Using wikis for online group projects: Student and tutor perspectives
Bibliographic record
Abstract
This paper presents a study of the use of wikis to support online group projects in two courses at the UK Open University. The research aimed to investigate the effectiveness of a wiki in supporting (i) student collaboration and (ii) tutors’ marking of the students’ collaborative work. The paper uses the main factors previously identified by the technology acceptance model (TAM) as a starting point to examine and discuss the experiences of these two very different user groups: students and tutors. Data was gathered from students via a survey and from tutors via a range of methods. The findings suggest that, when used in tandem with an online forum, the wiki was a valuable tool for groups of students developing a shared resource. As previous studies using the TAM have shown, usefulness and ease of use were both important to students’ acceptance of the wiki. However, the use of a wiki in this context was less well-received by tutors, because it led to an increase in their workload in assessing the quality of students’ collaborative processes. It was possible to reduce the tutor workload by introducing a greater degree of structure in the students’ tasks. We conclude that when introducing collaborative technologies to support assessed group projects, the perceptions and needs of both students and tutors should be carefully considered.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".