Voting Theory for Concept Detection

Zouaq, Amal; Gašević, Dragan; Hatala, Marek

doi:10.1007/978-3-642-30284-8_28

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…incoming, outgoing links and proximity) which have already been proposed in Zouaq et al, 2012 andLeake et al, 2004 and new distributional features (e.g. typography and cooccurrence) that are unique to presentation framework.…”

Section: Structural Feature Modelmentioning

confidence: 92%

See 1 more Smart Citation

Evaluation of Concept Importance in Concept Maps Mined from Lecture Notes - Computer Vs Human

Atapattu

Falkner

2014

Proceedings of the 6th International Conference on Computer Supported Education

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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, expertconstructed 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 (r s~1 ) for well-structured courses with rich grammar (well-fitted contents).

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Section: Structural Feature Modelmentioning

confidence: 92%

“…degree) (Zouaq et al, 2012) to rank the concepts according to their importance. However, we also use linguistic features, semantic information and the association between terms to mimic the human judgment using machine algorithms.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Concept Importance in Concept Maps Mined from Lecture Notes - Computer Vs Human

Atapattu

Falkner

2014

Proceedings of the 6th International Conference on Computer Supported Education

View full text Add to dashboard Cite

show abstract

“…proximity, incoming and outgoing links) (Leake et al, 2004) and graph-based metrics (e.g. degree) (Zouaq et al, 2012) to rank the concepts according to their importance. However, we also use linguistic features, semantic information and the association between terms to mimic the human judgment using machine algorithms.…”

Section: Related Workmentioning

confidence: 99%