The Effect of Multiple Intelligence Theory on Students’ Academic Success in The Subject of Geometric Shapes in Elementary School
Bibliographic record
Abstract
The aim of the research is to investigate whether "Teaching Mathematics for the 2nd grade of elementary school in an appropriate way for the theory of multiple intelligences on geometric subjects" has any impact on students’ academic achievement or not. The research is an experimental study and it was conducted with the students of the 2nd grade class in a primary school in Küçükçekmece province of Istanbul in 2016-2017 education year. A total of 60 students participated in the research, 30 in the experimental group and 30 in the control group. In the control group, while the subject "geometric objects" was taught using traditional methods, the same subject in the experimental group was taught by curriculums prepared in accordance with the Multiple Intelligence Theory. The study lasted for 4 weeks together with the applications of test development, pre-test, post-test and course work. The application was performed by researchers. The data obtained from the application were evaluated in the SPSS 22 Program. As a result of the evaluations made, it was concluded that the lesson which was taught by using the curriculums prepared according to the Multiple Intelligences Theory had a more positive effect on student achievement compared to the lesson which was taught using traditional methods.Keywords: Multiple Intelligence Theory, Mathematics, Geometry, Academic Achievement
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".