Implementation of ICT literacy in STEAM project learning for measuring student’s interest and motivation
Bibliographic record
Abstract
Chemistry learning is currently still dominated by memorizing textual concepts so that there is a lack of students’ interest and motivation in learning chemistry. The implementation of ICT literacy in STEAM project based learning is one of the learning innovations involving all aspects needed by students in 21st century. This study aims to analysis the implementation of ICT literacy in STEAM project learning for measuring student's interest and motivation. The method used is descriptive quantitative, data collection on 79 students selected by purposive sampling. The research data was obtained through a questionnaire that containing 16 items. Based on result and discussion, students’ motivation got the highest percentage on 88.4% in very high category, then students’ interest got 84,3% in high category, and students’ responses to the implementation of ICT literacy got the percentage 83.0% in high category. The results of regression analysis show that there is a very significant correlation (strong and positive) between ICT literacy and student interest and motivation. The implementation of ICT Literacy also provides significant test results for each regression coefficient, namely Y = 0.310 - 0.576 Interest + 1.493 Motivation. This shows that the ICT Literacy variable has a significant effect on student motivation, while the interest has an insignificant effect. Thus, the implementation of ICT literacy in STEAM project based learning is good for increase students' interest, motivation, and ICT literacy. These results can be used as innovations in the science learning process.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".