TEACHERS’ EXPERIENCES OF RE-ENGAGING DISENFRANCHISED YOUNG PEOPLE IN LEARNING THROUGH INQUIRY-BASED PEDAGOGIES: A PHENOMENOGRAPHIC STUDY
Bibliographic record
Abstract
The potential of inquiry-based pedagogies to improve the learning outcomes of students has gained some support in the literature. In Australia, a small group of schools has adopted inquiry-based pedagogies that foreground students’ interests in order to re-engage young people who have been disenfranchised from schooling, and also to provide an alternative education for those seeking a more responsive form of educational provision. This paper reports on a phenomenographic analysis of the experiences of teachers in these schools, which was part of a larger study that included interviews with students and their parents or caregivers, classroom observations, and documentary analysis. In developing their curriculum, participating schools drew inspiration from a U.S. organization, Big Picture Learning; hence, a core element involved students learning through their interests (LTI), a feature of Big Picture schooling. The building of inquiry skills, for both students and teachers, is foundational to the success of LTI. This paper aims to contribute to more responsive education provision for disenfranchised young people by documenting the range of variation in teachers’ experiences when implementing such a curriculum. We also consider the implications for sustainability of this approach in terms of the intellectual, physical, and emotional demands made on teachers.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".