Automatic concept type identification from learning resources

The objective of any tutoring system is to provide a meaningful learning to the learner. Therefore an automated tutoring system should be able to know whether a concept mentioned in a document is a prerequisite for studying that document, or it can be learned from it. This paper addresses the problem of identifying defined concepts and prerequisite concepts from learning resources in html format. In this paper a supervised machine learning approach was taken to address the problem, based on linguistic features which enclose contextual information and stylistic features such as font size and font weight. This paper shows that contextual information in addition to format information can give better results when used with the SVM classifier than with the (LP)2 algorithm.

[1]  Eric Brill,et al.  A Simple Rule-Based Part of Speech Tagger , 1992, HLT.

[2]  Peter Brusilovsky,et al.  Course Delivery Systems for the Virtual University , 2002 .

[3]  Hideki Mima,et al.  Automatic recognition of multi-word terms:. the C-value/NC-value method , 2000, International Journal on Digital Libraries.

[4]  Christine L Borgman,et al.  Access to Knowledge: New Information Technologies and the Emergence of the Virtual University , 2001 .

[5]  Peter Brusilovsky,et al.  An Adaptive E-Learning Service for Accessing Interactive Examples , 2004 .

[6]  Huiqing Liu,et al.  A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. , 2002, Genome informatics. International Conference on Genome Informatics.

[7]  Peter Brusilovsky,et al.  Concept-Based Courseware Engineering for Large Scale Web-based Education , 2000, WebNet.

[8]  Fabio Ciravegna,et al.  (LP) 2 , an Adaptive Algorithm for Information Extraction from Web-related Texts , 2001 .

[9]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[10]  Katerina T. Frantzi,et al.  Automatic recognition of multi-word terms , 1998 .

[11]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[12]  Johanna Völker,et al.  A Framework for Ontology Learning and Data-driven Change Discovery , 2005 .

[13]  D. Ausubel.  The psychology of meaningful verbal learning : an introduction to school learning / David P. Ausubel , 1963 .

[14]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[15]  J. Cid-Sueiro,et al.  Student modeling based on fuzzy inference mechanisms , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[16]  Fabio Ciravegna,et al.  Adaptive Information Extraction from Text by Rule Induction and Generalisation , 2001, IJCAI.

[17]  Rory McGreal,et al.  Learning objects: A practical definition , 2004 .

[18]  Sudeshna Sarkar,et al.  Automatic Extraction of Pedagogic Metadata from Learning Content , 2008, Int. J. Artif. Intell. Educ..

[19]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[20]  Robby Robson,et al.  WWW-Based Course-Support Systems: The First Generation , 1999 .