利用长短期记忆(LSTM)网络在在线英语教学中进行新媒体数据挖掘和分析的模型

Chen Chen, Muhammad Aleem
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引用次数: 0

摘要

为了维护和谐的师生关系,使教育者更深入地了解学生的学习进度,本研究通过网络平台收集学习者使用软件的数据。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、网络强度等多维特征形成学习观察值,从而分析出相应的学习状态,以便教师进行有针对性的教学改进。文章介绍了一种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。该模型能智能识别学生的学习状态。测试结果表明,所提出的模型利用相对简单的特征实现了高度精确的时间序列识别。这种超过 95% 的精确度对未来学习状态识别的应用具有重要意义,有助于教师智能地掌握学生的学习状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A model for new media data mining and analysis in online English teaching using long short-term memory (LSTM) network
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.
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