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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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s23198190","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T04:39:30Z","timestamp":1696221570000},"page":"8190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Student Learning Behavior Recognition Incorporating Data Augmentation with Learning Feature Representation in Smart Classrooms"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6960-509X","authenticated-orcid":false,"given":"Zhifeng","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longlong","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5799-6692","authenticated-orcid":false,"given":"Chunyan","family":"Zeng","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialong","family":"Yao","sequence":"additional","affiliation":[{"name":"Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104282","DOI":"10.1016\/j.compedu.2021.104282","article-title":"Understanding Socio-Technological Challenges of Smart Classrooms Using a Systematic Review","volume":"173","author":"Alfoudari","year":"2021","journal-title":"Comput. 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