Integration of play-based learning into the instructional delivery of tutors and lecturers in Ghana
Bibliographic record
Abstract
In this study, we explored the integration of play-based learning into the instructional delivery of tutors/lecturers in Ghana. Sixteen tutors/lecturers (12 males; 4 females) selected through homogenous sampling technique completed this current study. The findings indicated tutors/lecturers generally perceived play-based learning as an engaging and effective medium for teaching and learning. Tutors/lecturers affirmed play was crucial in motivating learners, fostering active participation, and connecting abstract ideas to real-world experiences. Relative to the application of play-based learning, tutors/lecturers had a preference for the guided approach. They primarily assumed a facilitator role during play-based learning and encouraged exploration, collaboration, and active learning. Limited use of play-based usage in assessment, classroom space constraints, time limitations, and large class sizes were some inhibitory factors impeding the application of play-based learning. Despite these challenges, tutors/lecturers recognize the potential of play-based learning to facilitate enjoyable, self-directed, and effective learning experiences and advocated for its broader adoption.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".