Relationships between Learning Styles, Perceived Advantages of Online Collaborative Learning and Practical Knowledge in Teaching among Taiwanese Student Teachers
Bibliographic record
Abstract
The purpose of this study is to investigate the effects of the three learning styles (collaborative, competitive, and individualistic) on the perceived advantage of collaborative learning (PAoCL) and practical knowledge in teaching (PKiT) among Taiwanese student teachers in an online collaborative environment. This study built a Facebook Group and developed the tasks of collaborative learning based on field-experience courses. The participants were required to share various practical experiences as the collaborative learning tasks. A total of 100 student teachers who enrolled in field-based courses between August 2016 and January 2017 participated in this study and were required to complete a validated survey in January 2017. This study determined the relationships between the three learning styles and PAoCL and PKiT and further identified predictors of online collaborative learning. The collaborative learning style of student teachers was positively associated with their PAoCL, while competitive learning style was correlated with their PKiT. Accordingly, teacher educators can encourage student teachers to share experiences about teaching practices during participating in field-experience courses through online collaboration. However, teacher educators should remind the student teachers to transfer the online information into PKiT.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".