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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.18290 |
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| _version_ | 1866908445079240704 |
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| author | Parasa, Niharika Sri Diwan, Chaitali Srinivasa, Srinath |
| author_facet | Parasa, Niharika Sri Diwan, Chaitali Srinivasa, Srinath |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_18290 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Riddle Generation using Learning Resources Parasa, Niharika Sri Diwan, Chaitali Srinivasa, Srinath Computation and Language 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. |
| title | Riddle Generation using Learning Resources |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2310.18290 |