Design of Smart Educational Robot as a Tool For Teaching Media Based on Contextual Teaching and Learning to Improve the Skill of Electrical Engineering Student

2nd International Conference on Vocational Education and Electrical Engineering

无线电电子学;教育

Zuhrie, M.S.^1 ; Basuki, I.^1 ; Asto, I.G.P.B.^2 ; Anifah, L.^2

Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia^1

Department of Computer Engineering, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia^2

关键词: Contextual Teaching and Learning;    Conventional machines;    Development model;    Electrical engineering students;    Engineering Competencies;    Focus of researches;    Mechanical design;    Research designs;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/336/1/012047/pdf
DOI  :  10.1088/1757-899X/336/1/012047

学科分类：发展心理学和教育心理学

来源: IOP

【 摘 要 】

The development of robotics in Indonesia has been very encouraging. The barometer is the success of the Indonesian Robot Contest. The focus of research is a teaching module manufacturing, planning mechanical design, control system through microprocessor technology and maneuverability of the robot. Contextual Teaching and Learning (CTL) strategy is the concept of learning where the teacher brings the real world into the classroom and encourage students to make connections between knowledge possessed by its application in everyday life. This research the development model used is the 4-D model. This Model consists of four stages: Define Stage, Design Stage, Develop Stage, and Disseminate Stage. This research was conducted by applying the research design development with the aim to produce a tool of learning in the form of smart educational robot modules and kit based on Contextual Teaching and Learning at the Department of Electrical Engineering to improve the skills of the Electrical Engineering student. Socialization questionnaires showed that levels of the student majoring in electrical engineering competencies image currently only limited to conventional machines. The average assessment is 3.34 validator included in either category. Modules developed can give hope to the future are able to produce Intelligent Robot Tool for Teaching.