Abstract:Learning to program at a stationary computer for any programming course can be boring and demotivated especially when dealing with complex syntax details. A more hands-on approach utilizing robotic module will lead to a better task-oriented interaction between students and their real-life surroundings hoping to increase student engagement with programming. Thus, this paper proposed a constructionist robotic module to facilitate learning in C programming curriculum utilizing a microcontroller board known as FRD… Show more
“…Arduino-series Platform Arduino, Arduino C [14,15] FRDM-KL05Z, C [16] TCLab Arduino Kit, MATLAB or Python [17] Arduino, LabView and Scratch [18] LEGO-series Platform LEGO EV3, MATLAB [19,20] LEGO EV3, Block-based Programming [21] LEGO EV3, EV3-G [22] LEGO EV3, LabView [23] LEGO EV3, Scratch [24] LEGO WeDo, Scratch [25] A review of this literature can provide insight into teaching trends and some details of the courses. However, most works in the literature focus on the course content, and few focus on the design methods of robotics experiments and students' hands-on experimental results.…”
There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students’ learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based on our teaching experience and multiple practical experiences of allowing students to do hands-on experiments. It consists of eight steps, mainly including experimental goals, experimental activities, robot assembly, robot control, in-class evaluation criteria, and after-class report requirements. The after-class report requirements designed in the proposed method can help students improve their report-writing abilities. A wall-following robotics experiment designed using the PDCA method is proposed, and some students’ learning outcomes and after-class reports in this experiment are presented to illustrate the effectiveness of the proposed method. This experiment also helps students to understand the fundamental application of multi-sensor fusion technology in designing an autonomous mobile robot. We can see that the proposed reference examples allow students to quickly assemble two-wheeled mobile robots with four different sensors and to design programs to control these assembled robots. In addition, the proposed in-class evaluation criteria stimulate students’ creativity in assembling different wall-following robots or designing different programs to achieve this experiment. We present the learning outcomes of three stages of the wall-following robotics experiment. Three groups of 42, 37, and 44 students participated in the experiment in these three stages, respectively. The ratios of the time required for the robots designed by students to complete the wall-following experiment, less than that of the teaching example, are 3/42 = 7.14%, 26/37 = 70.27%, and 44/44 = 100%, respectively. From the comparison of learning outcomes in the three stages, it can be seen that the proposed PDCA-based design method can indeed improve students’ learning outcomes and stimulate their active learning and creativity.
“…Arduino-series Platform Arduino, Arduino C [14,15] FRDM-KL05Z, C [16] TCLab Arduino Kit, MATLAB or Python [17] Arduino, LabView and Scratch [18] LEGO-series Platform LEGO EV3, MATLAB [19,20] LEGO EV3, Block-based Programming [21] LEGO EV3, EV3-G [22] LEGO EV3, LabView [23] LEGO EV3, Scratch [24] LEGO WeDo, Scratch [25] A review of this literature can provide insight into teaching trends and some details of the courses. However, most works in the literature focus on the course content, and few focus on the design methods of robotics experiments and students' hands-on experimental results.…”
There is a lack of research that proposes a complete and interoperable robotics experimental design method to improve students’ learning outcomes. Therefore, this study proposes a student-oriented method based on the plan-do-check-act (PDCA) concept to design robotics experiments. The proposed method is based on our teaching experience and multiple practical experiences of allowing students to do hands-on experiments. It consists of eight steps, mainly including experimental goals, experimental activities, robot assembly, robot control, in-class evaluation criteria, and after-class report requirements. The after-class report requirements designed in the proposed method can help students improve their report-writing abilities. A wall-following robotics experiment designed using the PDCA method is proposed, and some students’ learning outcomes and after-class reports in this experiment are presented to illustrate the effectiveness of the proposed method. This experiment also helps students to understand the fundamental application of multi-sensor fusion technology in designing an autonomous mobile robot. We can see that the proposed reference examples allow students to quickly assemble two-wheeled mobile robots with four different sensors and to design programs to control these assembled robots. In addition, the proposed in-class evaluation criteria stimulate students’ creativity in assembling different wall-following robots or designing different programs to achieve this experiment. We present the learning outcomes of three stages of the wall-following robotics experiment. Three groups of 42, 37, and 44 students participated in the experiment in these three stages, respectively. The ratios of the time required for the robots designed by students to complete the wall-following experiment, less than that of the teaching example, are 3/42 = 7.14%, 26/37 = 70.27%, and 44/44 = 100%, respectively. From the comparison of learning outcomes in the three stages, it can be seen that the proposed PDCA-based design method can indeed improve students’ learning outcomes and stimulate their active learning and creativity.
“…This may cause the program to fail to run properly in memory limited environments. In the process of algorithm design, it is necessary to consider how to optimize space complexity and reduce memory usage [7] .…”
This article aims to explore the application of algorithms in C language programming. Firstly, the article introduces the concept definition of algorithm design and elaborates on the concepts and characteristics of C language, including simplicity, efficiency, portability, and wide application. Next, the article discusses the problems and applications of commonly used algorithm design. Taking the factorial problem as an example, it proposes problems such as high time complexity, high spatial complexity, and instability, and presents solutions in combination with C language programming. Finally, the article proposes a suggestion to optimize the structure of C language by combining mathematical analysis methods. Through the explanation in this article, readers can better understand the application of algorithms in C language programming and improve program efficiency through optimization methods.
“…Students learn through building and programming small robots and social robots (Bers et al, 2014;Chen et al, 2017;Leonard et al, 2016). From some review articles (Mesquita et al, 2020;Johal et al, 2020), it is noted that robotics in education can be divided into three basic categories -robotics as a learning goal (Ahn et al, 2020), a learning aid (Miskon, et al, 2020), and a learning tool (Pozzi, et al, 2021). Robotics as a disciplinary goal focuses on the acquisition of technical skills and abilities necessary for professionals and engineers in the fields of Computer Science, Robotics, Artificial Intelligence (Miller and McBurney, 2008;Jacovi et al, 2021).…”
An educational robotics lab has been planned for undergraduate students in an Electronic Engineering degree, using the Project Based Learning (PBL) approach and the NAO robot. Students worked in a research context, with the aim of making the functions of the NAO robot as social and autonomous as possible, adopting in the design process the Wolfram Language (WL), from the Mathematica software. Interfacing the programming environment of the NAO with Mathematica, they solved in part the problem of autonomy of the NAO, thus realizing enhanced functions of autonomous movement, recognition of human faces and speech for improving the system social interaction. An external repository was created to streamline processes and stow data that the robot can easily access. Self-assessment processes demonstrated that the course provided students with useful skills to cope with real life problems. Cognitive aspects of programming by WL have also been collected in the students’ feedback.
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