A Critical Reflection of Collaborative Inquiry: To what extent is collaborative learning beneficial in my classroom?
Bibliographic record
Abstract
Twenty years ago, I was a Grade 6 student in a Grade 6/7 classroom. I remember learning material through direct teacher instruction. The structure was quite clear-cut; the teacher transmitted information and the students listened and absorbed the material. Tests were distributed, graded, and then new content would be introduced. Students were seated in rows, sometimes according to alphabetical order, which inhibited peer intermingling and interaction. This type of traditional teaching still exists in many classrooms despite educational advances that highlight the importance of collaborative student-centred learning rather than a teacher-centred classroom. I strongly maintain that “a major value of collaboration, the reason why it is so praised in our rhetoric, is that we can do more and better work collaboratively than we can alone” (Johnston-Parsons, M., 2010, p. 289). Student centred learning may be referred to as learning that “has student responsibility and activity at its heart, in contrast to the stronger emphasis on teacher-control and the coverage of academic content” found in much traditional teaching classrooms (Cannon, R., Ingleton, C., Kiley, M., & Rogers, T., 2000, pg.3). I further extend this definition and place emphasis on student centred learning as a classroom in which students play an active role in their learning, as oppose to a passive role in a teacher-centered learning environment.
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 | 0.058 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".