Stitching Together a Teacher's Body of Knowledge: Frankie N. Stein's ePortfolio
Bibliographic record
Abstract
In this article the authors report on research into how an ePortfolio (eP) process can address the critique that teacher education programs offer fragmented course experiences and too often focus on narrow instrumentalist approaches emphasising the ‘how to’ and the ‘what works' — implying that learning how to teach is about stitching together separate pieces of knowledge transmitted in an array of teacher education courses. In contrast, the authors believe that an eP process, systematically developed within a teacher education program, can create a complex and self-renewing system that grows from both individual and programmatic assessment of student learning. Using the eP entries of 45 elementary pre-service teachers and interviews with eight graduating pre-service teachers, they have crafted five ethnographic fictions. These narratives, drawing on themes generated in the data analysis, offer an insight into the lived experience of being a pre-service teacher in a teacher education program that uses an eP practice. Using a complexity theoretical lens the authors show how the eP process creates the conditions that enables pre-service teachers to communicate reflective thinking about teaching as they develop an understanding of learning and learners in emergent ways. The authors show how the eP process enables pre-service teachers to form a personal and collective sense of their forming teacher identity from course and practical experiences that can be integrated into an inter-connected sense of becoming a teacher.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".