Perceptions and experiences with academic group work in online and in-person classroom contexts: Perspectives across levels of undergraduate study
Bibliographic record
Abstract
Group work is a commonly used and highly regarded learning tool in tertiary education. While previous research has examined collaboration within higher education contexts, no study has investigated student preferences and experiences across different years of study. This is essential for a better understanding of how to effectively integrate collaborative learning into the undergraduate curriculum. The present study surveyed first year, second year and senior (3rd and 4th year) undergraduate students (n = 100 per group) to gather insights regarding their experiences with collaborative learning as a function of year of study and in both in-person and online contexts. Overall, preferences regarding group composition (e.g., group size) and experiences regarding group work (e.g., efficiency, motivation, satisfaction, stress) were consistent across the year of study. However, notable shifts in experiences were observed, particularly from first to second year, with respect to instructor-related variables (i.e., group formation strategies, leadership opportunities) and student-related variables (i.e., perceived difficulty producing assignments, level of collaboration in groups, learning experiences and enjoyment). Some differences were also observed between in-person and online contexts. Implications for future studies and instructional design are discussed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".