L’autorégulation conjointe de la cognition et des émotions : quel impact sur les apprentissages ?
Bibliographic record
Abstract
Les apprentissages consistent à résoudre des problèmes et à acquérir de nouvelles connaissances et compétences par le biais d’un ensemble de processus relevant de l’autorégulation. Deux aspects principaux rentrent en ligne de compte lorsque l’on cherche à améliorer la résolution de problèmes : la dimension émotionnelle et la métacognition. Les émotions, en tant que réactions organisées et utiles à une situation donnée, peuvent être tour à tour un atout ou un handicap lorsqu’il s’agit d’apprendre. Par ailleurs, la métacognition est constituée d’un ensemble de processus et de savoirs qui s’articulent autour de la prise de conscience et de la régulation de son propre fonctionnement, qu’il soit cognitif ou émotionnel. Grâce aux pratiques de l’attention (PA), issues de traditions permettant un travail sur la conscience et la régulation psychologique et physiologique, il est possible d’agir conjointement sur les cognitions et les émotions. Plusieurs travaux ont montré les nombreux bénéfices que présentent de telles approches et nous constatons également que les effets positifs sur l’autorégulation commencent à être de plus en plus étayés. Nous proposons donc de nouvelles approches holistiques permettant un travail global sur l’autorégulation qui prendraient en compte le traitement métacognitif des sphères cognitive et émotionnelle au bénéfice des apprenants. Simultaneous self-regulation of cognition and emotions and its consequences on learning Abstract: The learning process relies on problem-solving activities and the acquisition of knowledge and skills through self-regulation. Emotions and metacognitions are some of the key aspects that allow the improvement of problem-solving. The emotional dimension consists of structured and useful reactions in regard to a specific situation. Emotions can either be an asset or a disadvantage when one is involved in a learning situation. As for metacognition, it’s a compound of processes and knowledge (of cognitive or emotional nature) connected through self-regulation and self-awareness. Thanks to attentional practices (AP), one can regulate both cognitions and emotions. These AP come from various traditions focused on the exploration of the mind and self-regulation of psychological and physiological activities. Many studies show the positive effects of such practices on health, and some recent studies also report improvements in self-regulation thanks to AP. In this paper, we suggest that the creation of new holistic approaches would allow us to work on metacognition and emotions on a global scale, in order to improve the ability of individuals to engage in self-regulated learning efficiently.
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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 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".