Formative Value of an Active Learning Strategy: Technology Based Think-Pair-Share in an EFL Writing Classroom
Bibliographic record
Abstract
Think-Pair-Share (TPS) activities in classrooms provide an opportunity for students to revise, practice and reproducepreviously learned knowledge. Teachers also benefit from this active learning strategy by exploiting new learningmaterials, saving time by minimizing presentations and using it as a formative assessment tool. This article exploreshow a teacher can employ the strategy to both promote active learning and conduct formative assessment in atime-efficient way. To do this, a TPS activity was designed on an online platform along with an assessment rubric forstudent products. In 60 minutes, students thought individually on the topic provided, discussed and collaborated ingroups and finally wrote down their paragraphs on the online tool. Each group shared paragraphs simultaneously.The teacher examined the paragraphs in terms of the predefined learning outcomes and determined the points to berevised. The students answered an open-ended online questionnaire a day later and the qualitative data were analyzedthrough a coding system. The assessment results successfully showed the learning points to be revisited and theresults of the questionnaire supported the assessments of the teacher. The majority of the students revealed that theywere satisfied and willing to do the activity again in the future.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".