Zhongqi Ma
Pronouns: He/Him
Research Mentor(s): Ambuj Tewari, Associate Professor
Research Mentor School/College/Department: Statistics, College of Literature, Science, and the Arts
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 15
Presenter: 4
Abstract
Our research focuses on improving student masteries through question interleaving. Although previous researches by Rohrer and Taylor have shown that interleaving questions enhances student learning, there has been little research on question recommendation models that utilizes this concept to maximize student learning. We assume that question interleaving helps student learning. We aim to identify metrics that allow us to optimize the ordering of question interleaving and build a question recommendation model based on our findings. We are utilizing two datasets from online tutoring platforms that contain questions answered by students to analyze if our proposed metrics are statistically significant to base decisions on. We will also run simulations with Python on potential recommendation algorithms to see how well the result matches with a simple student ability model. The result of our findings aim to provide a question recommendation method that only relies upon the concept a question tests, but can be used in conjunction with further information. We hope our findings will align with our goal of having a question recommendation model that maximizes student learning. This allows for personalization in intelligent tutoring systems even before significant amounts of student/question data has been generated. The ultimate goal/implication of our research is to help improve learning results for all students simply by changing the order of their practice questions, without increasing their workload.
Authors: Zhongqi Ma, Laura Niss
Research Method: Computer Programming
