Funding: This study was supported by National Natural Science Foundation of China (61877027).
机构署名：
本校为第一机构
院系归属：
国家数字化学习工程技术研究中心
摘要：
Purpose: Student engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research. Design/methodology/approach: The video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different modal data have different granularity, this study proposed the Fast–Slow Neural Network (FSNN) to detect engagement through both observable...
