基于数据挖掘的个性化学习平台研究
|
摘要
网络学习目前普遍存在缺乏学习氛围、缺乏有效监督、没有根据学生的特点进行因材施教等问题。利用ASP.NET及SQL Server设计网络学习平台,通过Log4j记录学生与学习平台的交互,通过麦克风摄像头及OBS软件采集教师与学生的音视频,利用深度学习技术挖掘分析音视频中的语言和表情,帮助教师对众多学习者进行察言观色,及时了解学生的情况,调整教学内容和进度,并对学生进行监督。采用机器学习技术挖掘分析学生的基本信息、学习日志、课程评价、错题及成绩等数据,为学生提供个性化的学习服务。
At present, there are many problems in online learning, such as lack of learning atmosphere, lack of effective supervision, and lack of teaching students in accordance with their aptitude according to their characteristics. ASP.NET and SQL Server are used to design the network learning platform. Log4 j is used to record the interaction between students and learning platform.Microphone cameras and OBS software are used to collect the audio and video of teachers and students. Deep learning technology is used to mine and analyze the language and expression in audio and video. It helps teachers to observe many learners. When understanding the situation of students, adjust the teaching content and progress, and supervise students. Using machine learning technology to mine and analyze students' basic information, learning logs, curriculum evaluation, wrong questions and scores, etc.,to provide individual learning services for students.
At present, there are many problems in online learning, such as lack of learning atmosphere, lack of effective supervision, and lack of teaching students in accordance with their aptitude according to their characteristics. ASP.NET and SQL Server are used to design the network learning platform. Log4 j is used to record the interaction between students and learning platform.Microphone cameras and OBS software are used to collect the audio and video of teachers and students. Deep learning technology is used to mine and analyze the language and expression in audio and video. It helps teachers to observe many learners. When understanding the situation of students, adjust the teaching content and progress, and supervise students. Using machine learning technology to mine and analyze students' basic information, learning logs, curriculum evaluation, wrong questions and scores, etc.,to provide individual learning services for students.
引文
[1]龚维印,王力.基于卷积神经网络和XGBoost的文本分类[J].通信技术,2018(10):2337-2342.
[2] Francois Chollet.Python深度学习[M].张亮,译.北京:人民邮电出版社,2018.
[3]胡敏,张柯柯,王晓华,等.结合滑动窗口动态时间规整和CNN的视频人脸表情识别[J].中国图象图形学报,2018,23(8):1144-1153.
[4]潘峥嵘,贺秀伟.人脸表情识别在智能机器人中的应用研究[J].计算机技术与发展,2018(02):173-177.
[5]徐晓青,赵蔚,刘红霞.混合式学习环境下情绪分析应用与模型研究——基于元分析的视角[J].电化教育研究,2018(8):70-77.
[2] Francois Chollet.Python深度学习[M].张亮,译.北京:人民邮电出版社,2018.
[3]胡敏,张柯柯,王晓华,等.结合滑动窗口动态时间规整和CNN的视频人脸表情识别[J].中国图象图形学报,2018,23(8):1144-1153.
[4]潘峥嵘,贺秀伟.人脸表情识别在智能机器人中的应用研究[J].计算机技术与发展,2018(02):173-177.
[5]徐晓青,赵蔚,刘红霞.混合式学习环境下情绪分析应用与模型研究——基于元分析的视角[J].电化教育研究,2018(8):70-77.