Promoting Professional Conversations and Reflective Practice Among Educators: Unpacking Our Portfolios Using Duoethnography
Bibliographic record
Abstract
The use of portfolios is becoming more and more prevalent in university and college programs, especially professional programs such as medicine, nursing, and education (Jones, 2010; McCready, 2007; Ryan, 2011; Woodward & Nanlohy, 2010). Portfolio assessment requires students to demonstrate their competency and learning over time through the use of various artifacts and reflections. As researcher-participants, we employ duoethnography, a relatively new qualitative method (Sawyer & Norris, 2013), to examine our personal experiences with portfolio development and assessment. Using electronic and blended methods of communication, we engage in reflective practice as we examine the professional portfolios that we created in our respective teacher education programs. Throughout this chapter, we demonstrate how engaging in hybrid conversations, conducted primarily online by email, but also in-person, by text messaging or phone, can serve as a worthwhile approach for facilitating professional conversations and promoting continuous professional learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".