Computer Applications in Engineering Education最新文献

The recent technological advancements and consumer demands have revolutionized industrial production and workflows, requiring an interdisciplinary skilled workforce. Manufacturing education at universities can be vital in training a skilled workforce to meet Industry 4.0 requirements. A Manufacturing Process Innovation (MPI) course was conducted in the Spring of 2024 and 2025 to train undergraduate students in major Industry 4.0 technologies. The course was designed to enhance student learning and cross-curricular skills. The course curriculum was upgraded to include the main Industry 4.0 constituents like digital twins, AI, simulation, and IoT devices. A robotics module was integrated to teach fundamental concepts and enable students to operate an autonomous mobile robot. Team-based learning (TBL) and problem-based learning (PBL) frameworks were incorporated with conventional teaching to enhance course outcomes. The active learning methodologies improved student grades by 23.75% and 16% in Spring 2024 and Spring 2025, respectively, compared to the grades in conventional learning. Both TBL and PBL were positively rated in the student surveys and were found to be useful for learning complex concepts. The course helped foster cross-curricular skills like teamwork, problem-solving, and a sense of responsibility among students. Active learning frameworks integrated with key industrial technologies can improve curricular learning and cross-curricular skills. Robotics can be leveraged and integrated with core course concepts to train students for real-world scenarios. The findings can be helpful for manufacturing-related education programs and for integrating robotics modules in other engineering courses.

Work and energy are two critical concepts in Engineering Dynamics, a second-year undergraduate course required in many engineering programs. Research has shown that many students have difficulty learning these concepts and associated problem-solving. The present study aims to develop and assess computer simulation and animation (CSA) learning modules to enhance student learning of work and energy. Two technical problems in particle dynamics, an essential part of Engineering Dynamics, were developed and embedded into CSA learning modules I and II, respectively. To assess the effectiveness of the CSA learning modules, quasi-experimental quantitative research was conducted, involving a pre-/posttest by student participants in a comparison group and an intervention group. Using quasi-experimental quantitative research in the present study fills existing research gaps. In existing research, only questionnaire surveys were used to assess student learning outcomes, and a comparison group was also missing. As the data collected in the present study were in a non-normal distribution, non-parametric statistical analysis was performed, including a descriptive analysis and an independent-samples Mann–Whitney U test. The results show that compared to the comparison group, the intervention group increased normalized learning gains by 49 percentage points for CSA learning module I and by 47 percentage points for CSA learning module II. The difference in normalized learning gains between the two groups was statistically significant (p < 0.001). The CSA learning modules had a medium effect on student learning, with an effect size of 0.50 for CSA learning module I and 0.49 for CSA learning module II.

Traditional high-school genetics education often struggles with students' fragmented understanding of probability concepts and limited experimental sample sizes. This study developed a Python-based simulation tool for analyzing trait segregation, addressing these challenges. Additionally, it designed an interdisciplinary STEM teaching module to explore the collaborative cultivation path of probabilistic thinking and computational thinking. A mixed-methods analysis comparing an experimental group (programming simulations) with a control group (traditional hands-on experiments using physical models) revealed the following: (1) Conceptual mastery: The experimental group demonstrated a significantly superior understanding of the statistical implications behind the “3:1 phenotypic ratio” and the Law of Large Numbers in post-tests; (2) Empirical precision: Under the same number of experiments, the simulation data of the experimental group had better accuracy and stability; (3) Dynamic visualization: Real-time graphical modules illustrated how increasing trial repetitions from 100 to 10,000 reduced fluctuations in the dominant phenotype frequency from [65%,85%] to [74%,76%], empirically validating the Law of Large Numbers; (4) Cognitive transfer: Qualitative analysis revealed that 84% of the students successfully explored noncanonical inheritance scenarios by modifying code parameters, demonstrating advanced skills in problem abstraction and algorithmic iteration. This study confirms that using low-threshold technological tools as a medium to integrate mathematical probability, biological experimentation, and information technology practice can effectively overcome the cognitive barriers to understanding randomness inherent in traditional instruction, offering an innovative paradigm for STEM education characterized by maintaining disciplinary rigor while achieving cognitive synergy.

Under the current maritime education model, cadets generally lack a systematic understanding of navigation knowledge and practical experience in operating onboard equipment and instruments. To comply with the requirements of International Convention on Standards of Training, Certification, and Watchkeeping for Seafarers (STCW) for maritime engineering education, this study develops a high-fidelity, highly interactive teaching and training simulator of the BeiDou Satellite Navigator. On the basis of the Windows Presentation Foundation framework, the system simulates interactive navigation interfaces, the operational logic, and the visual design of real-world devices. At the algorithmic level, the system employs the Mercator projection for converting geographic to screen coordinates, the Bresenham algorithm for efficient straight-line rendering, and the Cohen–Sutherland algorithm to improve the accuracy of route visualization. It also incorporates spherical trigonometry and Vincenty formulae to construct a great-circle route model, enabling waypoint computation and total-distance accumulation. This simulator addresses the experimental teaching bottleneck of being unable to simulate offshore BeiDou navigation data, significantly enhancing students' operational capabilities. Its functions and interface closely replicate real-world equipment, providing an excellent human–machine interaction experience. Currently deployed in undergraduate navigation instrument courses to improve student proficiency, it fills the gap for specialized maritime training tools for the BeiDou system.

As artificial intelligence (AI) tools become more effective at supporting instructional preparation, their use in science, technology, engineering, and mathematics (STEM) education has gained increasing practical value. This study investigates how AI-curated video illustrations can support student learning in conceptually demanding fields, such as chemical engineering and nanotechnology. Over 15 weeks, students from a large public university in New York engaged with short, AI-selected animations prior to and during class time of a nanotechnology course, utilizing a flipped-classroom instructional model. Weekly surveys captured perceptions of comprehension, clarity, engagement, and retention, while final grades were compared with two prior cohorts (2023 and 2024) who received traditional lecture-based instruction. Results showed consistent increases in comprehension and retention scores, a statistically significant improvement in final grades over the traditional cohorts, and reduced grade variability. Thematic analysis of student feedback highlighted appreciation for visual clarity and brevity, alongside critiques of artificial narration and conceptual abstraction. Taken together, these findings suggest that, when paired with interactive instruction, AI-curated content can sharpen conceptual understanding and elevate pedagogical delivery in advanced STEM education.

With the development of digitalization and the advent of Industry 4.0, education in Smart Construction has undergone great revamping. Engineering System Analysis and Optimization (ESAO) is a cross-disciplinary undergraduate course under the curricula of the Smart Construction Major. However, due to its difficulty and poor linkage to real project-related problems, students lack interest in learning it. This study proposes developing a simulation program that is integrated into the course to improve learning effectiveness. The simulation program includes the following two models: (1) A subway station model that helps to understand the design and functions of a real subway station, the pedestrian flow in the subway station, and the evacuation pattern of pedestrians when a fire hazard occurs; (2) A road intersection model that was used to illustrate the functions of the intersection and explain how to select the optimal parameters to enhance the vehicle pass-through rate. In order to assess the effects of the simulation program, the study used both quantitative and qualitative analyses. In the quantitative analysis, the participants were assigned to two groups: (1) the experimental group and (2) the control group by comparing their exam scores. Additionally, the qualitative analysis, including interviews and casual conversations, was adopted to attain students' opinions about the simulation program. Based on the analysis results, the simulation program has been demonstrated to increase the competence, confidence, and interest of students when compared to conventional teaching approaches.