We present LearningQ, a challenging educational question generation dataset containing over 230K document-question pairs. It includes 7K instructor-designed questions assessing knowledge concepts being taught and 223K learner-generated questions seeking in-depth understanding of the taught concepts. We show that, compared to existing datasets that can be used to generate educational questions, LearningQ (i) covers a wide range of educational topics and (ii) contains long and cognitively demanding documents for which question generation requires reasoning over the relationships between sentences and paragraphs. As a result, a significant percentage of LearningQ questions (~30%) require higher-order cognitive skills to solve (such as applying, analyzing), in contrast to existing question-generation datasets that are designed mostly for the lowest cognitive skill level (i.e. remembering). To understand the effectiveness of existing question generation methods in producing educational questions, we evaluate both rule-based and deep neural network based methods on LearningQ. Extensive experiments show that state-of-the-art methods which perform well on existing datasets cannot generate useful educational questions. This implies that LearningQ is a challenging test bed for the generation of high-quality educational questions and worth further investigation. We open-source the dataset and our codes at https://dataverse.mpi-sws.org/dataverse/icwsm18.
|Title of host publication||Proceedings of the Twelfth International AAAI Conference on Web and Social Media|
|Publisher||Association for the Advancement of Artificial Intelligence (AAAI)|
|Number of pages||10|
|Publication status||Published - 2018|
|Event||Twelfth International AAAI Conference on Web and Social Media - Stanford University, Palo Alto, United States|
Duration: 25 Jun 2018 → 28 Jun 2018
|Conference||Twelfth International AAAI Conference on Web and Social Media|
|Abbreviated title||ICWSM 2018|
|Period||25/06/18 → 28/06/18|
- Automatic Question Generation
- Deep Neural Network
- Human Learning
- Bloom's Revised Taxonomy
Chen, G., Yang, J., Hauff, C., & Houben, G-J. (2018). LearningQ: A Large-scale Dataset for Educational Question Generation. In Proceedings of the Twelfth International AAAI Conference on Web and Social Media (pp. 481-490). Association for the Advancement of Artificial Intelligence (AAAI).