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Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms

A robust and scientifically grounded teaching evaluation system holds significant importance in modern education, serving as a crucial metric that reflects the quality of classroom instruction. However, current methodologies within smart classroom environments have distinct limitations. These includ...

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Autores principales: Wang, Zhifeng, Li, Longlong, Zeng, Chunyan, Yao, Jialong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575413/
https://www.ncbi.nlm.nih.gov/pubmed/37837019
http://dx.doi.org/10.3390/s23198190
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author Wang, Zhifeng
Li, Longlong
Zeng, Chunyan
Yao, Jialong
author_facet Wang, Zhifeng
Li, Longlong
Zeng, Chunyan
Yao, Jialong
author_sort Wang, Zhifeng
collection PubMed
description A robust and scientifically grounded teaching evaluation system holds significant importance in modern education, serving as a crucial metric that reflects the quality of classroom instruction. However, current methodologies within smart classroom environments have distinct limitations. These include accommodating a substantial student population, grappling with object detection challenges due to obstructions, and encountering accuracy issues in recognition stemming from varying observation angles. To address these limitations, this paper proposes an innovative data augmentation approach designed to detect distinct student behaviors by leveraging focused behavioral attributes. The primary objective is to alleviate the pedagogical workload. The process begins with assembling a concise dataset tailored for discerning student learning behaviors, followed by the application of data augmentation techniques to significantly expand its size. Additionally, the architectural prowess of the Extended-efficient Layer Aggregation Networks (E-ELAN) is harnessed to effectively extract a diverse array of learning behavior features. Of particular note is the integration of the Channel-wise Attention Module (CBAM) focal mechanism into the feature detection network. This integration plays a pivotal role, enhancing the network’s ability to detect key cues relevant to student learning behaviors and thereby heightening feature identification precision. The culmination of this methodological journey involves the classification of the extracted features through a dual-pronged conduit: the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN). Empirical evidence vividly demonstrates the potency of the proposed methodology, yielding a mean average precision (mAP) of 96.7%. This achievement surpasses comparable methodologies by a substantial margin of at least 11.9%, conclusively highlighting the method’s superior recognition capabilities. This research has an important impact on the field of teaching evaluation system, which helps to reduce the burden of educators on the one hand, and makes teaching evaluation more objective and accurate on the other hand.
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spelling pubmed-105754132023-10-14 Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms Wang, Zhifeng Li, Longlong Zeng, Chunyan Yao, Jialong Sensors (Basel) Article A robust and scientifically grounded teaching evaluation system holds significant importance in modern education, serving as a crucial metric that reflects the quality of classroom instruction. However, current methodologies within smart classroom environments have distinct limitations. These include accommodating a substantial student population, grappling with object detection challenges due to obstructions, and encountering accuracy issues in recognition stemming from varying observation angles. To address these limitations, this paper proposes an innovative data augmentation approach designed to detect distinct student behaviors by leveraging focused behavioral attributes. The primary objective is to alleviate the pedagogical workload. The process begins with assembling a concise dataset tailored for discerning student learning behaviors, followed by the application of data augmentation techniques to significantly expand its size. Additionally, the architectural prowess of the Extended-efficient Layer Aggregation Networks (E-ELAN) is harnessed to effectively extract a diverse array of learning behavior features. Of particular note is the integration of the Channel-wise Attention Module (CBAM) focal mechanism into the feature detection network. This integration plays a pivotal role, enhancing the network’s ability to detect key cues relevant to student learning behaviors and thereby heightening feature identification precision. The culmination of this methodological journey involves the classification of the extracted features through a dual-pronged conduit: the Feature Pyramid Network (FPN) and the Path Aggregation Network (PAN). Empirical evidence vividly demonstrates the potency of the proposed methodology, yielding a mean average precision (mAP) of 96.7%. This achievement surpasses comparable methodologies by a substantial margin of at least 11.9%, conclusively highlighting the method’s superior recognition capabilities. This research has an important impact on the field of teaching evaluation system, which helps to reduce the burden of educators on the one hand, and makes teaching evaluation more objective and accurate on the other hand. MDPI 2023-09-30 /pmc/articles/PMC10575413/ /pubmed/37837019 http://dx.doi.org/10.3390/s23198190 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Zhifeng
Li, Longlong
Zeng, Chunyan
Yao, Jialong
Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms
title Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms
title_full Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms
title_fullStr Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms
title_full_unstemmed Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms
title_short Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms
title_sort student learning behavior recognition incorporating data augmentation with learning feature representation in smart classrooms
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10575413/
https://www.ncbi.nlm.nih.gov/pubmed/37837019
http://dx.doi.org/10.3390/s23198190
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