An Exploratory Study for the Development of a Survey on Learning Team Process, Impact, and Tutor’s Role (PIT) in Facilitating Online Learning
Bibliographic record
Abstract
The aim of this article is to learn about teamwork in an online learning environment. to achieve this purpose, we developed a questionnaire based on three initial concepts, learning team process, learning teamwork impact, and tutor’s facilitation of learning teamwork. The PIT questionnaire is an instrument that could be used to identify critical factors that online students perceived as important in enhancing their learning and improve their experiences. We used some open-ended questions to support the questionnaire’s items analysis. We claim that learning team processes as well as tutor’s facilitations does have an impact on students’ experiences and learning. For a purposeful learning team, members should set clear goals outlining expectations and that every member should feel a sense of belonging and safe to contribute their ideas. The learning teamwork impact component contributed for the most variance in the PIT questionnaire. Apart from learning content, students indicated that with learning teams they gained collaborative skills, felt motivated and learned pertinent concepts from their peers from different backgrounds. We conclude that online learning teams are a form of community of learners, a place where students and tutors are actively and intentionally constructing knowledge together.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".