Curriculum Analytics: Application of Social Network Analysis for Improving Strategic Curriculum Decision-Making in a Research-Intensive University
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it