Educational Developers and Their Uses of Learning Theories: Conceptions and Practices
Bibliographic record
Abstract
This thesis reports on a study designed to understand how learning theories fit in the practice of educational developers; specifically, developers’ conceptions of learning theories, their use of theories, and, finally, factors that influence the way learning theories shape developers’ practice. To investigate these questions, a qualitative study was undertaken with eleven Canadian university educational developers, all formally associated with a campus-wide teaching and learning centre. By taking an exploratory approach, while drawing upon learning theories and educational development literature, aspects of educational developers’ understanding and use of learning theories were highlighted. \nThe findings showed that educational developers in this study: (i) conceptualize learning theories as lowercase ‘lt’ as opposed to uppercase ‘LT’, and (ii) define learning theories based on their prior disciplines. These practitioners didn’t associate learning theories with formal academic theories aimed at understanding a situation; instead they had formed their own synthesis of theories to help them perceive the characteristics of a particular situation. Also, the way the participants defined and conceptualized learning theories seemed to correspond to their prior disciplines and areas of study. Five definitions of learning theories were identified among educational developers: philosophy, language, educational-psychology, holistic, and neuroscience-based. In terms of how theories shape developers’ work, developers were categorized in three groups: (1) those who had a tendency to implicitly use learning theories –focusing more on practical explorations for achieving a desired outcome (seven in total); (2) developers who had a tendency to consciously use learning theories – taking more of a comprehensive approach by examining their assumptions and focusing on causes and effects that influence their practice (three in total); and, (3) one developer who had characteristics of both groups. Factors such as educational background, professional identities, and perceived audience readiness appeared to influence participants’ uses of learning theories. Seeing their work as part of a collective, and attending to the emotional needs of their audience also seemed to impact these practitioners’ work. \nConsidering the limited research examining how educational developers conceptualize learning theories and the way theories inform their practice, this study contributes in generating discussions and future research in a community that continues to grow and situate itself within the higher education landscape.
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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 | 0.050 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".