Zhuang Wei, Zhang Yunhong, Wang Yuan, He Kaiyang
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China.
School of Teacher and Education, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Sensors (Basel). 2024 Dec 9;24(23):7856. doi: 10.3390/s24237856.
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.
评估学生的学习效果对于深入理解学习过程、准确诊断学习障碍以及制定有效的教学策略至关重要。情绪作为影响学习结果的关键因素，为识别认知状态和情感体验提供了一个全新的视角。然而，传统的评估方法在特征提取方面存在片面性，在模型构建方面具有高复杂性，常常难以充分挖掘情感数据的深层价值。为应对这一挑战，我们创新性地提出了一种轻量级神经动力学模型：3D-BCLAM。该模型巧妙地整合了双向卷积长短期记忆（BCL）和动态注意力机制，以便以极低的计算成本高效捕捉时间序列中的情感动态变化。3D-BCLAM能够对学生的学习结果进行全面评估，不仅涵盖认知水平，还深入到情感维度进行详细分析。在公开数据集上进行测试时，3D-BCLAM展现出了卓越的性能，显著优于基于卷积神经网络（CNN）和循环神经网络（RNN）的传统机器学习和深度学习模型。这一成果不仅验证了3D-BCLAM模型的有效性，也为推动学生学习效果评估的创新提供了有力支持。
