Computer Applications in Engineering Education最新文献

This paper proposes a course designed for engineering students about digital image processing using tools from the Jupyter ecosystem, including Jupyter Notebooks, JupyterHub, and Jupyter Widgets. The course integrates theoretical concepts with different practical applications through project work and real-world case analysis to ensure involvement and comprehension. Jupyter Widgets represent one of the main features of the course and enable interactive learning through data manipulation capabilities that create dynamic visualizations. Two other important components of the course are case studies and projects. These components teach students how to solve real-world image processing problems and strengthen their problem-solving skills. Additionally, regular exercises reinforce learning and ensure that students can apply theoretical knowledge in practical scenarios. While a survey conducted among participants indicated a generally positive reception, the focus on Jupyter tools and real-world applications was particularly appreciated, demonstrating the course's success in bridging the gap between theory and practice. Future iterations of the course will continue to build on these strengths, further enhancing the educational experience and better preparing students for professional careers in engineering.

Scenario-based teaching supported by computerized solutions is an effective way to increase student engagement and to improve conceptual understanding in engineering education. In this study, we present bioXL3p, an Excel-based simulation tool designed to help undergraduate environmental engineering students learn fundamentals of activated sludge processes. A structured set of in-class and homework simulation activities, which aims to reinforce theoretical knowledge through guided simulations, was developed in which students are instructed to conduct computer simulations using bioXL3p. A total of 15 activities comprising 7 for carbon removal and fundamentals, 6 for nitrogen removal and 2 for phosphorus removal were proposed. The activities are well aligned with program outcomes from national and international accreditation frameworks such as ABET, EUR-ACE, and MÜDEK, focusing on problem analysis, simulation-based design, and lifelong learning skills. The method was implemented with a total of 23 students over 4 years. After excluding inconsistent responses, participants reported high perceived gains in conceptual understanding (mean = 4.53; N = 17) and confidence in applying activated sludge modeling to design problems. The accompanying database and supporting materials provide an open-source resource for integrating proposed method into environmental engineering curricula. Future development plans include more flexible versions of the tool (possible bioXL Pro and bioXL Ultimate), enabling model selection and user-defined model structures.

Human–computer interaction is a branch of computer science dedicated to the study of how human beings use computers and using the knowledge to develop hardware and software that are efficient and satisfying to users. The purpose of this paper was to develop an instructional approach in human–computer interaction in which students undertake a series of small projects requiring engineering and design skills. Accordingly, we designed three projects, (1) to design the user interface of a digital system taking into consideration performance-related and ergonomics-related parameters, (2) to design a new typeface for any specific application or user group, and (3) to test the usability of a digital system by objectively assessing measurable parameters, such as effectiveness and efficiency, and self-reported parameters, such as satisfaction and cognitive load. The projects involved interaction with end users and using state-of-the-art software tools. This approach was used to teach 104 undergraduate students during the autumn semester of 2024. An analysis showed that the projects provided the students an opportunity to collaborate with their peer and they performed better in examination.

Large language models (LLMs) brought the possibility of enhancing chatbot interactions in educational platforms by providing students adapted support throughout the learning process. This study presents the design and development process of an LLM-powered educational platform prototype and its evaluation in a single learning session on the Computer Networks and Telecommunications course at a technical faculty specialized in information systems and technologies. The proposed platform combines a microlearning paradigm and the availability of LLM chatbot support after each learning unit for students to clarify misconceptions and deepen knowledge and comprehension. The prototype was built using Angular for the client-side application and Express.js for the backend with Firebase Firestore as the database solution. It leveraged the OPENAI GPT-4o model through the available Application Programming Interface (API) for providing the chatbot, which was instructed to offer support for students in the context of the mentioned course. As a result, students generally found the chatbot to be a useful support tool in the learning process, with higher effectiveness reported for clarifying theoretical concepts compared to assisting with practical tasks. This perception aligns with expert evaluations of the chatbot's responses, which revealed statistically significant differences between theoretical and practical answers in terms of factual accuracy, relevance, completeness, and coherence, but not in fluency. Despite these observed differences, both student and expert assessments were overall positive across both types of questions. Even though single-session evaluation limits generalizability of results, these findings suggest feasibility and promising results within this educational context since the chatbot is perceived as a valuable component of the learning experience.

This study examines the impact of Napkin AI, a generative artificial intelligence tool, on student learning outcomes and knowledge retention via creative mind mapping. The research model, which combines the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) with the Information Adoption Model (IAM), comprises six principal independent variables: Social Influence, Learning Support Environment, Ease of Use, Perceived Benefit, Information Quality of Mind Mapping, and Usefulness of Mind Mapping. Mediating variables comprise Behavioral Intention to Use, Actual Use of Mind Mapping, and Cognitive Engagement in Mind Mapping, while Knowledge Retention and Learning Outcome serve as dependent variables. The study employed a quantitative approach with a sample of 570 Vietnamese university students majoring in STEM. Data analysis was conducted using Structural Equation Modeling (SEM) with IBM SPSS 25 and AMOS 24. The model exhibited a satisfactory fit (χ²/df = 1.906, GFI = 0.889, CFI = 0.935, TLI = 0.929, RMSEA = 0.040, PCLOSE = 1.000), with all direct routes being statistically significant (p < 0.001 to p = 0.037). The findings underscore the significance of user perception and involvement in improving the educational efficacy of AI-driven mind mapping tools, providing practical implications for technology integration in higher education.

This study presents the design and implementation of a low-cost 3-degree-of-freedom (3-DOF) helicopter platform aimed at strengthening control education through the integration of digital twins (DTs) and hardware-in-the-loop (HIL) systems in a Rapid Control Prototyping (RCP) framework. The methodology consisted of first modeling and understanding the nonlinear dynamics of the system, and then building the physical platform using a Raspberry Pi Zero W and a 3D-printed structure, enabling both affordability and a carry-it-home (CIH) approach. Once constructed, the platform was calibrated to match its DT, after which pole placement and Linear-Quadratic-Gaussian (LQG) controllers were designed, simulated, and tested on both the DT and the HIL setup. To assess the educational impact, pre- and post-course questionnaires were applied to gather students' perceptions and expectations. The results indicate that the experience not only improved comprehension of feedback control concepts but also provided a more comprehensive and practical understanding of DT and RCP technologies.

Discrete Mathematics is an important and challenging course for computer science and engineering students. It includes topics, such as logic, sets, proofs, number theory, graphs, trees, computation, relations, functions, and basic algorithmic concepts. These topics require strong analytical reasoning and consistent effort. As a result, many students find this course challenging to perform well. The aim of this study is to predict student performance in a Discrete Mathematics course at a reputed private university located in Bangladesh. Data were collected from both course instructors and students during the spring and summer semester of 2025. Instructors provided academic records, such as attendance, quizzes, assignments, and midterm scores. Students provided additional information, which included daily study time, subject interests, and use of learning platforms. The final data set included records for 240 students. K-means clustering with the Davies–Bouldin method was used to group similar students. Then, four machine learning (ML) models were trained and tested: Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors, and Naïve Bayes. The models were implemented using Python's scikit-learn library, with stratified sampling and fivefold cross-validation. Among the models, SVM achieved the highest accuracy of 96% after parameter tuning. Naïve Bayes had the lowest accuracy due to the assumption of feature independence. Key predictors of performance included mean score, attendance, and daily study hours. Findings show that ML can help instructors identify at-risk students early, provide focused academic support, and improve learning outcomes. While the results are promising, the study is limited by sample size and does not include psychological or emotional factors. Future work will explore larger data sets and apply interpretable Artificial Intelligence techniques for better model transparency.