Automatic Generation of Socratic Subquestions for Teaching Math Word Problems


Loading...

Date

2022-12

Publication Type

Conference Paper

ETH Bibliography

yes

Citations

Altmetric

Data

Abstract

Socratic questioning is an educational method that allows students to discover answers to complex problems by asking them a series of thoughtful questions. Generation of didactically sound questions is challenging, requiring understanding of the reasoning process involved in the problem. We hypothesize that such questioning strategy can not only enhance the human performance, but also assist the math word problem (MWP) solvers.In this work, we explore the ability of large language models (LMs) in generating sequential questions for guiding math word problem-solving. We propose various guided question generation schemes based on input conditioning and reinforcement learning.On both automatic and human quality evaluations, we find that LMs constrained with desirable question properties generate superior questions and improve the overall performance of a math word problem solver. We conduct a preliminary user study to examine the potential value of such question generation models in the education domain. Results suggest that the difficulty level of problems plays an important role in determining whether questioning improves or hinders human performance. We discuss the future of using such questioning strategies in education.

Publication status

published

Book title

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Journal / series

Volume

Pages / Article No.

4136 - 4149

Publisher

Association for Computational Linguistics

Event

Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

Organisational unit

09684 - Sachan, Mrinmaya / Sachan, Mrinmaya check_circle
09590 - Kapur, Manu / Kapur, Manu check_circle
02219 - ETH AI Center / ETH AI Center

Notes

Funding

Related publications and datasets