The Impact of An Instructional Program Based on Multiple Intelligences Theory on Ninth Grade EFL Students’ motivation towards Learning English in Jordan
Bibliographic record
Abstract
This study aimed to investigate the impact of an instructional program based on Multiple Intelligences Theory on motivation among ninth grade students in Amman during the academic year 2021-2022. The sample of the study consisted of 40 students in two sections who were randomly assigned to a control group (20 students) and to an experimental group (20 students). To achieve the purpose of the study, the researchers developed a questionnaire for data collection. Data were analyzed by using SPSS (i.e. means and standard deviations, and ANCOVA). The results of the study showed that the instructional program based on multiple intelligences theory was significantly more effective than the ordinary method in developing students’ motivation to learn English. Based on the results of this study, the researchers recommended that the principles of Multiple Intelligences Theory should be incorporated in EFL curriculum in Jordan to consolidate students’ motivation to learn English.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".