Supporting preservice teachers to navigate the tensions of teaching English Language Arts: An examination of two teacher educator's literacies pedagogies
Bibliographic record
Abstract
In 1997-1998, we taught in the same independently funded elementary school. As teachers of Grade 1 and Grade 2, the school leadership asked us to research and recommend a program to support early reading instruction and replace the outdated basal readers. Opinions were sometimes diverse as we met with parents and school board members to identify beliefs/priorities in early reading instruction. More than 25 years later, in our work as teacher educators in two provinces, we are embroiled in similar conversations about reading instruction in a field mapped by polarizing views expressed in reports, social media, and teacher professional texts. As teacher educators, we recognize: 1) this context as challenging for preservice teachers to enter professional practice and 2) our role in supporting preservice teachers in entering the field. This was the impetus for a research study examining our pedagogies. We ask: How are we, as teacher educators, supporting preservice teachers to navigate the theoretical and practical tensions of teaching Language Arts through our pedagogies? In this article, we share findings from a collaborative Self-Study of Teacher Education Practice (S-STEP) where we leverage our long-standing relationship as critical friends in research/teaching to examine our teacher education pedagogies in our elementary English Language Arts courses in 2022-2023. Data sources include teaching artifacts (e.g., syllabi, powerpoints, activities) and videorecordings of discussions as we share these artifacts over Zoom. Guided by posthuman perspectives, we consider the non-human and human entities that were a part of our pedagogies. Analysis is currently underway. Preliminary findings suggest a shift from sharing particular practices with each other to a focus on decision-making to support preservice teachers in weighing the desires of teaching practices in literacies as well as diffusing binaries. This article highlights teacher education practices as relational and moving in response to context.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".