Automatically predicting quiz difficulty level using similarity measures

Authors: Lin, C., Liu, D., Pang, W. and Apeh, E.

Start date: 7 October 2015

Journal: K-CAP 2015 Proceedings of the 8th International Conference on Knowledge Capture

ISBN: 978-1-4503-3849-3

DOI: 10.1145/2815833.2815842

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.

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Authors: Lin, C., Liu, D., Pang, W. and Apeh, E.

Journal: Proceedings of the 8th International Conference on Knowledge Capture, K-CAP 2015

ISBN: 9781450338493

DOI: 10.1145/2815833.2815842

© 2015 ACM. 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.

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