A Case Study to Foster the Individual-level DMC Through the AI-assisted Study Abroad Framework: A Phenomenological Approach
Bibliographic record
Abstract
Motivation is a complex dimension in language learning; however, the Directed Motivational Current (DMC) was recently introduced as a dynamic construct addressing engagement in long-term goal/vision for weeks, months, or years, rather than being limited to a single activity. This study sought to investigate the lived experiences related to enhancing individual-level DMC through the AI-assisted Study Abroad Framework from a phenomenological perspective. To this end, a longitudinal single-case design was used. The participant, an Iranian female migrant to Canada, was selected through purposive sampling. This longitudinal case study was conducted over two years. For data collection, semi-structured interviews, teacher-as-researcher observations, motometers, composite data display charts, and the DMC disposition scale were used. Data analysis was conducted using a phenomenological approach to capture diverse insights and lived experiences throughout this longitudinal study. The results showed that the Study Abroad Framework was practically effective in developing the core characteristics of the DMC construct. Moreover, by triangulating datasets, particularly motometer data, it was shown that the AI-assisted approach used in this study accelerated the development of the DMC construct by re-energizing the participant’s engagement in activities related to the three core components of DMC. The results have implications for teachers, practitioners, and curriculum planners seeking long-term learner engagement.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".