College instructor education: A model for effective student learning.
Bibliographic record
Abstract
The interconnection between teaching and learning in higher education has been the subject of academic investigation for some time. However, within the community college context, the effectiveness of pedagogically trained instructors on student learning has remained an under-examined area of scholarly research. This study advances a greater understanding regarding the importance of quality teaching within the community college system in Ontario and explores how institutional policy and practices support or impede the promotion of quality teaching. A mixed methods sequential explanatory design was employed at a Toronto college to gauge the perspectives of participants in three sub-groups of the College strata: administrators, instructors, and students. A pragmatic approach was utilised from which multiple methods of data collection (i.e., semi-structured interviews, focus groups, and online questionnaires) were selected to exhaustively address the primary research question. Key findings revealed that formal academic development in pedagogical education was perceived by the majority of participants as foundational to effective teaching practice and that more comprehensive academic development was needed to improve both current practice and student academic achievement. Most instructors and students concurred that learner-centred approaches, both in class and in field placements, led to a deeper level of learning. From a leadership standpoint, participants also believed that college policies and practices were misaligned with promoting quality instruction and that greater progress towards alignment was necessary; thus, there were serious leadership implications. This study adds to the current instructor education discourse by providing impetus for institutional change towards the professionalisation of college instructors and also recognises the inextricable tie between instructor education and student learning.
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.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".