Application Research of Computer-Assisted Technologies in EAP Module Learning
Bibliographic record
Abstract
Recent years have witnessed the rise of various computer-assisted technologies and the growing number of EAP modules in both English-speaking and non-English-speaking countries. Meanwhile, studies on computer-assisted language learning (CALL) have been prospering along the way. However, there has been by far few summaries on how these computer-assisted technologies lead to effective learning in EAP modules. The present study shows that while the link between computer-assisted technologies and learning outcomes has been made in previous scholarship in specific instances, it remains under-investigated how the role of computer-assisted technologies function to develop learning language skills and knowledge as a whole in EAP modules and how they are beneficial to the future professional development of the students. Further studies are necessary to explore how computer-assisted technologies are of benefit to learning in these two particular aspects by continuing exploration on how CALL is applicable in EAP modules that address learner professional needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".