mlsanityData to 14 Oct 2026

Riddle Generation using Learning Resources

arXiv 2310.18290 ↗First posted 27 Oct 2023cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 25 references that are in the map. First posted on arXiv 27 Oct 2023.

Builds on

2 of 25 references.

References · 2

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019Generalization through Memorization: Nearest Neighbor Language Modelscited by 800

Built on by

Not cited yet.