Narratives of Learning: The Personal Portfolio in the Portfolio Approach to Teaching and Learning
Bibliographic record
Abstract
This paper will explore how a portfolio approach to teaching and learning can help the educator incorporate unique forms of reflective practice into his or her daily work. By being able to express ideas more clearly to himself, the educator can better promote the relational construction of knowledge in his educational communities. This paper, as part of a larger body of research asks, how can a portfolio approach to teaching and learning help the educator develop unique forms of reflective practice that will help him express his ideas more clearly, first to himself and then secondly to his educational communities? Research methodology is primarily participatory action research and includes an autoethnographic review of the author’s work, reviews, interviews, observations, and focus groups with student teachers and professional teachers in the United Arab Emirates. The research concludes that in consideration of McLuhan’s (1964) notion that the “medium is the message,” the interactions that arise through the use of new media tools can lead us to relational, co-constructed ideas that are not those simply passed on from other texts. By making our thinking visible, the portfolio approach allows the educator to capture the contextual relationship between the author, the audience or community, and the knowledge being created.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".