Abstract
In undergraduate theses, a good methodology section should describe the series of steps that were followed in performing the research. To assist students in this task, we develop machine-learning models and an app that uses them to provide feedback while students write. We construct an annotated corpus that identifies sentences representing methodological steps and labels when a methodology contains a logical sequence of such steps. We train machine-learning models based on language modeling and lexical features that can identify sentences representing methodological steps with 0.939 f-measure, and identify methodology sections containing a logical sequence of steps with an accuracy of 87%. We incorporate these models into a Microsoft Office Add-in, and show that students who improved their methodologies according to the model feedback received better grades on their methodologies.- Anthology ID:
- 2020.bea-1.11
- Volume:
- Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, WA, USA → Online
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 115–123
- Language:
- URL:
- https://aclanthology.org/2020.bea-1.11
- DOI:
- 10.18653/v1/2020.bea-1.11
- Cite (ACL):
- Samuel González-López, Steven Bethard, and Aurelio Lopez-Lopez. 2020. Assisting Undergraduate Students in Writing Spanish Methodology Sections. In Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications, pages 115–123, Seattle, WA, USA → Online. Association for Computational Linguistics.
- Cite (Informal):
- Assisting Undergraduate Students in Writing Spanish Methodology Sections (González-López et al., BEA 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.bea-1.11.pdf