Improvement of professors' teaching: investigating motivating and inhibiting factors
Bibliographic record
Abstract
Professors have three main functions in universities: research, teaching and service. This study focuses on the teaching function. Effective teaching in higher education enhances students' learning while ineffective teaching can have detrimental impact on students' learning and their attitudes toward learning. In this regard, it is important that professors have the knowledge base for effective teaching, a base which is growing and changing rapidly. This requires that they engage in professional development activities to improve their teaching. Research suggests that professors are reluctant to dedicate time to improve their teaching. The main purpose of this study was to investigate the contextual and personal factors that contribute to a sense of reluctance or motivation for the improvement of teaching. Goal orientation and implicit theory of teaching skills guided this study to explore personal factors. Results revealed that mastery goal orientation and implicit theory of teaching skills are correlated with the time spent on activities for improving teaching and implementing new instructional methods, respectively. Professors' perceptions of barriers against and support for improvement of teaching were also studied. Recommendations forwarded by professors to enhance their engagement in the improvement of teaching including creating a reward system for teaching, designing more efficient teaching improvement opportunities, building communities of learning and practice, allocating funds for the improvement of teaching and considering teaching time release designated for improvement.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".