The Development of Instructional Model Based on Gardner’s Multiple Intelligence Theory to Improve the Mathematics Achievements of Second Grade Primary School Students
Bibliographic record
Abstract
This research aimed to 1) study the factors affecting the development of mathematics achievement of second-grade students in Qinhuang Town Primary School, 2) develop an instructional model based on Gardner’s Multiple Intelligence Theory, 3) compare the mathematics achievement of second-grade students before and after using an instructional model based on Gardner’s Multiple Intelligence Theory. The sample group was 30 second-grade students in Qinhuang Town Primary School. The research Instruments were 1) a questionnaire about the factors affecting elementary school students' mathematics achievement 2) an interview form about the factors affecting elementary school students' mathematics achievement 3) lesson plans 4) a mathematics achievement test 5) students' behavioral observation form 6) interview form about the opinions on teaching and learning. This study was conducted in three steps: a study of the factors affecting the development of second-grade students' mathematics achievement, the development of an instructional model based on Gardner’s Multiple Intelligence Theory, and an experimental and improvement process. Data were statistically analyzed, Qualitative data were analyzed through content analysis, and Quantitative data were analyzed through descriptive statistics, frequency, percentage, means, and standard deviation, and the different scores of mathematics achievement before and after using the instructional model were analyzed through t-test for dependent samples. The results of the study show that: 1) the factors affecting elementary school students' mathematics achievement include four aspects: 1) students 2) teachers 3) the learning process and the environment. 2) the instructional model consisted of four components: Principle, Objective, Learning Process, and Result. 3) the implementation of the instructional model based on the multiple intelligences students have after mathematics achievement was significantly higher than that before class with statistical significance at the level .01
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".