Abstract
Automatic test paper generation is highly helpful in teaching and learning. In order to generate a test paper that covers as many knowledge points as possible, it is needed to discover knowledge points from exam questions. However, the problem of automatically finding knowledge points is seldom investigated in existing work. To fill this gap, this paper proposes an ontology-based method to discover knowledge points from mathematical exam questions. Accordingly, a system for automatically generating mathematical test papers is also proposed. It composes a test paper by solving a pseudo-Boolean optimization problem. Its practicality is demonstrated by a task of generating mathematical test papers from hundreds of postgraduate entrance exam questions.
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Notes
- 1.
More information about our constructed math ontology and our system can be found at http://www.dataminingcenter.net/math/.
- 2.
More details about RapidMiner can be found at http://rapidminer.com/.
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Acknowledgements
This work is partly supported by the NSFC grants (61375056 and 61005043), the Guangdong Natural Science Foundation (S2013010012928), the Undergraduate Innovative Experiment Projects in Guangdong University of Foreign Studies (1184613038 and 201411846043), and the Business Intelligence Key Team of Guangdong University of Foreign Studies (TD1202).
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Du, J., Zhou, X., Lin, C., Liu, D., Cheng, J. (2014). An Ontology-Based System for Generating Mathematical Test Papers. In: Zhao, D., Du, J., Wang, H., Wang, P., Ji, D., Pan, J. (eds) The Semantic Web and Web Science. CSWS 2014. Communications in Computer and Information Science, vol 480. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45495-4_21
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DOI: https://doi.org/10.1007/978-3-662-45495-4_21
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