2017/09/03 by E. V, Vinu, E., Vinu, P. Sreenivasa Kumar +3 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Educational Technology and Assessment #FOS: Computer and information sciences #Intelligent Tutoring Systems and Adaptive Learning #Natural Language Processing Techniques #Online Learning and Analytics #Semantic Web and Ontologies #Topic Modeling #cs.AI
paper · pdf · doi:10.48550/arxiv.1709.00670
This manuscript is currently under review in the Semantic Web Journal (http://www.semantic-web-journal.net/system/files/swj1712.pdf)
arxiv created 2017/09/03 · openalex publication_date 2017/09/03 · arxiv updated 2017/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Semantics based knowledge representations such as ontologies are found to be very useful in automatically generating meaningful factual questions. Determining the difficulty level of these system generated questions is helpful to effectively utilize them in various educational and professional applications. The existing approaches for finding the difficulty level of factual questions are very simple and are limited to a few basic principles. We propose a new methodology for this problem by considering an educational theory called Item Response Theory (IRT). In the IRT, knowledge proficiency of end users (learners) are considered for assigning difficulty levels, because of the assumptions that a given question is perceived differently by learners of various proficiencies. We have done a detailed study on the features (factors) of a question statement which could possibly determine its difficulty level for three learner categories (experts, intermediates and beginners). We formulate ontology based metrics for the same. We then train three logistic regression models to predict the difficulty level corresponding to the three learner categories.