Development of an Instructional Model to Enhance Mathematical Literacy for Secondary Students
Bibliographic record
Abstract
This study aims (1) to develop an instructional model to enhance mathematical literacy for secondary students and (2) to examine the effects of using this instructional model. The sample group consisted of students from Lampabpla Wittayakarn School, under the jurisdiction of the Surin Secondary Educational Service Area Office in Thailand by using cluster random sampling, during the second semester of the 2021 academic year. Fifteen students from Grade 9/2 were selected as the experimental group, and 15 students from Grade 9/1 were selected as the control group. The data collection tools: 1) interviews; 2) focus group discussion; 3) mathematical literacy tests; and 4) student satisfaction questionnaires. The statistics used for data analysis include percentage, arithmetic mean, standard deviation, dependent and independent t-test. The research findings revealed that (1) the instructional model to enhance mathematical literacy for secondary students comprises six components: 1) principles and basic theoretical concepts, 2) objectives of the model, 3) instructional management procedures, 4) social system, 5) principles of response, and 6) support system. (2) results of using the instructional model showed that: 1) the experimental group of students had markedly higher mathematical literacy after the intervention compared to before, at the .05 significance level; 2) the experimental group had significantly higher mathematical literacy than the control group, at the .05 significance level; and 3) the experimental group reported the highest level of satisfaction with the instructional model to enhance mathematical literacy for secondary students, with an average score of 4.50 and a standard deviation of 0.17.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".