Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Conference paper
Title: Evaluation of concept importance in concept maps mined from lecture notes: computer vs human
Author: Atapattu Mudiyanselage, T.
Falkner, K.
Falkner, N.
Citation: Proceedings of the 6th International Conference on Computer Supported Education, 2014 / Zvacek, S., Restivo, M., Uhomoibhi, J., Helfert, M. (ed./s), vol.2, pp.75-84
Publisher: SciTePress
Issue Date: 2014
ISBN: 9789897580208
Conference Name: 6th International Conference on Computer Supported Education (CSEDU 2014) (1 Apr 2014 - 3 Apr 2014 : Barcelona, Spain)
Editor: Zvacek, S.
Restivo, M.
Uhomoibhi, J.
Helfert, M.
Statement of
Thushari Atapattu, Katrina Falkner and Nickolas Falkner
Abstract: Concept maps are commonly used tools for organising and representing knowledge in order to assist meaningful learning. Although the process of constructing concept maps improves learners’ cognitive structures, novice students typically need substantial assistance from experts. Alternatively, expert-constructed maps may be given to students, which increase the workload of academics. To overcome this issue, automated concept map extraction has been introduced. One of the key limitations is the lack of an evaluation framework to measure the quality of machine-extracted concept maps. At present, researchers in this area utilise human experts’ judgement or expert-constructed maps as the gold standard to measure the relevancy of extracted knowledge components. However, in the educational context, particularly in course materials, the majority of knowledge presented is relevant to the learner, resulting in a large amount of information that has to be organised. Therefore, this paper introduces a machine-based approach which studies the relative importance of knowledge components and organises them hierarchically. We compare machine-extracted maps with human judgment, based on expert knowledge and perception. This paper describes three ranking models to organise domain concepts. The results show that the auto-generated map positively correlates with human judgment (rs~1) for well-structured courses with rich grammar (well-fitted contents).
Keywords: Concept Map Mining; Concept Importance; Lecture Notes; Evaluation Methodology
Rights: Copyright © SCITEPRESS, Scienceand Technology Publications, Lda.
DOI: 10.5220/0004842300750084
Appears in Collections:Aurora harvest 7
Computer Science publications

Files in This Item:
File Description SizeFormat 
hdl_90995.pdfPublished version1.28 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.