Teens-Online: a Game Theory-Based Collaborative Platform for Privacy Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays, privacy education plays an important role in teenagers’ lives. Since this domain is strongly linked to their social life, it is preferable to provide a collaborative learning environment that teaches privacy, and at the same time, allows students to share knowledge, to interact with each other, to solve quizzes collaboratively and to discuss privacy issues and situations. To this end, we propose “Teens-online”, a collaborative e-learning platform for privacy awareness. The curriculum provided in this platform is based on the International Competency Framework on Privacy Education. Moreover, the proposed platform is equipped with a partner-matching mechanism based on matching game theory. This mechanism guarantees a stable student-student matching according to the student’s need (behavior and/or knowledge). Thus, mutual benefits will be attained by largely minimizing the chances of cooperating with incompatible students. Experimental results show that the average utility obtained by applying the proposed algorithm is much higher than the average utility obtained using other matching mechanisms. The results suggest that by adopting the proposed approach, each student can be paired with their optimal partners, which in turn can help them to engage more in learning activities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it