Undergraduate Students’ Willingness to Communicate in English during Remote Learning Classes
Bibliographic record
Abstract
Objectives: This study examines Jordanian undergraduate students’ willingness to communicate (WTC) in English during remote learning. Methods: The data were collected using a 61-item Likert scale questionnaire from 285 undergraduate students (118 males and 167 females) studying English at a public university. All the responses collected were analysed using descriptive analysis (e.g., mean, standard deviation) and Pearson’s Correlation coefficient. Results: The results of the descriptive analysis showed that students had an overall moderate level of online experiences (M 2.98, SD 0.98), and high levels of WTC (M= 3.61, SD= 0.969), self-perceived communicative competence (M=3.62, SD=1.038), L2 communication anxiety (M=2.91, SD=1.156) and virtual intercultural experience motivation (M=3.5, SD=1.111). On the other hand, the results of the 2-tailed correlation revealed that there was a statistically medium-positive correlation between students’ online experiences and their WTC in online classes (r=.359, p<.001), a strong positive correlation between students’ self-perceived communicative competence and their WTC in online classes (r=.664, p<.001), an insignificant low positive correlation between L2 communication anxiety and their WTC in online classes (r =.031, p < .605), a strong positive correlation between students’ virtual intercultural experience motivation and their WTC in online classes (r = .535, p<.001). Conclusions: The study shows that despite the lack of experience in e-learning, the students tend to have good self-perceived communicative competence. Pedagogical implications and suggestions for future studies are given based on the results of the study.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".