Exploring the Impact of the Integrated Think-Pair Share and Active Learning Management on Non-Credentialed Teacher Learning Assessment Competency
Bibliographic record
Abstract
This study investigates the effectiveness of an integrated approach combining think-pair share and active learning management in enhancing non-credentialed teachers' assessment competency and compares their learning achievement in educational assessment across pre, post, and delayed examination phases. Utilizing a one-group experimental design, 29 participants were selected through cluster sampling. Instruments included an integrated think-pair-share and active learning management system, along with assessments evaluating participants' assessment knowledge, skills, and attributes. Data analysis involved mean scores, standard deviation, effectiveness index (E1/E2), One - way repeated measure ANOVA, and pairwise comparisons. Results indicate that the integrated learning management plan effectively facilitated teaching assessment competency to non-credentialed teachers at the graduate level, leading to the attainment of expected learning levels within the class. Additionally, it significantly enhanced participants' competency compared to their baseline levels, with sustained effectiveness demonstrated over time.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".