Teacher Perspectives on the Usefulness of a Formative Assessment Tool to Support Their Implementation of Learning Through Play
Bibliographic record
Abstract
In this study, we explore teacher perspectives on the utility of a formative assessment tool, Teacher RePlay, to support their implementation of learning through play (LTP) activities in classrooms for students age 3 to 12 years in Bangladesh, Colombia, and Uganda. Research suggests that formative assessment tools may be useful to bridge support from professional development trainings to the classroom. The Teacher RePlay tool was designed to provide continued support to teachers subsequent to their participation in training sessions on implementing LTP. Through interviews, we explored the benefits and challenges teachers experienced while using the tool. Teachers reported the tool improved their planning and delivery of LTP activities and cited positive outcomes, including enhanced student engagement, attendance, and overall learning. Challenges in implementing LTP included class size limitations, time constraints, and technology access. The study underscores the importance of considering cultural contexts in implementing LTP and suggests future considerations, including alternative resource delivery formats and the creation of a support community for teachers. We highlight, as an alternative to the traditional professional development model, the promise of formative assessment tools as a means of providing continual and productive support to 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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".