用可复现方法评估计算机本科课程对新旧课程标准的覆盖情况
Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Longitudinal Framework Applied to CS2013 and CS2023

- 构建人机协同流程,通过语义检索匹配课程与知识单元
- 课程覆盖率达50%左右,但认知深度在新版标准下明显不足
- 可复用于评估能力层次与认知深度,适合课程改革参考
本科计算机科学教育受国际课程指南指导,约每十年修订一次,但高校缺乏可靠、可复现的方法来衡量课程对最新指南的覆盖程度及其随指南更新的变化。本文提出一种人机协同流程,纵向分析某认证计算机科学学士项目在《计算机科学课程2013》(CS2013)与《计算机科学课程2023》(CS2023)下的知识覆盖情况。该流程将课程与指南分别表示为结构化文本库,通过语义检索生成候选匹配,并由人工根据明确的覆盖定义确认。七种检索器中,互斥排名融合集成表现最佳,而知名长上下文模型反而不如小型句子模型,表明需实测评估检索器性能。两次映射经独立评审验证(CS2023 Cohen's kappa=0.64,CS2013=0.69)。项目覆盖了CS2023的49.7%和CS2013的50.9%的知识单元,十年间基本稳定。扩展至能力表述与认知深度分析发现:约88%的覆盖单元具备对应能力表述,但在CS2023下仅76%达到推荐深度,远低于旧版的95%,反映新版标准要求更高,非课程本身问题。纵向对比揭示了长期存在的结构性差距(如并行计算、编程语言基础、系统基础),以及由标准演进带来的差异。该工具可复用,作者可提供。
原文摘要 · Abstract (English)
Undergraduate computer science is governed by international curricular guidelines revised about once a decade, yet programs lack a reliable, reproducible way to measure how completely they cover the current guidelines and how that coverage shifts when the guidelines are restructured. We address this with a human-in-the-loop pipeline that measures a program's coverage of an external body of knowledge, applied longitudinally to one accredited BSc in Computer Science against Computer Science Curricula 2013 (CS2013) and 2023 (CS2023). The pipeline represents the program and each guideline as structured corpora, generates candidate course-to-knowledge-unit matches by semantic retrieval, and confirms them through human judgment under an explicit coverage definition. Of seven benchmarked retrievers, a reciprocal-rank-fusion ensemble was strongest, and a reputed long-context model underperformed a small sentence model, so retriever choice must be measured. Both maps were validated by an independent second rater (Cohen's kappa 0.64 for CS2023, 0.69 for CS2013). The program covers 49.7% of CS2023 and 50.9% of CS2013 knowledge units, near-constant across a decade. Extending the same retrieve-then-confirm design to competency articulation and cognitive depth shows that the program articulates the competency for ~88% of covered units under each guideline, yet delivers it at the recommended depth for 76% of present units under CS2023 against 95% under CS2013, a gap reflecting the newer guideline's raised expectations, not the program. The longitudinal comparison separates persistent structural gaps (parallel and distributed computing, foundations of programming languages, systems fundamentals), uncovered against both guidelines and ABET, from differences that reflect the standard's evolution. The instrument is reusable and available from the authors on request.
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