Doctoral Oral Defence--Rohit Murali
Name: Rohit Murali
Date: November 13th, 2025
Time: 12:30 PM
Location: Room 200, Graduate Student Centre (6371 Crescent Road)
Supervisor: David Poole
Title of Thesis: Predictive models for personalized support using interaction data in open learning environments
Abstract: Personalization during student learning has been shown to improve learners' experience by responding to learners in real-time. Various learner signals can be leveraged for personalization during learning such as student's affective states or students' predicted future learning performance. The work in this PhD focuses on predicting learner signals that can drive personalization using real-time sequential interaction data in various open-ended learning environments (OELEs).
First, we look at predicting two co-occurring emotion pairs (student experiences Boredom and/or Frustration; student experiences Curiosity and/or Anxiety) using eye-tracking and interface interaction data in an Intelligent Tutoring System (ITS). Our work combines two datasets with different eye-trackers. We test the effect that combining datasets has on predictions using interaction and eye-tracking data, in isolation and in combination with each
other. We conduct a statistical analysis of classifier performance that provides insights into the effectiveness of the different data modalities in terms of predicting emotion pairs. Our analysis shows that combining data from different eye-trackers is feasible, and can be utilised for real-world affect detection where data from different eye-trackers. Moreover, for each emotion pair, we were able to isolate classifiers that outperform a baseline in terms of overall accuracy.
Second, we look at predicting student learning performance using interface interaction data in a game-based OELE. Our work builds a data-informed intelligent pedagogical agent (IPA) that predicts students' future learning performance during self-directed interaction. Our work tests the effectiveness of these predictions to trigger help interventions for struggling students. We built a student model by extracting meaningful student behaviors on real-world interface interaction data obtained during interaction in online classrooms and including expert insights. Our results show that our student model performs better than a baseline and has the potential for adaptive support in self-directed interaction with the OELE. To the best of our knowledge, we are the first to build and evaluate an IPA for in-the-wild interaction with OELEs. In this regard, our study provided empirical evidence for research in OELEs that adaptive scaffolding can improve student learning performance.
Last, we systematically evaluated various predictive classifiers across multiple open-learning datasets. Our analysis provides insights into the strengths and weaknesses of different classifiers along with a discussion about the tradeoff between inherent interpretability and accuracy. These findings aim to contribute to the development of more effective adaptive support systems and scaffold research on building personalized systems in learning.