Constructivist Learning Environment Model for Rectifying Secondary Students’ Misconceptions in Learning Science: Design Development and Validation Phases
Bibliographic record
Abstract
This study aims to develop and validate a Constructivist Learning Environment Model to address secondary students’ misconceptions in learning Science. Employing a Design and Development approach (Richey & Klein, 2007), the research is conducted in two phases. Phase 1 focuses on model design, drawing from an extensive literature review to integrate five key components: psychological, pedagogical, misconceptions and concept change theory, media, and contextual factors. Phase 2 involves validation, with internal validity assessed through expert reviews and surveys, and external validity evaluated using pre-test and post-test measures on 60 high school students from Wangsammowittayakan School in Udonthani, Thailand. The results reveal modest improvements in student achievement, with the mean score on the achievement test rising from 32.43 (pre-test) to 34.63 (post-test), alongside a significant increase in students’ conceptual understanding, as evidenced by a mean score improvement on the conceptual change test from 11.70 to 13.50. This key contribution of the study is the development of a comprehensive model that systematically integrates psychological, pedagogical, misconceptions, media, and contextual components, offering an innovative and multifaceted approach to enhancing students’ understanding of complex scientific concepts and fostering conceptual change.
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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 | 0.059 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".