Teaching Inquiry in Secondary School Science: Beliefs and Practice, Challenges and Program Support
Bibliographic record
Abstract
In spite of a multi-decade mandate to enact inquiry in science, research reports that a large gap continues to exist in Ontario between the vision of science education presented in curriculum documents and what is enacted in the classroom. A three-staged, mixed methods design was chosen to examine teachers’ beliefs and practices that contribute to an understanding of this longstanding gap in teaching practice related to inquiry.\nThe participants in this study were secondary school science teachers currently employed by one medium-sized, urban & rural district public school board. Quantitative data was first collected through a self-reporting survey designed to explore teachers’ beliefs related to teaching and learning in inquiry. Completed questionnaires were submitted by 80 % (n = 83) of the population of science teachers. Qualitative data, collected through semi-structured interviews (n = 17), were used to confirm and expand the quantitative findings.\nQuantitative analysis resulted in the development of an empirical framework to illustrate the dimensionality of teachers’ beliefs and practices related to inquiry. Four types of science teachers were identified during qualitative analysis, each associated with a preferred type of inquiry and each identifiable by a cluster of beliefs. A stance was determined for each of these types of teachers representing their generalized view of teaching and learning related to inquiry including: utilitarian science, content-based science, authentic contextual science, and citizenship science. Additionally, each group of teachers could be associated with one of the four quadrants in my framework. Lastly, a beliefs profile was produced to represent each quadrant in this framework based on integration of the quantitative and qualitative findings.\nChallenges to enactment and types of program support to foster enactment of open-ended inquiry were identified by science teachers associated with each stance. A few of these challenges and types of program support represent newer areas for research that can inform educational leaders and teacher-educators and support decision-making so as to meet the diverse needs of both pre-service and in-service science teachers, thereby, fostering the enactment of open-ended inquiry as practical science.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".