Curricular analytics: graph-based measures of degree-program complexity (blocking, delay, centrality, structural complexity)¶
Research Method · All research methods
Evidence · 3 claims (3 for) · 1 study (1 associational),q2· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
Curricular analytics is the study of academic program design and layout, in which a degree plan is translated into a directed acyclic graph of courses and prerequisites. From the graph, four measures are computed: blocking factor (the number of courses reachable from a course node), delay factor (the maximum number of courses that must be completed in sequential order), centrality (the sum of the delay of all paths containing that course), and structural complexity (an unweighted linear combination of delay and blocking, with overall program complexity the sum of individual course complexities). The article uses these measures to compare 60 CS degrees against representation of women graduates.
Accounts¶
- Curricular analytics: graph-based measures of degree-program complexity (blocking, delay, centrality, structural complexity): Curricular analytics is the study of academic program design and layout, in which a degree plan is translated into a directed acyclic graph of courses and prerequisites. From the graph, four measures are computed: blocking factor (the number of courses reachable from a course node), delay factor (the maximum number of courses that must be completed in sequential order), centrality (the sum of the delay of all paths containing that course), and structural complexity (an unweighted linear combination of delay and blocking, with overall program complexity the sum of individual course complexities). The article uses these measures to compare 60 CS degrees against representation of women graduates. (Does Curricular Complexity in Computer Science Influence the Representation of Women CS Gr)
Claims¶
- CS programs with higher structural curricular complexity have lower representation of women graduates (ANOVA across three representation groups) [+M]
- Blocking factors of CS 1 and CS 2 differ significantly across women-representation groups, with lower-blocking programs having higher representation of women [+M]
- The authors' data supports that Calculus 1 need not be a prerequisite for lower-level CS courses [+W]
Related Research Methods¶
Key Sources¶
- Albert Lionelle, McKenna Quam, Carla Brodley, and Catherine Gill. 2024. Does Curricular Complexity in Computer Science Influence the Representation of Women CS Graduates?. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024). https://doi.org/10.1145/3626252.3630835
Page metadata
| Field | Value |
|---|---|
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research-method |
id |
curricular-analytics-graph-based-measures-of-degree-program-complexity-blocking-delay-cent |
title |
Curricular analytics: graph-based measures of degree-program complexity (blocking, delay, centrality, structural complexity) |
description |
Curricular analytics is the study of academic program design and layout, in which a degree plan is translated into a directed acyclic graph of courses and prerequisites. |
status |
review |
generated |
by process:settle-candidates on 2026-10-16 |
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unverified — no one has confirmed this page's content |
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