Determining the Most Effective Stage of the Think-Pair-Share Teaching Strategy
Bibliographic record
Abstract
This investigation was carried out to examine the effect of think-pair-share on high school learners’ academic attainment in fractions and the most effective stage of the think-pair-share. Two research questions and five hypotheses guided this study. An explanatory sequential mixed method design was used. Purposive and convenience sampling techniques were used to select the first-years and the 78 participants, respectively. Teacher-made fractions achievement tests and interviews were used as the data-gathering instruments. Students’ academic achievement was analysed using independent and paired samples t-tests whilst the interview data was analysed thematically. The study found that learners who received fractions instructions using the think-pair-share model outperformed their colleagues who were taught fractions without think-pair-share. Also, the performance of students at the pair stage was higher than the performance of the same students at the think stage on the same test items. It was again found that intolerance, lack of self-confidence and inability to build consensus on the part of some students affected their performances at the pair stage. It was, therefore, concluded that Think-Pair-Share is effective in teaching fractions and that the most effective stage of the Think-Pair-Share strategy is the pair stage. It was therefore recommended that teachers be encouraged to use the think-pair-share teaching strategy in their teaching and that think-pair-share should be made to form an integral part of the Senior High School mathematics curriculum. Again, teachers and the Ghana education service should consider assessing students in pairs. Also, teachers should encourage students to be tolerant and confident in themselves.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".