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Analysis of Teachers' Recognition of the Physics Lesson with Artificial Neural Network Models in Elementary School
New Physics: Sae Mulli 2017; 67: 1095~1108
Published online September 29, 2017;
© 2017 New Physics: Sae Mulli.

Seongsoo JEON1, Jong-Ho PARK*2

1 Hoewon Elementary School, Changwon 51291, Korea
2 Department of Science Education, Chinju National University of Education, Jinju 52673, Korea
Correspondence to:
Received June 30, 2017; Revised July 6, 2017; Accepted July 6, 2017.
cc This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
This research was done the implications of teachers' recognition of the physics area in science instructions in primary schools by using ANN models to analyze their recognition. One hundred twenty-five participants, who teach 3rd to 6th grade elementary students, responded to written survey questionnaires. After the educational conditions, including the grades of students, the gender of targets, whether or not they were homeroom teachers, and science lesson period per week had been set the diagram and the accuracy rate of classifications based on teachers' recognition of science instruction for understanding physics concepts were analyzed through the interrelationship among variables using ANN models by groups. In the interrelation between the students' grade and educational condition variables, and science instruction recognition factors, teachers' cognition of physics instruction presented differently, or was statistically meaningful, by their educational conditions. The participant groups were classified into four: female teachers who instruct science as homeroom teachers, subject teachers other than science, male teachers, and those in charge of 3rd and 4th grade students. 
PACS numbers: 01.40.Gm
Keywords: Artificial neural network, Science education, Physics education

January 2018, 68 (1)
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