Student Modeling Based on Fine-Grained Programming Process Snapshots

ICER '17 Proceedings of the 2017 ACM Conference on International Computing Education Research. New York, NY, USA : ACM, 2017 ISBN: 978-1-4503-4968-0I am studying the use of fine-grained programming process data for student modeling. The initial plan is to construct different types of program state representations such as Abstract Syntax Trees (ASTs) from the data. These program state representations could be used for both automatically inferring knowledge components that the students are trying to learn as well as for modeling students' knowledge on those specific components.Peer reviewe

Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW), 201

Designing a Personalized Semantic Web Browser

Web browsing is a complex activity and in general, users are not guided during browsing. Our hypothesis is that by using Semantic Web technologies and personalization methods, browsing can be supported better. However, existing personalization mechanisms on the Web are obstructive; users need to log in to multiple websites and enter their personal information and preferences, and the profiles are different for each site. There is a need for generic user profiles, which can also support the user’s browsing. In this paper, we propose a novel Semantic Web browser using an ontology-driven user modeling architecture to enable semantic and adaptive links. We also introduce a new behavior-based user model. With our approach, users need to log in to their Web browser only and personalization is achieved on different websites

Individualized Bayesian Knowledge Tracing Models

Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems. In the standard BKT implementation, there are only skill-specific parameters. However, a large body of research strongly suggests that student-specific variability in the data, when accounted for, could enhance model accuracy [5,6,8]. In this work, we revisit the problem of introducing student-specific parameters into BKT on a larger scale. We show that student-specific parameters lead to a tangible improvement when predicting the data of unseen students, and that parameterizing students’ speed of learning is more beneficial than parameterizing a priori knowledge.</p