Educational and Cybersecurity Applications of an Arabic CAPTCHA Gamification System
Bibliographic record
Abstract
A ubiquitous challenge-response mechanism, the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), primarily serves to distinguish between human users and automated bots.The presented work introduces an innovative Arabic CAPTCHA gamification system designed to concurrently address two critical aspects.The first aspect is centered on the pedagogical application of the system, particularly its employment in teaching young learners the Arabic alphabet.This is achieved by posing interactive queries based on displayed word images, thereby facilitating the practice and enhancement of Arabic letter recognition and typing proficiencies.The second aspect integrates a cybersecurity awareness component into the system.As learners engage with the game and advance through its levels, they are concurrently exposed to pertinent information and guidance regarding cyber threats and safe online practices.This dualpurpose approach serves to inform and empower learners, providing them with the necessary skills to navigate the digital landscape securely.The novelty of this work lies in the fusion of these two aspects, offering a uniquely comprehensive learning experience that not only bolsters language skills but also cultivates cybersecurity awareness, a critical facet of digital literacy in our increasingly interconnected world.To the authors' knowledge, this work represents the inaugural implementation of an Arabic CAPTCHA gamification system, making it an advantageous resource for anyone seeking to learn Arabic letters and word formations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".