← 研踪 PaperTrailarXiv:2611.05143 · v1
Better Plans, Better Outcomes: Integrating Historical Data into Student Planning in a Computing Course
首次提交 2026-11-04 00:20
版本更新 2026-11-04 00:20
雷达观测 2026-11-04 23:39
摘要
Effective planning is critical for academic success, but in many computing courses students often lack accurate benchmarks for assessing the difficulty and time required for academic tasks. This phenomenon is more pronounced in large courses where instructors have limited capacity to provide individualized guidance on planning and task estimation. Although many planning tools exist, still, they rarely leverage task-specific, empirically grounded data from actual course contexts. Thus, we explored whether providing task-specific, data-driven information---assignment previews and cohort-level historical statistics---could support more effective planning. We present a lightweight and data-driven planning intervention embedded in the programming assignment workflow of an undergraduate computing course. We conducted quantitative and qualitative studies with 252 students across five programming assignments. Our results indicated that, in authentic course settings, the intervention led to more conservative and executable plans, with significantly higher plan adherence. Students with stronger adherence also achieved better performance on both programming assignments and exams. Qualitative findings also showed both practical planning support and mental benefits from intervention. These results highlight the potential of data-driven intervention to support student planning in larger courses settings where individual guidance from an instructor is further limited.
作者备注：7 pages, 5 figures. Accepted for publication at the 58th ACM Technical Symposium on Computer Science Education (SIGCSE TS 2027)
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Better Plans, Better Outcomes: Integrating Historical Data into Student Planning in a Computing Course
Effective planning is critical for academic success, but in many computing courses students often lack accurate benchmarks for assessing the difficulty and time required for academic tasks. This phenomenon is more pronounced in large courses where instructors have limited capacity to provide individualized guidance on planning and task estimation. Although many planning tools exist, still, they rarely leverage task-specific, empirically grounded data from actual course contexts. Thus, we explored whether providing task-specific, data-driven information---assignment previews and cohort-level historical statistics---could support more effective planning. We present a lightweight and data-driven planning intervention embedded in the programming assignment workflow of an undergraduate computing course. We conducted quantitative and qualitative studies with 252 students across five programming assignments. Our results indicated that, in authentic course settings, the intervention led to more conservative and executable plans, with significantly higher plan adherence. Students with stronger adherence also achieved better performance on both programming assignments and exams. Qualitative findings also showed both practical planning support and mental benefits from intervention. These results highlight the potential of data-driven intervention to support student planning in larger courses settings where individual guidance from an instructor is further limited.
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