Abstract
We explore various supervised learning strategies for automated scoring of content knowledge for a large corpus of 130 different content-based questions spanning four subject areas (Science, Math, English Language Arts, and Social Studies) and containing over 230,000 responses scored by human raters. Based on our analyses, we provide specific recommendations for content scoring. These are based on patterns observed across multiple questions and assessments and are, therefore, likely to generalize to other scenarios and prove useful to the community as automated content scoring becomes more popular in schools and classrooms.
Cite
CITATION STYLE
Madnani, N., Loukina, A., & Cahill, A. (2017). A large scale quantitative exploration of modeling strategies for content scoring. In EMNLP 2017 - 12th Workshop on Innovative Use of NLP for Building Educational Applications, BEA 2017 - Proceedings of the Workshop (pp. 457–467). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-5052
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
