Open Collections

Abstract

Personalization during student learning can 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 building robust predictive models that can drive personalization using real-time sequential interaction data in various open-ended learning environments (OELEs). First, we look at predicting co-occurring emotions using eye-tracking and interface interaction data in an OELE. 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. 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) leveraging interaction data in online classrooms and including expert insights 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. 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.