Fostering Autonomous English Language Learners : Reflections on Four Years of Practice at a Japanese National University
Bibliographic record
Abstract
The authors have designed a credit-bearing English self-directed learning (SDL) course for incoming first-year students at a Japanese national university to foster their language learning autonomy. There have been three iterations of the course from 2016 to 2018 with about 60 students per iteration. Major course design components include: (1) introduction to SDL concepts and scaffolding of SDL skills in the first quarter through four sets of tasks [speaking, listening, reading, CALL] based on goal-orientedness and CEFR can-do principles, (2) development of autonomous goal-setting skills in the second quarter through student-generated task activities, (3) self- and peer-assessment of student-generated tasks, and (4) class-by-class reflection on selfprogress through a student language portfolio hosted on an online learning management system. The authors will introduce the core concepts of the course including the logistics in creating and implementing this course. Also, they will share student reflections on their
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".