edutec-bea-shared-task-2024
Science Score: 31.0%
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Low similarity (2.1%) to scientific vocabulary
Keywords
bea-workshop
shared
task
Last synced: 6 months ago
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Repository
Basic Info
- Host: GitHub
- Owner: SGombert
- License: apache-2.0
- Language: Python
- Default Branch: main
- Homepage: https://edutec.science
- Size: 19.5 KB
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- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
bea-workshop
shared
task
Created almost 2 years ago
· Last pushed almost 2 years ago
Metadata Files
Readme
License
Citation
README.md
edutec-bea-shared-task-2024
Our submission for the BEA 2024 Shared Task on Predicting Item Difficulty and Item Response Time.
If you use this code for scientific purposes, please cite:
Gombert, S., Menzel, L., Di Mitri, D., & Drachsler, H. (2024, June). Predicting Item Difficulty and Item Response Time with Scalar-mixed Transformer Encoder Models and Rational Network Regression Heads. Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024). Mexico City, Mexico: Association for Computational Linguistics.
Owner
- Name: Sebastian Gombert
- Login: SGombert
- Kind: user
- Location: Darmstadt, Germany
- Company: DIPF - Leibniz Institute for Research and Information in Education
- Repositories: 1
- Profile: https://github.com/SGombert
Citation (citation.bib)
@inproceedings{gombert-etal-2024-DART-MCQ,
title = "{{Predicting Item Difficulty and Item Response Time with Scalar-mixed Transformer Encoder Models and Rational Network Regression Heads}}",
author = "Gombert, Sebastian and Menzel, Lukas and Di Mitri, Daniele and Drachsler, Hendrik",
booktitle = "Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)",
year = "2024",
month = "June",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
}