Abstract
Mastery learning is a common personalization strategy in adaptive educational systems. A mastery criterion decides whether a learner should continue practice of a current topic or move to a more advanced topic. This decision is typically done based on comparison with a mastery threshold. We argue that the commonly used mastery criteria combine two different aspects of knowledge estimate in the comparison to this threshold: the degree of achieved knowledge and the uncertainty of the estimate. We propose a novel learner model that provides conceptually clear treatment of these two aspects. The model is a generalization of the commonly used Bayesian knowledge tracing and logistic models and thus also provides insight into the relationship of these two types of learner models. We compare the proposed mastery criterion to commonly used criteria and discuss consequences for practical development of educational systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beck, J.E., Gong, Y.: Wheel-spinning: students who fail to master a skill. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS (LNAI), vol. 7926, pp. 431–440. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39112-5_44
Conati, C., Gertner, A., Vanlehn, K.: Using Bayesian networks to manage uncertainty in student modeling. User Model. User Adap. Inter. 12(4), 371–417 (2002)
Corbett, A.T., Anderson, J.R.: Knowledge tracing: modeling the acquisition of procedural knowledge. User Model. User Adap. Inter. 4(4), 253–278 (1994)
Falakmasir, M., Yudelson, M., Ritter, S., Koedinger, K.: Spectral Bayesian knowledge tracing. In: Proceedings of Educational Data Mining, pp. 360–363 (2015)
González-Brenes, J., Huang, Y., Brusilovsky, P.: General features in knowledge tracing: applications to multiple subskills, temporal item response theory, and expert knowledge. In: Proceedings of Educational Data Mining, pp. 84–91 (2014)
Kaeser, T., Klingler, S., Schwing, A.G., Gross, M.: Dynamic Bayesian networks for student modeling. IEEE Trans. Learn. Technol. (2017)
Käser, T., Klingler, S., Gross, M.: When to stop?: Towards universal instructional policies. In: Proceedings of Learning Analytics & Knowledge, pp. 289–298. ACM (2016)
Khajah, M., Wing, R.M., Lindsey, R.V., Mozer, M.C.: Integrating latent-factor and knowledge-tracing models to predict individual differences in learning. In: Proceedings of Educational Data Mining (2014)
Lewis, C., Sheehan, K.: Using Bayesian decision theory to design a computerized mastery test. Appl. Psychol. Meas. 14(4), 367–386 (1990)
Pavlik, P.I., Cen, H., Koedinger, K.R.: Performance factors analysis-a new alternative to knowledge tracing. In: Proceedings of Artificial Intelligence in Education, pp. 531–538. IOS Press (2009)
Pelánek, R.: Applications of the ELO rating system in adaptive educational systems. Comput. Educ. 98, 169–179 (2016)
Pelánek, R.: Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques. User Model. User Adap. Inter. 27(3), 313–350 (2017)
Pelánek, R., Řihák, J.: Experimental analysis of mastery learning criteria. In: Proceedings of User Modelling, Adaptation and Personalization, pp. 156–163. ACM (2017)
Pelánek, R., Řihák, J., Papoušek, J.: Impact of data collection on interpretation and evaluation of student model. In: Proceedings of Learning Analytics & Knowledge, pp. 40–47. ACM (2016)
Ritter, S., Yudelson, M., Fancsali, S.E., Berman, S.R.: How mastery learning works at scale. In: Proceedings of ACM Conference on Learning@Scale, pp. 71–79. ACM (2016)
Rollinson, J., Brunskill, E.: From predictive models to instructional policies. In: Proceedings of Educational Data Mining, pp. 179–186 (2015)
Streeter, M.: Mixture modeling of individual learning curves. In: Proceedings of Educational Data Mining. pp. 45–52 (2015)
Vos, H.J.: A bayesian procedure in the context of sequential mastery testing. Psicológica 21(1), 191–211 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Pelánek, R. (2018). Conceptual Issues in Mastery Criteria: Differentiating Uncertainty and Degrees of Knowledge. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10947. Springer, Cham. https://doi.org/10.1007/978-3-319-93843-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-93843-1_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93842-4
Online ISBN: 978-3-319-93843-1
eBook Packages: Computer ScienceComputer Science (R0)