Using hidden Markov models to characterize student behaviors in learning-by-teaching environments

被引：0

作者：

Jeong, Hogyeong ^{[1
]}

Gupta, Amit ^{[1
]}

Roscoe, Rod ^{[1
]}

Wagster, John ^{[1
]}

Biswas, Gautam ^{[1
]}

Schwartz, Daniel ^{[2
]}

机构：

[1] Vanderbilt Univ, Nashville, TN 37235 USA

[2] Stanford Univ, Stanford, CA 94305 USA

来源：

INTELLIGENT TUTORING SYSTEM, PROCEEDINGS | 2008年 / 5091卷

基金：

美国国家科学基金会;

关键词：

learning by teaching environments; metacognition; behavior analysis; hidden Markov modeling;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Using hidden Markov models (HMMs) and traditional behavior analysis, we have examined the effect of metacognitive prompting on students' learning in the context of our computer-based learning-by-teaching environment. This paper discusses our analysis techniques, and presents evidence that HMMs can be used to effectively determine students' pattern of activities. The results indicate clear differences between different interventions, and links between students learning performance and their interactions with the system.

引用

页码：614 / +

页数：3

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