An Integrated Framework to Motivate Student Engagement in Science Education for Sustainable Development
Bibliographic record
Abstract
Science teachers continue to face decreased motivation, lower achievement levels, and decreased enrollment in post-secondary science programs. Teachers ask themselves this question: How do I motivate my students to achieve? Student-centered pedagogies, such as an in-depth pedagogy informed by Self-Determination Theory, can improve students’ motivation by addressing students’ basic psychological needs for autonomy, competency, and relatedness. Problem-based learning presents students with relevant situations and actively engages them in developing plausible solutions to problems. Environmental sustainability encompasses issues concerning our ecological and social environments. Teachers can focus on these issues to develop authentic problem-based learning units that offer a student-relevant pathway to improve motivation and scientific literacy. We propose a pedagogical framework, drawing on Self-Determination Theory, to promote students’ motivation to engage keenly with environmental sustainability education through problem-based learning. This framework is designed for secondary science classrooms to inform science teachers’ pedagogical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".