-study using eye-tracking. Educational Technology and Society, 18(4), 24–36
Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The Development and Psychometric Properties of LIWC2015. https://repositories.lib.utexas.edu/handle/2152/31333
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 (1st ed., pp. 451–502). US: Academic Press. https://doi.org/10.1016/b978-012109890-2/50043-3
Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory into Practice, 41(4), 219–225. https://doi.org/10.1207/s15430421tip4104_3
Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x
Reiser, B. J. (2004). Scaffolding complex learning: The mechanisms of structuring and problematizing student work. Journal of the Learning Sciences, 13(3), 273–304. https://doi.org/10.1207/s15327809jls1303_2
Scheiter, K., Ackerman, R., & Hoogerheide, V. (2020). Looking at mental effort appraisals through a metacognitive lens: Are they biased? Educational Psychology Review, 32(4), 1003–1027. https://doi.org/10.1007/s10648-020-09555-9
Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19(3), 285–307. https://doi.org/10.1007/s10648-007-9046-3
Schwonke, R. (2015). Metacognitive load—Useful, or extraneous concept? Metacognitive and self-regulatory demands in computer-based learning. Educational Technology and Society, 18(4), 172–184
Seufert, T. (2018). The interplay between self-regulation in learning and cognitive load. Educational Research Review, 24, 116–129. https://doi.org/10.1016/j.edurev.2018.03.004
Seufert, T. (2020). Building bridges betweenself-regulation and cognitive load—An invitation for a broad and differentiated attempt. Educational Psychology Review, 32(4), 1151–1162. https://doi.org/10.1007/s10648-020-09574-6
Siadaty, M., Gasevic, D., & Hatala, M. (2016). Trace-based micro-analytic measurement of self-regulated learning processes. Journal of Learning Analytics, 3(1), 183–214. https://doi.org/10.18608/jla.2016.31.11
Stuijfzand, B. G., Van Der Schaaf, M. F., Kirschner, F. C., Ravesloot, C. J., Van Der Gijp, A., & Vincken, K. L. (2016). Medical students’ cognitive load in volumetric image interpretation: Insights from human-computer interaction and eye movements. Computers in Human Behavior, 62, 394–403. https://doi.org/10.1016/j.chb.2016.04.015
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1016/0364-0213(88)90023-7
Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), Psychology of Learning and Motivation (pp. 37–76). US: Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002-8
Sweller, J., & Paas, F. (2017). Should self-regulated learning be integrated with cognitive load theory? A commentary. Learning and Instruction, 51, 85–89. https://doi.org/10.1016/j.learninstruc.2017.05.005
Syed, M., & Nelson, S. C. (2015). Guidelines for establishing reliability when coding narrative data. Emerging Adulthood, 3(6), 375–387. https://doi.org/10.1177/2167696815587648
Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676
Valcke, M. (2002). Cognitive load: updating the theory? Learning and Instruction, 12(1), 147–154. https://doi.org/10.1016/S0959-4752(01)00022-6
van Loon, M. H., de Bruin, A. B., van Gog, T., & van Merriënboer, J. J. (2013). Activation of inaccurate prior knowledge affects primary-school students’ metacognitive judgments and calibration. Learning and Instruction, 24, 15–25. https://doi.org/10.1016/j.learninstruc.2012.08.005
van Loon, M. H., & Roebers, C. M. (2017). Effects of feedback on self-evaluations and self-regulation in elementary school. Applied Cognitive Psychology, 31(5), 508–519. https://doi.org/10.1002/acp.3347
Winne, P. H. (1985). Steps toward promoting cognitive achievements. The Elementary School Journal, 85(5), 673–693. https://doi.org/10.1086/461429
Winne, P. H. (2001). Self-regulated learning viewed from models of information Self-regulated learning viewed from models of information processing. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-Regulated Learning and Academic Achievement: Theoretical Perspectives (pp. 145–178)
Winne, P. H. (2018). Theorizing and researching levels of processing in self-regulated learning. British Journal of Educational Psychology, 88(1), 9–20. https://doi.org/10.1111/bjep.12173
Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated engagement in learning. In D. Hacker, J. Dunlosky, & A. Graesser (Eds.), Metacognition in Educational Theory and Practice (pp. 277–304). Hillsdale, NJ: Erlbaum. https://doi.org/10.4324/9781410602350
Wirth, J., Stebner, F., Trypke, M., Schuster, C., & Leutner, D. (2020). An interactive layers model of self-regulated learning and cognitive load. Educational Psychology Review, 32(4), 1127–1149. https://doi.org/10.1007/s10648-020-09568-4
Zheng, J., Li, S., & Lajoie, S. P. (2020). The role of achievement goals and self-regulated learning behaviors in clinical reasoning. Technology Knowledge and Learning, 25(3), 541–556. https://doi.org/10.1007/s10758-019-09420-x
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaert, P. R. Pintrich, & M. Zerdner (Eds.), Handbook of self-regulation (1st ed., pp. 13–39). US: Academic Press. https://doi.org/10.1016/b978-012109890-2/50031-7
Funding
This research was made possible by the China Scholarship Council (CSC).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethical approval:
This study was approved by the Research Ethics Board of McGill University.
Competing interests
The authors declare that they have no competing interests.
Disclosure statement
None.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, T., Li, S., Huang, X. et al. Examining students’ cognitive load in the context of self-regulated learning with an intelligent tutoring system. Educ Inf Technol 28, 5697–5715 (2023). https://doi.org/10.1007/s10639-022-11357-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-022-11357-1