The Effect of Using STEAM Approach on Motivation Towards Learning Among High School Students in Jordan
Bibliographic record
Abstract
This study aimed at investigating the effect of applying integrated Science, Technology, Engineering, Art, and Mathematics (STEAM) approach on motivation among students of grade 10 in a private school in Amman. In this context the researchers used a quasi-experimental method. The sample of this study involved 32 high school students; the individuals of the study were intentionally chosen and distributed randomly into two groups: the control group consisted of 19 students who studied Geography in a conventional way, and the experimental group consisted of 13 students who studied the same content using STEAM approach. To achieve the study goals, the researchers developed an instrument to measure motivation towards learning geography that focused on the following constructs of motivation: Internal Motivation, Grade Motivation, Class Anxiety, Career Motivation, Self-Efficacy, and Teacher Obedience. After verifying its validity and reliability, the instrument was applied on the study sample. The results of the analysis of covariance (ANCOVA) showed no significant difference on motivation in all of its constructs except for (Class Anxiety) which was in favor of the experimental group. The study recommended providing teachers with enough and valuable training opportunities on how to activate STEAM approach to ensure meaningful learning for students and to increase their awareness to future careers including STEAM jobs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".