Curriculum Analysis Purposes and Methodologies: A Systematic Literature Review
Bibliographic record
Abstract
requirements. Given the inconsistent formats of curriculum documentation such as course syllabi and descriptions, analyzing curricular data can be challenging. This work aims to explore documentation, methodologies, and purposes used for conducting curriculum analyses through a systematic literature review (SLR) of academic databases including Scopus, Web of Science, ERIC, and the CEEA Proceedings. The breadth and depth of data from the SLR were examined through a thematic analysis of 92 papers. Nine sources of curriculum documentation, seven common analysis methodologies, and four main purposes of curriculum analysis were found. Key findings include a lack of sophisticated methods of automated curriculum analysis, trade-offs between accessibility and detail of curriculum documentation, and opportunities for reporting on curricular interventions. This research contributes to the field by systematically describing methodologies used for curriculum analysis in engineering education. We hope that this research may be used as a starting point for researchers and curriculum developers who are interested in performing a curriculum analysis.
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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 | 0.260 | 0.472 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.055 | 0.045 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".