Application of Multiple Intelligence Theory to Increase Student Motivation in Learning History
Bibliographic record
Abstract
This study aimed at investigating the enhancement of motivation among low achievement students in the History lesson, after the multiple intelligence theory was integrated in teachers’ teaching practices. The teachers were expected to apply a new approach with various teaching activities to motivate students to learn. The sample consisted of 68 low achievement students, who were then divided into two groups: 34 students were treated in the treatment group, while another 34 students were put in the control group. This is a quasi-experiment of non equivalent control group design. The questionnaire was distributed to students of both groups, to test the effectiveness of the integration approach. Analysis of the mean and standard deviation was conducted for both groups, while the null hypothesis was tested by the t- test. Based on the pre-test, there was no significant difference between the two groups. The post-test recorded significant motivational differences between the two groups studied. It was determined that the integrated History lesson with multiple intelligences had increased the level of motivation among students in the treatment group. This shows that diversity of methods and activities undertaken were able to change students’ perception about the History subject and had increased their interests to learn History. Hence, it can be concluded that integrated multiple intelligence activities are able to increase students' motivation to learn History.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".