Inspiring Future Program Evaluators through Innovative Curriculum for Undergraduates
Bibliographic record
Abstract
Abstract: Instruction in program evaluation is challenging given the inherent interdisciplinary nature of the field. As well, there is no one discipline typically dedicated to evaluation training, and few formal programs and university course offerings exist. Despite these limitations, training and education at the postsecondary level continues to be vital in supporting the professionalization of program evaluation, especially as it is a requirement for credentialing. The current article presents an innovative project comprising both education and hands-on training of program evaluation practices for undergraduate students. The project involved in-class lectures targeting specific program evaluation competencies and a program evaluation assignment in an upper-level undergraduate psychology course. Students were asked to develop a logic model and identify psychometrically sound evaluation measures based on an existing community organization's program or on a theoretical example. At the end of the course, students (N = 58) completed surveys to assess their achieved evaluation competencies and experience with program evaluation. Overall, students gained evaluation-specific skills and knowledge, and the assignment was successful in promoting interest in program evaluation as a discipline. It is our hope that the current project can support faculty to integrate program evaluation in engaging and meaningful ways into their own curriculum.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".