Category Archives: Education

Interesting way of explaining pointers and arrays of C in teaching programming

W. Rong, T. Xu, Z. Sun, Z. Sun, Y. Ouyang and Z. Xiong, An Object Tuple Model for Understanding Pointer and Array in C Language, IEEE Transactions on Education, vol. 66, no. 4, pp. 318-329, Aug. 2023 DOI: 10.1109/TE.2023.3236027.

Contribution: In this study, an object tuple model has been proposed, and a quasi-experimental study on its usage in an introductory programming language course has been reported. This work can be adopted by all C language teachers and students in learning pointer and array-related concepts. Background: C language has been extensively employed in numerous universities as an introductory programming practice. However, the pointer and array have long been recognized as some of the most difficult concepts for novice students learning C language. To help students become familiar with the concept of pointer and array and also their related operations, a comprehensive understanding from memory management\u2019s perspective might be helpful. Research Questions: 1) How does the object tuple model help students understand all kinds of object types from a generalized perspective? 2) Why is it important to let the students consider multiple arrays from a 1-D perspective? and 3) How do the memory-oriented operations from the object\u2019s perspective help students comprehensively understand the pointer and array? Methodology: The students were divided into experimental and control groups, and the object tuple model was presented in the experimental group. An examination was conducted at end of the semester, and test data were gathered for further analysis. Findings: The proposed object tuple model is effective in giving students clear guidance and helping them further understand the pointer and array in C language.

Measuring conceptual understanding of Systems & Signals university subjects

C. Crockett, H. C. Powell and C. J. Finelli, Conceptual Understanding of Signals and Systems in Senior Undergraduate Students, IEEE Transactions on Education, vol. 66, no. 2, pp. 113-122, April 2023 DOI: 10.1109/TE.2022.3199079.

Contribution: This article proposes a new definition of conceptual understanding (CU) specific to engineering. It then measures CU of signals and systems (S&S) in senior undergraduate students and describes how students approach conceptual problems. Background: Previous studies across multiple engineering subjects show students have low CU at the end of courses. However, little is known about CU semesters after a course. Research Questions: What is the CU of S&S concepts among electrical engineering senior students? Methodology: This mixed method study uses quantitative concept inventory data (n=467) and think-aloud interviews (n=12) to measure CU. The results come from two universities. Findings: Seniors\u2019 scores on the concept inventory are typical of scores presented at the end of an S&S course. Many struggled with the concept of linearity, made a common error when finding the maximum value in graphical convolution, and had low confidence on relating frequencies in time to a Fourier transform representation, but seniors had relatively high CU of time invariance and filtering.

On the increasing problem of writing quality of engineering students

F. C. Berry, M. L. Phillips, J. Condron and P. A. Sanger, Improving Writing Quality of Capstone Reports, IEEE Transactions on Education, vol. 64, no. 4, pp. 383-389, Nov. 2021, DOI: 10.1109/TE.2021.3059739.

Contributions: The main contribution is to share a series of practical methods that improve the writing quality of capstone reports. Background: The ability to write well is critical to the success of an engineering technology graduate. However, the evidence points to the fact that industries are disappointed with the quality of writing skills graduates demonstrate. Intended Outcomes: A faculty review of capstone reports showed little improvement in writing quality from the first course to the second in a two-semester capstone sequence. Therefore, the instructors explored what actions were needed to improve the writing quality of the capstone reports. Application Design: Several changes in the capstone courses were developed and implemented. The changes included: 1) using instructional technology as a scaffolding to help frame the writing required for the course and 2) engaging students in iterative writing with feedback. Findings: The assessment data showed a significant improvement, at the 5% level. The iterative process of writing and rewriting the report, coupled with frequent meetings with faculty mentors, proved to be a powerful combination for improving the writing process of the students.

A MATLAB toolbox for controlling and programming KUKA robots and a list of robotics toolboxes

M. Safeea and P. Neto, KUKA Sunrise Toolbox: Interfacing Collaborative Robots With MATLAB, IEEE Robotics & Automation Magazine, vol. 26, no. 1, pp. 91-96, 2019 DOI: 10.1109/MRA.2018.2877776.

Collaborative robots are increasingly present in our lives. The KUKA LBR iiwa, equipped with the KUKA Sunrise.OS controller, is one example of a collaborative/sensitive robot. This tutorial presents the KUKA Sunrise Toolbox (KST), a MATLAB toolbox that interfaces with KUKA Sunrise.OS. KST contains functionalities for networking, soft control in real time, point-to-point motion, parameter setters/getters, general purpose, and physical interaction. It includes approximately 100 functions and runs on a remote computer connected with the KUKA Sunrise controller via Transmission Control Protocol/Internet Protocol (TCP/IP). The potentialities of the KST are demonstrated in nine application examples.

On the use of flipped classroom for control engineering classes and its problem with the required (longer) time for learning

Y. Kim and C. Ahn, Effect of Combined Use of Flipped Learning and Inquiry-Based Learning on a System Modeling and Control Course, IEEE Transactions on Education, vol. 61, no. 2, pp. 136-142, DOI: 10.1109/TE.2017.2774194.

Contribution: This paper illustrates how to design and implement curricula in terms of the combined use of flipped learning and inquiry-based learning in an engineering course. Background: Elementary courses in engineering schools are conventional and foundational, and involve a considerable amount of knowledge. Throughout such courses, students are also expected to develop insight, which cannot be obtained by only listening to instructors. Having relevant discussions is also difficult for most instructors. Intended Outcomes: The combined use of flipped learning and inquiry-based learning would be beneficial to broaden student achievement. Application Design: Based on an epistemological approach about knowledge and knowing, this paper applies the combined use of flipped learning and inquiry-based learning to enhance student knowledge and advance ways of thinking on a System Modeling and Control course. Findings: The extended learning time and the collective responsibility for learning are discussed as critical issues in applying the combined use of flipped learning and inquiry-based learning in an engineering school.

Interesting study about the concepts to be taught in control system engineering

R. M. Reck, Common Learning Objectives for Undergraduate Control Systems Laboratories,IEEE Transactions on Education, vol. 60, no. 4, pp. 257-264, DOI: 10.1109/TE.2017.2681624.

Course objectives, like research objectives and product requirements, help provide clarity and direction for faculty and students. Unfortunately, course and laboratory objectives are not always clearly stated. Without a clear set of objectives, it can be hard to design a learning experience and determine whether students are achieving the intended outcomes of the course or laboratory. In this paper, a common set of laboratory objectives, concepts, and components of a laboratory apparatus for undergraduate control systems laboratories were identified. A panel of 40 control systems faculty members completed a multi-round Delphi survey to bring them toward consensus on the common aspects of their laboratories. These panelists identified 15 laboratory objectives, 26 concepts, and 15 components common to their laboratories. Then an 45 additional faculty members and practitioners completed a follow-up survey to gather feedback on the results. In both surveys, each participant rated the importance of each item. While average ratings differed slightly between the two groups, the order in which the items were ranked was similar. Important examples of common learning objectives include connecting theory to what is implemented in the laboratory, designing controllers, and modeling systems. The most common component in both groups was MathWorks software. Some of the common concepts include block diagrams, stability, and PID control. Defining common aspects of undergraduate control systems laboratories enables common development, detailed comparisons, and simplified adaptation of equipment and experiments between campuses and programs.

An interesting simulation educational software for control systems engineering based on controlling a quadrotor

S. Khan, M. H. Jaffery, A. Hanif and M. R. Asif, Teaching Tool for a Control Systems Laboratory Using a Quadrotor as a Plant in MATLAB, IEEE Transactions on Education, vol. 60, no. 4, pp. 249-256, DOI: 10.1109/TE.2017.2653762.

This paper presents a MATLAB-based application to teach the guidance, navigation, and control concepts of a quadrotor to undergraduate students, using a graphical user interface (GUI) and 3-D animations. The Simulink quadrotor model is controlled by a proportional integral derivative controller and a linear quadratic regulator controller. The GUI layout’s many components can be easily programmed to perform various experiments by considering the simulation of the quadrotor as a plant; it incorporates control systems (CS) fundamentals such as time domain response, transfer function and state-space form, pole-zero location, root locus, frequency domain response, steady-state error, position and disturbance response, controller design and tuning, unity, and the use of a Kalman filter as a feedback sensor. 3-D animations are used to display the quadrotor flying in any given condition selected by the user. For each simulation, users can view the output response in the form of 3-D animations, and can run time plots. The quadrotor educational tool (QET) helps students in the CS laboratory understand basic CS concepts. The QET was evaluated based on student feedback, grades, satisfaction, and interest in CS.

Modelling the implicit complexity of problem solving in exams

A. Shoufan, “Toward Modeling the Intrinsic Complexity of Test Problems,” in IEEE Transactions on Education, vol. 60, no. 2, pp. 157-163, May 2017.
DOI: 10.1109/TE.2016.2611666.

The concept of intrinsic complexity explains why different problems of the same type, tackled by the same problem solver, can require different times to solve and yield solutions of different quality. This paper proposes a general four-step approach that can be used to establish a model for the intrinsic complexity of a problem class in terms of solving time. Such a model allows prediction of the time to solve new problems in the same class and helps instructors develop more reliable test problems. A complexity model, furthermore, enhances understanding of the problem and can point to new aspects interesting for education and research. Students can use complexity models to assess and improve their learning level. The approach is explained using the K-map minimization problem as a case study. The implications of this research for other problems in electrical and computer engineering education are highlighted. An important aim of this paper is to stimulate future research in this area. An ideal outcome of such research is to provide complexity models for many, or even all, relevant problem classes in various electrical and computer engineering courses.

Personalizing the assessments generated automatically for students in order to minimize plagiarism: the case of programming

S. Manoharan, “Personalized Assessment as a Means to Mitigate Plagiarism,” in IEEE Transactions on Education, vol. 60, no. 2, pp. 112-119, May 2017.
DOI: 10.1109/TE.2016.2604210.

Although every educational institution has a code of academic honesty, they still encounter incidents of plagiarism. These are difficult and time-consuming to detect and deal with. This paper explores the use of personalized assessments with the goal of reducing incidents of plagiarism, proposing a personalized assessment software framework through which each student receives a unique problem set. The framework not only auto-generates the problem set but also auto-marks the solutions when submitted. The experience of using this framework is discussed, from the perspective of both students and staff, particularly with respect to its ability to mitigate plagiarism. A comparison of personalized and traditional assignments in the same class confirms that the former had far fewer observed plagiarism incidents. Although personalized assessment may not be cost-effective in all courses (such as language courses), it still can be effective in areas such as mathematics, engineering, science, and computing. This paper concludes that personalized assessment is a promising approach to counter plagiarism.

A method to predict the grading of students during a course

Yannick Meier, Jie Xu, Onur Atan, and Mihaela van der Schaar, Predicting Grades, in IEEE Transactions on Signal Processing , vol.64, no.4, pp.959-972, Feb.15, 2016, DOI: 10.1109/TSP.2015.2496278.


To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on maximizing the accuracy of the prediction while overseeing the importance of issuing timely and personalized predictions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students’ performance in a course. We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past seven years. We demonstrate that for 85% of the students we can predict with 76% accuracy whether they are going do well or poorly in the class after the fourth course week. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.