Cross-Course Generalizability of SRL-Aligned Predictive Models Using Digital Learning Traces
STEM dropout rates remain high at universities, particularly in computer science programs with theory-intensive courses. Digital learning environments now capture rich behavioral data that could help identify struggling students early, yet the generalizability of data-driven prediction models across courses and institutions remains uncertain. Guided by self-regulated learning (SRL) theory, this study analyzed multimodal digital-trace data from three undergraduate theoretical computer science cou
Record details
Published: 14 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Children & education · Environment
Retrieved: 14 July 2026
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ethics.ai (14 April 2026), “Cross-Course Generalizability of SRL-Aligned Predictive Models Using Digital Learning Traces,” evidence record 5863, https://ethics.ai/record/5863 (originally published by arXiv).
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