Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs

Xu Wang, Simin Fan, Jessica Houghton, Lu Wang


Abstract
NLP-powered automatic question generation (QG) techniques carry great pedagogical potential of saving educators’ time and benefiting student learning. Yet, QG systems have not been widely adopted in classrooms to date. In this work, we aim to pinpoint key impediments and investigate how to improve the usability of automatic QG techniques for educational purposes by understanding how instructors construct questions and identifying touch points to enhance the underlying NLP models. We perform an in-depth need finding study with 11 instructors across 7 different universities, and summarize their thought processes and needs when creating questions. While instructors show great interests in using NLP systems to support question design, none of them has used such tools in practice. They resort to multiple sources of information, ranging from domain knowledge to students’ misconceptions, all of which missing from today’s QG systems. We argue that building effective human-NLP collaborative QG systems that emphasize instructor control and explainability is imperative for real-world adoption. We call for QG systems to provide process-oriented support, use modular design, and handle diverse sources of input.
Anthology ID:
2022.naacl-main.22
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
291–302
Language:
URL:
https://aclanthology.org/2022.naacl-main.22
DOI:
10.18653/v1/2022.naacl-main.22
Bibkey:
Cite (ACL):
Xu Wang, Simin Fan, Jessica Houghton, and Lu Wang. 2022. Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 291–302, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs (Wang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.22.pdf
Code
 olivia-fsm/p2mcq