EFL Female Students’ Perceptions towards Cheating in Distance Learning Programmes
Bibliographic record
Abstract
Distance education is becoming more demanding at universities all around the world in general and in the Kingdom of Saudi Arabia (KSA) in particular, than ever before. This study aimed at highlighting the students’ perceptions of cheating on distance learning at King Abdulaziz University (KAU). The study will further explore the ways they use to cheat, causes for cheating, and some solutions to minimize cheating. This study was conducted at the end of the second semester in 2019. Data was collected from 57 female distance learning students who graduated from the English Language Department (ELD) at KAU. A mixed-methods research design was adopted in this study via a custom designed, fifteen closed ended and one, open ended item questionnaire. The quantitative section was analysed by frequency and percentages and the open-ended questions were analysed thematically. The results indicated that the majority of female students cheated by helping each other, obtain the correct answers from another student or send the correct responses to all their classmates. Another way which some students admitted they used was using electronic websites to copy and paste the answers in the test’s screen. The study further concluded that reasons for cheating in distance learning programs were due to technical problems, frequent absence of virtual classes, and wanting grades, not necessarily knowledge. Recommendations for the possible solutions to eliminate cheating, the participants recommended increasing the students’ awareness of cheating policy and finding solutions to technological issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".