RESEARCH-BASED LEARNING IN AN ONLINE COURSE-BASED MASTER OF EDUCATION PROGRAM
Bibliographic record
Abstract
In this case study research, we examined graduate student experiences with research-based learning in an online course-based master's degree in education at a Canadian university. Data were gathered during two sequential phases, starting with exit surveys and then interviews with graduates from the master of education program. Findings indicated the way students were grouped and progressed through their courses in cohorts and using a signature pedagogy called collaboratories of practice, where students were provided with opportunities to engage in field-focused inquiry online alongside their peers and with guided support from their instructor, which served to support students with continuity throughout their program and with support for developing and applying research-based learning skills. Participants described the cohort structure and signature pedagogy as key elements that contributed to their research-based learning experiences. These findings are consistent with earlier results from studying two previous cohorts of postgraduate students. Study results serve to inform scholarship and program designs for research-based learning in course-based, online master's degree programs. More broadly, the results will also benefit faculties and institutions developing structural supports and pedagogies for new online courses and program offerings.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".