1 Reconceptualizing the Evaluation of Teaching in Higher Education
Bibliographic record
Abstract
Trends within higher education in the United States and Canada suggest that, although there are calls for recognition of teaching as a scholarly activity, teaching is not perceived as a significant aspect of scholarly work. Furthermore, policies, procedures, and criteria for the evaluation of teaching in higher education contribute to the marginalization of teaching within the reward structures of universities and colleges. Evaluation policies, procedures, and criteria tend to (1) emphasize technical, rather than substantive aspects of teaching, (2) focus on process rather than outcomes, (3) lack strategic concern for the use of evaluation data within the institution, and (4) are devoid of the very substance through which academics derive a sense of identity-- their discipline. Recommendations are offered for evaluating three aspects of teaching: planning, implementation, and results. Within each aspect, conceptual arguments and practical solutions are suggested for establishing criteria, deciding on sources of data, and determining the nature of data that must be gathered. The goal is to set in place evaluation policies, procedures, and criteria that will be perceived as rigorous and credible alongside more traditional forms of scholarship, while respecting the diversity of contexts and disciplinary identities within universities and colleges. Seven principles for evaluation of teaching are proposed.
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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 | 0.276 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.010 | 0.094 |
| Scholarly communication | 0.049 | 0.025 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.006 | 0.009 |
| 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".