Student modeling in intelligent tutoring systems is mostly concerned with modeling correctness of students' answers. As interactive problem solving activities become increasingly common in educational systems, it is useful to focus also on timing information associated with problem solving. We argue that the focus on timing is natural for certain types of educational problems and we describe a simple model of problem solving times which assumes a linear relationship between a latent problem solving skill and a logarithm of a time to solve a problem. The model is closely related to models from two different areas: the item response theory and collaborative filtering. We describe two parameter estimation techniques for the model and several extensions – models with multidimensional skill, learning, or variability of performance. We describe an application of the proposed models in a widely used computerized practice system. Using both simulated data and real data from the system we evaluate the model, analyse its parameter values, and discuss the insight into problem difficulty which the model brings.
%0 Journal Article
%1 citeulike:13653854
%A Pelánek, Radek
%A Jarusek, Petr
%B International Journal of Artificial Intelligence in Education
%D 2015
%I Springer New York
%K rhpaws sspaws student-modeling yhpaws
%N 4
%P 493--519
%R 10.1007/s40593-015-0048-x
%T Student Modeling Based on Problem Solving Times
%U http://dx.doi.org/10.1007/s40593-015-0048-x
%V 25
%X Student modeling in intelligent tutoring systems is mostly concerned with modeling correctness of students' answers. As interactive problem solving activities become increasingly common in educational systems, it is useful to focus also on timing information associated with problem solving. We argue that the focus on timing is natural for certain types of educational problems and we describe a simple model of problem solving times which assumes a linear relationship between a latent problem solving skill and a logarithm of a time to solve a problem. The model is closely related to models from two different areas: the item response theory and collaborative filtering. We describe two parameter estimation techniques for the model and several extensions – models with multidimensional skill, learning, or variability of performance. We describe an application of the proposed models in a widely used computerized practice system. Using both simulated data and real data from the system we evaluate the model, analyse its parameter values, and discuss the insight into problem difficulty which the model brings.
@article{citeulike:13653854,
abstract = {{Student modeling in intelligent tutoring systems is mostly concerned with modeling correctness of students' answers. As interactive problem solving activities become increasingly common in educational systems, it is useful to focus also on timing information associated with problem solving. We argue that the focus on timing is natural for certain types of educational problems and we describe a simple model of problem solving times which assumes a linear relationship between a latent problem solving skill and a logarithm of a time to solve a problem. The model is closely related to models from two different areas: the item response theory and collaborative filtering. We describe two parameter estimation techniques for the model and several extensions – models with multidimensional skill, learning, or variability of performance. We describe an application of the proposed models in a widely used computerized practice system. Using both simulated data and real data from the system we evaluate the model, analyse its parameter values, and discuss the insight into problem difficulty which the model brings.}},
added-at = {2018-03-19T12:24:51.000+0100},
author = {Pel\'{a}nek, Radek and Jaru\v{s}ek, Petr},
biburl = {https://www.bibsonomy.org/bibtex/2814ff48bda3bac766a230d05ae0d3be6/aho},
booktitle = {International Journal of Artificial Intelligence in Education},
citeulike-article-id = {13653854},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/s40593-015-0048-x},
citeulike-linkout-1 = {http://link.springer.com/article/10.1007/s40593-015-0048-x},
doi = {10.1007/s40593-015-0048-x},
interhash = {f73b1842b21264c619574df462f6c1bb},
intrahash = {814ff48bda3bac766a230d05ae0d3be6},
keywords = {rhpaws sspaws student-modeling yhpaws},
number = 4,
pages = {493--519},
posted-at = {2015-10-25 02:17:31},
priority = {2},
publisher = {Springer New York},
timestamp = {2018-03-19T12:24:51.000+0100},
title = {{Student Modeling Based on Problem Solving Times}},
url = {http://dx.doi.org/10.1007/s40593-015-0048-x},
volume = 25,
year = 2015
}