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 18
Intelligent tutoring systems often recommend questions based on the estimation of students’ current ability estimates, the randomness, or the expectations from the instructors. To help students improve their masteries in a concept, we are going to propose a question recommendation model by utilizing question interleaving. Previous researches in math classes have shown that interleaving questions enhance student learning. Hence, assuming interleaving will benefit students’ learning, we examine how to effectively reordering the concepts of the questions within a session. Analyzing mainly on two online skill-builder datasets, we demonstrate that there exists a statistically significant difference in what we call sub-abilities from which interleaving can be optimized. We come up with a simple method that only depends on knowing a question concept id and a binary outcome for correctness. We are writing a Python library for simulation to compare how ideal results match with the proposed recommendation algorithms. The result of our findings aims to generate a personalized question recommendation for the students without gathering extensive data from them while maintaining the flexibility to include other models. The ultimate goal of our research is to provide students from different backgrounds with an efficient way to improve learning results.