Like or Not Like Studying Science: Exploring Students’ Personal and Cultural Characteristics
Bibliographic record
Abstract
Abstract This research examines students’ interests in studying science in the UK, focusing on the context of declining interest in science subjects. A total of 1618 students aged 10 to 14 participated, responding to a questionnaire covering various constructs, such as subject preferences, stereotypes of science people, science extracurricular activities, career pathways, and views of science. Descriptive and inferential analyses, including three models of multiple regression, revealed several key findings. Students from non-Western backgrounds showed lower engagement in science extracurricular activities compared to their Western counterparts. Engagement in science-related activities positively influenced students’ views of science. Interestingly, students’ educational background (primary or secondary education) had a negative impact on their views of science. The study also highlighted a preference for non-STEM subjects over specific STEM fields, with curiosity and hands-on learning influencing favourite subjects. Addressing stereotypes and promoting gender equity in science education are essential. Early educational experiences and science extracurricular activities positively impacted students’ liking for science. Students’ views of science were influenced by hands-on experiences, gender, and educational background, indicating the need to challenge stereotypes. These insights inform science education policy and practice to promote interest and engagement among young learners in the UK.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".