Evaluation of Student Engagement, Communication, and Collaboration During Online Group Work: Experiences of Fourth-Year Veterinary Medicine Students
Bibliographic record
Abstract
Accelerated by the COVID-19 pandemic, online teaching has become widely established in higher education in recent years. However, little is known about the influence of the online environment on collaborative student activities, which are an integral part of veterinary education. This study explored engagement, collaboration, and communication among fourth-year veterinary students working in groups on online case-based learning (CBL) activities. Data were collected by questionnaire (93/135) and anonymous peer assessment (98/135) at the end of the trimester. While most students (67%) enjoyed group work and 75% considered it of benefit to their learning, the results indicated that the students' interaction was mainly limited to task management and collating individual answers on shared documents. Rather than meeting online, students communicated by chat and messenger apps. Agreement of roles, rules, and the group contract were largely treated as box-ticking exercises. Conflict was the only factor that affected group work satisfaction and was largely avoided rather than addressed. Interestingly lack of student engagement in group work was not related to overall academic performance and had no impact on their end-of-term exam results. This study highlights high student satisfaction and engagement with online group CBL activities even when collaboration and communication were limited. Achieving higher levels of collaborative learning involving co-regulation of learning and metacognitive processing of learning content may require more specific, formal training in relevant skill sets from an early stage of the veterinary curriculum.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".