{
  "id": 5863,
  "url": "https://arxiv.org/abs/2604.22812v1",
  "title": "Cross-Course Generalizability of SRL-Aligned Predictive Models Using Digital Learning Traces",
  "summary": "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",
  "authors": "Jakob Schwerter, Loreen Sabel, Judith Bose, Matthew L. Bernacki, Di Xu, Marko Schmellenkamp et al.",
  "category": "research",
  "topics": "regulation,children-education,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-14T15:30:13.000Z",
  "fetched_at": "2026-07-14T16:32:06.465Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5863",
  "original_url": "https://arxiv.org/abs/2604.22812v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}