Peer-review of teaching materials in Canadian and Australian universities: A content analysis
Bibliographic record
Abstract
BACKGROUND: Peer-review of teaching materials (PRTM) has been considered a rigorous method to evaluate teaching performance to overcome the student evaluation's psychometric limitations and capture the complexity and multidimensionality of teaching. The current study aims to analyze the PRTM practices in Canadian and Australian universities in their faculty evaluation system. MATERIALS AND METHODS: This is a qualitative content analysis study in which all websites of Canadian and Australian universities ( n = 46) were searched based on the experts› opinion. Data related to PRTM were extracted and analyzed employing an integrative content analysis, incorporating both inductive and deductive elements iteratively. Data were coded and then organized into subcategories and categories using a predetermined framework including the major design elements of a PRTM system. The number of universities for each subcategory was calculated. RESULTS: A total of 21 universities provided information on PRTM on their websites. The main features of PRTM programs were organized under the seven major design elements. Universities applied PRTM mostly ( n = 11) as a summative evaluation. Between half to two-thirds of the universities did not provide information regarding the identification of the reviewers and candidates, preparation of reviewers, and logistics (how often and when) of the PRTM. Almost all universities ( n = 20) defined the criteria for review in terms of teaching philosophy ( n = 20), teaching activities ( n = 20), teaching effectiveness ( n = 19), educational leadership ( n = 18), teaching scholarship ( n = 17), and professional development ( n = 14). CONCLUSION: The major design elements of PRTM, categories and subcategories offered in the current study provide a practical framework to design and implement a comprehensive and detailed PRTM system in the academic 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.
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 | 0.039 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".