New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization

被引：75
作者：
Koedinger, Kenneth R. [1 ,2 ]
Brunskill, Emma [3 ]
Baker, Ryan S. J. D. [4 ]
McLaughlin, Elizabeth A. [1 ]
Stamper, John [1 ,5 ]
机构：
[1] Carnegie Mellon Univ, Human Comp Interact Inst, Pittsburgh, PA 15213 USA
[2] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA 15213 USA
[3] Carnegie Mellon Univ, Dept Comp Sci, Pittsburgh, PA 15213 USA
[4] Columbia Univ, Teachers Coll, New York, NY 10027 USA
[5] Pittsburgh Sci Learning Ctr DataShop Pslcdatashop, Pittsburgh, PA USA
来源：
AI MAGAZINE | 2013年 / 34卷 / 03期
基金：
美国国家科学基金会;
关键词：
AUTOMATIC DETECTION; GENERATION; MODEL;
D O I：
10.1609/aimag.v34i3.2484
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Increasing widespread use of educational technologies is producing vast amounts of data. Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for data-driven development and optimization of educational technologies, focusing on intelligent tutoring systems. We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference.
引用
收藏
页码：27 / 41
页数：15
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