The Design and Psychometric Properties of a Peer Observation Tool for Use in LMS-Based Classrooms in Medical Sciences
Bibliographic record
Abstract
In peer observation of teaching, an experienced colleague in the educational environment of a faculty member observes the educational performance of that faculty member and provides appropriate feedback. The use of peer review as an alternative source of evidence of teaching effectiveness is increasing. However, no research has been done in the field of tool design and development to peer review in classrooms that use a learning management system (LMS). This study used mixed methods. In the qualitative stage, after studying sources and interviewing professors active in virtual education, a question bank was prepared and a 26-item initial questionnaire created. In the quantitative stage, the psychometric properties of the developed instruments, such as the face, content, and structural validity, were examined, and reliability tests were performed. IBM SPSS Statistics (Version 20) was used for analysis. Five categories, including content preparation, content presentation, effective interactions, motivation management, and support services, and 26 subcategories were determined to be effective indicators in peer observation in LMS-based classes in medical sciences. During content analysis, 9 items were removed due to lack of necessary criteria. Then, using principal component analysis and varimax rotation in the present mode )Watkins, 2018), 5 components with eigenvalues higher than 1 were extracted, which explained a total of 70.55% of the total variance. The inter-cluster correlation coefficient (ICC) was 0.88. Thus, the peer observation measurement tool, designed with 17 expressions using the answer method “yes/no”, showed good validity and reliability. The research results demonstrate that the evaluation of virtual classes of professors by their peers is effective and that the results can be used in e-learning promotion plans.
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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 | 0.040 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".