Authors:
Thushari Atapattu
;
Katrina Falkner
and
Nickolas Falkner
Affiliation:
University of Adelaide, Australia
Keyword(s):
Concept Map Mining, Concept Importance, Lecture Notes, Evaluation Methodology.
Related
Ontology
Subjects/Areas/Topics:
Assessment Software Tools
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
Information Technologies Supporting Learning
;
Intelligent Learning and Teaching Systems
;
Learning Organizations
;
Learning/Teaching Methodologies and Assessment
;
Ontologies and Meta-Data Standards
;
Social Context and Learning Environments
;
Teacher Evaluation
;
Technology Enhanced Learning
;
Ubiquitous Learning
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 introdu
ces 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).
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