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The Design and Implementation of Effective Teaching Based on Human Factors Engineering

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Advances in Human Factors in Training, Education, and Learning Sciences (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1211))

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Abstract

With the popularization of mobile communication technology and equipment,students obtain knowledge from many sources by mobile phone or searching engine, the traditional mode of teaching does not adapt to the current situation, also the model of higher education is being challenged. It’s a important problem that how to attract students to study actively in class. The basic concept of human factors engineering is people-oriented, for any human-machine system, the limitation of human physiological and psycho-logical characteristics and behavioral capabilities should be considered first in order to improve the efficiency of the system, as well as teaching system. This paper put forward to some points about effective teaching based on human factors engineering. Effective means valid and efficient, which cover the whole procedure of making teaching objectives, analyzing student’s features, selecting teaching materials, design the classroom activities in order to attract students to take the initiative to learn. The author also discuss about the characteristics of student’s memory and attention based on the human in-formation processing model and the laws of human cognition in teaching system, and summarized the implementation of effective teaching when taught the course “Human Factors Engineering”. By several effective strategies and steps, students get more chances to do real things, enjoy joining classroom activities, so not only learn knowledge of the course but also learn how to solve practical problems.

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Correspondence to Qing Xue .

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Xue, Q., Gong, L. (2020). The Design and Implementation of Effective Teaching Based on Human Factors Engineering. In: Nazir, S., Ahram, T., Karwowski, W. (eds) Advances in Human Factors in Training, Education, and Learning Sciences. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1211. Springer, Cham. https://doi.org/10.1007/978-3-030-50896-8_11

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