Concept Drift Detection for Knowledge Tracing
Year: 2025
Type: conference-paper
Abstract: Knowledge Tracing models have been used to predict and understand student learning processes for over two decades, spanning multiple generations of student learners who have different relationships with the technologies used to provide them instruction and practice. Given that student experiences of education have changed dramatically in that time span, can we assume that the student learning process modeled by KT is stable over time? We investigate the robustness of four different KT models over five school years and find evidence of significant model decline that is more pronounced in the more sophisticated models. We then propose multiple avenues of future work to better predict and understand this phenomenon. In addition, to foster more longitudinal testing of novel KT architectures, we will be releasing student interaction data spanning those five years.
Authors: Morgan Lee, Neil Heffernan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6912047322
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