面向在线智慧学习的教育数据挖掘技术研究

doi:10.16451/j.cnki.issn1003-6059.201801007

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摘要

随着教育信息化进程的深入,学生在线学习数据得到不断积累,为数据驱动的教育评估和智能辅助教学提供良好条件.然而,已有的面向在线智慧学习的教育数据挖掘模型很难从海量、稀疏、高噪的数据中准确分析试题特征和学生学业水平,也较少考虑学生及教师的个性化需求.文中针对上述问题开展若干面向在线智慧学习的教育数据挖掘技术研究工作,以教育学习所涉及的试题、学生、教师为对象,以个性化推荐等技术同教育领域知识相结合为手段,以提高学生学业水平为目标.具体介绍用于试题分析和检索的试题文本表征模型、基于认知诊断的个性化学习资源推荐方法、针对教师的教学建议和指导等方法,以及这些技术所依托的应用平台——科大讯飞在线教育系统“智学网”.最后简单讨论面向在线智慧学习的教育数据挖掘技术未来可能的研究方向.

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关键词 ：在线智慧学习, 教育评估, 认知诊断, 推荐系统

Abstract：

With the rapid informationization of education, extensive data records from online education of students are accumulated, and it provides a good opportunity for both data-driven educational assessment and intelligent tutoring. However, existing models are hard to accurately analyze the characteristics of questions and the academic levels of students from the massive and sparse data with high noise. Meanwhile, it is difficult for these models to satisfy the personalized needs of students and teachers. In this paper, educational data mining studies on these problems are summarized. To improve the student academic level, these studies focus on modeling three objects in education (i.e., questions, students and teachers) and apply effective techniques, such as personalized recommendation methods, combined with the domain knowledge from education. Specifically, a question text embedding framework is presented for question analysis and question retrieval. Then, personalized recommendation methods on learning resources are illustrated based on the cognitive diagnosis of students. Moreover, the way of providing effective guidance and suggestions for teachers is showed. Some of these research achievements are applied to the online educational system “ZHIXUE” in iFlyTek. Finally, the possible research directions in the future are discussed.

Key words： Online Intelligent Learning Educational Assessment Cognitive Diagnosis Recommender System

收稿日期: 2017-12-08

基金资助:

国家自然科学基金项目(No.61672483,U1605251)、中国科学院青年创新促进会会员专项项目(No.2014299)资助

作者简介: 刘淇,博士,副教授,主要研究方向为数据挖掘、知识发现、机器学习.E-mail:qiliuql@ustc.edu.cn.陈恩红(通讯作者),博士,教授,主要研究方向为机器学习、数据挖掘、社会网络、个性化推荐系统.E-mail:cheneh@ustc.edu.cn.朱天宇,硕士研究生,主要研究方向为数据挖掘、推荐系统.E-mail:zhtianyu@mail.ustc.edu.cn.黄振亚,博士研究生,主要研究方向为教育数据挖掘、推荐系统.E-mail:huangzhy@mail.ustc.edu.cn.吴润泽,博士研究生,主要研究方向为教育数据挖掘、认知诊断.E-mail:wrz179@mail.ustc.edu.cn.苏喻,博士研究生,主要研究方向为数据挖掘、图像识别.E-mail:firesysysy@163.com.胡国平,博士,工程师,主要研究方向为智能语音、语言核心技术研究.E-mail:gphu@iflytek.com.

引用本文:

刘淇, 陈恩红, 朱天宇, 黄振亚, 吴润泽, 苏喻, 胡国平. 面向在线智慧学习的教育数据挖掘技术研究[J]. 模式识别与人工智能, 2018, 31(1): 77-90. LIU Qi, CHEN Enhong, ZHU Tianyu, HUANG Zhenya, WU Runze, SU Yu, HU Guoping. Research on Educational Data Mining for Online Intelligent Learning. , 2018, 31(1): 77-90.

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