知识追踪综述

doi:10.3969/j.issn.1000-5641.2019.05.001

华东师范大学学报(自然科学版) ›› 2019, Vol. 2019 ›› Issue (5): 1-15.doi: 10.3969/j.issn.1000-5641.2019.05.001

• 数据驱动的计算教育学 • 上一篇下一篇

知识追踪综述

刘恒宇, 张天成, 武培文, 于戈

东北大学计算机科学与工程学院, 沈阳 110819

收稿日期:2019-07-29 出版日期:2019-09-25 发布日期:2019-10-11
通讯作者: 张天成,男,副教授,研究方向为大数据分析与挖掘、时空数据管理、智慧教育.E-mail:tczhang@mail.neu.edu.cn. E-mail:tczhang@mail.neu.edu.cn
作者简介:刘恒宇,男,博士研究生,研究方向为智慧教育.E-mail:l372511387@163.com.
基金资助:
国家自然科学基金（U1811261，61602103）

A review of knowledge tracking

LIU Heng-yu, ZHANG Tian-cheng, WU Pei-wen, YU Ge

Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Received:2019-07-29 Online:2019-09-25 Published:2019-10-11

摘要/Abstract

摘要： 在教育领域中，科学地、有针对性地对学生的知识状态进行有效追踪具有十分重要的意义.根据学生的历史学习轨迹，可以对学生与习题的交互过程进行建模.在此基础上，能够自动地对学生各个阶段的知识状态进行追踪，进而预测学生表现，实现个性化导学和自适应学习.首先，对知识追踪及其应用背景进行介绍，总结知识追踪所涉及的教育学与数据挖掘理论；其次，总结基于概率图、矩阵分解、深度学习的知识追踪研究现状，并根据方法的不同特点进行更为细致的分类；最后对目前的知识追踪技术进行分析比较，并对未来的研究方向进行展望.

关键词: 知识追踪, 认知诊断, 深度学习, 概率图模型

Abstract: In the field of education, scientifically and purposefully tracking the progression of student knowledge is a topic of great significance. With a student's historical learning trajectory and a model for the interaction process between students and exercises, knowledge tracking can automatically track the progression of a student's learning at each stage. This provides a technical basis for predicting student performance and achieving personalized guidance and adaptive learning. This paper first introduces the background of knowledge tracking and summarizes the pedagogy and data mining theory involved in knowledge tracking. Then, the paper summarizes the research status of knowledge tracking based on probability graphs, matrix factorization, and deep learning; we use these tools to classify the tracking methods according to different characteristics. Finally, the paper analyzes and compares the latest knowledge tracking technologies, and looks ahead to the future direction of ongoing research.

Key words: knowledge tracking, cognitive diagnosis, deep learning, probability map model

中图分类号:

TP301.6

刘恒宇, 张天成, 武培文, 于戈. 知识追踪综述[J]. 华东师范大学学报(自然科学版), 2019, 2019(5): 1-15.

LIU Heng-yu, ZHANG Tian-cheng, WU Pei-wen, YU Ge. A review of knowledge tracking[J]. Journal of East China Normal University(Natural Sc, 2019, 2019(5): 1-15.

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知识追踪综述

A review of knowledge tracking

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