Assessing Acceptance Toward Wiki Technology in the Context of Higher Education
Bibliographic record
Abstract
This study investigated undergraduate students’ intention to use wiki technology. An extension of the Technology Acceptance Model (TAM) has been used by taking into account not only students’ wiki perceived utility and usability, but also Big Five personality characteristics and two other variables, social norms, and facilitating conditions, as proposed in the Unified Theory of Acceptance and Use of Technology (UTAUT). Students’ beliefs before (pre-wiki scenario) and after (post-wiki scenario) the actual use of the wiki system were investigated, with 85 and 86 participants respectively. The hypotheses were tested using partial least squares analysis. For the pre-wiki scenario, 8/15 hypotheses were confirmed and 11/15 for the post-wiki scenario. The relationship between perceived ease of use and perceived usefulness was found to be of the highest magnitude. The most notable difference across the two scenarios was that the relation between perceived ease of use and attitudes towards use was significant only in the first scenario. The results demonstrate that the proposed TAM-extended model could predict students’ wiki acceptance.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".