Computer Applications in Engineering Education最新文献

This study empirically examines the associations between AutoCAD-based spatial learning and engineering students perceived spatial visualization skills (SVS), measured via self-reported spatial self-efficacy (SS) rather than objective spatial tests, particularly in developing country contexts such as Vietnam. By integrating SERVQUAL and the Unified Theory of Acceptance and Use of Technology (UTAUT), the research examines perceived learning experiences alongside behavioral factors shaping students' adoption of AutoCAD in education. Given the cross-sectional survey design, the identified relationships are interpreted as associative rather than causal. Data were collected from 322 engineering students in Vietnam using a structured survey, and Structural Equation Modeling (SEM) with AMOS was employed to analyze relationships among Facilitating Conditions (FC), Software Interaction (SI), Learning Motivation (LM), SS, and Active Engagement (AE). Results indicate that SI shows the strongest association with SS (β = 0.502, p < 0.001), highlighting the role of intuitive software use in SS. LM was found to be significantly associated with perceived SVS (β = 0.323, p < 0.001), underscoring the importance of motivation in technology-enhanced learning contexts. However, the findings suggest that unguided AE was negatively associated with perceived SVS (β = –0.142, p = 0.011), suggesting that engagement without instructional scaffolding may be counterproductive, and that Performance Expectancy (PE) alone may not translate into meaningful engagement without adequate instructional support. The findings highlight the importance of supportive learning environments that ensure infrastructure readiness, user-friendly software, and clear instructional strategies. The study proposes an integrated SERVQUAL–UTAUT framework to jointly capture service quality and technology acceptance factors in AutoCAD-based learning. Situated in a developing-country context, the study offers implications for more inclusive technology adoption in engineering education and provides practical insights for educators and policymakers.

Laboratories are essential to engineering education, yet traditional setups often face challenges such as limited accessibility for students with disabilities and constraints imposed by events like pandemics. To address these issues, this paper introduces an innovative remote laboratory platform to overcome these barriers. The platform integrates specialized control boards and hardware with advanced laboratory management software, offering a versatile and accessible educational tool. Its fault detection and control system ensures reliable operation while safeguarding both equipment and users from potential risks. The platform supports both in-person and remote access to laboratory resources, providing a cost-effective and high-precision alternative to conventional labs. Furthermore, it can be easily and swiftly implemented in existing traditional labs at a reasonable cost. This remote laboratory system enhances educational equity and operational flexibility, with practical applications in teaching, research, and industrial settings.

While retrieval-augmented generation (RAG) systems have substantially improved the factual accuracy of Large Language Models (LLMs) in educational contexts, they exhibit a fundamental limitation: an inability to adapt responses to a student's specific academic proficiency. This is a particularly critical gap in Artificial Intelligence (AI) education, where a learner's foundational knowledge in subjects like mathematics, programming, and core AI concepts exhibits significant heterogeneity. To address this, we introduce Personalized RAG for Education (PRAG-EDU), a novel context-aware RAG framework that dynamically calibrates response complexity by leveraging students' historical module grades as pedagogical signals. Unlike conventional RAG implementations that treat all learners uniformly, our model integrates these academic profiles with retrieved course materials to generate responses precisely tailored to individual proficiency levels. We establish the first benchmark for grade-aware educational RAG within the AI domain, comprising 250 expert-validated question-answer pairs linked to specific academic profiles and difficulty-calibrated reference responses. Through a rigorous evaluation of seven open-source LLMs against our framework, we demonstrate that PRAG-EDU achieves a 23.7% improvement in BERTScore F1 (0.555 vs. 0.451) and 18.3% higher ROUGE-L over non-personalized baselines. A qualitative analysis of 250 student evaluations further confirms its pedagogical efficacy, with expert raters awarding an average of 4.09/5 stars, significantly outperforming the next-best model (Qwen3:1.7B at 3.94). This work reveals a notable trade-off between factual alignment and generative fluency, as our method leads in accuracy while a model like Smollm2:1.7B excels in expressiveness. Ultimately, this research bridges the gap between technical RAG implementations and domain-specific educational theory by operationalizing academic performance data as a personalization mechanism, offering a scalable solution for heterogeneous AI engineering classrooms.

Cable accessories installation (CAI) education is a crucial component of practical teaching in the power engineering major. It aims to cultivate students' mastery of standardized installation procedures and operational skills, ensuring the safe and stable operation of the power system. Due to the traditional teaching of CAI mostly adopting the “apprenticeship” approach, which relies on on-site practical training and experience transmission for instruction, there are problems such as tight teaching resources and high operational risks in actual application, making it challenging to meet the urgent demand of modern engineering education for practical ability cultivation. Therefore, this paper constructs a virtual training system (VTS) for CAI. This system integrates virtual reality and 3D modeling technologies to realistically simulate the structure of cable accessories and the construction environment. It combines the cognitive characteristics and skill training requirements of CAI to create an integrated teaching framework based on the “demonstration—practice—examination—evaluation” approach. Finally, to verify the application effect of the system, 40 college students were invited to participate in the training experiment and were randomly divided into an experimental group and a control group. The experimental process included three stages: pre-test, training, and post-test. The results show that VTS had a comparable learning effect to the traditional teaching mode and demonstrated significant effectiveness in reducing cognitive load. Meanwhile, the system could stimulate students’ interest in professional knowledge during the training process, help them discover their strengths and abilities, and further enhance their initiative and enthusiasm for professional learning.

Manual grading of engineering drawing assignments is often time-consuming, inconsistent, and error-prone, particularly in large-scale engineering education. To address these challenges, we present “CAD-AG”, an automated grading system designed to evaluate two-dimensional engineering drawings exported from any CAD tool in DXF format. The system streamlines the grading process by comparing student submissions with a reference solution through geometric feature extraction and rule-based error detection. Key criteria assessed include shape accuracy, scale, alignment, and element completeness. Developed in Python and deployed via a web-based platform, CAD-AG enhances accessibility and usability. In testing with over 1000 student submissions, the system achieved 100% grading accuracy and significantly reduced average grading time. These results demonstrate the potential of automated grading to improve efficiency and consistency in engineering education, providing timely and objective feedback to support both instructors and learners.