Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…

We propose a novel tensor factorization approach, Feedback-Driven Tensor Factorization (FDTF), for modeling student learning process and predicting student performance. This approach decomposes a tensor that is built upon students' attempt sequence, while considering the quizzes students select to work with as its feedback. FDTF does not require…

This paper describes an approach for developing adaptive electronic textbooks and presents InterBook, an authoring tool based on this approach which simplifies the development of adaptive electronic textbooks on the World Wide Web. The following topics are discussed: (1) content structuring, including the domain and user models, the glossary, and…

This paper describes the electronic ACT-R Bookshelf, a system which supports learning ACT-R, a well-known theory in the field of cognitive psychology, over the World Wide Web. ACT-R Bookshelf is a collection of electronic books on various aspects of ACT-R. The primary role of ACT-R Bookshelf is to serve as a 24-hour information resource for…