Abstract
One of the primary challenges in online learning environments is to retain learner engagement. Several different instructional strategies are proposed both in online and offline environments to enhance learner engagement. The Concept Attainment Model is one such instructional strategy that focuses on learners acquiring a deeper understanding of a concept rather than just its dictionary definition. This is done by searching and listing the properties used to distinguish examples from non-examples of various concepts. Our work attempts to apply the Concept Attainment Model to build conceptual riddles, to deploy over online learning environments. The approach involves creating factual triples from learning resources, classifying them based on their uniqueness to a concept into ‘Topic Markers’ and ‘Common’, followed by generating riddles based on the Concept Attainment Model’s format and capturing all possible solutions to those riddles. The results obtained from the human evaluation of riddles prove encouraging.
Supported by Gooru (https://gooru.org/).
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Parasa, N.S., Diwan, C., Srinivasa, S., Ram, P. (2024). An Automated Approach for Generating Conceptual Riddles. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14650. Springer, Singapore. https://doi.org/10.1007/978-981-97-2266-2_13
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