A Longitudinal Understanding of an International Practicum through the Disruptive Learning Narrative Framework
Bibliographic record
Abstract
The pedagogical cornerstone to any teacher education program is the practicum experience. The purpose of this critical ethnographic study was to explore the longitudinal effects of a three-week international teaching practicum on teacher candidates in Kenya, which occurred 5-6 years earlier. We applied the Disruptive Learning Narrative (DLN) framework to our longitudinal study to elucidate an understanding and critical perspective of the intense and uncomfortable experiences on the international practicum (Sharma, Allen, & Ibrahim, 2017). Sixteen of the 46 teacher candidates responded to an email invitation to participate in a semi-structured interview. Disruptive Learning Narrative (DLN) Framework, composed of three overlapping elements, facilitates understanding of teacher candidates’ lived international experiences. The “DLN unearths the covert tensions underlying the teacher education program and lays them bare for analysis in our teacher educator observation and narratives” (Sharma, Allen, & Ibrahim, 2017, p. 29). Based on our analysis, that while the DLN framework lays bare the disruptive experiences of an international practicum, what lays beyond is a consideration of overcoming limited transformation across participants.
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 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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".