‘Science is My True Villain’: Exploring STEM Classroom Dynamics Through Student Drawings
Bibliographic record
Abstract
Classroom dynamics including interactions among peers and with teachers is a key component of students’ STEM experiences, strongly influencing students’ motivation to engage in the learning activities. Classroom dynamics and dialogue have been predominantly studied through longitudinal ethnographic observations of the classroom while the perspective of the students who are undergoing these experiences is largely unaccounted for. This research article showcases an empirical study that used student drawings to explore STEM classroom dynamics. In contrast with interviews, drawing allows the participant to illuminate their tacit knowledge and communicate ideas without interference from the researcher. The participants (n=32), 9th grade students from 16 public schools in Northern India, were asked to create a poster with drawings and words to show their experiences and feelings in mathematics and science classrooms. A thematic analysis of students’ work was performed to draw inferences about the classroom dynamics. The posters provided an opportunity for students to authentically express themselves and represent the social and emotional consequences of teacher behaviour in STEM classrooms. Concurring with previous classroom dynamics research, findings identified a strong need to reassess teaching practices in rural Indian context.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".