Designing Group Work in Online Courses to Develop Preservice Teachers’ Professional Collaboration Skills
Bibliographic record
Abstract
With post-secondary institutions increasing offerings of online courses, there is much to learn about how online group work is designed. This is of particular importance for professional certification courses where group work is used to develop skills needed to prepare students for their chosen field, such as K-12 education. As part of case study research, the authors synthesize findings collected from both instructors and students at Western Canadian post-secondary institutions offering online courses in their Bachelor of Education degree pathways. Seeking to understand how group work can be designed to build essential professional skills required in the teaching profession through online course delivery, data was collected through one online survey, semi-structured interviews, and course documents. Findings suggest four design considerations for online group work: (1) clearly articulate the purpose of group work, (2) provide learner support through teaching presence, (3) be intentional in how groups are established, and (4) leverage digital tools for collaboration. The results will serve to benefit faculty, students and educational policy makers in understanding how group learning in online courses can be designed to develop critical professional skills. This will particularly benefit post-secondary institutions providing online courses in professional fields.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".