Investigating Students’ Development of Computer-Aided Design Self-Efficacy: An Analysis of Pre-Course CAD Exposure
Notice bibliographique
Résumé
Abstract With the increasing demand for new and innovative technologies, engineers are called on to be at the forefront of designing new products. As a result, undergraduate engineering programs must equip students with both technical skills and internal beliefs that they are capable of success in the profession post-graduation. For mechanical engineers, knowledge of computer-aided design (CAD) software is an invaluable skill in order to contribute to product development in a wide variety of industries. However, students at the undergraduate level enter university with varying levels of knowledge and beliefs in their capabilities of using CAD software. Therefore, there is currently a lack of research investigating how students develop self-efficacy in relation to CAD prior to their undergraduate degree. As there currently does not exist a validated scale to measure CAD self-efficacy, in this paper, we explore the related concepts of undergraduate engineering students' initial 3D Modeling and Engineering Design self-efficacy before formal CAD instruction at the university level. Bandura's Theory of Self-Efficacy suggests there are four main sources of self-efficacy: mastery experiences, social persuasion, vicarious experiences and physiological states [1]. Therefore, we aim to answer the question: "What prior CAD learning experiences influence undergraduate engineering students' self-efficacy with 3D Modeling and Engineering Design?" [2]. Adapting validated measurement tools for 3D Modeling and Engineering Design self-efficacy, we surveyed second-year mechanical engineering students to target beginner CAD users regarding their prior instruction and knowledge of CAD as well as their perceived self-efficacy in these areas [3]–[6]. Hierarchical multiple regression was used to analyze various reported levels of pre-course CAD exposure and test if they predict students' 3D Modeling and Engineering Design self-efficacy [7]. The results indicate that students' use of video tutorials and personal projects to learn CAD software is a significant predictor (p < .01) of their 3D Modeling self-efficacy. Our findings did not discover any of our survey's forms of CAD exposure to be a significant predictor of Engineering Design self-efficacy. These research findings provide a deeper understanding of the experiences that assist students in developing self-efficacy and familiarity with technical software in the pre- and early stages of their undergraduate degree [8]. The intention is to inform educators about how they can design an effective CAD curriculum accommodating students of all skill sets and to provide the foundation for developing and validating a CAD self-efficacy scale. Future work will focus on the implications of blended and project-based learning settings on students' development of 3D Modeling self-efficacy based on the post-course survey. As a result of this research, students will be able to maximize their learning and become better prepared for upper-year undergraduate studies and their careers in industry as mechanical design engineers [8].
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».