3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness
Evaluating students’ learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective f...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7856 |
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| author | Wei Zhuang Yunhong Zhang Yuan Wang Kaiyang He |
| author_facet | Wei Zhuang Yunhong Zhang Yuan Wang Kaiyang He |
| author_sort | Wei Zhuang |
| collection | DOAJ |
| description | Evaluating students’ learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students’ learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment. |
| format | Article |
| id | doaj-art-3e78942ed95a4dc3a99ce480b4ff4538 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3e78942ed95a4dc3a99ce480b4ff45382025-08-20T02:50:41ZengMDPI AGSensors1424-82202024-12-012423785610.3390/s242378563D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning EffectivenessWei Zhuang0Yunhong Zhang1Yuan Wang2Kaiyang He3School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Teacher and Education, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaSchool of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaEvaluating students’ learning effectiveness is of great importance for gaining a deeper understanding of the learning process, accurately diagnosing learning barriers, and developing effective teaching strategies. Emotion, as a key factor influencing learning outcomes, provides a novel perspective for identifying cognitive states and emotional experiences. However, traditional evaluation methods suffer from one sidedness in feature extraction and high complexity in model construction, often making it difficult to fully explore the deep value of emotional data. To address this challenge, we have innovatively proposed a lightweight neurodynamic model: 3D-BCLAM. This model cleverly integrates Bidirectional Convolutional Long Short-Term Memory (BCL) and dynamic attention mechanism, in order to efficiently capture emotional dynamic changes in time series with extremely low computational cost. 3D-BCLAM can achieve a comprehensive evaluation of students’ learning outcomes, covering not only the cognitive level but also delving into the emotional dimension for detailed analysis. Under testing on public datasets, 3D-BCLAM has demonstrated outstanding performance, significantly outperforming traditional machine learning and deep learning models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). This achievement not only validates the effectiveness of the 3D-BCLAM model, but also provides strong support for promoting the innovation of student learning effectiveness assessment.https://www.mdpi.com/1424-8220/24/23/7856emotion recognitionbrain-computer interfacestudent learning effectiveness |
| spellingShingle | Wei Zhuang Yunhong Zhang Yuan Wang Kaiyang He 3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness Sensors emotion recognition brain-computer interface student learning effectiveness |
| title | 3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness |
| title_full | 3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness |
| title_fullStr | 3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness |
| title_full_unstemmed | 3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness |
| title_short | 3D-BCLAM: A Lightweight Neurodynamic Model for Assessing Student Learning Effectiveness |
| title_sort | 3d bclam a lightweight neurodynamic model for assessing student learning effectiveness |
| topic | emotion recognition brain-computer interface student learning effectiveness |
| url | https://www.mdpi.com/1424-8220/24/23/7856 |
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