Teens-Online: a Game Theory-Based Collaborative Platform for Privacy Education
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".