Investigating Students’ Development of Computer-Aided Design Self-Efficacy: An Analysis of Pre-Course CAD Exposure
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".