Adaptive Practicing Design to Facilitate Self-Regulated Learning

Hongxin Yan; Fuhua Lin; Kinshuk

doi:10.21432/cjlt28768

Authors

Hongxin Yan University of Eastern Finland
Fuhua Lin Athabasca University
Kinshuk University of North Texas

DOI:

https://doi.org/10.21432/cjlt28768

Keywords:

adaptive practicing, confidence-based assessment, knowledge tracing, question sequencing, self-regulated learning, wheel-spinning

Abstract

Online higher education provides exceptional flexibility in learning but demands high self-regulated learning skills. The deficiency of self-regulated learning skills in many students highlights the need for support. This study introduces a confidence-based adaptive practicing system as an intelligent assessment and tutoring solution to enhance self-regulated learning in STEM disciplines. Unlike conventional intelligent tutoring systems that depend entirely on machine control, confidence-based adaptive practicing integrates learner confidence and control options into the AI-based adaptive mechanism to improve learning autonomy and model efficiency, establishing an AI-learner shared control approach. Based on Vygotsky’s zone of proximal development (ZPD) concept, an innovative knowledge-tracing framework and model called ZPD-KT was designed and implemented in the confidence-based adaptive practicing system. To evaluate the effectiveness of the ZPD-KT model, a simulation of confidence-based adaptive practicing was conducted. Findings showed that ZPD-KT significantly improves the accuracy of knowledge tracing compared to the traditional Bayesian knowledge-tracing model. Also, interviews with experts in the field underlined the potential of the confidence-based adaptive practicing system in facilitating self-regulated learning and the interpretability of the ZPD-KT model. This study also sheds light on a new way of keeping humans apprised of adaptive learning implementation.

Author Biographies

Hongxin Yan, University of Eastern Finland

Hongxin Yan is a Learning Designer at Athabasca University in Alberta, Canada and a Doctoral Student at the University of Eastern Finland (UEF). His research interests include adaptive and personalized learning, artificial intelligence (AI) in education, learning analytics, and related fields. Email: hongya@student.uef.fi ORCID: 0000-0002-3729-0844

Fuhua Lin, Athabasca University

Fuhua Lin is a Professor in the Faculty of Science and Technology at Athabasca University in Alberta, Canada. His research focuses on adaptive learning systems, artificial intelligence in education, and virtual reality applications for training. He has led multiple NSERC/CFI/Alberta Innovates-funded projects to advance personalized learning technologies. Email: oscarl@athabascau.ca

Kinshuk, University of North Texas

Kinshuk is a full Professor and the Dean of the College of Information at the University of North Texas, USA. His research interests include learning analytics, mobile learning, ubiquitous learning, personalized learning, and adaptivity. Email: kinshuk@ieee.org

References

Beck, J. E., & Gong, Y. (2013). Wheel-spinning: Students who fail to master a skill. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial intelligence in education. AIED2013 (pp. 431–440). Springer. https://doi.org/10.1007/978-3-642-39112-5_44

Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417–444. https://doi.org/10.1146/annurev-psych-113011-143823

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. https://doi.org/10.1016/j.iheduc.2015.04.007

Broadbent, J. (2017). Comparing online and blended learners’ self-regulated learning strategies and academic performance. The Internet and Higher Education, 33, 24–32. https://doi.org/10.1016/j.iheduc.2017.01.004

Brusilovsky, P. (2007). Adaptive navigation support. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 263–290). Springer. https://doi.org/10.1007/978-3-540-72079-9_8

Brusilovsky, P. (2024). AI in education, learner control, and human-AI collaboration. International Journal of Artificial Intelligence in Education, 34, 122–135. https://doi.org/10.1007/s40593-023-00356-z

Clement, B., Roy, D., Oudeyer, P.-Y., & Lopes, M. (2015). Multi-armed bandits for intelligent tutoring systems. Journal of Educational Data Mining, 7(2), 20–48. https://doi.org/10.48550/arXiv.1310.3174

Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4, 253–278. https://doi.org/10.1007/BF01099821

Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325–346. https://doi.org/10.1080/00461520.1991.9653137

Doignon, J.-P., & Falmagne, J.-C. (1999). Knowledge spaces. Springer-Verlag. https://doi.org/10.1007/978-3-642-58625-5

Doroudi, S., Aleven, V., & Brunskill, E. (2019). Where’s the reward? International Journal of Artificial Intelligence in Education, 29, 568–620. https://doi.org/10.1007/s40593-019-00187-x

Ebbinghaus, H. (1913). Memory: A contribution to experimental psychology. Annals of Neurosciences, 20(4), 155–156. https://www.doi.org/10.5214/ans.0972.7531.200408

Ekstrand, B. (2015). What it takes to keep children in school: A research review. Educational Review, 67(4), 459–482. https://doi.org/10.1080/00131911.2015.1008406

Gardner-Medwin, T., & Curtin, N. (2007, May 29–31). Certainty-based marking (CBM) for reflective learning and proper knowledge assessment. In REAP International Online Conference on Assessment Design for Learner Responsibility (pp. 1–7). REAP. https://www.ucl.ac.uk/lapt/REAP_cbm.pdf

Holstein, K., Aleven, V., & Rummel, N. (2020). A conceptual framework for human–AI hybrid adaptivity in education. In I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial intelligence in education. AIED 2020 (pp. 240–254). Springer. https://doi.org/10.1007/978-3-030-52237-7_20

Hsu, H. C. K., Wang, C. V., & Levesque-Bristol, C. (2019). Re-examining the impact of self-determination theory on learning outcomes in the online learning environment. Education and Information Technologies, 24, 2159–2174. https://doi.org/10.1007/s10639-019-09863-w

Jansen, R. S., van Leeuwen, A., Janssen, J., & Kester, L. (2019). Supporting learners’ self-regulated learning in Massive Open Online Courses. Computers & Education, 146, 103771. https://doi.org/10.1016/j.compedu.2019.103771

Lehmann, T., Hähnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323. https://doi.org/10.1016/j.chb.2013.07.051

Luo, Y., Lin, J., & Yang, Y. (2021). Students’ motivation and continued intention with online self-regulated learning: A self-determination theory perspective. Zeitschrift für Erziehungswissenschaft, 24, 1379–1399. https://doi.org/10.1007/s11618-021-01042-3

Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., & Koper, R. (2011). Recommender systems in technology-enhanced learning. In F. Ricci, L. Rokach, B. Shapira, & P. Kantor (Eds.), Recommender systems handbook (pp. 387–415). Springer. https://doi.org/10.1007/978-0-387-85820-3_12

Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, A. (2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review, 56(4), 3005–3054. https://doi.org/10.1007/s10462-022-10246-w

Nkambou, R., Bourdeau, J., & Mizoguchi, R. (Eds.). (2010). Advances in intelligent tutoring systems. Springer. https://doi.org/10.1007/978-3-642-14363-2

Novacek, P. (2013). Confidence-based assessments within an adult learning environment. In G. Demetrios, J. Sampson, M. Spector, D. Ifenthaler, & P. Isaías (Eds.), IADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2013) (pp. 403–406). International Association for Development of the Information Society. https://eric.ed.gov/?id=ED562245

Nuryadin, A., Lidinillah, D. A. M., Prehanto, A., Maesaroh, S. S., Putri, I. R., & Desmawati, S. A. (2024). Self-regulated learning in STEM education: A bibliometric mapping analysis of research using Scopus database. International Journal of Education in Mathematics, Science and Technology, 12(4), 919–941. https://doi.org/10.46328/ijemst.4015

Papoušek, J., & Pelánek, R. (2017). Should we give learners control over item difficulty? In M. Tkalcic, D. Thakker, P. Germanakos, K. Yacef, C. Paris, & O. Santos (Eds.), UMAP ’17: Adjunct publication of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 299–303). ACM. https://doi.org/10.1145/3099023.3099080

Pelánek, R. (2017). Bayesian knowledge tracing, logistic models, and beyond: An overview of learner modeling techniques. User Modeling and User-Adapted Interaction, 27(3), 313–350. https://doi.org/10.1007/s11257-017-9193-2

Preheim, M., Dorfmeister, J., & Snow, E. (2023). Assessing confidence and certainty of students in an undergraduate linear algebra course. Journal for STEM Education Research, 6, 159–180. https://doi.org/10.1007/s41979-022-00082-6

Rahdari, B., Brusilovsky, P., He, D., Thaker, K. M., Luo, Z., & Lee, Y. J. (2022). HELPeR: An interactive recommender system for ovarian cancer patients and caregivers. In J. Golbeck, F. M. Harper, V. Murdock, M. Ekstrand, B. Shapira, J. Basilico, K. Lundgaard, & E. Oldridge (Eds.), RecSys ’22: Proceedings of the 16th ACM Conference on Recommender Systems (pp. 644–647). https://doi.org/10.1145/3523227.3551471

Remesal, A., Corral, M. J., García-Mínguez, P., Domínguez, J., SanMiguel, I., Macsotay, T., & Suárez, E. (2023). Certainty-based self-assessment: A chance for enhanced learning engagement in higher education. An experience at the University of Barcelona. In D. Guralnick, M. E. Auer, & A. Poce (Eds.), Creative approaches to technology-enhanced learning for the workplace and higher education. TLIC 2023 (pp. 689–700). Springer. https://doi.org/10.1007/978-3-031-41637-8_56

Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60, Article 101832. https://doi.org/10.1016/j.cedpsych.2019.101832

Smrkolj, Š., Bančov, E., & Smrkolj, V. (2022). The reliability and medical students’ appreciation of certainty-based marking. International Journal of Environmental Research and Public Health, 19(3), Article 1706. https://doi.org/10.3390/ijerph19031706

Sorgenfrei, C., & Smolnik, S. (2016). The effectiveness of e-learning systems: A review of the empirical literature on learner control. Decision Sciences Journal of Innovative Education, 14(2), 154–184. https://doi.org/10.1111/dsji.12095

Vainas, O., Bar-Ilan, O., Ben-David, Y., Gilad-Bachrach, R., Lukin, G., Ronen, M., & Sitton, D. (2019). E-Gotsky: Sequencing content using the zone of proximal development. ArXiv. https://doi.org/10.48550/arXiv.1904.12268

Viberg, O., Khalil, M., & Baars, M. (2020). Self-regulated learning and learning analytics in online learning environments: A review of empirical research. In C. Rensing & H. Drachsler (Chairs), LAK ’20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 524–533). ACM. https://doi.org/10.1145/3375462.3375483

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds.). Harvard University Press.

Weber, G., & Brusilovsky, P. (2001). ELM-ART: An adaptive versatile system for Web-based instruction. International Journal of Artificial Intelligence in Education, 12, 351–384. https://telearn.hal.science/hal-00197328v1

Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G. J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human–Computer Interaction, 35(4-5), 356–373. https://doi.org/10.1080/10447318.2018.1543084

Yan, H., Lin, F., & Kinshuk. (2021). Including learning analytics in the loop of self-paced online course learning design. International Journal of Artificial Intelligence in Education, 31, 878–895. https://doi.org/10.1007/s40593-020-00225-z

Yan, H., Lin, F., & Kinshuk. (2022). Removing learning barriers in self-paced online STEM education. Canadian Journal of Learning and Technology, 48(4), 1–18. https://doi.org/10.21432/cjlt28264

Zimmerman, B. J. (2020). Attaining self-regulation: A social cognitive perspective. In Handbook of Self-Regulation (3rd ed., pp. 13–39). Elsevier.

Zohaib, M. (2018). Dynamic difficulty adjustment (DDA) in computer games: A review. Advances in Human‐Computer Interaction, 2018, Article 5681652. https://doi.org/10.1155/2018/5681652