@inproceedings{758eb262279747c6a0cd10f1a9ed41ae,
title = "Assessing Student Performance with Multi-granularity Attention from Online Classroom Dialogue",
abstract = "Accurately judging students' ongoing performance is very crucial for real-world educational scenarios. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in the online learning environment. We propose a novel neural network armed with a multi-granularity attention mechanism to capture the personalized pedagogical instructions from the very noisy teacher-student dialogue transcriptions. We conduct experiments on a real-world educational dataset and the results demonstrate the superiority and availability of our model in terms of various evaluation metrics.",
keywords = "AI in education, assessment, classroom dialogue, student modeling",
author = "Jiahao Chen and Zitao Liu and Shuyan Huang and Yaying Huang and Xiangyu Zhao and Boyu Gao and Weiqi Luo",
note = "Publisher Copyright: {\textcopyright} 2023 Copyright held by the owner/author(s).; 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 ; Conference date: 21-10-2023 Through 25-10-2023",
year = "2023",
month = oct,
day = "21",
doi = "10.1145/3583780.3615143",
language = "英语",
series = "International Conference on Information and Knowledge Management, Proceedings",
publisher = "Association for Computing Machinery ",
pages = "3798--3802",
booktitle = "CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management",
address = "美国",
}