Knowledge Attitude and Practice of Students Towards Online Communication in EFL
Bibliographic record
Abstract
Online learning refers to education that occurs through the Internet using technological tools such as tablets, smartphones, laptops, and PCs. This study intends to determine how structural equation modeling (SEM) can be used to evaluate the adequacy of the variables included in the knowledge, attitude, and practices (KAP) model concerning online communication in English as a foreign language (EFL). An exploratory study was conducted with 112 female undergraduate English language program (ELP) students at the King Khalid University of Saudi Arabia. A KAP questionnaire was distributed to those students using a Google form. The data analysis was carried out using SPSS version 20. The SEM analysis was conducted using Analysis of Moment Structures (AMOS) software version 5.0. The results showed that the mean score of KAP toward online communication in EFL was 3.78, 3.90, and 3.70, respectively. The mean practice score is lower than the mean knowledge and attitude score. The SEM analysis demonstrated that the variables used in the KAP model are positively related (p<0.05). Variables used in the KAP questionnaire are adequately fit to evaluate the ELP students’ KAP toward online communication in EFL. Hence, policymakers can utilize these variables to assess the students’ KAP toward online communication in EFL at HEIs. The findings of KAP can aid universities in framing and implementing appropriate strategies to motivate online communication and enhance the student’s English language skills.
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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 | 0.003 | 0.012 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".