Teacher experience with the accelerative integrated methodology (AIM) : a multiple case study
Bibliographic record
Abstract
This multiple case study explores different dimensions of the Accelerative Integrated Methodology (AIM) in an attempt to investigate how a primary English as a second language (ESL) teacher could implement it into her practice. This research examined how three French as a second language (FSL) teachers integrated AIM to their practice and identified the benefits and drawbacks of AIM on teachers and learners as they were perceived by the participants. The data was collected through a semi-structured interview conducted with each experienced candidate. Different artifacts (lesson plans, video excerpts, etc.) were also collected to help illustrate and understand each case. The results emerged following an inductive thematic analysis and a document analysis. The findings show many similarities and discrepancies between how the participants chose to include AIM in their classes and about their opinions regarding the programme. Many links could also be made between this study and previous research on AIM. However, additional studies on the subject are encouraged to supplement the literature about AIM, especially to explore the use of AIM in ESL contexts across the province of Quebec, the effects of AIM on learners with different educational needs, and to uncover how AIM could better address different cultural backgrounds.
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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".