Solving STEM-Relevant Problems: A Study with Prospective Primary School Teachers
Bibliographic record
Abstract
Solving a problem requires and promotes a diversity of competencies, which include conceptual knowledge, technical and methodological knowledge, and transversal competencies. Everyday STEM-relevant problems are contextualized, ill structured, and multidisciplinary in nature. By focusing on daily life issues, they promote students’ engagement in the problem-solving process and enable them to perceive how science relates to their lives. This paper aims to characterize the processes followed by prospective primary school teachers when solving three STEM-relevant problems that have different features. The qualitative analysis of 77 participants’ answers showed that complete problem-solving pathways were one among a variety of other paths identified. Most strategies adopted by the participants led them to ignore the contextual conditions of the problem and to reach solutions that did not attend to them. The affective relationship with the object may increase the problem solver’s tendency to ignore the contextual conditions, but this issue deserves further research. The results shed some light on the features of the problems that teacher educators should select if they wish for their prospective teachers to learn and succeed in solving everyday STEM-relevant issues. This is required to promote their future students’ engagement in problem-based learning processes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".