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Authors: Petch Sajjacholapunt and Mike Joy

Affiliation: University of Warwick, United Kingdom

ISBN: 978-989-758-107-6

Keyword(s): Information Retrieval, Technical Terms Extraction, Technology Enhanced Learning.

Related Ontology Subjects/Areas/Topics: Authoring Tools and Content Development ; Computer-Supported Education ; Course Design and e-Learning Curriculae ; e-Learning ; e-Learning Hardware and Software ; e-Learning Platforms ; Information Technologies Supporting Learning ; Simulation and Modeling ; Simulation Tools and Platforms ; Social Context and Learning Environments

Abstract: Digital materials not only provide opportunities as enablers of e-learning development, but also create a new challenge. The current e-materials provided on a course website are individually designed for learning in classrooms rather than for revision. In order to enable the capability of e-materials to support a students revision, we need an efficient system to associate related pieces of different e-materials. In this case, the features of each item of e-material, including the structure and the technical terms they contain, need to be studied and applied in order to calculate the similarity between relevant e-materials. Even though difficulties regarding technical term extraction and the similarities between two text documents have been widely discussed, empirical experiments for particular types of e-learning materials (for instance, lecture slides and past exam papers) are still rare. In this paper, we propose a framework and relatedness model for associating lecture slides and p ast exam paper materials to support revision based on Natural Language Processing (NLP) techniques. We compare and evaluate the efficiency of different combinations of three weighted schemes, term frequency (TF), inverse document frequency (IDF), and term location (TL), for calculating the relatedness score. The experiments were conducted on 30 lectures ( 900 slides) and 3 past exam papers (12 pages) of a data structures course at the authors’ institution. The findings indicate the appropriate features for calculating the relatedness score between lecture slides and past exam papers. (More)

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Paper citation in several formats:
Sajjacholapunt, P. and Joy, M. (2015). Analysing Features of Lecture Slides and past Exam Paper Materials - Towards Automatic Associating E-materials for Self-revision.In Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-107-6, pages 169-176. DOI: 10.5220/0005371201690176

@conference{csedu15,
author={Petch Sajjacholapunt. and Mike Joy.},
title={Analysing Features of Lecture Slides and past Exam Paper Materials - Towards Automatic Associating E-materials for Self-revision},
booktitle={Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2015},
pages={169-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005371201690176},
isbn={978-989-758-107-6},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Analysing Features of Lecture Slides and past Exam Paper Materials - Towards Automatic Associating E-materials for Self-revision
SN - 978-989-758-107-6
AU - Sajjacholapunt, P.
AU - Joy, M.
PY - 2015
SP - 169
EP - 176
DO - 10.5220/0005371201690176

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