Computer Applications in Engineering Education最新文献

Game-based learning (GBL) has demonstrated effectiveness across various disciplines, yet its application in engineering education remains limited. Understanding engineering concepts is essential not only for engineering students but for all STEM undergraduates. This study investigates the potential of GBL to enhance STEM students' comprehension of engineering concepts, and explores the underlying influence pathways by integrating self-determination theory (SDT), flow theory (FT), and personal gaming history. Structural equation modeling was employed to analyze the relationships among these variables. Results indicate that completing game tasks significantly improves students' understanding of engineering concepts. Among the influence pathways, need frustration emerged as a major barrier (path coefficient = −0.355), while need satisfaction strongly promoted both flow experience (0.779) and game motivation (0.703). Additionally, personal gaming experience, flow experience, and game motivation positively influenced game satisfaction (path coefficients = 0.114, 0.559, and 0.329, respectively). These findings support the effectiveness of GBL in engineering education. To optimize learning outcomes, game design and use should address students' basic psychological needs, minimize need frustration, and foster flow and motivation, thereby enhancing engagement and conceptual understanding.

Engineering Geology is a mandatory course for undergraduates majoring in civil engineering at colleges and universities, where much of the knowledge is inherently abstract. The teaching methods employed by instructors play a crucial role in helping students understand the course material. This study highlighted several effective visual teaching methods that facilitate students' comprehension of abstract geological concepts. It specifically discussed various visual teaching methods, including classroom object diagrams, classroom video playback, and field geological practices. Visual teaching methods for different slope models, tunnel models, and fractured rock mass models were developed using numerical software. An online questionnaire survey was conducted to assess the acceptance of these methods among second-year undergraduates. The results indicated that the combined effect of these methods was most favorable. Therefore, this visual teaching strategy holds promise for broader application in engineering geology education.

The use of virtual reality (VR) in higher education and training has been widely explored in various fields, with divergent results. This study examines whether VR is a suitable learning support tool compared to traditional methods, such as 2D static slides or physical models, focusing on usability, workload, and learning effectiveness in an engineering education context. A total of 184 first-year BSc engineering students voluntarily participated in an experimental activity. Participants were divided into three groups, each experiencing a different learning condition: slides-based, VR-based, and physical model-based. The study design ensured comparability across conditions using 3 out of 4 levels provided by the ICAP (Interactive, Constructive, Active, Passive) framework. Usability was assessed using the System Usability Scale, workload via the NASA Task Load Index, and learning outcomes through a task-based test. The study found no significant differences in test scores across the three conditions. However, participants in the VR-based and physical model-based conditions reported significantly higher usability and lower perceived workload compared to the slides-based condition. The VR-based condition provided an immersive environment while maintaining comparability in cognitive engagement. The findings demonstrate that VR can serve as an effective alternative to traditional learning methods, particularly for enhancing usability and reducing cognitive workload. However, its integration must be carefully designed to ensure cognitive engagement and optimize learning outcomes. This study highlights VR's potential in engineering education and underscores the importance of aligning tools with pedagogical goals.

This study explores the potential of generative artificial intelligence (AI) tools in supporting students preparing for mathematical competitions, focusing on the Mathematical Kangaroo competition in the context of the Serbian-speaking region. The research analyzed tools such as ChatGPT-free, ChatGPT-paid, AI Math Solver, Math Mentor, and o1-preview, assessing their accuracy and efficiency in solving tasks of varying difficulty levels and domains (algebra, geometry, logic, and numbers), as well as different formats (text and image-based). Testing included tasks in both Serbian and English, allowing for the evaluation of language barriers in tool performance. The results indicate that tools perform better with text-based task formats, with o1-preview standing out for its exceptionally high accuracy in this format. All tools achieve the highest precision in numbers and algebra, while results are significantly lower in geometry and logic, highlighting challenges in processing visual information and logical reasoning. The conclusions of this study emphasize the importance of generative AI in improving mathematics education but highlight the need for further development of tools that can better handle visual tasks, support local languages, and be more specialized in solving mathematical problems in general.

The integration of generative artificial intelligence (AI) technologies, such as large language models like ChatGPT, in higher education has introduced innovative ways to enhance personalized learning, assist students, and support educators. However, these advancements come with significant ethical implications, particularly concerning privacy, bias, and pedagogy from a socio-technical perspective. This study explores the ethical dimensions of employing ChatGPT in higher educational systems across ASEAN countries. Employing a systematic literature review and thematic analysis of data from news articles, university policies, and journal papers, the research identifies key ethical concerns related to data privacy, algorithmic bias, and the impact on educational practices. Findings highlight the need for responsible generative AI usage and propose recommendations to ensure that ChatGPT and similar AI tools positively contribute to higher education while upholding academic integrity and equity from a socio-technical viewpoint.

This article introduces a new web-based open-source software application called UYAMAK, which stands for Unleash Your Applied Mathematics Abilities & Keenness. UYAMAK enables users to design, simulate, and analyze mathematical models using block diagrams. The software can not only be downloaded and installed on computers running Windows but also be used directly from web browsers without installation, making it compatible with any operating system. Due to this feature, the proposed program can be used in hybrid courses. A wide range of mathematical topics covered in undergraduate and graduate courses can be explored through UYAMAK, as it includes libraries for performing basic operations in arithmetic, trigonometry, complex numbers, Boolean logic, matrices, calculus, and statistics. Additionally, it allows for the simulation of discrete or continuous dynamical systems described by differential equations, transfer functions, or state-space representations. Simulation results can be visualized through displays, scopes, XY plots, and 3D graphs. Moreover, UYAMAK also simulates the 3D model of a 6-DOF articulated robot, whose joints can be controlled to follow a desired trajectory generated using various signal sources provided by UYAMAK, including step, ramp, sinusoidal, sawtooth, rectangular, and triangular waveforms. Several simulations are presented to demonstrate the potential of the proposed software, which has been evaluated in engineering courses. The results indicate that the proposed software helps students better understand and verify the mathematics taught in class.

In the rapidly evolving field of education, the timely updating of course content is an essential task, especially in disciplines such as computer science and artificial intelligence, where knowledge evolves swiftly. However, traditional methods of course revision are labor-intensive, time-consuming, and struggle to keep up with the latest academic developments and industry practices. Moreover, directly utilizing content generated by agent tools often results in a disorganized structure and questionable quality. To address these challenges, this paper proposes a dynamic updating method of course content based on the collaboration of multiple intelligent agents. By integrating multiple intelligent agents and knowledge graph technology, this method enables automated retrieval, filtering, and integration of the latest knowledge points, thereby generating a structured, accurate, and comprehensive course content structure. For engineering education, this method can help educators quickly adapt to the needs of technological development, which not only effectively alleviates the burden on educators but also significantly enhances the efficiency and quality of course updating, offering a novel solution for educational content management and promoting the development of engineering education towards intelligence and dynamism. Experimental results indicate that compared to traditional manual revision and direct utilization of single agent, this method maintains high accuracy (over 95%) and high coverage (over 96%), while reducing the knowledge point update cycle to less than 0.5 h and controlling the task execution cycle within 1.5 h.