The Perceptions of Participation in a Mobile Collaborative Learning among Pre-Service Teachers
Bibliographic record
Abstract
This study uses Facebook as a platform and arranges certain learning tasks to identify the feasibility of mobile collaborative learning for pre-service teachers. The pre-service teachers’ sense of community and perceptions of collaborative learning are investigated. A total of 153 pre-service teachers volunteered to participate in an Intern Mobile Collaborative Learning Facebook Group from July 2015 to October 2015. During participating in the Facebook Group, pre-service teachers were required to achieve various tasks regarding collaborative learning. A questionnaire, consisting of three sections, frequency, sense of community, and perceptions of learning and perceptions of collaborative learning, was developed and validated. All participants were required to fill in the questionnaire at the last week of the project’s schedule. This study concludes that high browsing frequency on Facebook Group could positively facilitate the sense of community and perceptions of collaborative learning among pre-service teachers; while high frequency of posting and responding to messages on Facebook Group merely promotes perceptions of collaborative learning. The conclusion identifies that assigned tasks like posting and responding to messages regarding school field-based experiences are necessary in mobile collaborative learning among pre-service teachers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".