Research Article
Smart Farm Teaching Aids Based on STEM Concepts
@INPROCEEDINGS{10.1007/978-3-031-43135-7_2, author={Hsin-Te Wu and Kuo-Chun Tseng}, title={Smart Farm Teaching Aids Based on STEM Concepts}, proceedings={Bio-inspired Information and Communications Technologies. 14th EAI International Conference, BICT 2023, Okinawa, Japan, April 11-12, 2023, Proceedings}, proceedings_a={BICT}, year={2023}, month={9}, keywords={Artificial Intelligence Smart Farming Interdisciplinary Teaching STEM Digital Transformation}, doi={10.1007/978-3-031-43135-7_2} }- Hsin-Te Wu
Kuo-Chun Tseng
Year: 2023
Smart Farm Teaching Aids Based on STEM Concepts
BICT
Springer
DOI: 10.1007/978-3-031-43135-7_2
Abstract
In view of the above-mentioned problems, this paper is based on STEM education and constructs teaching aids for smart farms, allowing students to practice the teaching aids developed by this paper in the field. The teaching aids of this paper are mainly based on the detection of farm and honeycomb status. Determine whether there are any abnormalities between the farm and the activity status of bees, such as: crop growth, bee reproduction, etc. Students can increase their interest in IT practical learning through teaching aid assembly and program operation. In addition, students should correct the teaching aid parameters during actual operation. Improving the recognition accuracy will further arouse students’ interest in artificial intelligence theory learning and achieve STEM education concepts. This paper will mainly use traditional artificial intelligence practical teaching methods and the innovative teaching methods proposed by this paper for learning comparison. The teaching method will be evaluated through the T test method through front and back questionnaires, teaching evaluation and student achievement scores. Help show whether the teaching method of this paper has achieved the expected goal. This article aims to cultivate more talents through Artificial Intelligence of Things (AIoT) teaching aids, analyzing data using t-tests in Statistical Product and Service Solutions (SPSS). Therefore, the experimental results prove that the STEM 4.0 approach proposed in this study can enhance students’ learning performance and willingness.
