A Case Study of College Students’ Attitudes toward Computer-Aided Language Learning
Bibliographic record
Abstract
Nowadays computers are becoming smaller and more powerful, they are put into use in many areas, and one of the important implementations is the assistance with language learning, which is called CALL. In China, college English teaching has experienced lots of frustrations and difficulties. At the end of 20th century, the mode of CALL gradually appeared in China, but it was still immature and not systematic. At the beginning of 21th century that CALL was carried out extensively in China. During this period, many universities and colleges have tried experiments on online learning and many of them have got quite fruitful achievements. Henan Polytechnic University is one of them. After careful consideration, the university decided to adopt the English online learning system produced by Higher Education Press to carry out a comprehensive college English teaching reform in the non-English majors’ students, who are freshmen and sophomores. In the four academic semesters, students have 4 periods of classroom-based learning and another 2 periods of online learning every week. So far, the university has worked on the experiment for 10 years, and now, it is the high time to check the students’ attitudes toward English online learning. Only in this way can we get the valuable first-hand suggestions to improve online learning mode.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".