@inproceedings{palomino-etal-2024-edtec,
title = "{E}d{T}ec-{QB}uilder: A Semantic Retrieval Tool for Assembling Vocational Training Exams in {G}erman Language",
author = "Palomino, Alonso and
Fischer, Andreas and
Kuzilek, Jakub and
Nitsch, Jarek and
Pinkwart, Niels and
Paassen, Benjamin",
editor = "Chang, Kai-Wei and
Lee, Annie and
Rajani, Nazneen",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-demo.3",
pages = "26--35",
abstract = "Selecting and assembling test items from a validated item database into comprehensive exam forms is an under-researched but significant challenge in education. Search and retrieval methods provide a robust framework to assist educators when filtering and assembling relevant test items. In this work, we present EdTec-QBuilder, a semantic search tool developed to assist vocational educators in assembling exam forms. To implement EdTec-QBuilder{'}s core search functionality, we evaluated eight retrieval strategies and twenty-five popular pre-trained sentence similarity models. Our evaluation revealed that employing cross-encoders to re-rank an initial list of relevant items is best for assisting vocational trainers in assembling examination forms. Beyond topic-based exam assembly, EdTec-QBuilder aims to provide a crowdsourcing infrastructure enabling manual exam assembly data collection, which is critical for future research and development in assisted and automatic exam assembly models.",
}
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%0 Conference Proceedings
%T EdTec-QBuilder: A Semantic Retrieval Tool for Assembling Vocational Training Exams in German Language
%A Palomino, Alonso
%A Fischer, Andreas
%A Kuzilek, Jakub
%A Nitsch, Jarek
%A Pinkwart, Niels
%A Paassen, Benjamin
%Y Chang, Kai-Wei
%Y Lee, Annie
%Y Rajani, Nazneen
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: System Demonstrations)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F palomino-etal-2024-edtec
%X Selecting and assembling test items from a validated item database into comprehensive exam forms is an under-researched but significant challenge in education. Search and retrieval methods provide a robust framework to assist educators when filtering and assembling relevant test items. In this work, we present EdTec-QBuilder, a semantic search tool developed to assist vocational educators in assembling exam forms. To implement EdTec-QBuilder’s core search functionality, we evaluated eight retrieval strategies and twenty-five popular pre-trained sentence similarity models. Our evaluation revealed that employing cross-encoders to re-rank an initial list of relevant items is best for assisting vocational trainers in assembling examination forms. Beyond topic-based exam assembly, EdTec-QBuilder aims to provide a crowdsourcing infrastructure enabling manual exam assembly data collection, which is critical for future research and development in assisted and automatic exam assembly models.
%U https://aclanthology.org/2024.naacl-demo.3
%P 26-35
Markdown (Informal)
[EdTec-QBuilder: A Semantic Retrieval Tool for Assembling Vocational Training Exams in German Language](https://aclanthology.org/2024.naacl-demo.3) (Palomino et al., NAACL 2024)
ACL