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Enregistrement W4391599320 · doi:10.18260/1-2--43520

Making Learning Fun: Implementing a Gamified Approach to Materials Science and Engineering Education

2024· article· en· W4391599320 sur OpenAlexafffund
Shayna Earle, Liza‐Anastasia DiCecco, Dakota M. Binkley, Muhammad Arshad, Andrew Lucentini, Gerald Tembrevilla, Bosco Yu

Notice bibliographique

Revuenon disponible
Typearticle
Langueen
DomainePsychology
ThématiqueEducational Games and Gamification
Établissements canadiensMcMaster UniversityUniversity of VictoriaMount Saint Vincent UniversityUniversity of British ColumbiaNatural Sciences and Engineering Research Council of Canada
Organismes subventionnairesMcMaster University
Mots-clésComputer scienceEngineering educationMultimediaMathematics educationHuman–computer interactionEngineering managementEngineeringPsychology

Résumé

récupéré en direct d'OpenAlex

Abstract Materials science plays a critical role in educating future engineers, where knowledge of materials selection is essential for design and problem-solving. However, many programs rely on traditional lecture styles to convey this fundamental knowledge. While these teaching styles can be effective, they provide little opportunity to actively engage and expose learners to memorable experiential learning elements. The COVID-19 pandemic presented a new opportunity to focus on developing unique teaching tools to reach students on virtual platforms. Although the development of these tools was critical in today's technology-driven society, pandemic teaching and learning remained challenging, which likely contributed to the amplification of virtual gamified learning. In redesigning our first-year engineering curriculum within the Faculty of Engineering at McMaster University into the new Integrated Cornerstone Design Projects in Engineering (ENG 1P13) course, an opportunity to re-evaluate our teaching approach was presented, which allowed us to further explore ways to increase student engagement and learner creativity. This work focuses on the introduction of a gamified active-learning approach to teach materials science within the first-year curriculum. The purpose of this intervention was to enhance the learner experience to demystify the fundamentals by connecting theory to practice. Although pedagogical literature highlights the effectiveness of gamified learning strategies to enhance the learning experience, limited examples were found within the materials science and engineering fields. In this work, two types of materials science games along with other interactive lab components were successfully implemented in an adaptable fashion for in-person and virtual teaching styles for over 1200 learners. The first type is adapted based on popular board games in efforts to design relatable understandable games such that the students could focus on learning the new materials and not the game rules, "Materials Battleships", and "Materials Taboo", where gamified strategies are incorporated to introduce students to materials properties and materials selection. The second involves the design of custom virtual emulators that challenge learners to explore the mechanical and electrical behaviour of materials. The games challenged learners to explore various materials and science concepts in a fun way. Our survey responses from participating students were used to evaluate the approach; these findings highlight that gamification stimulated students' interest in material science and motivation to participate. While the majority of students surveyed found the new activities enjoyable, the results also indicate higher learning engagement and increased interest in materials science for upper-level stream selection choice after the open first year. The analysis of these surveys targets what factors were effective in increasing engagement as well as effectiveness in teaching content. The success of gamified learning for material science coupled with the targeted data for improvement and adaption creates a space for significant improvement in the material science curriculum.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,757
Score d'incertitude au seuil0,316

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
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 savante0,0000,000
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 Opus0,048
Tête enseignante GPT0,372
Écart entre enseignants0,325 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
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

Citations5
Publié2024
Routes d'admission2
Résumé présentoui

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