Abstract
Intelligent and adaptive online education systems aim to make high-quality education available for a diverse range of students. However, existing systems usually depend on a pool of hand-made questions, limiting how fine-grained and open-ended they can be in adapting to individual students. We explore targeted question generation as a controllable sequence generation task. We first show how to fine-tune pre-trained language models for deep knowledge tracing (LM-KT). This model accurately predicts the probability of a student answering a question correctly, and generalizes to questions not seen in training. We then use LM-KT to specify the objective and data for training a model to generate questions conditioned on the student and target difficulty. Our results show we succeed at generating novel, well-calibrated language translation questions for second language learners from a real online education platform.- Anthology ID:
- 2021.acl-short.88
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 692–701
- Language:
- URL:
- https://aclanthology.org/2021.acl-short.88
- DOI:
- 10.18653/v1/2021.acl-short.88
- Cite (ACL):
- Megha Srivastava and Noah Goodman. 2021. Question Generation for Adaptive Education. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 692–701, Online. Association for Computational Linguistics.
- Cite (Informal):
- Question Generation for Adaptive Education (Srivastava & Goodman, ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2021.acl-short.88.pdf
- Code
- meghabyte/acl2021-education