‘Complexifying’ our approach to evaluating educational development outcomes: bridging theoretical innovations with frontline practice
Bibliographic record
Abstract
Increasing instructional quality in higher education is a key goal of educational development (ED) work, yet demonstrating complex outcomes remains challenging and lacks practical guidance. Evaluating ED services often relies on a reductionist approach characterized by linear assumptions of causal pathways to measure the extent to which instructional outcomes have been achieved and uses proxies such as short-term participant satisfaction. This paper advances a complexity-informed approach for guiding the evaluation of complex outcomes of ED services across individuals and activities within institutions that is adaptable across institutional contexts. To do this, we position the need for innovation in evaluation approaches within current ED literature and practice, and outline key implications of four complexity principles for guiding our approach. Then we describe an iterative process for developing and implementing the evaluation approach within a larger Centre for Teaching and Learning self-study. We describe the transferability of the evaluation approach to contexts beyond the study, and conclude with theoretical, practical, and methodological implications for evidence-based decision-making and strategic planning of ED work.
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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 | 0.156 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".