EFL College Students’ Attitudes towards Mobile Learning
Bibliographic record
Abstract
Recently, cell phones have received much attention in the context of EFL/ESL learning. Mobile learning, in general, and distant learning, in particular, in educational contexts has been approached by educationalist all over the world (Hwang & Chang, 2011). Presently, countries pay ample attention to mobile learning in education. Despite the fact that devices such as cell phones might divert students’ attention, yet, no one can deny their importance as high-tech educational tools. This study investigates EFL college students’ attitudes towards cell phones learning. For the sake of satisfying the study’s objectives, a questionnaire has been designed and randomly distributed to 300 female undergraduate students enrolled during the First Academic Term (2014/2015). The questionnaire is consisted of 3 sections and 39 items. Section 1, students’ attitudes towards the usefulness of mobile learning (26 items), section 2, investigation of students’ opinions towards reasons where and why they and their instructors use cell phones (12 items), and section 3, an open-ended question, asking students if they have any comments on the importance of cell phones (1 item). The questionnaire consisted 5-Point Likert type scale. Data were quantitatively analysed using SPSS, and ANOVA tests. Percentages, means, and standard deviations, were used for the sake of the analysis. The open-ended question was analysed qualitatively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".