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.
%0 Conference Paper
%1 Jeong:2008:its
%A Jeong, Hogyeong
%A Gupta, Amit
%A Roscoe, Rod
%A Wagster, John
%A Biswas, Gautam
%A Schwartz, Daniel
%B ITS'08: Proc. 9th Int'l Conf. on Intelligent
Tutoring Systems
%C Montreal, Canada
%D 2008
%E Woolf, Beverley P.
%E Aïmeur, Esma
%E Nkambou, Roger
%E Lajoie, Susanne
%I Springer
%K imported thesis
%P 614--625
%R 10.1007/978-3-540-69132-7_64
%T Using Hidden Markov Models to Characterize Student Behavior Patterns
in Computer-based Learning-by-Teaching Environments
%V 5091
%X 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.
%@ 978-3-540-69130-3
@inproceedings{Jeong:2008:its,
abstract = {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.},
added-at = {2017-03-16T11:50:55.000+0100},
address = {Montreal, Canada},
author = {Jeong, Hogyeong and Gupta, Amit and Roscoe, Rod and Wagster, John and Biswas, Gautam and Schwartz, Daniel},
biburl = {https://www.bibsonomy.org/bibtex/28289b9251b836cfbef6e7a7722499494/krevelen},
booktitle = {ITS'08: Proc. 9th Int'l Conf. on Intelligent
Tutoring Systems},
crossref = {its:2008},
doi = {10.1007/978-3-540-69132-7_64},
editor = {Woolf, Beverley P. and A\"imeur, Esma and Nkambou, Roger and Lajoie, Susanne},
interhash = {31347266f5583378e168935e55a374b5},
intrahash = {8289b9251b836cfbef6e7a7722499494},
isbn = {978-3-540-69130-3},
keywords = {imported thesis},
owner = {Rick},
pages = {614--625},
publisher = {Springer},
series = {LNCS},
timestamp = {2017-03-16T11:54:14.000+0100},
title = {Using Hidden Markov Models to Characterize Student Behavior Patterns
in Computer-based Learning-by-Teaching Environments},
volume = 5091,
year = 2008
}