Authors:
Petch Sajjacholapunt
and
Mike Joy
Affiliation:
University of Warwick, United Kingdom
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
past 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.
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