Reading, Writing and Pedagogy: The Impact of Ontario Education Faculties' P/J Programs on Graduates' Knowledge and Ability to Teach
Bibliographic record
Abstract
The purpose of this study was to determine the impact of Ontario’s Primary/Junior teacher preparation programs on graduates’ knowledge and ability to apply what they know about literacy education. The research examined the content of the programs, the extent to which this content reflected evidence-based components of literacy instruction, and the degree of variability across programs. A range of strategies including self-reporting by graduates, interviews, and reviews of course materials (e.g., course outlines, course topic schedules, reading lists) were used to examine the breadth and depth of what was covered in the required literacy courses. A proportional sample of 210 graduates representing all nine English-speaking faculties of education in Ontario completed surveys and submitted course materials. Twenty-nine of these also participated in in-depth interviews. Interviewees were asked to reflect upon their understanding and ability to apply what they had learned, as well as to offer their impressions of their programs and to discuss their personal feelings of preparedness to teach literacy. Quantitative and qualitative research methods were used to describe and summarize findings. Process/Outcome Matrices revealed various themes. In summary, faculty literacy programs varied greatly with respect to both length and content. Course materials and descriptions from graduates also suggested that variability across sections within programs was as great as that across programs. Although courses covered theories of child development they generally lacked a theoretical framework for literacy instruction. Moreover, many respondents reported learning little about how to teach reading and writing. Such responses were more prevalent in certain programs. Many graduates believed that explicit instruction is a necessary component of “good” literacy teaching, however, most graduates were not able to demonstrate an understanding of the complexities of language and literacy, and many could not describe how to implement effective literacy instruction, or to address the diverse needs of students. Recommendations include: lengthening the required faculty literacy courses in order to provide more breadth and depth of coverage; refining the manner in which instruction in literacy education is provided at faculties; increasing the vigilance with which governing bodies oversee faculty literacy course content; and improving practicum placement experiences.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".