What Really Makes Secondary School Students “Want” to Study Physics?
Bibliographic record
Abstract
This paper reports on a mixed-methods study with high school students. The study focused on the reasons they give with regard to “what they find interesting about their physics lesson” and “what makes them want to study their physics lesson” during a school year. The sample consisted of 219 students, who attended public high schools, located in various geographical regions of Greece. Journal entries made by all students—that is, students from junior high and senior high schools—were content-analyzed through a grounded theory approach. A total of eight categories were identified. Quantitative differences between these categories, and between the two groups of students, were also identified. Even though some of the identified categories are well-known motivators in science education, three specific categories deserve particular attention: “connection to one’s own self”, “purpose”, and “utility”. Notwithstanding the limitations of the present research design (i.e., volunteer sample, lack of standardization in students” and especially in teachers’ activities), these categories, along with two quantitative indicators—that is, number of journal entries and student percentages—challenge us to rethink what makes the ideas of science, especially those of physics, meaningful or simply relevant to the life of the students.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".