Enhancing Inquiry-Based Science Instruction: The Role of Professional Learning Communities and Instructional Coaching for Elementary Science Teachers
Bibliographic record
Abstract
Inquiry-based science instruction fosters critical thinking, problem-solving, and scientific literacy by engaging students in exploration, questioning, and reflective learning. However, implementing inquiry-based practices presents significant challenges for elementary teachers, who often balance multiple subjects and may feel less confident facilitating open-ended investigations. This study examines how school-based coaching and professional learning communities (PLCs) support teachers in adopting inquiry-based science instruction. Grounded in distributed cognition theory of learning, this research draws on qualitative data from classroom observations and artifacts from coaching sessions and PLC meetings. Using a multiple case study design, this study identifies four case studies to illustrate varying levels of alignment between PLC discussions and classroom implementation. Findings emphasize the importance of sustained, flexible professional development and coaching that integrates collaborative learning into consistent classroom practice. Distributing cognitive demands through coaching and PLCs strengthens inquiry-based instruction and enhances science teaching in diverse educational contexts. This study offers insights for educators, school leaders, and policymakers aiming to advance student-centered science education.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".