The Impact of Teachable Machine on Middle School Teachers' Perceptions of Science Lessons after Professional Development

被引:0
作者
Kurz, Terri L. [1 ]
Jayasuriya, Suren [2 ,3 ]
Swisher, Kimberlee [2 ]
Mativo, John [4 ]
Pidaparti, Ramana [5 ]
Robinson, Dawn T. [6 ]
机构
[1] Arizona State Univ, Teachers Coll, Tempe, AZ 85287 USA
[2] Arizona State Univ, Sch Arts Media & Engn, Tempe, AZ 85287 USA
[3] Arizona State Univ, Engn & Sch Elect Comp & Energy Engn, Tempe, AZ 85287 USA
[4] Univ Georgia, Environm Civil Agr & Mech Engn, Athens, GA 30602 USA
[5] Univ Georgia, Coll Engn, Athens, GA 30602 USA
[6] Univ Georgia, Dept Sociol, Athens, GA 20602 USA
来源
EDUCATION SCIENCES | 2024年 / 14卷 / 04期
基金
美国国家科学基金会;
关键词
middle school science; technology; Teachable Machine; STEM education; in-service teachers; professional development; TECHNOLOGY;
D O I
10.3390/educsci14040417
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Technological advances in computer vision and machine learning image and audio classification will continue to improve and evolve. Despite their prevalence, teachers feel ill-prepared to use these technologies to support their students' learning. To address this, in-service middle school teachers participated in professional development, and middle school students participated in summer camp experiences that included the use of Google's Teachable Machine, an easy-to-use interface for training machine learning classification models. An overview of Teachable Machine is provided. As well, lessons that highlight the use of Teachable Machine in middle school science are explained. Framed within Personal Construct Theory, an analysis of the impact of the professional development on middle school teachers' perceptions (n = 17) of science lessons and activities is provided. Implications for future practice and future research are described.
引用
收藏
页数:17
相关论文
共 58 条
  • [1] Abassian A., 2020, Investigations in Mathematics Learning, V12, P53
  • [2] Agustian D., 2021, P 2021 3 INT C CYBER, P1
  • [3] Anderson J., 2017, P 41 C INT GROUP PSY, VVolume 1, P141
  • [4] Anyanwu K., 2021, P SOC INFORM TECHNOL, P1140
  • [5] Arseven A., 2015, Universal Journal of Educational Research, V3, P973
  • [6] Lessons Learned: Authenticity, Interdisciplinarity, and Mentoring for STEM Learning Environments
    Ayar, Mehmet C.
    Yalvac, Bugrahan
    [J]. INTERNATIONAL JOURNAL OF EDUCATION IN MATHEMATICS SCIENCE AND TECHNOLOGY, 2016, 4 (01): : 30 - 43
  • [7] Bauer J., 2005, Journal of Technology and Teacher Education, V13, P519
  • [8] Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification
    Carney, Michelle
    Webster, Barron
    Alvarado, Irene
    Phillips, Kyle
    Howell, Noura
    Griffith, Jordan
    Jongejan, Jonas
    Pitaru, Amit
    Chen, Alexander
    [J]. CHI'20: EXTENDED ABSTRACTS OF THE 2020 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, 2020,
  • [9] Personal Paths of Mutual Recognition: Relational Patterns and Narrative Identities from a Personal Construct Perspective
    Chiari, Gabriele
    [J]. JOURNAL OF CONSTRUCTIVIST PSYCHOLOGY, 2023, 36 (04) : 504 - 523
  • [10] Clarke G., 2012, CHILDHOOD EDUC, V88, P136, DOI 10.1080/00094056.2012.662140