“The Dream Team:” A Case Study of Teamwork in Higher Education
Bibliographic record
Abstract
The integration of collaborative practices in essay writing within higher education constitutes a pivotal advantage, frequently producing outcomes surpassing those of independent composition endeavors. However, although collaboration is necessary and can yield many positive outcomes, a collaborative effort is not always successful. A paucity of empirical studies has highlighted the causes of the dysfunctions of teamwork in Jamaica. In higher education, participants often express frustration and unwillingness to engage in teamwork because of the various dysfunctions they are likely to experience. Consequently, in response to this gap, this case study explored both functional and dysfunctional attributes Academic Writing participants encountered at a university in Jamaica, as they worked collaboratively to complete their expository and argumentative essays. This mixed methods study collected data from interviews, peer reviews, and a questionnaire. The findings identified both functional and dysfunctional qualities. The results showed that the major functional attributes were clear communication, respect, commitment, and accountability. The main dysfunctional attributes were lack of trust, miscommunication, commitment, disrespect, and limited time management skills. Finally, this paper highlights best practices that educators can use to create and promote functional and effective teams in the teaching and 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".