StuBot: Learning by Teaching a Conversational Agent Through Machine Reading Comprehension

Nayoung Jin, Hana Lee


Abstract
This paper proposes StuBot, a text-based conversational agent that provides adaptive feedback for learning by teaching. StuBot first asks the users to teach the learning content by summarizing and explaining it in their own words. After the users inputted the explanation text for teaching, StuBot uses a machine reading comprehension (MRC) engine to provide adaptive feedback with further questions about the insufficient parts of the explanation text. We conducted a within-subject study to evaluate the effectiveness of adaptive feedback by StuBot. Both the quantitative and qualitative results showed that learning by teaching with adaptive feedback can improve learning performance, immersion, and overall experience.
Anthology ID:
2022.findings-emnlp.219
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3008–3020
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.219
DOI:
10.18653/v1/2022.findings-emnlp.219
Bibkey:
Cite (ACL):
Nayoung Jin and Hana Lee. 2022. StuBot: Learning by Teaching a Conversational Agent Through Machine Reading Comprehension. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3008–3020, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
StuBot: Learning by Teaching a Conversational Agent Through Machine Reading Comprehension (Jin & Lee, Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.219.pdf
Dataset:
 2022.findings-emnlp.219.dataset.zip
Video:
 https://aclanthology.org/2022.findings-emnlp.219.mp4