ABSTRACT
Mastery Grids is an intelligent interface that provides access to different kinds of practice content for an introductory programming course. A distinctive feature of the interface is a parallel topic-level visualization of student progress and the progress of their peers. This contribution presents an extended version of the original system that features a fine-grained visualization of student knowledge on the level of the detailed concepts that are associated with the course. The student model is based on a Bayesian-network which is built using students performance history in the learning activities.
Supplemental Material
- P. Brusilovsky, S. Somyurek, J. Guerra, R. Hosseini, V. Zadorozhny, and P. Durlach. 2016. Open Social Student Modeling for Personalized Learning. IEEE Transactions on Emerging Topics in Computing 4, 3 (2016), 450--461.Google ScholarCross Ref
- M. Druzdzel. 1999. SMILE: Structural Modeling, Inference, and Learning Engine and GeNIe: a development environment for graphical decision-theoretic models. In Proc. 16th National Conf. on Arti?cial Intelligence (AAAI). 902--903. Google ScholarDigital Library
- A. Elliot and K. Murayama. 2008. On the measurement of achievement goals: Critique, illustration, and application. Journal of Educational Psychology 100, 3 (2008), 613--628.Google ScholarCross Ref
- J. Guerra, R. Hosseini, S. Somyurek, and P. Brusilovsky. 2016. An Intelligent Interface for Learning Content Combining an Open Learner Model and Social Comparison to Support Self-Regulated Learning and Engagement. In Proceedings of the 21st International Conference on Intelligent User Interfaces IUI '16. 152--163. Google ScholarDigital Library
- Y. Huang, J. Guerra, and P. Brusilovsky. 2016. Modeling skill combination patterns for deeper knowledge tracing. In Proc. 6th Workshop on Personalization Approaches in Learning Environments (PALE). The 24th conference on User Modeling, Adaptation, and Personalization (UMAP).Google Scholar
Index Terms
- Concept-Level Knowledge Visualization For Supporting Self-Regulated Learning
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