Making spaces in professional learning for democratic literacy education in the early years
Bibliographic record
Abstract
Introduction This chapter provides narrative illustrations from Ontario, Canada, of a case study of professional learning to support early primary teachers (that is, teachers of children aged 3.8– 8 years) in designing and implementing multimodal pedagogies. We offer these illustrations as a resource for hope (Williams, 1966), or a way of thinking about teacher professional learning that can support rich, meaningful and democratic literacy education in an era when neoliberal discourses dominate. Specifically, we present the example of teacher-participant ‘Esther’s’ first-grade classroom that was unruly and unfocused, and then transformed into a hive of innovative, multimodal pedagogies and practices. We detail how the professional learning introduced new pedagogical repertoires, materials and ideas into the classroom, providing resistance to reductive literacy pedagogies. The changes promoted connected, purposeful literacies, joyful engagement in learning, and new relationships between children, teachers, materials and their meaning-making. We frame these findings within a view of professional learning that defies neoliberal approaches to professional development aimed at increasing test scores while deprofessionalising teachers. Context of the study The study took place in Ontario, Canada. Governance of education in Canada is a provincial/territorial responsibility. As the most populous province, Ontario is a potentially powerful influence in the country. In terms of literacy education, Ontario is also a global force, with Allan Luke identifying Ontario as an example of successful curricular reform (Literacy Research, 2018) and the British Broadcasting Corporation naming Canada an ‘education superpower’, pointing to Ontario's ‘strong base in literacy’ as part of the reason for its potency (Coughlan, 2017). There is evidence that Ontario's lauded literacy education is entangled in neoliberal discourses that limit spaces for teachers to privilege children in curriculum-making (Schwab, 1973), to support the expansion of children's literacy options (that is, the opportunities that children have to be active makers of meaning and curriculum through engagement with multiple modes, media and genres) (Heydon, 2013), to exercise their professional discernment in developing and enacting literacy-related pedagogies (Hibbert and Iannacci, 2005), and to participate in professional learning that supports teachers to enact these (Hibbert et al, 2013). We understand curriculum-making (Schwab, 1973) as the process of forging responses to questions such as what should be learned and how, and agree with the literature that centres children and teachers in this process (for example, Harste, 2003).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.026 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".