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Using Clustering in a Cognitive Tutor to Identify Mathematical Misconceptions

Andersson, Johan LU and Johansson, Hannes (2015) In LU-CS-EX 2015-45 EDA920 20151
Department of Computer Science
Abstract
We have implemented an Intelligent Tutoring System (ITS) prototype for
teaching multi-column addition and subtraction to children aged 5-10, using a
digitalized version of the Montessori bank game exercises. An Intelligent Tutoring System is a piece of software that teaches a certain subject to its users, and that typically uses artificial intelligence related algorithms to personalize the educational process.

Our Intelligent Tutoring System focuses on collecting erroneous input from
the user and analyzing it using an experimental clustering algorithm in order
to find common misconceptions. The system is based on the assumption that
if there is a lot of user errors that are similar, they might correspond to a misconception. To find... (More)
We have implemented an Intelligent Tutoring System (ITS) prototype for
teaching multi-column addition and subtraction to children aged 5-10, using a
digitalized version of the Montessori bank game exercises. An Intelligent Tutoring System is a piece of software that teaches a certain subject to its users, and that typically uses artificial intelligence related algorithms to personalize the educational process.

Our Intelligent Tutoring System focuses on collecting erroneous input from
the user and analyzing it using an experimental clustering algorithm in order
to find common misconceptions. The system is based on the assumption that
if there is a lot of user errors that are similar, they might correspond to a misconception. To find which errors are “similar”, we use clustering. An ITS like
this could support teaching by making the students become aware of their misconceptions, so that they can overcome them. Normally, ITS use bug libraries
to systematize misconception handling. A bug library is a collection containing
information about possible errors, that can be used to help identify these
errors when encountered. Creating bug libraries takes a lot of effort, and if
they could be avoided, a typical ITS implementation would take considerably
less time.

While we found that we could identify some misconceptions of a computer
player, the clustering approach needs to be generalized further in order to enable effective application on humans. We conclude that if this approach were
to be explored more in detail, it could prove to be a viable alternative to the
bug library. (Less)
Please use this url to cite or link to this publication:
author
Andersson, Johan LU and Johansson, Hannes
supervisor
organization
course
EDA920 20151
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
Intelligent Tutoring System, Cognitive Tutor, Clustering, Educational Software
publication/series
LU-CS-EX 2015-45
report number
LU-CS-EX 2015-45
ISSN
1650-2884
language
English
id
8080716
date added to LUP
2015-10-20 09:22:58
date last changed
2015-10-20 09:22:58
@misc{8080716,
  abstract     = {We have implemented an Intelligent Tutoring System (ITS) prototype for
teaching multi-column addition and subtraction to children aged 5-10, using a
digitalized version of the Montessori bank game exercises. An Intelligent Tutoring System is a piece of software that teaches a certain subject to its users, and that typically uses artificial intelligence related algorithms to personalize the educational process.

Our Intelligent Tutoring System focuses on collecting erroneous input from
the user and analyzing it using an experimental clustering algorithm in order
to find common misconceptions. The system is based on the assumption that
if there is a lot of user errors that are similar, they might correspond to a misconception. To find which errors are “similar”, we use clustering. An ITS like
this could support teaching by making the students become aware of their misconceptions, so that they can overcome them. Normally, ITS use bug libraries
to systematize misconception handling. A bug library is a collection containing
information about possible errors, that can be used to help identify these
errors when encountered. Creating bug libraries takes a lot of effort, and if
they could be avoided, a typical ITS implementation would take considerably
less time.

While we found that we could identify some misconceptions of a computer
player, the clustering approach needs to be generalized further in order to enable effective application on humans. We conclude that if this approach were
to be explored more in detail, it could prove to be a viable alternative to the
bug library.},
  author       = {Andersson, Johan and Johansson, Hannes},
  issn         = {1650-2884},
  keyword      = {Intelligent Tutoring System,Cognitive Tutor,Clustering,Educational Software},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-CS-EX 2015-45},
  title        = {Using Clustering in a Cognitive Tutor to Identify Mathematical Misconceptions},
  year         = {2015},
}