Bibliographic record
Abstract
Abstract: Motivating students is a major issue for current Intelligent Tutoring Systems. Modern theories of motivation (such as the Self Determination Theory) have shown the positive motivational impact of autonomy-support. Following this idea, we have proposed in a previous work an autonomy-supportive motivational design for eLearning systems. Later, the importance to take learner’s culture into account appeared obvious to us if we wanted to correctly enhance/maintain the learner’s motivation. Both these findings resulted in an ITS called MOCAS (Motivationally and Culturally Aware System). MOCAS is basically composed of a virtual world in which several pedagogical agents with different roles, behaviors and knowledge cooperate to provide a motivational (i.e. autonomy-supportive) and culturally adapted teaching to learners. In the real world, pedagogical behaviors frequently determine whether a preceptor will be well accepted/respected or not by his/her pupils (and in many cases, this rating will be culturally dependant). But whatever preceptors are rated, they can not change from day to day what they truly are and how they behave. In Intelligent Tutoring Systems, pedagogical agents could have this ability to dynamically evolve. In this paper we define how to genetically adapt the crowd of pedagogical agents that is inside our MOCAS. This process allows the production of generations of pedagogical agents whose behaviors are more and more fitting the learners’ motivational and cultural needs.
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 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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".