Abstract
In this paper, we propose Learn, a unified, easy-to-use tool to apply question generation and selection in classrooms. The tool lets instructors and TAs create assignments that can write and re-write themselves. Given existing course materials, for example a reference textbook, Learn can generate questions, select the highest quality questions, show the questions to students, adapt question difficulty to student knowledge, and generate new questions based on how effectively old questions help students learn. The modular, composable nature of the tools for handling each sub-task allow instructors to use only the parts of the tool necessary to the course, allowing for integration in a large number of courses with varied teaching styles. We also report on the adoption of the tool in classes at the University of Pennsylvania with over 1000 students. Learn is publicly released at https://learn.withmartian.com.- Anthology ID:
- 2023.eacl-demo.30
- Volume:
- Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
- Month:
- May
- Year:
- 2023
- Address:
- Dubrovnik, Croatia
- Editors:
- Danilo Croce, Luca Soldaini
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 267–276
- Language:
- URL:
- https://aclanthology.org/2023.eacl-demo.30
- DOI:
- 10.18653/v1/2023.eacl-demo.30
- Cite (ACL):
- Shriyash Upadhyay, Chris Callison-burch, and Etan Ginsberg. 2023. Learn With Martian: A Tool For Creating Assignments That Can Write And Re-Write Themselves. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 267–276, Dubrovnik, Croatia. Association for Computational Linguistics.
- Cite (Informal):
- Learn With Martian: A Tool For Creating Assignments That Can Write And Re-Write Themselves (Upadhyay et al., EACL 2023)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2023.eacl-demo.30.pdf