The Effect of Mobile Learning on Students’ Reading Self-Efficacy: A Case Study of the APP “English Liulishuo”
Bibliographic record
Abstract
A number of studies have been conducted regarding self-efficacy in the field of foreign language learning. Yet, with the popularity of mobile learning, research on the relationship between mobile learning and self-efficacy in this field is still limited. To bridge the gap, the study aims to investigate the effects of mobile learning on students’ reading self-efficacy, i.e. whether the use of mobile learning can improve students’ English reading self-efficacy. A questionnaire is employed to collect data from 294 non-English major students in universities. To survey the effect of mobile learning on students’ reading self-efficacy, the data is accessed by the software SPSS 20.0. Results of independent T test demonstrate that for overall students, reading self-efficacy for students who have used the app is significantly different from those who haven’t in overall reading skills and in the four dimensions of reading skills, i.e. basic reading skills, applied reading skills, reading task skills, and advanced reading skills. As for students with relatively better reading performance, the results are consistent. However, for students with relatively weak reading performance, the reading self-efficacy of students who have used the app only shows significant differences in overall reading skills and in the two dimensions of basic reading skills and applied reading skills, but shows no difference in the dimensions of reading task skills and advanced reading skills. Finally, practical suggestions for mobile learning and students’ English reading are given.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".