Inquiry based learning: Exploring elementary years teacher candidate experiences in the University of Northern British Columbia bachelor of education degree program
Bibliographic record
Abstract
This qualitative study explored the learning experiences of Elementary Years teacher candidates in the Bachelor of Education program at the University of Northern British Columbia. Within the theoretical framework of Spirals of Inquiry (Halbert & Kaser, 2015), the main goal of the study was to answer the research question, “In what ways does embedding Inquiry-Based Learning into the UNBC teacher education program affect the Elementary Years teacher candidate experience?”. Using the extant professional literature, I made the argument for researching this topic and laid a strong literature-based foundation for Inquiry-Based Learning. Within a qualitative research paradigm and utilizing case study methodology, the three research methods, interviews, participant journals, and observation log, revealed 97 codes and five main themes: Inquiry-Based Learning, Practicum Experience, Teacher Educator Andragogy, Self Development, and Relationships. These five themes were supported by the professional literature. The study concludes with three primary recommendations and lessons learned from the literature.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".