Modeling Student Knowledge and Performance
When Learning from Multiple Types of Materials
NSF Grant No. 1755910
PI: Shaghayegh (Sherry) Sahebi
Email: ssahebi [at] albany [dot] edu
Project Duration: 9/1/2018 - 8/31/2020

Modeling Student Knowledge and Performance When Learning from Multiple Types of Materials

As the national interest in higher and professional education has been increasing, interest in online learning systems has also grown rapidly. Online learning systems aim to contribute to the society by providing high quality, affordable, and accessible education, at scale. Delivering such high-impact goals requires automatic tools that can help us understand students' learning process and answer questions such as what knowledge is gained by watching a video lecture (domain knowledge modeling), what is a student's state of knowledge (student knowledge modeling), and how a specific student would perform on a test (predicting student performance). In this project our goal is to achieve a better understanding of students' learning process in online educational systems, when learning from different learning material types, such as videos, quizzes, etc. To do this, we model student knowledge growth as they interact with all types of learning materials. We develop multi-view machine learning algorithms that minimize the error of student performance prediction while maximizing the correlations among multiple views to the learning data. We evaluate our student models by measuring how we can predict students' performance on their next quiz or problem.

Learning from Different Learning Material Types

Related Publications

Project Duration

From 9/1/2018 to 8/31/2020

This material is based upon work supported by the National Science Foundation under Grant No. 1755910. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.