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W3024052457 abstract "In modern times, e-learning has become the most convenient environment to attain knowledge about and across various domains. While there are plenty of e-learning resources available across the ever-expanding web, imbibing knowledge is not the only factor that contributes to the edification of an individual. The assessment also must be given a quintessential role because both learning and assessment are two sides of a coin. In this paper, OntoQuest, which is a scheme for generation of multiple-choice questions is done based on the domain or subject of choice from the user. An equivalence set based summarization scheme has been proposed. Synonymization and Anonymization are used for the generation of best key and distractors respectively. Domain and Granular Ontologies are crawled for relevant sub-topics and auxiliary topics. WordNet integrates dynamic knowledge for improving the overall accuracy. Jaccard similarity is encompassed for the generation of the correct key. OntoQuest is a reliable framework that yields an overall accuracy of 95.46% and 95.05% for key and distractor generation respectively, which is higher and consistent compared to the existing models available." @default.
W3024052457 created "2020-05-21" @default.
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W3024052457 date "2019-12-01" @default.
W3024052457 modified "2023-10-18" @default.
W3024052457 title "OntoQuest: An Ontological Strategy for Automatic Question Generation for e-assessment using Static and Dynamic Knowledge" @default.
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W3024052457 doi "https://doi.org/10.1109/icinpro47689.2019.9092034" @default.
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