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)
1 Downloads (Pure)

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 publicationProceedings of The 8th International Conference on Knowledge Capture (K-Cap)
PublisherACM
Pages1-8
Number of pages8
ISBN (Print)978-1-4503-3849-3
DOIs
Publication statusPublished - 2015
EventK-CAP 2015 - The 8th International Conference on Knowledge Capture - USA, New York, United States
Duration: 7 Oct 201510 Oct 2015

Conference

ConferenceK-CAP 2015 - The 8th International Conference on Knowledge Capture
CountryUnited States
CityNew York
Period7/10/1510/10/15

Fingerprint

Semantics
Experiments

Cite this

Lin, C., Liu, D., Pang, W., & Apeh, E. (2015). Automatically Predicting Quiz Difficulty Level Using Similarity Measures. In Proceedings of The 8th International Conference on Knowledge Capture (K-Cap) (pp. 1-8). [1] ACM. https://doi.org/10.1145/2815833.2815842

Automatically Predicting Quiz Difficulty Level Using Similarity Measures. / Lin, Chenghua; Liu, Dong ; Pang, Wei; Apeh, Edward.

Proceedings of The 8th International Conference on Knowledge Capture (K-Cap). ACM, 2015. p. 1-8 1.

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

Lin, C, Liu, D, Pang, W & Apeh, E 2015, Automatically Predicting Quiz Difficulty Level Using Similarity Measures. in Proceedings of The 8th International Conference on Knowledge Capture (K-Cap)., 1, ACM, pp. 1-8, K-CAP 2015 - The 8th International Conference on Knowledge Capture, New York, United States, 7/10/15. https://doi.org/10.1145/2815833.2815842
Lin C, Liu D, Pang W, Apeh E. Automatically Predicting Quiz Difficulty Level Using Similarity Measures. In Proceedings of The 8th International Conference on Knowledge Capture (K-Cap). ACM. 2015. p. 1-8. 1 https://doi.org/10.1145/2815833.2815842
Lin, Chenghua ; Liu, Dong ; Pang, Wei ; Apeh, Edward. / Automatically Predicting Quiz Difficulty Level Using Similarity Measures. Proceedings of The 8th International Conference on Knowledge Capture (K-Cap). ACM, 2015. pp. 1-8
@inproceedings{0b88aa5f05ac4de1a3f14acb6d5d7c0f,
title = "Automatically Predicting Quiz Difficulty Level Using Similarity Measures",
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.",
author = "Chenghua Lin and Dong Liu and Wei Pang and Edward Apeh",
note = "ACKNOWLEDGMENTS This work is supported by the BBC Connected Studio programme the award made by the RCUK Digital Economy theme to the dot.rural",
year = "2015",
doi = "10.1145/2815833.2815842",
language = "English",
isbn = "978-1-4503-3849-3",
pages = "1--8",
booktitle = "Proceedings of The 8th International Conference on Knowledge Capture (K-Cap)",
publisher = "ACM",

}

TY - GEN

T1 - Automatically Predicting Quiz Difficulty Level Using Similarity Measures

AU - Lin, Chenghua

AU - Liu, Dong

AU - Pang, Wei

AU - Apeh, Edward

N1 - ACKNOWLEDGMENTS This work is supported by the BBC Connected Studio programme the award made by the RCUK Digital Economy theme to the dot.rural

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

U2 - 10.1145/2815833.2815842

DO - 10.1145/2815833.2815842

M3 - Conference contribution

SN - 978-1-4503-3849-3

SP - 1

EP - 8

BT - Proceedings of The 8th International Conference on Knowledge Capture (K-Cap)

PB - ACM

ER -