Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
This study strives to understand how mathematical modelling is perceived by novice, intermediate and expert modellers, through comparing and contrasting their understanding and habits of modelling. The study adopted a qualitative methodology based on observations, interviews and surveys of 78 participants. This included 14 experts who are professors, 11 intermediates consisting of graduate students and post-doctoral fellows, and 53 undergraduates or novices. The study incorporated interviews of the professors and the post-graduate participants, while questionnaires were utilized to understand the perspective of the undergraduate students. The study revealed that the majority of expert participants see modelling as a collaborative effort. There is a dichotomy among them regarding whether mathematical modelling is the setting up of a mathematical model alone, which is deemed an art, or if it includes the solving of the model, which is more a science. These differences have implications on how modelling is taught and how novices and intermediates in turn will view the modelling process. Experts also vary in their opinion on whether models must be verifiable or not. One key feature of the experts approach is that they begin by assuming that they do not understand the question asked and work to ensure that they do. This is despite their superior ability to solve problems. Intermediate participants were more forth- coming with their emotions on modelling than experts; they cited research as opposed to collaboration as their primary means of dealing with barriers arising during the modelling process, and gave credit to intuition as a skill needed for solving - something not mentioned among the experts. Novices were the most descriptive about their feelings when modelling. They conveyed a tendency to be more passive when encountering barriers, waiting for help or giving up as opposed to actively working through the problems. Many of our results, including those mentioned above, have implications for the teaching of effective mathematical modelling.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle