Encouraging Students with Different Profiles of Perceptions to Pursue Science by Choosing Appropriate Teaching Methods for Each Age Group
Bibliographic record
Abstract
This research aimed at identifying student profiles of perceptions by means of a clustering method using a validated questionnaire. These profiles describe students’ attraction to science and technology ( S&T ) studies and careers as a variable driven by school S&T self-concept and interest in school S&T . In addition to three rather predictable student profiles ( confident enthusiast , average ambitious , and pessimistic dropout ), the fourth fairly well-populated profile called confident indifferent was produced. Our second and third research questions allowed us to describe each profile in terms of the instructional methods to which their population was exposed (including the degree to which they were actively involved) and the instructional methods to which they would like more exposure. An analysis of the evolution of the profiles’ population over time is also presented. The results suggest that pedagogical variety and active involvement in the decision to pursue S&T are important. The perception of the utility and importance of S&T both in and out of school may also play an important role in these decisions. Minor pedagogical preferences were also found in certain age groups.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".