Perceptions of Digital Academic Dishonesty in English Writing at Applied Colleges
Bibliographic record
Abstract
Since the last decade, English instructors have often found digital academic English writing dishonesty (Tayan, 2017) among Saudi EFL graduating students due to the serious issues of adapting the online ghostwrites and tools, fabricating the English writing practices (Al-Khairy, 2013), plagiarism (Madkhali, 2017; Jenkins, 2018), neglecting proper citation and referencing, a lack of critical thinking and analysis, limited vocabulary and weak language proficiency (Fareed et al., 2016, Alsowat, 2017), and inadequate grammar and syntax, all of which affect their English writing skills ethically. However, such serious issues and digital factors devalue the hard work of honest students and negatively impact the integrity of the educational system. Therefore, this research attempt investigated critically the perceptions of digital academic dishonesty by Saudi EFL students in English writing at the Applied Colleges of King Khalid University. The objective of this study was to examine the awareness and understanding of digital academic dishonesty among diploma students enrolled in applied colleges. A descriptive quantitative analysis of 134 students' questionnaires was conducted, and data was gathered regarding their perceptions of academic dishonesty and such mediums by which students take part in academically dishonest activities. Also, the study explored the prevalent types of digital academic dishonesty that Saudi students engage in and the causes behind such behaviors. The investigation identified such common types of digital academic dishonesty as plagiarism, unauthorized collaboration, utilizing essay mills, and falsification of sources, among others. By investigating the motivations behind such behaviors, the study, therefore, aimed to shed light on suggestions to reduce digital academic dishonesty in the context of English writing assignments.
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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 | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".