Computer Applications in Engineering Education最新文献

In engineering education, supporting theoretical knowledge with practical applications significantly enhances learning effectiveness. In this study, a Unity-based 3D serious game was developed for Arduino-based microcontroller education, and its instructional effectiveness was evaluated through a controlled experimental design. The game integrates fundamental microcontroller commands, hardware connections, and real-world tasks within an immersive learning environment. To examine both cognitive and experiential outcomes, a comparative pre-test/post-test design was employed with an experimental group exposed to game-based laboratory instruction and a control group receiving conventional instruction. Learning performance was assessed using curriculum-aligned achievement tests, while students' interaction experiences were evaluated through validated self-report instruments. Perceived system usability was measured using the Task-Computer System Usability Questionnaire-Short Version, and flow experience and concentration were assessed using the Flow Short Scale. The results indicate that students engaging with the serious game achieved significantly higher learning gains than those receiving traditional instruction. Furthermore, perceived system usability positively correlated with flow experience, highlighting that effective interaction design enhances engagement and immersion. Overall, the findings underscore the potential of curriculum-aligned serious games as an effective and engaging instructional approach for microcontroller education in engineering programs.

The rapid expansion of the Internet of Things (IoT) demands instructional models that effectively bridge theory and practice. This study explores the integration of Project-Based Learning (PBL) with the ADDIE instructional design model in an undergraduate Information Technology Projects course. As a capstone activity, students designed and built a Smart Lamp using an ESP32 microcontroller, sensors, MQTT communication, and a mobile user interface, iterating through ADDIE phases with formative feedback. A mixed-methods evaluation combined SAM-based rubrics, user-experience testing, and self- and peer-assessment (n = 30 students; n = 20 external users). Results indicated strong technical performance (overall M = 4.2/5 across competencies), high collaboration indicators (M = 4.4/5), and positive end-user satisfaction (M = 4.4/5). A nonparametric exploratory comparison revealed no significant differences between students with and without prior IoT experience, highlighting the inclusiveness of the approach. Overall, findings demonstrate that coupling PBL with ADDIE fosters motivation, knowledge transfer, and effective hardware–software integration while providing a replicable structure for IoT education. Implications for course design include aligning learning outcomes with ADDIE phases, incorporating iterative usability testing, and integrating cloud-connected prototypes as authentic assessments for future implementations.

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.