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	Deep learning practice for high school student engagement in STEM careers
	Santana, Otacilio Antunes; de Sousa, Barbara Alves; do Monte, Sandra Razana Silva; de Freitas Lima, Mayara Lopes; Ferraz e Silva, Caina
通讯作者	Santana, OA (corresponding author), Univ Fed Pernambuco, Dept Biofis & Radiobiol, Recife, PE, Brazil.
会议名称	IEEE Global Engineering Education Conference (IEEE EDUCON)
会议日期	APR 27-30, 2020
会议地点	ELECTR NETWORK
英文摘要	The use of deep learning activities at the educational stage is quite relevant for student engagement (positive or negative) in Science, Technology, Engineering, and Mathematics (STEM) careers. Physical contact with hardware, visual and interactive contact with software, and contact with algorithms are practices in which students observe the complexity of command could result in ingenious automation and task accomplishment (universal approximation theorem and/or probabilistic inference). Deep learning came to this work as a tool for solving a problem: How do we identify forage palm (Opuntia ficus-indica Mill, Cactaceae) for feeding goats, in an area under Semi-arid climate? High school students on advisory teachers of various disciplines (biology, chemical, physical, mathematics, geography, and others) attempt to answer to this question: Did student engagement happen by new context, problem to be solved, data collection (hardware), or data analysis (algorithm and software)? Sixty-seven high school students from Brazilian public schools (from 14 to 17 years old) participated in the proposed following teacher. High school students get more involved by practical and technological issues (mechanisms and hardware) than in scenario (new context) or by evaluation and discussion of the purpose. This data about the preference of one stage over other stages of Deep Learning determined the application for undergraduate courses in STEM. This was the most relevant data of this work, despite total engagement, students' preferences in one stage point out the student's academic vocation.
英文关键词	K-12 Education Engineering Career Problem-Based Learning Engineering Education Remote Labs
来源出版物	PROCEEDINGS OF THE 2020 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON 2020)
ISSN	2165-9567
出版年	2020
页码	164-169
ISBN	978-1-7281-0930-5
出版者	IEEE
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000617739900031
WOS关键词	VOCATIONAL-EDUCATION ; MATHEMATICS STEM ; TECHNOLOGY ; MOTIVATION ; SCIENCE ; LAB
WOS类目	Education, Scientific Disciplines ; Engineering, Multidisciplinary
WOS研究方向	Education & Educational Research ; Engineering
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/365575
作者单位	[Santana, Otacilio Antunes] Univ Fed Pernambuco, Dept Biofis & Radiobiol, Recife, PE, Brazil; [de Sousa, Barbara Alves; do Monte, Sandra Razana Silva; Ferraz e Silva, Caina] Univ Fed Pernambuco, Programa Posgrad Rede Nacl Ensino Ciencias Ambien, Recife, PE, Brazil; [de Freitas Lima, Mayara Lopes] Univ Fed Rural Pernambuco, Programa Posgrad Ensino Ciencias, Recife, PE, Brazil
推荐引用方式 GB/T 7714	Santana, Otacilio Antunes,de Sousa, Barbara Alves,do Monte, Sandra Razana Silva,et al. Deep learning practice for high school student engagement in STEM careers[C]:IEEE,2020:164-169.