Curriculum Analytics: Application of Social Network Analysis for Improving Strategic Curriculum Decision-Making in a Research-Intensive University
Bibliographic record
Abstract
This paper provides insight into the use of curriculum analytics to enhance learning-centred curricula in diverse higher education contexts. Engagement in evidence-based practice to evaluate and monitor curricula is vital to the success and sustainability of efforts to reform undergraduate and graduate programs. Emerging technology-enabled inquiry methods have enormous potential to inform evidence-based practice in complex curriculum settings. For example, curriculum analytics, including data from student learning outcomes, graduate qualities, course selection and assessment activities, can be mined from various student learning systems and analysed to inform curriculum renewal strategies and demonstrate impact at both the program and course level. Curriculum analytics can serve to foster a culture of inquiry and scholarship regarding program improvements that is characterised by information sharing within and across disciplinary borders. This paper presents an innovative technology that draws on social network methodologies for assessing and visualising the integration and linkages across individual courses that ultimately form a student’s complete program of study. Insights are grounded in the literature and curriculum leadership experiences in a Canadian research-intensive university setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".