Learning by challenging: A social network and privacy based approach
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
Considerable research in the field of Intelligent Tutoring Systems (ITS) has focused on helping students increase learning by taking advantage of technological progress. As the use of social media by learners continues to increase, however, ITS should go beyond teaching based on isolated environments (platforms) and turn toward community-based learning within social networks. In this paper we introduce a new approach to learning based on social networks. This approach takes advantage of the increasing enthusiasm among learners for spending time in social networks. The goal is to use some of that time for learning, by replacing one of the various social game applications with an Intelligent Tutoring Systems (ITS). During a learning session, learners are encouraged to improve their scores by challenging either a score predefined by the system or the scores posted by their Facebook friends. In this paper, we describe a new system called LBC (Learning By Challenging) that enables the user to learn and to share their knowledge and resources in a social environment. It also provides an environment that protects the learner's privacy if he or she so desires.
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.
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 | 0.000 |
| 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 | 0.000 |
| 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