Autonomous learning in a CALL EFL classroom: an exploratory case study
Bibliographic record
Abstract
The increasing use of both computers and the Internet in universities and other Higher Education institutions in recent decades has had widespread effects on the EFL education programs (Kim, 2008; Terrill, 2000). The appearance of new forms of digital media, online learning communities, science simulations and electronic software presents new learning opportunities for EFL learners, which do not require the constant intervention of a teacher or which can be pursued outside the framework of a formal educational institution (Reeder, 2012; Warschauer, 1996). Computer and internet technologies are in many ways driving self-directed approaches to learning. We are starting to see a change in our understanding of self-directed learning as a set of specific abilities to access and effectively employ different learning environments with technology playing an important facilitative and enhancing role. The focus of this study is materials analysis of a Self-Study Listening Project (SSLP), which was implemented in the context of CALL EFL class at a Japanese university. This study uses an exploratory case study approach to address its two questions: (1) is SSLP, as documented in the CALL-course syllabus, likely to promote autonomous learning inside and outside the classroom? And (2) is SSLP, as documented in the CALL-course syllabus, likely to promote the development of the EFL listening skills? The materials analysis of SSLP revealed two main findings: (1) SSLP is likely to have impact on development of the interdependent autonomous learning skills of students; (2) SSLP is likely to have impact on development of the listening skills of students; a balanced strategy approach to listening instruction was taken in the course of implementation of SSLP; top-down and bottom-up approaches to listening were utilized; the use of various audiovisual materials coincided with multiple cognitive, metacognitive, and socioaffective activities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".