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A complex systems approach to analyzing pedagogical agents’ scaffolding of self-regulated learning within an intelligent tutoring system

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Abstract

Self-regulated learning (SRL), learners’ monitoring and control of cognitive, affective, metacognitive, and motivational processes, is essential for learning. However, cognitive and metacognitive SRL strategies are not typically used accurately leading to poor learning outcomes. Intelligent tutoring systems (ITSs) attempt to address this issue by prompting and scaffolding learners to engage in SRL via using pedagogical agents. However, current literature does not examine the extent to which learners’ deployed strategies are functional or dysfunctional in relation to pedagogical agent scaffolding. The current study collected 117 undergraduate students’ data as they learned with MetaTutor, an ITS about the human circulatory system. Participants were randomly assigned to either the (1) Prompt and Feedback Condition where pedagogical agents scaffolded cognitive and metacognitive SRL strategies or (2) Control Condition where no prompts or feedback were provided. Results demonstrated that learners who received prompts by the pedagogical agents to engage in SRL had higher learning gains as well as greater frequencies across most strategies compared to those in the Control Condition who relied on self-initiated strategy use. While sequential transitions across all strategies were not significant between conditions, further analysis grounded in Complex Systems Theory found that learners who were prompted to engage in strategies demonstrated a significantly lower degree of repetition and balance between repetitive and novel patterns of strategy use. The findings suggest that pedagogical agents within MetaTutor successfully scaffolded the functional deployment of cognitive and metacognitive SRL strategies and are indicative of higher learning after interacting with ITSs.

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Data Availability

Due to IRB restrictions, the data used for this project will only be available upon reasonable request to authors.

References

  • Aleven, V., Mclaren, B., Roll, I., & Koedinger, K. (2006). Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education, 16(2), 101–128.

    Google Scholar 

  • Allen, L. K., Likens, A. D., & McNamara, D. S. (2017). Recurrence quantification analysis: A technique for the dynamical analysis of student writing. In V. Rus & Z. Markov (Eds.), Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (pp. 240–245). AAAI.

  • Amon, M. J., Vrzakova, H., & D’Mello, S. K. (2019). Beyond dyadic coordination: Multimodal behavioral irregularity in triads predicts facets of collaborative problem solving. Cognitive Science, 43, e12787.

    Article  Google Scholar 

  • Amon, M. J., Mattingly, S., Necaise, A., Mark, G., Chawla, N., Dey, A., & D’Mello, S. (2022). Flexibility versus routineness in multimodal health indicators: A sensor-based longitudinal in situ study of information workers. ACM Transactions on Computing for Healthcare, 2691–1957. https://doi.org/10.1145/3514259

  • Azevedo, R., & Aleven, V. (Eds.). (2013). International handbook of metacognition and learning technologies. Springer.

    Google Scholar 

  • Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students’ learning with hypermedia? Journal of Educational Psychology, 96(3), 523–535. https://psycnet.apa.org/doi/10.1037/0022-0663.96.3.523

    Article  Google Scholar 

  • Azevedo, R., & Dever, D. A. (2022). Metacognition in multimedia learning. In R. E. Mayer & L. Fiorella (Eds.), The Cambridge handbook of multimedia learning (pp. 132–142). Cambridge University Press.

    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 

  • Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition–Implications for the design of computer-based scaffolds. Instructional Science, 33, 367–379.

    Article  Google Scholar 

  • Azevedo, R., & Wiedbusch, M. D. (2023). Theories of metacognition and pedagogy applied in AIED systems. In B. du Boulay, A. Mitrovic, & K. Yacef (Eds.), Handbook of artificial intelligence in education. Springer.

    Google Scholar 

  • Azevedo, R., & Witherspoon, A. M. (2009). Self-regulated learning with hypermedia. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 319–339). Routledge.

    Google Scholar 

  • Azevedo, R., Bouchet, F., Duffy, M., Harley, J., Taub, M., Trevors, G., Cloude, E., Dever, D., Wiedbusch, M., & Wortha, F. (2022). Lessons learned and future directions of MetaTutor: Leveraging multichannel data to scaffold self-regulated learning with an intelligent tutoring system. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.813632

    Article  Google Scholar 

  • Azevedo, R., Cromley, J. G., & Seibert, D. (2004). Does adaptive scaffolding facilitate students’ ability to regulate their learning with hypermedia? Contemporary Educational Psychology, 29(3), 344–370.

    Article  Google Scholar 

  • Azevedo, R., Johnson, A., Chauncey, A., & Graesser, A. (2011). Use of hypermedia to convey and assess self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 102–121). Routledge.

    Google Scholar 

  • Azevedo, R., Landis, R. S., Feyzi-Behnagh, R., Duffy, M., Trevors, G., Harley, J. M., Bouchet, F., Burlison, J., Taub, M., Pacampara, N., Yeasin, M., Rahman, A. K. M. M., Tanveer, M. I., & Hossain, G. (2012). The effectiveness of pedagogical agents’ prompting and feedback in facilitating co-adapted learning with MetaTutor. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Lecture Notes in Computer Science, Vol. 7315, International Conference on Intelligent Tutoring Systems (pp. 212–221). Springer. https://doi.org/10.1007/978-3-642-30950-2_27

    Chapter  Google Scholar 

  • Azevedo, R., Martin, S. A., Taub, M., Mudrick, N. V., Millar, G. C., & Grafsgaard, J. F. (2016). Are pedagogical agents’ external regulation effective in fostering learning with intelligent tutoring systems? In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Lecture Notes in Computer Science, Vol. 9684, International Conference on Intelligent Tutoring Systems (pp. 197–207). Springer. https://doi.org/10.1007/978-3-319-39583-8_19

    Chapter  Google Scholar 

  • Azevedo, R., Moos, D. C., Greene, J. A., Winters, F. I., & Cromley, J. G. (2008). Why is externally-regulated learning more effective than self-regulated learning with hypermedia? Educational Technology Research & Development, 56(1), 45–72.

    Article  Google Scholar 

  • Azevedo, R., Taub, M., & Mudrick, N. V. (2018). Understanding and reasoning about real-time cognitive, affective, and metacognitive processes to foster self-regulation with advanced learning technologies. In P. A. Alexander, D. H. Schunk, & J. A. Greene (Eds.), Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 254–270). Routledge. https://doi.org/10.4324/9781315697048

    Chapter  Google Scholar 

  • Baker, R. S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26, 600–614.

    Article  Google Scholar 

  • Bannert, M., & Reimann, P. (2012). Supporting self-regulated hypermedia learning through prompts. Instructional Science, 40, 193–211. https://doi.org/10.1007/s11251-011-9167-4

    Article  Google Scholar 

  • Baumeister, R. F., & Heatherton, T. F. (1996). Self-regulation failure: An overview. Psychological Inquiry, 7(1), 1–15. https://doi.org/10.1207/s15327965pli0701_1

    Article  Google Scholar 

  • Belland, B. R. (2017a). Instructional scaffolding: Foundations and evolving definition. In Instructional Scaffolding in STEM Education: Strategies and Efficacy Evidence (pp. 17–54). https://doi.org/10.1007/978-3-319-02565-0

  • Belland, B. R. (2017b). Computer-based scaffolding strategy. In Instructional Scaffolding in STEM Education: Strategies and Efficacy Evidence (pp. 107–126). https://doi.org/10.1007/978-3-319-02565-0

  • Bernacki, M. L., Byrnes, J. P., & Cromley, J. G. (2012). The effects of achievement goals and self-regulated learning behaviors on reading comprehension in technology-enhanced learning environments. Contemporary Educational Psychology, 37, 148–161.

    Article  Google Scholar 

  • Bouchet, F., Harley, J. M., & Azevedo, R. (2013). Impact of different pedagogical agents’ adaptive self-regulated prompting strategies on learning with MetaTutor. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Lecture Notes in Computer Science, Vol. 7926, International Conference on Artificial Intelligence in Education (pp. 815–819). Springer. https://doi.org/10.1007/978-3-642-39112-5_120

    Chapter  Google Scholar 

  • Castro-Alonso, J. C., Wong, R. M., Adesope, O. O., & Paas, F. (2021). Effectiveness of multimedia pedagogical agents predicted by diverse theories: A meta-analysis. Educational Psychology Review, 33(3), 989–1015. https://doi.org/10.1007/s10648-020-09587-1

    Article  Google Scholar 

  • Cloude, E. B., Wortha, F., Wiedbusch, M. D., & Azevedo, R. (2021a). Goals matter: Changes in metacognitive judgments and their relation to motivation and learning with an intelligent tutoring system. In P. Zaphiris & A. Ioannou (Eds.), Lecture Notes in Computer Science, Vol. 12784, Learning and Collaboration Technologies: New Challenges and Learning Experiences. Springer. https://doi.org/10.1007/978-3-030-77889-7_15

    Chapter  Google Scholar 

  • Cloude, E. B., Wortha, F., Dever, D. A., & Azevedo, R. (2021b). Negative emotional dynamics shape cognition and performance with MetaTutor: Toward building affect-aware systems. Proceedings of the International Conference on Affective Computing and Intelligent Interaction, 9. IEEE. https://doi.org/10.1109/ACII52823.2021.9597462

  • Coco, M. I., Monster, D., Leonardi, G., Dale, R., & Wallot, S. (2020). Unidimensional and multidimensional methods for recurrence quantification analysis with crqa. arXiv preprint arXiv:2006.01954.

  • Craig, S. D., & Schroeder, N. L. (2017). Reconsidering the voice effect when learning from a virtual human. Computers & Education, 114, 193–205. https://doi.org/10.1016/j.compedu.2017.07.003

    Article  Google Scholar 

  • D’Mello, S., Olney, A., Williams, C., & Hays, P. (2012). Gaze tutor: A gaze-reactive intelligent tutoring system. International Journal of Human-Computer Studies, 70, 377–398.

    Article  Google Scholar 

  • Davis, R. O., Park, T., & Vincent, J. (2022). A meta-analytic review on embodied pedagogical agent design and testing formats. Journal of Educational Computing Research, 07356331221100556. https://doi.org/10.1177/2F07356331221100556

  • Dever, D. A., Amon, M. J., Vrzáková, H., Wiedbusch, M. D., Cloude, E. B., & Azevedo, R. (2022). Capturing sequences of learners’ self-regulatory interactions with instructional material during game-based learning using auto-recurrence quantification analysis. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2022.813677

    Article  Google Scholar 

  • Dresp-Langley, B. (2020). Seven properties of self-organization in the human brain. Big Data and Cognitive Computing, 4, 10. https://doi.org/10.3390/bdcc4020010

    Article  Google Scholar 

  • Efklides, A. (2019). Gifted students and self-regulated learning: The MASRL model and its implications for SRL. High Ability Studies, 30(1–2), 79–102. https://doi.org/10.1080/13598139.2018.1556069

    Article  Google Scholar 

  • Elliot, A. J., Murayama, K., & Pekrun, R. (2011). A 3 × 2 achievement goal model. Journal of Educational Psychology, 103(3), 632–648. https://doi.org/10.1037/a0023952

    Article  Google Scholar 

  • Favela, L. H. (2019). Integrated information theory as a complexity science approach to consciousness. Journal of Consciousness Studies, 26, 21–47.

    Google Scholar 

  • Feyzi-Behnagh, R., Azevedo, R., Legowski, E., Reitmeyer, K., Tseytlin, E., & Crowley, R. S. (2014). Metacognitive scaffolds improve self-judgments of accuracy in a medical intelligent tutoring system. Instructional Science, 42, 159–181. https://doi.org/10.1007/s11251-013-9275-4

    Article  Google Scholar 

  • Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. C. (2006). The International Personality Item Pool and the future of public-domain personality measures. Journal of Research in Personality, 40, 84–96.

    Article  Google Scholar 

  • Graesser, A. C. (2020). Learning science principles and technologies with agents that promote deep learning. In R. S. Feldman (Ed.), Learning science: Theory, research, and practice (pp. 2–33). McGraw-Hill.

    Google Scholar 

  • Graesser, A. C., & D'Mello, S. (2012). Emotions during the learning of difficult material. In B. H. Ross (Ed.), The psychology of learning and motivation (pp. 183–225). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-394293-7.00005-4

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

    Chapter  Google Scholar 

  • Hadwin, A. F., & Winne, P. H. (2001). CoNoteS2: A software tool for promoting self-regulation. Educational Research and Evaluation, 7(2–3), 313–334. https://doi.org/10.1076/edre.7.2.313.3868

    Article  Google Scholar 

  • Haken, H., & Portugali, J. (2016). Information and self-organization. Entropy, 19(1), 18. https://doi.org/10.3390/e19010018

    Article  Google Scholar 

  • Haridas, M., Gutjahr, G., Raman, R., Ramaraju, R., & Nedungadi, P. (2020). Predicting school performance and early risk of failure from an intelligent tutoring system. Education and Information Technologies, 25, 3995–4013.

    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.

    Article  Google Scholar 

  • Harrell, F. E. (2022). Hmisc: Harrell Miscellaneous. R package version 4.7–0. https://CRAN.R-project.org/package=Hmisc

  • Heylighen, F. (2008). Complexity and self-organization. In M. J. Bates & M. N. Maack (Eds.), Encyclopedia of library and information sciences (pp. 1215–1224). Taylor & Francis.

    Google Scholar 

  • Hidayah, I., Adji, T. B., & Setiawan, N. A. (2019). Development and evaluation of adaptive metacognitive scaffolding for algorithm-learning system. IET Software, 13(4), 305–312. https://doi.org/10.1049/iet-sen.2018.5072

    Article  Google Scholar 

  • Hilpert, J. C., & Merchand, G. C. (2018). Complex systems research in educational psychology: Aligning theory and method. Educational Psychologies, 53, 185–202.

    Article  Google Scholar 

  • Holden, R. J. (2009). People or systems? To blame is human. The fix is to engineer. Professional Safety, 54, 34–41.

    Google Scholar 

  • Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6, 27–52. https://doi.org/10.18608/jla.2019.62.3

    Article  Google Scholar 

  • Jansen, R. S., Van Leeuwen, A., Janssen, J., Jak, S., & Kester, L. (2019). Self-regulated learning partially mediates the effect of self-regulated learning interventions on achievement in higher education: A meta-analysis. Educational Research Review, 28, 100292. https://doi.org/10.1016/j.edurev.2019.100292

    Article  Google Scholar 

  • Johnson, W. L., & Lester, J. C. (2016). Face-to-face interaction with pedagogical agents, twenty years later. International Journal of Artificial Intelligence in Education, 26, 25–36. https://doi.org/10.1007/s40593-015-0065-9

    Article  Google Scholar 

  • Johnson, A. M., Azevedo, R., & D’Mello, S. K. (2011). The temporal and dynamic nature of self-regulatory processes during independent and externally assisted hypermedia learning. Cognition and Instruction, 29(4), 471–504.

    Article  Google Scholar 

  • Kay, J., Bartimote, K., Kitto, K., Kummerfeld, B., Liu, D., & Reimann, P. (2022). Enhancing learning by open learner model (OLM) driven data design. Computers and Education: Artificial Intelligence, 3, 100069.

    Google Scholar 

  • Kim, Y., & Baylor, A. L. (2016). Research-based design of pedagogical agent roles: A review, progress, and recommendations. International Journal of Artificial Intelligence in Education, 26(1), 160–169. https://doi.org/10.1007/s40593-015-0055-y

    Article  Google Scholar 

  • Kochmar, E., Vu, D. D., Belfer, R., Gupta, V., Serban, I. V., & Pineau, J. (2020). Automated personalized feedback improves learning gains in an intelligent tutoring system. In I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial Intelligence in Education. (Vol. 12164). Springer. https://doi.org/10.1007/978-3-030-52240-7_26

    Chapter  Google Scholar 

  • Kramarski, B. (2017). Teachers as agents in promoting students’ SRL and performance: applications for teachers’ dual-role training program. In D. H. Schunk & J. A. Greene (Eds.), Educational Psychology Handbook Series. Handbook of Self-regulation of Learning and Performance (2nd ed., pp. 223–239). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781315697048

    Chapter  Google Scholar 

  • Kramarski, B., & Heaysman, O. (2021). A conceptual framework and a professional development model for supporting teachers “triple SRL–SRT processes” and promoting students’ academic outcomes. Educational Psychologist, 56(4), 298–311. https://doi.org/10.1080/00461520.2021.1985502

    Article  Google Scholar 

  • Lallé, S., Taub, M., Mudrick, N. V., Conati, C., & Azevedo, R. (2017). The impact of student individual differences and visual attention to pedagogical agents during learning with MetaTutor. In E. André, R. Baker, X. Hu, M. Rodrigo, & B. du Boulay (Eds.), Lecture Notes in Computer Science, Vol. 10331, Artificial Intelligence in Education (pp. 149–161). Springer. https://doi.org/10.1007/978-3-319-61425-0_13

    Chapter  Google Scholar 

  • Lane, H. C. (2016). Pedagogical agents and affect: Molding positive learning interactions. In S. Y. Tettegah & M. Gartmeier (Eds.), Emotions, technology, design, and learning (pp. 47–62). Academic Press. https://doi.org/10.1016/B978-0-12-801856-9.00003-7

    Chapter  Google Scholar 

  • Li, S., Zheng, J., & Lajoie, S. P. (2020). The relationship between cognitive engagement and students’ performance in a simulation-based training environment: An information-processing perspective. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1848879

    Article  Google Scholar 

  • Li, S., Zheng, J., Huang, X., & Xie, C. (2022a). Self-regulated learning as a complex dynamical system: Examining students’ stem learning in a simulation environment. Learning and Individual Differences, 95, 102144.

    Article  Google Scholar 

  • Li, W., Wang, F., Mayer, R. E., & Liu, T. (2022b). Animated pedagogical agents enhance learning outcomes and brain activity during learning. Journal of Computer Assisted Learning, 38(3), 621–637. https://doi.org/10.1111/jcal.12634

    Article  Google Scholar 

  • Lindquist, T. M., & Olsen, L. M. (2007). How much help, is too much help? An experimental investigation of the use of check figures and completed solutions in teaching intermediate accounting. Journal of Accounting Education, 25, 103–117.

    Article  Google Scholar 

  • Lippert, A., Shubeck, K., Morgan, B., Hampton, A., & Graesser, A. (2020). Multiple agent designs in conversational intelligent tutoring systems. Technology, Knowledge and Learning, 25(3), 443–463. https://doi.org/10.1007/s10758-019-09431-8

    Article  Google Scholar 

  • Martha, A. S. D., Santoso, H. B., Junus, K., & Suhartanto, H. (2019). A scaffolding design for pedagogical agents within the higher-education context. Proceedings of the International Conference on Education Technology and Computers, 11, 139–143. https://doi.org/10.1145/3369255.3369267

    Article  Google Scholar 

  • Marx, J. D., & Cummings, K. (2007). Normalized change. American Journal of Physics, 75(1), 87–91.

    Article  Google Scholar 

  • Matcha, W., Gašević, D., Uzir, N. A., Jovanović, J., & Pardo, A. (2019). Analytics of learning strategies: Associations with academic performance and feedback. Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 461–470.

  • Matsuda, N., Weng, W., & Wall, N. (2020). The effect of metacognitive scaffolding for learning by teaching a teachable agent. International Journal of Artificial Intelligence in Education, 30, 1–37. https://doi.org/10.1007/s40593-019-00190-2

    Article  Google Scholar 

  • Mayer, R. E. & Fiorella, L. (2022). The Cambridge Handbook of Multimedia Learning (3rd ed.). https://doi.org/10.1017/9781108894333

  • McCarthy, K. S., Likens, A. D., Johnson, A. M., Guerrero, T. A., & McNamara, D. S. (2018). Metacognitive overload!: Positive and negative effects of metacognitive prompts in an intelligent tutoring system. International Journal of Artificial Intelligence in Education, 28, 420–438.

    Article  Google Scholar 

  • Mitchell, M. (2009). Complexity: A guided tour. Oxford University Press.

    Book  Google Scholar 

  • Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2018). Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 1–22. https://doi.org/10.1080/10494820.2018.1558257

  • Musso, M. F., Boekaerts, M., Segers, M., & Cascallar, E. C. (2019). Individual differences in basic cognitive processes and self-regulated learning: Their interaction effects on math performance. Learning and Individual Differences, 71, 58–70. https://doi.org/10.1016/j.lindif.2019.03.003

    Article  Google Scholar 

  • Nelson, T. O., & Narens, L. (1990). Metamemory: A theoretical framework and new findings. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (pp. 125–173). Academic Press.

    Google Scholar 

  • Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1

    Article  Google Scholar 

  • Panadero, E., Jonsson, A., & Botella, J. (2017). Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses. Educational Research Review, 22, 74–98. https://doi.org/10.1016/j.edurev.2017.08.004

    Article  Google Scholar 

  • Park, S., & Sperling, R. (2012). Academic procrastinators and their self-regulation. Psychology, 3, 12–23. https://doi.org/10.4236/psych.2012.31003

    Article  Google Scholar 

  • Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18(4), 315–341. https://doi.org/10.1007/s10648-006-9029-9

    Article  Google Scholar 

  • Peverly, S. T., Brobst, K. E., Graham, M., & Shaw, R. (2003). College adults are not good at self-regulation: A study on the relationship of self-regulation, note taking, and test taking. Journal of Educational Psychology, 95(2), 335–346. https://psycnet.apa.org/doi/10.1037/0022-0663.95.2.335

    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 Press. https://doi.org/10.1016/B978-012109890-2/50043-3

    Chapter  Google Scholar 

  • Pintrich, P., Smith, D., García, T., & McKeachie, W. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). University of Michigan.

    Google Scholar 

  • Puntambekar, S., & Hubscher, R. (2005). Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist, 40(1), 1–12. https://doi.org/10.1207/s15326985ep4001_1

    Article  Google Scholar 

  • R Core Team. (2017). R: A language and environment for statistical computing. R foundation for statistical computing. Vienna, Austria: URL https://www.R-project.org/

  • Revelle, W. (2016). psych: Procedures for Personality and Psychological Research. Northwestern University, Evanston, Illinois, USA, http://CRAN.R-project.org/package=psych Version = 1.6.6.

  • Richardson, M. J., & Chemero, A. (2014). Complex dynamical systems and embodiment. In L. Shapiro (Ed.), The Routledge handbook of embodied cognition (pp. 39–50). Routledge/Taylor & Francis Group.

    Google Scholar 

  • Rosenberg, M., Schooler, C., Schoenbach, C., & Rosenberg, F. (1995). Global self-esteem and specific self-esteem: Different concepts, different outcomes. American Sociological Review, 60(1), 141–156. https://doi.org/10.2307/2096350

    Article  Google Scholar 

  • Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55, 68–78.

    Article  Google Scholar 

  • Schiff, D. (2021). Out of the laboratory and into the classroom: The future of artificial intelligence in education. AI & Society, 36, 331–348. https://doi.org/10.1007/s00146-020-01033-8

    Article  Google Scholar 

  • Schroeder, N. L., Adesope, O. O., & Gilbert, R. B. (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research, 49(1), 1–39. https://doi.org/10.2190/2FEC.49.1.a

    Article  Google Scholar 

  • Schroeder, N. L., Romine, W. L., & Craig, S. D. (2017). Measuring pedagogical agent persona and the influence of agent persona on learning. Computers & Education, 109, 176–186. https://doi.org/10.1016/j.compedu.2017.02.015

    Article  Google Scholar 

  • Schunk, D. H., & Greene, J. A. (2018). Historical, contemporary, and future perspectives on self-regulated learning and performance. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 1–15). Routledge.

    Google Scholar 

  • Sharma, P., & Harkishan, M. (2022). Designing an intelligent tutoring system for computer programing in the Pacific. Education and Information Technologies. https://doi.org/10.1007/s10639-021-10882-9

    Article  Google Scholar 

  • Sikström, P., Valentini, C., Kärkkäinen, T., & Sivunen, A. (2022). How pedagogical agents communicate with students: A two-phase systematic review. Computers & Education, 104564. https://doi.org/10.1016/j.compedu.2022.104564

  • Simons, K. D., & Klein, J. D. (2007). The impact of scaffolding and student achievement levels in a problem-based learning environment. Instructional Science, 35(1), 41–72. https://doi.org/10.1007/s11251-006-9002-5

    Article  Google Scholar 

  • Sottilare, R. (2020). Agent-based methods in support of adaptive instructional decisions. In R. Sottilare & J. Schwarz (Eds.), Adaptive Instructional Systems. HCII 2020. Lecture Notes in Computer Science. (Vol. 12214). Springer. https://doi.org/10.1007/978-3-030-50788-6_12

    Chapter  Google Scholar 

  • Stahl, E., & Bromme, R. (2007). The CAEB: An instrument for measuring connotative aspects of epistemological beliefs. Learning and Instruction, 17(6), 773–785. https://doi.org/10.1016/j.learninstruc.2007.09.016

    Article  Google Scholar 

  • Taub, M., & Azevedo, R. (2019). How does prior knowledge influence eye fixations and sequences of cognitive and metacognitive SRL processes during learning with an Intelligent Tutoring System? International Journal of Artificial Intelligence in Education, 29(1), 1–28.

    Article  Google Scholar 

  • Taub, M., Martin, S. A., Azevedo, R., & Mudrick, N. V. (2015). The role of pedagogical agents on learning: Issues and trends. In F. Neto, R. Souza, & A. Gomes (Eds.), Handbook of research on 3-D virtual environments and hypermedia for ubiquitous learning (pp. 362–386). IGI Global.

    Google Scholar 

  • Taub, M., Mudrick, N. V., Azevedo, R., Millar, G. C., Rowe, J., & Lester, J. (2016). Using multi-level modeling with eye-tracking data to predict metacognitive monitoring and self-regulated learning with CRYSTAL ISLAND. In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Lecture Notes in Computer Science, Vol. 9684, International Conference on Intelligent Tutoring Systems. Springer. https://doi.org/10.1007/978-3-319-39583-8_24

    Chapter  Google Scholar 

  • Teuscher, C. (2022). Revisiting the edge of chaos: Again? Biosystems, 218, 104693. https://doi.org/10.1016/j.biosystems.2022.104693

    Article  Google Scholar 

  • Trevors, G., Duffy, M., & Azevedo, R. (2014). Note-taking with MetaTutor: Interactions between an intelligent tutoring system and prior knowledge on note-taking and learning. Educational Technology Research and Development, 62, 507–528.

    Article  Google Scholar 

  • VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369

    Article  Google Scholar 

  • Vogel-Walcutt, J. J., Carper, T. M., Bowers, C., & Nicholson, D. (2010). Increasing efficiency in military learning: Theoretical considerations and practical applications. Military Psychology, 22(3), 311–339. https://doi.org/10.1080/08995605.2010.492701

    Article  Google Scholar 

  • Vrzakova, H., Amon, M. J., Stewart, A. E., & D'Mello, S. K. (2019). Dynamics of visual attention in multiparty collaborative problem solving using multidimensional recurrence quantification analysis. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1–14).

  • Wallot, S., Roepstorff, A., & Mønster, D. (2016). Multidimensional Recurrence Quantification Analysis (MdRQA) for the analysis of multidimensional time-series: A software implementation in MATLAB and its application to group-level data in joint action. Frontiers in Psychology, 1835. https://doi.org/10.3389/fpsyg.2016.01835

  • Webber, C. L., Jr., & Zbilut, J. P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. Tutorials in Contemporary Nonlinear Methods for the Behavioral Sciences, 94, 26–94.

    Google Scholar 

  • Webber, C. L., & Marwan, N. (2016). Recurrence quantification analysis: Theory and best practices. Springer International Publishing.

  • Wiedbusch, M. D., & Azevedo, R. (2020). Modeling metacomprehension monitoring accuracy with eye gaze on informational content in a multimedia learning environment. In ACM Symposium on Eye Tracking Research and Applications (pp. 1–9)

  • Wiedbusch, M., Dever, D., Wortha, F., Cloude, E. B., & Azevedo, R. (2021). Revealing data feature differences between system- and learner-initiated self-regulated learning processes within hypermedia. In R. A. Sottilare & J. Schwarz (Eds.), Lecture Notes in Computer Science, Vol. 12792, Adaptive Instructional Systems. Design and Evaluation. Springer. https://doi.org/10.1007/978-3-030-77857-6_34

    Chapter  Google Scholar 

  • Winne, P. H. (2018). Theorizing and researching levels of processing in self-regulated learning. British Journal of Educational Psychology, 88(1), 9–20.

    Article  Google Scholar 

  • Winne, P., & Azevedo, R. (2022). Metacognition and self-regulated learning. In R. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (pp. 93–113). Cambridge University Press. https://doi.org/10.1017/9781108888295.007

    Chapter  Google Scholar 

  • Winne, P. H., & Marzouk, Z. (2019). Learning strategies and self-regulated learning. In J. Dunlosky & K. A. Rawson (Eds.), The Cambridge handbook of cognition and education (pp. 696–715). Cambridge University Press.

  • Yang, T. C., Chen, M. C., & Chen, S. Y. (2018). The influences of self-regulated learning support and prior knowledge on improving learning performance. Computers & Education, 126, 37–52.

    Article  Google Scholar 

  • Zheng, L. (2016). The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: A meta-analysis. Asia Pacific Education Review, 17(2), 187–202. https://doi.org/10.1007/s12564-016-9426-9

    Article  Google Scholar 

  • Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of Metacognition in Education (pp. 141–160). Routledge.

    Google Scholar 

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Acknowledgements

The research reported in this manuscript was supported by funding from the National Science Foundation (DUE#1761178, DRL#1661202, DRL#1916417). The authors would also like to thank the members of the SMART Lab at the University of Central Florida.

Funding

The research presented in this paper is supported by funding from the National Science Foundation (DRL #1916417).

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Authors and Affiliations

  1. School of Modeling, Simulation, and Training, University of Central Florida, Orlando, FL, USA

    Daryn A. Dever, Nathan A. Sonnenfeld, Megan D. Wiedbusch, Mary Jean Amon & Roger Azevedo

  2. Department of Psychology, University of Central Florida, Orlando, FL, USA

    S. Grace Schmorrow

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  1. Daryn A. Dever
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Correspondence to Daryn A. Dever.

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Dever, D.A., Sonnenfeld, N.A., Wiedbusch, M.D. et al. A complex systems approach to analyzing pedagogical agents’ scaffolding of self-regulated learning within an intelligent tutoring system. Metacognition Learning 18, 659–691 (2023). https://doi.org/10.1007/s11409-023-09346-x

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