Scholarlink

Many educational recommender systems (EdRecSys) rely on commercial recommendation strategies that emphasize content relevance while neglecting learners’ views on recommendation effectiveness. To address this, we conducted a co-design study with computer science students in an introductory programming course to explore their vision of an ideal EdRecSys. The subjects shared preferences and concerns related to three areas: recommendation approaches, transparency, and control. We used Zimmerman’s model of self-regulated learning to contextualize their expectations within a broader educational framework. Findings offer actionable insights for designing learner-centered AIED systems that foster engagement, agency, and self-regulation.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Using Self-regulated Learning Theory to Inform the Design of Educational Recommender Systems for Introductory Programming

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Jordan Barria-Pineda,
  • Deniz Sonmez Unal,
  • Kamil Akhuseyinoglu,
  • Peter Brusilovsky,
  • Erin Walker

摘要

Many educational recommender systems (EdRecSys) rely on commercial recommendation strategies that emphasize content relevance while neglecting learners’ views on recommendation effectiveness. To address this, we conducted a co-design study with computer science students in an introductory programming course to explore their vision of an ideal EdRecSys. The subjects shared preferences and concerns related to three areas: recommendation approaches, transparency, and control. We used Zimmerman’s model of self-regulated learning to contextualize their expectations within a broader educational framework. Findings offer actionable insights for designing learner-centered AIED systems that foster engagement, agency, and self-regulation.