Negotiating Multiple Literacies: Student Teachers' Conceptions, Experiences, and Practices
Bibliographic record
Abstract
This longitudinal and qualitative study makes an important contribution to literacy teacher education because it highlights the need for more systematic research to examine how the concept of multiple literacies is engaged in teacher education to prepare beginning teachers for the demands of contemporary literacy teaching. This study examined how a group of middle school student teachers constructed conceptions of literacy, enacted literacy pedagogy, and negotiated their role as teachers of literacy, over two years of teacher education studies. Data collection methods included questionnaires (n=22), four stages of semi-structured interviews with a purposive sample of eight student teachers (32 interviews), and document analysis. Findings suggest the student teachers entered teacher education with restricted notions of literacy due in large part to their school literacy histories. While the student teachers participated in a rich array of literacy practices in their out-of-school interactions (e.g., home, community, peer), their initial conceptions of literacy were largely grounded in the autonomous model of literacy they encountered in their prior schooling (Street, 1984). However, as the student teachers entered into conversation with the broader field of literacy (e.g., multiliteracies, critical literacy, out-of-school literacies) in their literacy methods courses, their conceptions of literacy steadily broadened and they developed a more nuanced understanding of literacy pedagogy. They came to recognize the need for contemporary literacy pedagogy to foster students' critical navigation of diverse texts and digitally mediated technologies. Moreover, the student teachers who had the opportunity to teach literacy during practice teaching sought to translate their expanded conceptions of literacy to the middle school classroom. This research identified several factors that facilitated student teachers' expanded conceptions of literacy including: specific course content (e.g., understanding literacy as a situated social practice), particular pedagogies (e.g., integration of digital technologies), and having ample opportunity to observe and enact a multifaceted approach to literacy teaching during practice teaching. This research contributes to our understanding of the role literacy teacher education plays in the lives of beginning 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".