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
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3008–3020
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.219
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.219.pdf