Chen Chen, Aleem Muhammad
Department of Language and Literature, College of Technology, Hubei Engineering University, Xiaogan, China.
National University of Computer and Emerging Sciences, Islamabad, Islamabad, Pakistan.
PeerJ Comput Sci. 2024 Feb 15;10:e1869. doi: 10.7717/peerj-cs.1869. eCollection 2024.
To maintain a harmonious teacher-student relationship and enable educators to gain a more insightful understanding of students' learning progress, this study collects data from learners utilizing the software through a network platform. These data are mainly formed by the user's learning characteristics, combined with the screen lighting time, built-in inertial sensor attitude, signal strength, network strength and other multi-dimensional characteristics to form the learning observation value, so as to analyze the corresponding learning state, so that teachers can carry out targeted teaching improvement. The article introduces an intelligent classification approach for learning time series, leveraging long short-term memory (LSTM) as the foundation of a deep network model. This model intelligently recognizes the learning status of students. The test results demonstrate that the proposed model achieves highly precise time series recognition using relatively straightforward features. This precision, exceeding 95%, is of significant importance for future applications in learning state recognition, aiding teachers in gaining an intelligent grasp of students' learning status.
为了维持和谐的师生关系，并使教育工作者能够更深入地了解学生的学习进展，本研究通过网络平台从使用该软件的学习者那里收集数据。这些数据主要由用户的学习特征形成，结合屏幕点亮时间、内置惯性传感器姿态、信号强度、网络强度等多维度特征，形成学习观测值，从而分析相应的学习状态，以便教师进行有针对性的教学改进。本文介绍了一种针对学习时间序列的智能分类方法，利用长短期记忆（LSTM）作为深度网络模型的基础。该模型能够智能识别学生的学习状态。测试结果表明，所提出的模型使用相对简单的特征实现了高精度的时间序列识别。这种超过95%的精度对于学习状态识别的未来应用具有重要意义，有助于教师智能掌握学生的学习状态。
