Abstract The knowledge‐based prerequisite framework (KPF) is an alternative to the course‐based prerequisite framework (CPF), which is widely used for curriculum design. The KPF is more flexible because it only requires essential prerequisite knowledge, while the CPF is more rigid and requires students to take all prerequisite courses. Since the number of prerequisite knowledge terms is, in general, much greater than the number of prerequisite courses, flexibility can cause additional complexity. Furthermore, the KPF inevitably requires handling semantics of defined knowledge terms. This work presents a novel Artificial Intelligence (AI) Planning mathematical model that enables the KPF by automatically verifying prerequisite knowledge and incorporating hierarchical semantic relationships among knowledge terms into the model. The proposed model significantly improves the quality of course planning solutions by finding hidden or better solutions that could not be obtained without semantics consideration. The results of the comprehensive experiments show the optimality of the solutions obtained by the mathematical model and demonstrate the outperformance of incorporation of the semantics into the mathematical model, in terms of the quality of solutions. Finally, the experimental results on scalability show the necessity of the development of efficient heuristic algorithms.

Abstract Computational Thinking (CT) can be defined as the thought processes involved in formulating problems so that their solutions can be represented as sequential steps and algorithms. It is a key skill for children in the 21st century. However, it is unclear how CT can be developed most effectively in children. Several pedagogical methodologies have been proposed and are being investigated. The main aim of this paper is to test the hypothesis which states that using three‐dimensional (3D) simulated robots helps in the learning of programming and CT concepts, such as directions, loops, conditionals, and functions. The research questions are: Does this hypothesis hold true? Are some concepts easier or better learned than others and to what extent? The goal is to measure and evaluate the effect of using as a learning tool a platform with 3D simulated robots and realistic physics, and compare it with the standard Scratch learning tool which does not use robotics but a two‐dimensional (2D) cartoon avatar they are already familiar with. For practical reasons, a quasiexperimental design with nonequivalent groups and 85 second‐year Secondary Education students (ages 12–13) was performed. They were separated into control and experimental groups and followed a seven‐session intervention with the baseline 2D Scratch and the 3D simulated robots platform, respectively. Both used a visual block programming language and the same activities. To have quantitative and reliable results, a widely accepted CT test has been used, pre‐ and postintervention. Also qualitative feedback is presented. The obtained results show that using the platform with simulated 3D robots significantly helps when developing students' CT. With it, the students do learn basic programming concepts and reach higher scores in the CT test. This improvement applies to all CT‐analyzed concepts except in functions where the grades are maintained. Furthermore, students manage to master the activities on the 3D simulated robots platform, which reflects on the empowerment the platform has got in them.

Abstract This study focuses on promoting women's participation in engineering education (WPEE), which is crucial for inclusive and innovative development in the engineering fields and key to achieving the United Nations Sustainable Development Goals (SDGs). Due to the global shortage of women engineers, there is a need. to find effective ways to increase WPEE. This study aims to identify key factors that influence WPEE, which should be prioritized in policymaking. By adopting a three‐round Delphi survey and an improved fuzzy DEMATEL model, the findings reveal that the factors influencing WPEE are complex and multifaceted. Within the Chinese context, six factors, including hobbies and interest, employment expectation, parental occupation, incentive measures, social attitudes, and employment prospects, have been identified as key determinants of WPEE, exhibiting greater centrality and causality than others. This study not only provides empirical evidence from China but also introduces a novel approach to identifying key factors promoting WPEE, offering significant insights into global policy and practice.

Abstract The field of computer vision is characterized by computationally intensive algorithms and techniques with strict real‐time requirements. Field programmable gate arrays (FPGAs) are based on a concurrent paradigm which allows the design of efficient hardware architectures and has positioned FPGAs as an ideal device for implementing compute‐intensive applications. For this reason, FPGA technology has had a great impact in areas such as computer vision, where one of the main objectives for researchers working in this field is to create efficient automatic object recognition systems. Therefore, the need to provide undergraduates with the necessary skills to design FPGA‐based object recognition systems is evident. With this aim in mind, it is essential that specialization courses related to the design of these systems include the required resources for the student to apply the theoretical knowledge in solving practical problems. In this article, we present a development tool designed to help students, teachers, and researchers during the design‐modeling‐implementation process of object recognition systems based on FPGAs. The proposed tool operates under a modular approach as this facilitates the working on any of the phases of a recognition system and it is considered as a hybrid because the other phases can be developed using a software language. An empirical evaluation involving undergraduates enrolled in a Computer Engineering program was conducted to create a hardware architecture for the DAISY descriptor that uses the homogeneous features of objects immersed in images to produce an efficient representation. By considering similar descriptors such as Scale‐Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG), DAISY is computed by convolving orientation maps instead of using weighted sums of gradient norms, which offers the same kind of invariance at a lower computational cost for the dense case. The results obtained during such an evaluation indicated that students consider this FPGA‐based tool to be an alternative to receiving practical training on designing systems for solving problems related to the area of object recognition.

Abstract Mastering the concept of distributed forces is vital for students who are pursuing a major involving engineering mechanics. Misconceptions related to distributed forces that are typically acquired in introductory Physics courses should be corrected to increase student success in subsequent mechanics coursework. The goal of this study was to develop and assess a guided instructional activity using augmented reality (AR) technology to improve undergraduate engineering students' understanding of distributed forces. The AR app was accompanied by a complementary activity to guide and challenge students to model objects as beams with progressively increasing difficulty. The AR tool allowed students to (a) model a tabletop as a beam with multiple distributed forces, (b) visualize the free body diagram, and (c) compute the external support reactions. To assess the effectiveness of the activity, 43 students were allocated to control and treatment groups using an experimental nonequivalent groups preactivity/postactivity test design. Of the 43 students, 35 participated in their respective activity. Students in the control group collaborated on traditional problem‐solving, while those in the treatment group engaged in a guided activity using AR. Students' knowledge of distributed forces was measured using their scores on a 10‐item test instrument. Analysis of covariance was utilized to analyze postactivity test scores by controlling for the preactivity test scores. The treatment group demonstrated a significantly greater improvement in postactivity test scores than that of the control group. The measured effect size was 0.13, indicating that 13% of the total variance in the postactivity test scores can be attributed to the activity. Though the effect size was small, the results suggest that a guided AR activity can be more effective in improving student learning outcomes than traditional problem‐solving.

Abstract Recently, automatic judgment systems have been widely used in various computer science lectures, and these systems are mainly developed for languages with a console‐based practice environment. On the other hand, embedded systems such as Arduino are executed in a hardware‐based environment and their verification is conducted by manually checking the operation of the hardware. Therefore, it is not easy to automatically judge the Arduino system developed by students. In the embedded system, hardware and software have to be developed at the same time, and it increases effort and time for the instructors to teach students, especially in practice‐based learning. The study in this article proposes an Arduino practice judgment system based on the function execution log in virtual execution environment. It performs both Fritzing‐based hardware configuration checking and source code testing based on virtual execution environment, where hardware operations are replaced by mock‐up functions. In addition, more diverse practices are possible by providing an experimental environment where students can freely enter input data and check the results. A case study shows that the developed system can be applied to the lectures and help reduce instructors' work and increase students' understanding through the system.

Abstract This article explores the integration of artificial intelligence (AI) applications into project‐based learning (PBL) education as a means to enhance students' education. Specifically, the implementation of AI in the context of power generation is addressed, focusing on achieving power generation at minimum wind velocity and classifying turbine faults using AI techniques. The researchers have proposed a novel generating unit which is going to generate 1 KW of electric power at a specific flow rate of air and the generated power will be stored in the battery bank through the charge controller and then the load is driven from the battery through an inverter. Iron or core losses (Hysteresis, Eddy Current losses) can be acknowledged as one of the major reasons for the inefficiency of conventional generators, therefore anovel coreless model generator was proposed which also improved efficiency and reduces drag. Wind Turbine prototype was fabricated and deployed for the testing and validation of the proposed novel design. The design produced outstanding power ratings and electrical generation characteristics compared with other existing strategies at minimal air flow. Results proved that the proposed coreless axial flux generator has the capability to produce a better power rating compared with the existing wind turbine generators. Proposed Axial flux achieved 10.73 watt power at wind velocity at around 80 rpm. At a wind velocity of 10 m/s and around 800 rpm 313–330 kwh was produced by the proposed generator while the conventional generator produced around 300 kwh. The proposed generator design performed 35% better in terms of production efficiency under load and no load conditions. Moreover, faults in turbines are very common due to the various temperatures, therefore the faults have been classified using state‐of‐the‐art AI‐based classifiers. A comparison of space vector modulation (SVM) and Naive Bayes classifiers was performed in the study to classify wind turbine faults. It was found that both classifiers performed well in achieving high accuracy. SVM achieved a slightly higher accuracy of 0.9861 compared with Naive Bayes, which achieved an accuracy of 0.967. Based on the results, it can be inferred that SVM may be a more suitable classifier for wind turbine fault classification. The case study results demonstrated the potential of AI applications in PBL education, offering students a multidisciplinary learning experience that enhances their technical knowledge, problem‐solving skills, and teamwork abilities.

Abstract Optional university courses are designed to allow undergraduate students to specialize in relevant fields to enhance their skills and knowledge for their future careers. However, there are some cases in which students prioritize enrolling in courses that are easy to pass. This choice results in having students with low motivation and commitment, who mainly focus on doing just enough to pass the course, missing the opportunity to boost their skills. In this study, an eclectic approach is proposed, applying a mixture of active learning methods together with the theory of multiple intelligences to improve students' performance, motivation, and commitment throughout the course. The study was applied to the 56 students enrolled in the optional Micro‐Robotics Application spring course in the year 2021 at the University of Cádiz (Spain). Results demonstrate that this combination of active learning methodologies increased students' motivation, prompting them to give their best in terms of commitment, performance, and creativity. Furthermore, they were convinced that during the course they not only learned relevant robotic knowledge but also acquired essential skills needed for their future. Finally, this study highlights the benefits and future directions for implementing active learning methodologies in science, technology, engineering, and mathematics courses.