Computer Applications in Engineering Education最新文献

This paper presents a project-based learning approach to improve students' ability to apply systematic design methodologies during their transition from general Electrical Engineering (EE) courses to Integrated Circuit (IC) design programs. With the advent of Electronic Design Automation (EDA) tools and Artificial Intelligence (AI) techniques, there is a growing need for students to develop a more systematic understanding of circuit design. A structured training program is designed to enhance the ability of mixed IC design of fourth-year students, who are expected to use open source tools to design key circuit components which are aligned with MATLAB. With these components, the mixed signal system can be built and optimized top-down, allowing significantly accelerated development and deeper knowledge learning. A team of students is required to design MATLAB-aligned circuit components and a benchmark testing framework is developed to ensure the alignment of functionality between the proposed work and MATLAB Simulink. The results indicate that the proposed project-based learning approach effectively supports fourth-year students in transitioning to IC design, with strong recommendations for its continued implementation in future academic years. The project establishes a comprehensive and free EDA framework for teaching mixed signal IC design, offering significant advantages over traditional commercial tools. Moreover, the use of hardware-defined code provides greater flexibility in the design environment, allowing students to utilize the EDA framework across multiple platforms without the need for expensive commercial licenses.

Predicting learning outcomes is an important problem in educational research, as it enables timely intervention for learners and supports educators in implementing personalized strategies to improve training quality. This task is especially important in online and blended learning environments, where students must independently manage their learning progress. Deep learning (DL) models, particularly Long Short-Term Memory (LSTM) networks, have shown promise in improving prediction accuracy with time-series data. However, single-network models often face high computational costs and struggle to capture the diversity of learner behaviors, resulting in limited performance. To address this, we propose a novel architecture called MoELA to solve the pass/fail classification problem, based on the Mixture of Experts (MoE) framework, which uses multiple expert networks to flexibly model different behavior groups. Each expert includes an LSTM layer integrated with an attention mechanism to effectively process time-series data and emphasize critical learning moments. The number of experts is determined by clustering learner behavior using the Fuzzy C-Means (FCM) algorithm. Experiments were conducted on the Open University Learning Analytics Dataset (OULAD). To mitigate class imbalance in the Pass/Fail prediction task, we introduced SMOTERN, a data balancing method that combines SMOTE with noise removal. Results show that MoELA models significantly outperform the traditional four-layer stacked LSTM. The best performance was achieved by the MoELA4 model trained on SMOTERN-balanced data, with Accuracy, AUC, and F1 scores of 0.9238, 0.9568, and 0.8637, respectively. Additionally, the proposed architecture requires fewer parameters and achieves faster training and prediction times compared to the stacked LSTM model.

Learning efficacy constitutes a fundamental metric in educational assessment, maintaining critical significance across all domains of knowledge acquisition and skill development in pedagogical contexts. This study aims to implement and evaluate a restructured pedagogical framework for introductory programming education, utilizing quantitative metrics to optimize learning efficacy based on empirical collegiate teaching data and established educational indicators. A longitudinal study was conducted utilizing a dual-phase methodology. Phase I (2016–2018) implemented a hybrid assessment approach, integrating a proprietary Moodle learning management system with a web-based online judge platform for summative evaluations. Phase II (2019–2024) introduced the Judge-oriented Pedagogical Framework (JOPF), incorporating synchronous pre- and post-assessment protocols under controlled conditions. The study encompassed four distinct demographic cohorts: undergraduate freshmen (54 contact hours/18 weeks), in-service high school educators (54 contact hours/18 days), and secondary students in sequential phases (30 contact hours/10 weeks per phase). Real-time performance analytics were systematically monitored through a dual-observation protocol involving both instructors and participants. Descriptive multi-year trends indicated consistent improvements in learning outcomes following implementation of the JOPF. Semester completion rates rose, and cohort performance stabilized across iterations. Instructor observations and cohort patterns point to practical advantages of judge-based weekly assessments over prior multiple-choice quizzes, and both instructors and students reported positive acceptance. The study demonstrates the superior effectiveness of programmatic online assessment protocols compared to conventional learning management systems. This methodology enables precise evaluation of learning outcomes through empirical assessment metrics, facilitating enhanced learning efficacy. The framework establishes a bidirectional feedback mechanism that optimizes educational outcomes for both pedagogical facilitators and learners.

As the requirements of geotechnical engineering education on students’ practical ability and innovative thinking continue to improve, the traditional classroom teaching mode is difficult to effectively meet the teaching needs of the complex mechanical behavior of fractured rock mass, such as multi-scale characteristics and nonlinear response. On the basis of this, an integrated education and teaching paradigm with the Conceive-Design-Implement-Operate (CDIO) of “Rock mechanics test—Numerical simulation” is constructed to intuitively and efficiently improve the students’ cognitive level and engineering practical ability on the fractured engineering rocks and their mechanical properties. The teaching process revolves around the uniaxial compression mechanics test of red sandstone with arc-shaped prefabricated fissures, covering four stages of conception, design, realization, and operation, combined with numerical simulation relying on Particle Flow Code in Three Dimensions (PFC3D) discrete element computer software, to carry out the whole process of fissure expansion of the three-dimensional modeling, calibration of the physical parameters and simulation of the nonlinear response, and then form the virtual-actual combination of teaching and learning paths based on the engineering computing platform. By comparing the physical test and computer simulation results, high consistency verification is realized in terms of fracture extension path, stress-strain response, and fracture morphology, which reflects the cognitive gain and accuracy advantage of engineering computational technology based on the discrete element method in the teaching session. The four-phase teaching structure effectively enhances students’ practical ability, numerical modeling skills, and scientific research thinking, and constructs a closed-loop teaching system with virtual-real synergy and computational simulation drive as the core, which has good transfer-ability and promotion potential and can provide a replicable reference paradigm for the informatization reform of geotechnical engineering courses.

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.