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Good systems thinking skills are beneficial to engineering projects since, besides being multidisciplinary, they address the needs of large human populations. Therefore, it is paramount that engineers should not only excel in the technical parts but also be good system thinkers and aware of the potential impacts of their activity. In this sense, this paper describes how to implement a project assignment in a system dynamics course for electrical engineering students to teach systemic thinking skills and connect the technical content, industry, and social responsibility. The effects on student performance and retention, and an evaluation of their perceptions of learning gains, are presented. Five weeks are dedicated to the implementation of a project-based project assignment. A formative assessment provides students developmental feedback based on the competencies shown during the creative process. It is complemented by a summative assessment module, which evaluates the adequacy of the finished content, which is developed as an online portfolio. Results indicate that the concepts of systemic thinking and the holistic view of engineering projects were successfully implemented through project assignments. Moreover, students’ perceptions of their knowledge gains and comparisons of their performance and retention before and after the intervention indicate a positive influence on the learning process.

Students’ motivation in computer graphic courses is a challenge for educators. Computer courses entail abstract notions and algorithmic features, and these difficulties affect the students’ motivation. A problem-based learning serious game framework named Immersivio was designed. The researchers designed a quantitative study with a between subject design and implemented a treatment course using the problem-based serious game. The game was designed based on the principles of problem-based learning and serious games, i.e., higher order thinking, collaborative learning, and cognitive thinking and information visualization theory. A non-random sampling procedure (purposive sampling) was used in this quasi-experimental study and the participants formed to 2 groups, the experimental group (n = 24) and the control group (n = 26). MANOVA test results indicated that the experimental group participants who had experienced the Immersivio game were more motivated to learn computer graphics in terms of extrinsic motivation, intrinsic motivation, interest, attainment, cost, identification with academics, self-efficacy, and instrumentality. This study has implications for basic and advanced computer graphic lecturers and educators in this field.

The volume integral equation (VIE) that describes the forward scattering problem is generally solved by iterative methods, such as the conjugate gradient (CG) method. In this work, we unfold the CG method into an iterative deep neural network to accelerate solving the VIE. After the dielectric scatterer’s relative permittivity and the incident field are input into the network, the total field is trained to converge to the ground truth iteratively. In the neural network, Green’s function is taken as an explicit operator to describe wave physics, and the fast Fourier transform (FFT) is applied to accelerate the computation of volume integrations. The global influence of all points in the space is compressed into a layer by the volume integration. In numerical tests, we validate the accuracy, efficiency, and generalization ability of the proposed neural network, and investigate the feasibility of changing the input size and the frequency in the prediction. Results show that the network is scale-independent and adaptable to predict fields in a narrow frequency band. This work provides us a new perspective of incorporating both learned parameters and physics into numerical algorithms for fast computation, and has the potential of being applied in deep-learning-based inverse scattering problems. [ABSTRACT FROM AUTHOR]

Contribution: This multicase study compares computer orchestrated learning together (COLT) and student orchestrated learning together (SOLT) in cooperatively preparing first year university students for collaborative activities. COLT using computer orchestrated group learning environment (COGLE) was perceived efficient, effective, and inclusive for team effectiveness as it helps build domain knowledge and trust between neurologically typical (NT) and/or neurologically atypical (NAT) teammates. This study confirms existing and identifies two new antecedents for trust, namely, resolving cognitive conflicts and real-time updates to domain knowledge. Background: Industrial and organizational psychology literature links effective teamworking with domain knowledge and trust. Building team trust within a short period can be challenging, in particular in mixed teams of NT and NAT students. Facilitating teamwork can be resource hungry. Educational studies on trust and teamwork are therefore rare. Research Questions: This study investigates how orchestration affects teamwork by asking: 1) how does computer and student orchestration affect domain knowledge acquisition in NT and NAT students? and 2) how does computer and student orchestration affect the development of trust between NT and/or NAT students? Methodology: Both qualitative and quantitative data were captured at multiple points within multiple (literal and theoretical replication) cases. Case summaries and a cross-case analysis provided further data and the methodological triangulation needed for analytical generalization. Findings: COGLE’s scaffolding and nonsocial prompts for cooperation, shared goal orientation, shared monitoring, and shared working not only helped NT and NAT students learn together but also helped develop team-trust quickly. Delayed interactions, low team-trust, and clique formation were seen in SOLT teams. [ABSTRACT FROM AUTHOR]

Contribution: In the research presented in this article, a procedure for determining student workload has been designed, tested, and validated. This procedure also makes it possible to analyze how different teaching variables are affected by workload and to visualize the learning environment generated in the course. Background: When the project-based learning (PBL) methodology is used in a course, if the students’ workload is not properly planned and controlled, important variables in the educational environment, such as the teaching organization and the students’ approach to learning, can be affected. The PBL methodology may even become unfeasible as it affects the other courses with which it shares year and semester. This justifies the need to know the student workload of a subject. The previous research has presented procedures for establishing student workload. However, they use questionnaires with a high number of questions. A questionnaire with many questions affects the reliability of students’ answers. The questionnaire presented in this article reduces the number of questions without affecting the results obtained and it is easily applicable to other courses. Research Questions: The questions to be answered in this research are: 1) is the workload of the students, when using the PBL methodology in a course, in line with the one established in the syllabus? and 2) is the learning environment created in the course as expected? Methodology: The methodology used to obtain the necessary data is based on students completing weekly surveys, two critical incident questionnaires (CIQs), and a final survey. The aim was always to minimize the number of questions to be answered, ensuring that the data collected provided a true picture of the workload and the educational environment. Findings: The analysis of the data obtained by applying the procedure provides mechanisms to control the workload, ensure involvement and motivation, improve students’ academic results and show the learning environment created in the course. The implementation of this method, during three academic years, has allowed to test its effectiveness, validate some of the initial hypotheses, and confirm the conclusions of some previous studies. [ABSTRACT FROM AUTHOR]

Contributions: The study contributes to the social cognitive career theory (SCCT) by explaining high-school students’ career choices and finding possible relations between self-efficacy, interpersonal skills, what inspires them to choose a career, and their actual choices. The practical contribution of this research lies in understanding the impact of the For Inspiration and Recognition of Science and Technology (FIRST) program on its participants and graduates. Background: The FIRST program incorporates project-based learning that fosters the design and production of innovative robotics by teams of students who compete annually. Research Questions: 1) Do the FIRST program activities increase STEM exposure and encourage STEM career choices, and if so, how? 2) What are the factors that affect these choices? Are there correlations between those factors? If so, what are they? 3) Is the effect of the FIRST program on FIRST high-school students’ exposure and career choices different from that effect on FIRST graduates? If so, what are the differences and does gender play a role in these differences? Methodology: The research participants included 119 FIRST high-school students and 297 FIRST graduates. The research applied a convergent parallel mixed-methods approach, with data collected both qualitatively via interviews and quantitatively via questionnaires. Findings: Analysis of the data showed that the FIRST program increased participants’ STEM exposure and career choice in STEM domains. A significant, positive, strong correlation was found between interpersonal skills, STEM exposure, career choice, family and school support, and external motivation. [ABSTRACT FROM AUTHOR]

A dual-gripper robotic cell consists of multiple processing machines and one material handling robot, which can perform an unloading or a loading task one at a time but can hold two parts at the same time. We address a scheduling problem of the robotic cell that determines a robot task sequence when two part types are processed in a different set of machines and all machines have variable processing times within a given interval. The objective is to minimize the makespan. This study proposes a learning-based method, i.e., a reinforcement learning (RL) approach, for the first time, to address a dual-gripper robotic cell scheduling problem. The problem is modeled with a Petri net, a graphical and mathematical modeling tool, which is used as an environment in RL. The states, actions, and rewards are defined by using flow shop scheduling properties, features from a Petri net, and knowledge from previous studies of scheduling robotized tools. Then, the RL approach is compared to the first-in-first-out (FIFO) rule, which is generally used in practice, a swap sequence, which is widely used for cyclic scheduling of dual-gripper robotic cells, and a lower bound. The extensive experiments show that the proposed method performs better than FIFO and the swap sequence; moreover, the gap between the makespan of the proposed method and the lower bound is not large. Note to Practitioners—We address a scheduling problem of dual-gripper robotic cells with two-part types when all machines have processing time variations. We propose an RL approach to obtain an efficient robot task sequence in order to minimize makespan. The proposed method is performed offline, and a robot task sequence is then obtained instantaneously. The proposed method performs better than the FIFO rule used in practice and the swap sequence used for cyclic scheduling of robotic cells. It can be easily extended for scheduling other configurations of robotic cells. [ABSTRACT FROM AUTHOR]

Visual Question Answering (VQA) has attracted extensive research focus recently. Along with the ever-increasing data scale and model complexity, the enormous training cost has become an emerging challenge for VQA. In this article, we show such a massive training cost is indeed plague. In contrast, a fine-grained design of the learning paradigm can be extremely beneficial in terms of both training efficiency and model accuracy. In particular, we argue that there exist two essential and unexplored issues in the existing VQA training paradigm that randomly samples data in each epoch, namely, the “difficulty diversity” and the “label redundancy”. Concretely, “difficulty diversity” refers to the varying difficulty levels of different question types, while “label redundancy” refers to the redundant and noisy labels contained in individual question type. To tackle these two issues, in this article we propose a fine-grained VQA learning paradigm with an actor-critic based learning agent, termed FG-A1C. Instead of using all training data from scratch, FG-A1C includes a learning agent that adaptively and intelligently schedules the most difficult question types in each training epoch. Subsequently, two curriculum learning based schemes are further designed to identify the most useful data to be learned within each inidividual question type. We conduct extensive experiments on the VQA2.0 and VQA-CP v2 datasets, which demonstrate the significant benefits of our approach. For instance, on VQA-CP v2, with less than 75 percent of the training data, our learning paradigms can help the model achieves better performance than using the whole dataset. Meanwhile, we also shows the effectivenesss of our method in guiding data labeling. Finally, the proposed paradigm can be seamlessly integrated with any cutting-edge VQA models, without modifying their structures. [ABSTRACT FROM AUTHOR]

Contribution: This article explores the impact of the combination of various pedagogical approaches to deliver high-quality learning experiences for online-based engineering students. Though existing research extensively studied online education approaches, limited research has investigated how to bridge the gap between online cloud-based and campus-based students more effectively for obtaining hands-on engineering skills. Background: Given that engineers aim to solve real-world problems, engineering graduates need to obtain relevant experiences for employability. However, it is challenging to deliver such experiences in online education, which motivates innovative ways to integrate practical experiments in Web-based resources. Intended Outcomes: Practical and industry-relevant skills with flexibility in terms of time and pace of learning are intended to be achieved in the teaching framework which is expected to improve learning experiences for online students. Application Design: The adopted mixed pedagogical approach revolves around real-life problem-based learning delivered in the online mode using recorded experiments on energy-efficient design for three cohorts of the fourth-year engineering students, two of which are completely cloud-based students and the other one has a mix of on-campus and cloud-based students. Findings: The effectiveness of the adopted approach is measured through quantitative and qualitative evaluation tools. The evaluation demonstrated that cloud student engagement and motivation improved substantially by integrating explicit, analytical, as well as embodied learnings while enabling them to perform equally well as compared to the campus-based students. [ABSTRACT FROM AUTHOR]

Contribution: This article shares the learning ecosystem of a project-based safety-critical systems (SCS) course, identifying course contents that support self-directed learning and how assignments develop students’ knowledge and understanding toward meeting the demand of industries. Background: The syllabus for undergraduate and postgraduate students of the engineering stream has been periodically updated by universities and higher education institutes to meet the demand of industries. Due to high reliability and safety requirements, the development process of SCS differs from that of other industrial systems in many ways. This article seeks to further understand ways that a project-based learning approach in an undergraduate and postgraduate SCS can earn a systematic knowledge to meet industrial demands. Research Question: In what ways can a project-based learning and experimental approach in the undergraduate and postgraduate course can embed the expertise on the development of SCS among the students to meet the industrial demands and increase the employability. Methodology: In-depth reviews with 21 experienced industry experts from ten different organizations, working on different domains, were conducted in the context of the development of SCS in order to investigate critical aspects related to the development of SCS. Then, students and faculty members of different universities and institutes were interviewed to know the existing course structure of SCS. Findings: The salient differences between the university students and practices were analyzed, based on which a systematic course on the development of SCS is proposed that can meet the industry demand. [ABSTRACT FROM AUTHOR]

Contribution: This article adds to the results of previous systematic mapping study by addressing a more ample context of problem-based learning (PBL) in computing education. Background: PBL is defined as an instructional method of constructivist teaching that uses real problems as a motivating element for learning. Although PBL was born in medical education, it has been used in computing education to facilitate the students’ engagement and learning capacity, contributing to developing skills, such as teamwork, holistic vision, critical thinking, and solving problem. Considering that approach much more descriptive than prescriptive, it favors the implementation of diverse methodologies on its behalf. Research Question: “How the PBL approach has been applied in teaching computing?” Methodology: From the initial set of 3837 studies, 102 primary studies were selected and categorized through Kitchenham methodology for analysis from 1999 to 2019. Findings: Based on these studies, it was possible to verify a greater adoption of PBL in undergraduate education and a great diversity of strategies involving real-world problems, developing technical and no-technical skills and making the student much more motivated and engaged. Challenges were also mapped, which shows a very interesting field for new research. [ABSTRACT FROM AUTHOR]

Face analysis includes a variety of specific problems as face detection, person identification, gender and ethnicity recognition, just to name the most common ones; in the last two decades, significant research efforts have been devoted to the challenging task of age estimation from faces, as witnessed by the high number of published papers. The explosion of the deep learning paradigm, that is determining a spectacular increasing of the performance, is in the public eye; consequently, the number of approaches based on deep learning is impressively growing and this also happened for age estimation. The exciting results obtained have been recently surveyed on almost all the specific face analysis problems; the only exception stands for age estimation, whose last survey dates back to 2010 and does not include any deep learning based approach to the problem. This paper provides an analysis of the deep methods proposed in the last six years; these are analysed from different points of view: the network architecture together with the learning procedure, the used datasets, data preprocessing and augmentation, and the exploitation of additional data coming from gender, race and face expression. The review is completed by discussing the results obtained on public datasets, so as the impact of different aspects on system performance, together with still open issues. [ABSTRACT FROM AUTHOR]

Black high school (HS) boys are an untapped source for initiatives in broadening participation in computer science (CS). This article describes a five week program that employs active and problem-based learning activities to teach tech innovation and entrepreneurship to local teens in the Washington, DC, USA area. Called #WatchMeCode, the program's objective is to build black HS boys' confidence in coding, communicating their programming skills, and rebuilding their thoughts about computer science. The program uses a constructivist pedagogical approach allowing students to build their understanding of computer science and technology through Internet-search-based history lessons, podcasts, and presentations by other black male tech entrepreneurs. Additionally, students ventured out into their community with hands-on design-thinking and customer discovery skills to observe their environment, its issues/problems, and developed new technology with their unique perspectives in mind. Results showed that participants became interested and confident in computer science by creating their own tech innovations. [ABSTRACT FROM AUTHOR]

Congestion control is a fundamental network task that modulates the data transmission rates of traffic sources to efficiently utilize network capacity. With the advent of machine learning, congestion control based on deep reinforcement learning is the subject of extensive attention. At present, research on machine-learning-based congestion control is mainly focused on single-task scenarios -- to design a control strategy that is only used to reduce congestion. This article studies the congestion control problem based on machine learning in multi-task scenarios. Specifically, we propose a congestion control model based on multi-task deep reinforcement learning. The model takes congestion control as the main task and load balancing as the auxiliary task. Compared to the single-task method, our model can better represent the network environment by learning the shared representation of congestion features and load balancing features. Moreover, network traffic control may involve both congestion control and load balancing. Therefore, learning multiple tasks jointly while exploiting commonalities and differences across tasks can help reduce the cost of task coordination. We use software defined networking to decouple the data and control planes, making network control strategies more flexible than traditional networks. To the best of our knowledge, this is the first time multi-task learning has been applied to network traffic control. Experimental results show that the method is efficient. [ABSTRACT FROM AUTHOR]

Contribution: Increased understanding of the influence of a project-based multidisciplinary design and innovation course on students’ engineering design thinking, specifically regarding problem framing. Background: Entrepreneurship-related education has been offered to engineering and technology students, but its influence has been discussed mainly in terms of students’ self-efficacy or awareness of entrepreneurship or technological innovation, relying mostly on their self-reported data. Research Questions: 1) How do undergraduate students perceive the relationship between entrepreneurial mindsets and engineering design thinking after a project-based design course that integrates entrepreneurial activities and 2) How differently do they frame a design situation after this course? Methodology: This qualitative case study involved 14 undergraduate students pursuing a minor in design and innovation at a polytechnic institute. For more systematic data collection and analysis, a conceptual framework was developed based on literature about cognitive operations for problem framing and entrepreneurial engineering mindsets. Primary data were collected through semi-structured interviews and pre- and post-activities focused on problem framing. The interview and activity data were analyzed through multiple qualitative analysis approaches based on the conceptual framework. Findings: The analysis of the interview responses reveals the participants’ thoughts on the role of an entrepreneurial mindset in engineering design practice, and the comparison between their pre- and post-problem framing activities shows that they tended to focus more on Customer and Social problem elements and less on Technical and Business problem elements after the course. However, important questions remain to better understand the impact of this change on their design abilities. [ABSTRACT FROM AUTHOR]

Contribution: This paper introduces the real-world limitations and non-technical aspects of power electronics (PEs) projects to students through innovative tutorial activities. Background: Many electrical engineering curricula offer a PE courses (PECs) for third- or fourth-year undergraduate students. Prior research on PEs education mainly focused on improving students’ experimental skills through developing practical experiments, laboratory activities, and problem/project-based learning. An instructional approach that instead employs real-world knowledge and skills is worth evaluating. Intended Outcomes: Students should be able to consider real-world technical and non-technical limitations when applying theory to design PE circuits and converters, and be able to select and carry out appropriate tests to troubleshoot circuits. Application Design: Prior research on engineering education emphasized the importance of introducing real-world limitations to the students as part of their curriculum. This paper suggests that the tutorial activities presented in a PEC can help students acquire skills in designing and troubleshooting a circuit or system according to desired technical aspects, real-world limitations, and available data. Findings: Evidence of the validity of this approach in a PEC at two Australian universities, over four academic years, is provided. Students receiving the new tutorial activities had percentage scores some 10–15 points higher than those who had traditional tutorials. Another evaluation reveals the students’ vibrant participation in the activities during the new tutorial sessions. [ABSTRACT FROM AUTHOR]

Contribution: This paper reports engineering students ’ practical epistemic cognition by studying their interactional work in situ. Studying “epistemologies in action” the study breaks away from mainstream approaches that describe this in terms of beliefs or of stage theories. Background: In epistemology, knowledge is traditionally seen as “justified true belief,” neglecting knowledge related to action. Interest has increased in studying the epistemologies people use in situated action, and their development of epistemic fluency. How appropriate such approaches are in engineering and design education need further investigation. Research Questions: 1) How do students in the context of a design project use epistemic tools in their interactional work? and 2) What are the implications of the findings in terms of how students’ cognitive and epistemological development could be conceptualized? Methodology: A collaborative group of six students were video recorded on the 14th day of a fifth-semester design project, as they were preparing for a formal critique session. The entire, almost 6 h, session was recorded by four video cameras mounted in the design studio, with an additional fifth body-mounted camera. The video data collected was analyzed using video ethnographic, conversation analysis, and embodied interaction analysis methods. Findings: The results show that the students use a wealth of bodily material resources as an integral and seamless part of their interactions as epistemic tools, in their joint production of understanding and imagining. The analysis also suggests that students’ epistemological and cognitive development, individually and as a group, should be understood in terms of developing “epistemic fluency.” [ABSTRACT FROM AUTHOR]

Contribution: This paper reports a curriculum development in hybrid problem-based learning (h-PBL), addresses the design, implementation, effectiveness, and assessment issues of h-PBL, and explains the mixed results observed regarding the impact of problem-based learning (PBL) on student grades from a hybrid perspective. Background: The effect of PBL on student learning is difficult to analyze. Empirical research on h-PBL has been scant in the engineering education field. Intended Outcomes: The hybrid approach described in this paper can be used to guide other course designs. Future research directions are also provided in order to better capture the positive effects of PBL on student learning. Application Design: A 3-D imaging project was developed and implemented using h-PBL. A non-parametric hypothesis test was conducted to compare four-year student performance data collected after the implementation of h-PBL with four-year student performance data collected under traditional lecturing; both sets of students were taught by the same instructor. Findings: Student project grades improved after h-PBL, whereas student cumulative course grades did not show significant improvement. The study suggests that higher ratios and weights of PBL, better timing in introducing PBL, and more integrated course components in the hybrid approach may further improve student performance. [ABSTRACT FROM AUTHOR]

Background: Engineering programs must equip students to solve open-ended workplace problems. However, the literature points to actual or potential difficulties faced by students in solving open-ended or complex problems. During Fall 2014, the authors’ students experienced difficulties in solving open-ended bio-signals laboratory problems of designing input signals to analyze unknown systems via MATLAB programming. These difficulties resulted in low performance. Intended Outcomes: To support, or scaffold, problem-solving in subsequent semesters, a strategy of frequent and timely monitoring and feedback was used. The hypotheses were that these scaffolding strategies would be associated with enhanced performance on open-ended projects, and would support students in similar future work once removed. Application Design: Based upon strategies from the scaffolding literature, assignments that guided problem decomposition were used. Flipped instruction challenged students to prepare for the laboratory by reviewing worked programming examples and completing online assessments. The laboratory sessions were reserved for collaborative, hands-on programming, with instructor oversight, as in a problem-based learning environment. Students submitted frequent progress reports for self-monitoring and feedback throughout each project. Findings: A statistical comparison of project scores across semesters revealed performance improvements with scaffolding. Post-scaffolding assessment in a follow-up course determined scaffolding to be helpful and applicable by these students for similar projects. These preliminary results are important for STEM students and instructors encountering challenges with open-ended problem-solving of this nature, and they provide quantitative evidence recently called for by the STEM scaffolding literature. [ABSTRACT FROM AUTHOR]

The case study presented in this paper describes the pedagogical aspects and experience gathered while using an e-learning tool named IPA-PBL. Its main purpose is to provide additional motivation for adopting theoretical principles and procedures in a computer networks course. In the proposed model, the sequencing of activities of the learning process is grouped into three phases based on educational goals. In this way, the same tool is used on several courses with different curricula. In IPA-PBL, problem-based learning (PBL) is applied as a pedagogical strategy, as well as a set of concrete methods implemented in the software. Together with the pedagogical model, specific domain ontology is designed. In this way, the learner's knowledge can be analyzed in order to collect data necessary for the dynamic adaptation of system behavior. The results collected while using IPA-PBL are compared to those obtained without using the system. Statistical analysis, together with pertaining considerations and conclusions, are also presented in the paper. [ABSTRACT FROM PUBLISHER]

This paper describes a study of teaching a programming language in a C programming course by having students assemble and program a low-cost mobile robot. Writing their own programs to define the robot’s behavior raised students’ motivation. Working in small groups, students programmed the robots by using the control structures of structured programming. Over the course, there was a significant improvement in students’ academic performance and motivation. The approach took into account four of the motivational factors that feature in the ARCS model: 1) attention; 2) relevance; 3) confidence; and 4) satisfaction. The main achievements of the approach were to be able to: 1) catch and hold students’ attention throughout the course; 2) increase students’ confidence in the learning process; 3) achieve a high student satisfaction level with their acquired skills; and 4) demonstrate to students the practical usefulness of the knowledge they had learned. This paper also determines the extent to which this strategy was effective in meeting the teaching goals. [ABSTRACT FROM PUBLISHER]

The problem-based learning (PBL) approach has been successfully applied to teaching software engineering thanks to its principles of group work, learning by solving real problems, and learning environments that match the market realities. However, the lack of well-defined methodologies and processes for implementing the PBL approach represents a major challenge. The approach requires great flexibility and dynamism from all involved, whether in mapping content, in teacher performance, or laying out the process of how learners should go about solving problems. This paper suggests that management processes can help in implementing PBL throughout its life cycle (planning, implementation, monitoring, and enhancement), and proposes an assessment model called PBL-SEE for use in software engineering education (SEE). Two examples of its use are provided. The results show how the model can be applied and how the resulting information can be used to make the PBL initiatives “authentic,” in that they bring the reality of the labor market to the learning environment, while keeping to PBL principles. [ABSTRACT FROM PUBLISHER]

This paper presents a multidisciplinary problem-based learning (PBL) project consisting of the development of a robot arm prototype and the implementation of its control system. The project is carried out as part of Industrial Informatics (II), a compulsory third-year course in the Automation and Electronic Engineering (AEE) degree program at the School of Engineering at Universitat Politècnica de València, València, Spain. The robot arm is a low-cost prototype, initially controlled with a joystick. The aim of the project is, first, to design and implement a full control system for the robot, including the necessary circuitry for microcomputer (ARM Cortex)-based control and, second, to pit the designed robot in competition against other students' robots. Incorporation of multidisciplinary PBL required changes in the organization of the course, which features various learning activities and their continuous evaluation; it also required knowledge of five other courses in the AEE program. PBL methodology is compared to a traditional method; the results obtained proved highly satisfactory. Moreover, the PBL robotic experience was highly rated by some 900 students. [ABSTRACT FROM AUTHOR]