Automatically Predicting Quiz Difficulty Level Using Similarity Measures

Chenghua Lin, Dong Liu, Wei Pang, Edward Apeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

In this paper, we present a semi-automatic system (Sherlock) for quiz generation using Linked Data and textual descriptions of RDF resources. Sherlock is distinguished from existing quiz generation systems in its ability to control the difficulty level of the generated quizzes. We cast the problem of perceiving the level of knowledge difficulty as a similarity measure problem and propose a novel hybrid semantic similarity measure using linked data. Extensive experiments show that the proposed similarity measure outperforms four strong baselines in both the pilot evaluation using a synthetic gold standard as well as with human evaluation, giving more than 47% gain in clustering accuracy over the baselines.
Original languageEnglish
Title of host publicationK-CAP 2015: Proceedings of the 8th International Conference on Knowledge Capture
PublisherAssociation for Computing Machinery
Number of pages8
ISBN (Print)978-1-4503-3849-3
DOIs
Publication statusPublished - Oct 2015

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  • Cite this

    Lin, C., Liu, D., Pang, W., & Apeh, E. (2015). Automatically Predicting Quiz Difficulty Level Using Similarity Measures. In K-CAP 2015: Proceedings of the 8th International Conference on Knowledge Capture Association for Computing Machinery. https://doi.org/10.1145/2815833.2815842