Affective Data Mining
Affective Data Mining is a computational methodology within affective computing that identifies, extracts, and analyzes emotional and cognitive states from multimodal educational data to infer learners' affective conditions during learning processes.
190
Publications
10.1K
Citations
638
Authors
284
Institutions
Publications per year
2017–2026
101
190
1–5 of 190
638
8
h-index
7
h-index
6
h-index
6
h-index
Universidad Nacional de Educación a Distancia, Spain
5 publications · 194 citations
5
h-index
1–5 of 638
284
Nanyang Technological University
Singapore, Singapore
9 h-index
18 publications
2K citations

Worcester Polytechnic Institute
Worcester, United States
8 h-index
19 publications
729 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
7 h-index
13 publications
821 citations
Columbia University
New York, United States
4 h-index
9 publications
569 citations
University of Memphis
Memphis, United States
4 h-index
13 publications
1.2K citations

University of Stirling
Stirling, United Kingdom
4 h-index
6 publications
950 citations

Universidad Nacional de Educación a Distancia
Madrid, Spain
4 h-index
14 publications
638 citations
Southwest University
Chongqing, China
4 h-index
9 publications
143 citations
1–8 of 284
Conference · 4 publications · 117 citations
4
h-index
4 publications · 479 citations
4
h-index
4 publications · 107 citations
4
h-index
4 publications · 193 citations
4
h-index
4 publications · 400 citations
4
h-index
1–5