Computer Applications in Engineering Education最新文献

Smart antenna and digital antenna systems are considered as a crucial technology for current and future communication systems supporting 5G/6G and nonterrestrial-networks communications. Based on the information of the received signal/s, smart antenna systems toward the radiation pattern to the desired direction/s. This technique is known as adaptive beamforming. The theories and the applications of adaptive beamforming are taught in engineering educational courses. This paper proposes a set of real-time interactive experiments for adaptive beamforming algorithms in antenna array processing subject. The proposed experiments can be performed with the computer-aided experimental studies tool prepared in the MATLAB® environment. The users can easily make comparisons and can perform trade-offs between different types of adaptive beamforming algorithms, scenarios, and antenna architecture. This is used as a teaching tool for a graduate course on adaptive beamforming algorithms.

Teachers can create engaging learning environments in engineering courses through pedagogical innovation using Information and Communication Technologies (ICT). Introductory chemistry courses in Science, Technology, Engineering and Mathematics (STEM) disciplines typically include the study of molecular models and periodic properties, which are inherently visual concepts. Unfortunately, these concepts are often taught using two-dimensional drawings, although three-dimensional (3D) printing offers an engaging approach to learning chemistry in these disciplines. Undergraduate engineering students are provided with modelling software and a 3D printer, allowing them to construct 3D models of basic Valence Shell Electron Pair Repulsion theory shapes using polylactic acid. The results of this research showed that the average score of the students in the control group was lower than that of the students in the 3D technology group. This research has found that the learning curve for modelling and the time required for printing are high, limiting the practical application of this exercise in student laboratory classes. However, in an appropriate environment, 3D printing technology has the potential to be a valuable tool for teaching and learning molecular models and periodic properties of chemistry courses in STEM disciplines.

The dynamic map is applied instead of traditional static maps in the teaching of geographic information science (GIS)-related majors so as to meet the need for teaching animations and teaching interaction in new teaching methods such as mobile personalized learning, massive open online course, and Flipped Classroom. The research introduces the concept and the making tools of dynamic maps, studies the making principles and key technologies of dynamic maps, and constructs the relevant theories such as the “frame” model based on time granularity, the dynamic interpolation algorithm based on spatial characteristics, dynamic symbolic logic model, and algorithm. Based on the above principles and theories, taking a dynamic map of the change in the population of each city in Shandong province as an example, the research demonstrates the making method and process of the dynamic maps for teaching with Flash MX. At last, it analyzes the role of dynamic map in GIS-related courses teaching and evaluates the teaching effect of dynamic maps.

The study presents the advantages of, and possible uses for, Orange software for data mining in combination with processing spatial data by ArcGIS Pro software in education. To present suitability of Orange software in education, the scientific method of Physics of Notation by D. Moody is used to evaluate the Orange software's visual vocabulary. All nine principles are applied in the presented evaluation. As a result, a high level of effective cognition of the Orange visual vocabulary is proven by this method. Namely, the semantic transparency of visual vocabulary, thanks the explicit inner icons, is semantically immediate. Also, principle of dual coding is used properly by automatic text labels of graphical symbols with the opportunity to rename labels. Renaming is also a way to ensure the partial overloading of symbols found by the first principle of semiotic clarity. The principle of cognitive interaction is partially fulfilled by automatically reorganizing connector lines between symbols to reduce the crossing of lines. A high level of effective cognition is beneficial for students. The evaluation of the visual notation of Orange software is presented to inform teachers and the geoinformatics community of the highly effective cognitive aspects of Orange software. The two practical lectures of processing in Orange and ArcGIS Pro software are shown to the teachers and students of geoinformatics community as examples of machine learning tasks. They are cluster analyses carried out with the density-based spatial clustering of applications with noise method, first for the location of cafés in Olomouc town and the second example concerns finding similar European towns based on their land use arrangement, using the neural network and following hierarchical clustering. Both examples could provide inspiration for the geoinformatics community to adopt Orange data mining software.

Recent advancements in artificial intelligence (AI) and machine learning (ML) have driven research and development across multiple industries to meet national economic and technological demands. Consequently, companies are investing in AI, ML, and data analytics workforce development efforts to digitalize operations and enhance global competitiveness. As such, evidence-based educational research around ML is essential to provide a foundation for the future workforce as they face complex AI challenges. This study explored students' conceptual ML understanding through a scientific argumentation framework, where we examined how they used evidence and reasoning to support claims about their ML models. This framework lets us gain insight into students' conceptualizations and helped scaffold student learning via a cognitive apprenticeship model. Thirty students in a mechanical engineering classroom at Purdue University experimented with neural network ML models within a computational notebook to create visual claims (ML models) with textual explanations of their evidence and reasoning. Accordingly, we qualitatively analyzed their learning artifacts to examine their underfit, fit, and overfit models and explanations. It was found that some students tended toward technical explanations while others used visual explanations. Students with technically dominant explanations had higher proficiency in generating correctly fit models but lacked explanatory evidence. Conversely, students with visually dominant explanations provided evidence but lacked technical reasoning and were less accurate in identifying fit models. We discuss implications for both groups of students and offer future research directions to examine how positive pedagogical elements of learning design can optimize ML educational material and AI workforce development.

This paper describes a methodological study carried out between 2018 and 2022, at Rey Juan Carlos University, focused on the subject monitoring and control systems within a master's program in Industrial Engineering. The study proposes an innovative teaching strategy using problem-based learning and project-based learning methodologies. The projects undertaken are based on Internet of Things (IoT) systems aimed at enhancing weather stations, services and facilitating real-time decision-making. Inspired by our experience in the development of Industry 4.0 projects, we have designed a methodological strategy for this subject that focuses on providing students with the necessary knowledge and skills in the field of Control and Monitoring Systems and the IoT to develop real monitoring and control systems. The approach emphasizes interdisciplinary problem-solving, with students working collaboratively in stable teams. Throughout the 16-week course, tasks of increasing complexity are completed, resulting in the development of a complete system. The practical approach of the course and its relation to real applications motivates students, resulting in better performance. The acquired techniques and skills from the course are broadly applicable across engineering disciplines.

This systematic literature review explores the impact of innovative teaching approaches on student motivation and academic achievement in online blended learning. A thorough search of five electronic databases for studies published between January 2009 and May 2023 yielded 1468 records. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Population, Intervention, Comparison, Outcome and Study-design (PICOS) frameworks as the basis for the eligibility criteria, 47 studies were eligible and reviewed. The findings revealed that the effects of motivation were influenced by various factors, such as the blended course design, instructor's support, learning environment and the student's characteristics. The common innovative teaching and learning techniques and tools which advanced better teaching and learning were found to be interactive lessons, the use of virtual reality technology, artificial intelligence, project-based learning, inquiry-based learning, jigsaw, cloud computing, flipped classroom, peer teaching, peer feedback, crossover teaching and personalised teaching. These techniques positively and significantly affected motivation and academic achievement. Furthermore, results also suggest that educators should carefully consider the needs and preferences of their students when designing their courses and curricula to motivate and support students to achieve their full potential. Based on these findings, instructor support through innovative teaching and learning is vital to sustaining meaningful, innovative interactions that motivate students and promote better academic achievements in innovative online blended learning. Therefore, this study proposed a framework that illustrates that when students are well motivated, they develop personal and academic qualities such as interest, confidence, belonging, cooperation and trust in the educational experiences, resulting in better academic achievement.

Thermodynamics is a branch of physics of high importance for engineering applications but is usually considered by most students as a rather obscure field, full of abstract concepts. Therefore, simple algorithms, which can exemplify the use of thermodynamic principles for practical situations, should be viewed as valuable teaching tools with large applications in engineering undergraduate courses. This point served as motivation for the present work, which proposes an alternative and simple computational approach for solving chemical equilibrium problems via successive reaction quotient calculations, both for single and multireactional systems. The code was written using MATLAB software; its fundamental theory was explained through a step-by-step approach and applied to both Shift and Boudouard reactions. Comparisons with ASPEN HYSYS and HSC Chemistry simulations corroborate its versatility and thermodynamic consistency. The full script is available in its entirety at the as supporting information together with the necessary text (.txt) files. Also, a user guide was provided to help students to replicate the results presented in the article.

Professional training presents a significant challenge for organizations, particularly in captivating and engaging employees in these learning initiatives. With the ever-evolving landscape of workplace education, various learning modes have emerged within organizations, and e-learning stands out as a prominent choice. This increasingly cost-effective and adaptable solution has revolutionized training by facilitating numerous learning activities, including the seamless integration of educational games driven by cutting-edge technologies. However, incorporating serious games into educational and professional settings introduces its own set of challenges, particularly in quantifying their tangible impact on learning and assessing their adaptability across diverse contexts. Organizations require a consistent framework to guide best practices in implementing e-learning combined with serious games in professional training. The primary objective of this research is to bridge this gap. Rooted in the methodology of Design Science Research, it aims to provide a comprehensive framework for creating and assessing adaptive serious games that achieve desired learning and engagement outcomes. The overarching goal is to enhance the teaching–learning process in professional training, ultimately elevating student engagement and boosting learning outcomes to new heights. The proposal is grounded in a review of literature, expert insights, and user experiences with Serious Games in professional training, considering learning outcomes and forms of adaptation as essential characteristics for developing or evaluating Serious Games. The result is a framework designed to guide learners toward improved learning outcomes and increased engagement. The proposal underwent evaluation through triangulation, involving focus groups and expert interviews. Additionally, it was utilized in the development and assessment of a Serious Game, offering new insights and application suggestions. This experiment provided an evaluation of the framework based on real courses. In summary, this investigation contributes to the development of evidence-based approaches for the effective use of Serious Games in professional training.

Computational thinking (CT) has been promoted worldwide by educational systems and is an essential skill for technological citizens. Various strategies have been planned and developed to help in introducing, improving, and delivering CT. One of the strategies is by creating and developing the supporting tools for CT learning. In this article, educational robotics (ER) is chosen as the focus tool to support CT learning. Each CT and ER has a massive field of study. There are various available reports determining the suitability of CT subject integrated with ER for students' learning. However, all students do not develop similar style of learning and thinking. There is difference in their personal traits. There is a lack of research that designed CT learning through ER specifically based on student's preferences. Besides, it resulted in a challenge to determine the suitability of CT and ER for different kind of preferences. Therefore, this study aimed to develop an adaptive learning (AL) framework for students to deliver learning of CT through ER. The framework consists of three submodels: domain model, student model, and adaptation model. One case study is defined, which is learning the introductory level of CT through ER (CTER). At the end of the study, it can be observed that the AL framework produced positive results in performance and perception for various student categories. It was noted that students utilizing the AL framework had superior understanding of CTER. Individually or collaboratively, all students who applied or did not apply the AL framework in studying the CTER introduction had positive learning outcomes.