arXiv:2606.19469v2 Announce Type: replace
Abstract: Undergraduate computer science is governed by international curricular guidelines revised about once a decade, yet programs lack a reliable way to measure how completely they cover the current guideline and how coverage shifts when it changes. Existing analyses rely on topic models or manual tagging, seldom report reliability, do not benchmark the matching method, and examine topical overlap at a single point in time. We address these gaps with a staged pipeline that separates candidate generation from confirmation, applied to one accredited Bachelor of Science in Computer Science against Computer Science Curricula 2013 (CS2013) and 2023 (CS2023). Semantic retrieval proposes candidate course-to-knowledge-unit matches, a large language model confirms each against an explicit coverage rule, and an independent expert validates the resulting map. Benchmarking seven retrievers against pooled relevance judgments, we find that no automatic configuration reaches acceptable precision and recall, peaking at an F1 of 0.55 and inflating apparent coverage once tuned for recall, establishing retrieval as a candidate generator, not a measurement. Each map was validated by two independent experts and reconciled to a consensus, with substantial first-pass agreement (Cohen's kappa 0.64 and 0.69); the reported coverage is the lenient end of a sensitivity band whose strict end lies about seven points lower. Coverage of CS2023 is 48.4 percent of knowledge units, 59.4 percent by recommended hours, and about 28 percent of topics, and sixty-nine percent of covered units rest on a single course. The program articulates most competencies it covers yet meets the recommended cognitive depth far less often under CS2023 than under CS2013, a gap that survives a sensitivity analysis of the mapping, while structural gaps stay separable from artifacts of the standard's evolution. The instrument is reusable and released.
Abstract: Undergraduate computer science is governed by international curricular guidelines revised about once a decade, yet programs lack a reliable way to measure how completely they cover the current guideline and how coverage shifts when it changes. Existing analyses rely on topic models or manual tagging, seldom report reliability, do not benchmark the matching method, and examine topical overlap at a single point in time. We address these gaps with a staged pipeline that separates candidate generation from confirmation, applied to one accredited Bachelor of Science in Computer Science against Computer Science Curricula 2013 (CS2013) and 2023 (CS2023). Semantic retrieval proposes candidate course-to-knowledge-unit matches, a large language model confirms each against an explicit coverage rule, and an independent expert validates the resulting map. Benchmarking seven retrievers against pooled relevance judgments, we find that no automatic configuration reaches acceptable precision and recall, peaking at an F1 of 0.55 and inflating apparent coverage once tuned for recall, establishing retrieval as a candidate generator, not a measurement. Each map was validated by two independent experts and reconciled to a consensus, with substantial first-pass agreement (Cohen's kappa 0.64 and 0.69); the reported coverage is the lenient end of a sensitivity band whose strict end lies about seven points lower. Coverage of CS2023 is 48.4 percent of knowledge units, 59.4 percent by recommended hours, and about 28 percent of topics, and sixty-nine percent of covered units rest on a single course. The program articulates most competencies it covers yet meets the recommended cognitive depth far less often under CS2023 than under CS2013, a gap that survives a sensitivity analysis of the mapping, while structural gaps stay separable from artifacts of the standard's evolution. The instrument is reusable and released.
This article has been indexed from cs.AI updates on arXiv.org
Read the original article: