TY - GEN
T1 - Combining Deep Learning and Computer Vision Techniques for Automatic Analysis of the Learning Process in STEM Education
AU - Lee, Hsin Yu
AU - Chang, Wei Cyun
AU - Huang, Yueh Min
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - STEM education has been a focus in recent years, evidenced by the increasing number of studies conducted on STEM education to enhance the future competitiveness of learners. Compared with traditional teaching methods, learning outcomes in STEM education focus on what is learned during the process of collaboration and problem-solving rather than on the score of the final exam or final project. However, most assessment tools measure learning outcomes using questionnaires or interviews, which lack objective standards and require time for data processing. We address these problems with a system that combines deep learning and computer vision techniques to automatically recognize the learner’s learning process in STEM education. System verification reveals an average precision of 87.1% and an average recall of 86.4%, which is sufficient to keep track of the learning process.
AB - STEM education has been a focus in recent years, evidenced by the increasing number of studies conducted on STEM education to enhance the future competitiveness of learners. Compared with traditional teaching methods, learning outcomes in STEM education focus on what is learned during the process of collaboration and problem-solving rather than on the score of the final exam or final project. However, most assessment tools measure learning outcomes using questionnaires or interviews, which lack objective standards and require time for data processing. We address these problems with a system that combines deep learning and computer vision techniques to automatically recognize the learner’s learning process in STEM education. System verification reveals an average precision of 87.1% and an average recall of 86.4%, which is sufficient to keep track of the learning process.
UR - http://www.scopus.com/inward/record.url?scp=85137979140&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-15273-3_3
DO - 10.1007/978-3-031-15273-3_3
M3 - Conference contribution
AN - SCOPUS:85137979140
SN - 9783031152726
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 22
EP - 32
BT - Innovative Technologies and Learning - 5th International Conference, ICITL 2022, Proceedings
A2 - Huang, Yueh-Min
A2 - Cheng, Shu-Chen
A2 - Barroso, João
A2 - Sandnes, Frode Eika
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Innovative Technologies and Learning, ICITL 2022
Y2 - 29 August 2022 through 31 August 2022
ER -