Students’ Perceptions of Autonomous Out-of-Class Learning through the Use of Computers
Bibliographic record
Abstract
This study investigates the attitudes towards, and practices of, computer-assisted autonomous learning in learning English of 160 students from three different higher education institutions in China. To do this, a questionnaire was completed by 160 participants, and follow-up in-depth interviews were undertaken with six participants and six of their teachers. The results from the findings and data analysis demonstrate students’ attitudes towards computer-assisted autonomous English learning. Furthermore, the students have a positive view of computer-assisted autonomous learning. Also, it is believed that, with the development of Information Technology (IT), some English language learning problems, such as inefficient learning strategies and limited oral and listening ability that English teaching in China has faced for many years, may be solved. Finally, both the students and the teachers have made favourable comments on the effectiveness of computer assisted language learning, which is more effective than other ways to learn English. Based on the findings of this study, some main implications are presented. Recommendations are also made for enhancing teacher training, updating English coursebooks with relevant websites and investing more funds in learning facilities for higher education students.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".