Investigating University Students’ Preferences to Science Communication Skills: A Case of Prospective Science Teacher in Indonesia
Bibliographic record
Abstract
The purpose of this study was to investigate Indonesian university students’ preferences to science communication skills. Data collected from 251 students who were majoring in science education program. The Learning Preferences to Science Communication (LPSC) questionnaire was developed with Indonesian language and validated through an exploratory factor analysis (EFA) of participants’ responses. The differences between student levels were also explored for their significance using ANOVA test in order to draw a clear line among different learning preferences. The results indicated that, first, the instrument used in this study had satisfactory in validity and reliability. The construct validities of the LPSC were vary from .48 and .83 and explained 64.54% of the variance. Overall, the Cronbach alpha coefficient of the instrument was .91. Second, university students as prospective teacher in junior and freshman level performed higher preference in visual (V) and aural (A) than others. Moreover, senior student depicted higher confident in read or write (R/W) than junior and sophomore level. However, university student in sophomore level performed less confident in both visual and aural. Third, the results also showed the significant intra-relationships among dimensions of learning preferences. The implications delineated contribute to the improvement of science teacher education program in Indonesia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".