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Enregistrement W2614078181 · doi:10.18260/1-2--6784

Situated Learning And Motivation Strategies To Improve Cognitive Learning In Ce

2020· article· en· W2614078181 sur OpenAlexaffabout
Alexandre Cabral, Rolland Viau, Denis Bédard

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueEvaluation of Teaching Practices
Établissements canadiensUniversité de Sherbrooke
Organismes subventionnairesnon disponible
Mots-clésSituatedSituated cognitionContext (archaeology)Situated learningTheme (computing)Mathematics educationPerceptionPedagogyPsychologyCognitionComputer scienceArtificial intelligenceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract SITUATED LEARNING AND MOTIVATION STRATEGIES TO IMPROVE COGNITIVE LEARNING IN CE Alexandre Cabral, Rolland Viau and Denis Bédard Université de Sherbrooke, Quebec, Canada Abstract This papers describes the results obtained and the main observations made during a year long research project whose main purpose was to integrate situated learning and some motivational tools in an undergraduate civil engineering course (Soil Mechanics I). New teaching material was developed almost from scratch around a main theme and several secondary themes. Oriented discussions and exercises were prepared in order to help the students acknowledge the new professional skills they had acquired. The motivational tools served as means to create a better teaching and learning environment in the classroom and in the laboratory. The response of the students was constantly monitored. The results show that the various activities strategically planned to motivate the students to become active learners and to situate them in the context of the practice of Civil Engineering had a positive effect on several aspects, including their perception the of the significance of the knowledge being acquired, of the reality of their future profession and of the importance of the tools they might need. Another significant increase relates to the perception the students ended up with of their capacity to transfer the knowledge acquired to other situations. 1. General problem Two of the challenges facing higher education, in particular professional education, are the capacity these programs have (1) to foster the students’ ability to use their newly acquired knowledge in other contexts such as practice training sessions and (2) to maintain the students’ motivation throughout each course. The programs should aim at encouraging the students to draw links between what they are learning in class and the more applied context of the profession for which they are being prepared. Many professors in universities and colleges orient their instruction so that the students achieve such a transfer of information. However, in regards to student learning and to capacity to use this knowledge, comparing goals to outcomes in terms of teaching does not present an encouraging perspective, especially in the context of professional training (Bédard and Turgeon, 1995). Higher education today is also struggling with students’ loss of motivation and engagement in the parts of the curriculum which tend to present a more abstract set of knowledge that cannot easily be linked to the profession. Students generally enter university programs with high expectations about the usefulness of the knowledge that will be acquired. What the students are offered in the first place are courses presenting abstract concepts, generally given in a lecturing format. Therefore, they tend to disengage from learning activities and from their study.

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.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,015
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,289
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,015
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,111
Tête enseignante GPT0,422
Écart entre enseignants0,311 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2020
Routes d'admission2
Résumé présentoui

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