‘Complexifying’ our approach to evaluating educational development outcomes: bridging theoretical innovations with frontline practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it