@inproceedings{f5adfaf0666e4230a07aeb9eb5936e15,
title = "Data Fusion in Classroom-Based Multimodal Learning Analytics: A Systematic Literature Review",
abstract = "In classroom environments, this paper presents a systematic review of Multimodal Learning Analytics (MMLA) following PRISMA guidelines. It explores the relationship between learning indicators, multimodal data types, and data fusion strategies to improve MMLA's decision-making efficacy. The main findings include: 1) Complex mappings exist between learning indicators and multimodal data, including within learning indicator layers; 2) Engagement stands out as the foremost learning indicator; 3) Video data predominates in MMLA applications; 4) Feature-level fusion is the leading strategy for data integration. The study also highlights both theoretical and technological challenges, emphasizing the vital influence of learning theories.",
author = "Yuxuan Wang and Xiaoqing Gu",
note = "Publisher Copyright: {\textcopyright} ISLS.; 18th International Conference of the Learning Sciences, ICLS 2024 ; Conference date: 10-06-2024 Through 14-06-2024",
year = "2024",
doi = "10.22318/icls2024.626650",
language = "英语",
series = "Proceedings of International Conference of the Learning Sciences, ICLS",
publisher = "International Society of the Learning Sciences (ISLS)",
pages = "951--954",
editor = "Robb Lindgren and Tutaleni Asino and Kyza, \{Eleni A.\} and Chee-Kit Looi and Keifert, \{D. Teo\} and Enrique Suarez",
booktitle = "ISLS Annual Meeting 2024",
address = "美国",
}