Applying the Collaborative Approaches to Evaluation (CAE) Principles in an Educational Evaluation: Reflections from the Field
Bibliographic record
Abstract
Abstract: This practice note presents reflections on the application of the collaborative approaches to evaluation (CAE) principles used as a guide in planning, implementing, and managing a collaborative evaluation in a higher educational setting. The reflection is based the evaluation of a technology integration program intended to enhance K–12 teacher preparation in a school of education at a public university in the southeast United States. The evaluation was conducted during a one-year period by the author and a diverse team of novice and experienced evaluators. Discussion of the principles and their influence on collaborative practice are based on an analysis of evaluator reflections, meetings with stakeholders, and a culminating interview with stakeholders that were recorded and documented throughout the evaluation. Key takeaways from our reflection and analysis highlight the ways in which the CAE principles encourage reflection, the emphasis of some principles based on the specificities of context, and challenges applying the principles that emerged throughout the evaluation.
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How this classification was reachedexpand
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 | 0.044 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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; both teacher heads agree on what is shown here.
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".