Abstract
Knowledge of the association between assessment questions and the skills required to solve them is necessary for analysis of student learning. This association, often represented as a Q-matrix, is either hand-labeled by domain experts or learned as latent variables given a large student response data set. As a means of automating the match to formal standards, this paper uses neural text classification methods, leveraging the language in the standards documents to identify online text for a proxy training task. Experiments involve identifying the topic and crosscutting concepts of middle school science questions leveraging multi-task training. Results show that it is possible to automatically build a Q-matrix without student response data and using a modest number of hand-labeled questions.- Anthology ID:
- W17-5036
- Volume:
- Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 319–326
- Language:
- URL:
- https://aclanthology.org/W17-5036
- DOI:
- 10.18653/v1/W17-5036
- Cite (ACL):
- Farah Nadeem and Mari Ostendorf. 2017. Language Based Mapping of Science Assessment Items to Skills. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications, pages 319–326, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Language Based Mapping of Science Assessment Items to Skills (Nadeem & Ostendorf, BEA 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-5036.pdf