Amorcage cognitif pour amelioration de l'acquisition de la connaissance dans un systeme tutoriel intelligent
Bibliographic record
Abstract
This thesis aims at defining a new learning method to improve knowledge acquisition for intelligent tutoring systems. Learning is a complex phenomenon interlinking both emotional and cognitive mechanisms on conscious and unconscious levels. We are interested in understanding the unconscious mechanisms involved in human reasoning for knowledge acquisition. The importance of these unconscious processes is well documented in neuroscience, but remains largely unexplored in our research field. In this thesis, we put forward a new pedagogical approach in the field of education based on a taxonomy of human perception in neuroscience. We show that this method improves on reasoning which in turn enhances overall learning and inductive capabilities for knowledge acquisition in a problem solving environment. In a first part, we present the implementation of our new method in a tutorial system to improve reasoning hence leading to better learning. Furthermore, acknowledging the importance of emotional mechanisms in learning, we therefore recorded, in this first part, the emotional reactions of users using physiological sensors. The effectiveness of our method for learning and its positive impact on emotions has been validated on 31 participants. In a second part, we go further in our research and enhance our approach in order to improve reasoning for a better induction of knowledge. Induction in a bottom-up logical reasoning approach where one constructs general rules from observed examples. To better understand the impact of our method on the cognitive processes involved in this type of reasoning, we used sensors to record the users’ brain electrical activity. The validation of our approach was carried out on a cohort of 43 volunteers. We showed the effectiveness of our method on the induction of knowledge and sustainability of measuring user’s reasoning by brain recordings after applying proper signal processing algorithms to the data. Following the two parts, we finish the thesis by presenting the implementation of a new intelligent tutoring system incorporating the results found throughout this work. Keywords : cognitive priming, unconscious cognition, intelligent tutoring systems, subliminal agent, emotion, brain, biometric
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".