The need for soft computing technologies to facilitate effective automated tutoring is pervasive – from machine learning techniques to predict content significance and generate appropriate questions, to interpretation of noisy spoken responses and statistical assessment of the response quality, through user modeling and determining how best to respond to the learner in order to optimize learning gains. This chapter focuses primarily on the domain-independent
semantic analysis of learner responses, reviewing prior work in intelligent tutoring systems and educational assessment. We present a new framework for assessing the semantics of learner responses and the results of our initial implementation of a machine learning approach based on this framework.
Description
SpringerLink - Abstract
Studies in Fuzziness and Soft Computing, 2008, Volume 230/2008, 201-230, DOI: 10.1007/978-3-540-79005-1_11
%0 Journal Article
%1 Nielsen08
%A Nielsen, Rodney D.
%A Ward, Wayne
%A Martin, James H.
%D 2008
%J Studies in Fuzziness and Soft Computing
%K intelligent semantic_analysis tutoring
%P 201-230
%T Soft Computing in Intelligent Tutoring Systems and Educational Assessment
%V 230/2008
%X The need for soft computing technologies to facilitate effective automated tutoring is pervasive – from machine learning techniques to predict content significance and generate appropriate questions, to interpretation of noisy spoken responses and statistical assessment of the response quality, through user modeling and determining how best to respond to the learner in order to optimize learning gains. This chapter focuses primarily on the domain-independent
semantic analysis of learner responses, reviewing prior work in intelligent tutoring systems and educational assessment. We present a new framework for assessing the semantics of learner responses and the results of our initial implementation of a machine learning approach based on this framework.
@article{Nielsen08,
abstract = {The need for soft computing technologies to facilitate effective automated tutoring is pervasive – from machine learning techniques to predict content significance and generate appropriate questions, to interpretation of noisy spoken responses and statistical assessment of the response quality, through user modeling and determining how best to respond to the learner in order to optimize learning gains. This chapter focuses primarily on the domain-independent
semantic analysis of learner responses, reviewing prior work in intelligent tutoring systems and educational assessment. We present a new framework for assessing the semantics of learner responses and the results of our initial implementation of a machine learning approach based on this framework.},
added-at = {2011-06-20T11:23:47.000+0200},
author = {Nielsen, Rodney D. and Ward, Wayne and Martin, James H.},
biburl = {https://www.bibsonomy.org/bibtex/26cb9dea1efca76f9cdf092209a12297f/jennymac},
description = {SpringerLink - Abstract
Studies in Fuzziness and Soft Computing, 2008, Volume 230/2008, 201-230, DOI: 10.1007/978-3-540-79005-1_11},
interhash = {170b5db70521c12927acf469d1c3c59a},
intrahash = {6cb9dea1efca76f9cdf092209a12297f},
journal = {Studies in Fuzziness and Soft Computing},
keywords = {intelligent semantic_analysis tutoring},
pages = {201-230},
timestamp = {2011-06-20T11:23:47.000+0200},
title = {Soft Computing in Intelligent Tutoring Systems and Educational Assessment},
volume = {230/2008},
year = 2008
}