Multi-modal Learner Modeling
Multi-Modal Learner Modeling is a learner modeling approach that integrates multiple types of data modalities—such as behavioral, physiological, linguistic, and interaction traces—to construct a comprehensive and dynamic representation of individual learners, thereby enabling more nuanced inferences about cognitive, affective, and metacognitive states than single-modality models.
14
Publications
809
Citations
73
Authors
42
Institutions
Publications per year
2017–2026
11
Parents
14
Multimodality in meta-learning: A comprehensive survey
Yao Ma, Shilin Zhao, Weixiao Wang et al. · Knowledge-Based Systems · 2022 · 84 citations
Q-Instruct: Improving Low-Level Visual Abilities for Multi-Modality Foundation Models
Haoning Wu, Zicheng Zhang, Erli Zhang et al. · 2024 · 69 citations
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42

Malmö University
Malmo, Sweden
2 h-index
3 publications
43 citations
University College London
London, United Kingdom
2 h-index
3 publications
44 citations
Tallinn University
Tallinn, Estonia
1 h-index
1 publications
14 citations
Institute for Infocomm Research
Singapore, Singapore
1 h-index
1 publications
69 citations

Shanghai Jiao Tong University
Shanghai, China
1 h-index
3 publications
207 citations
Wested
San Francisco, United States
1 h-index
1 publications
27 citations
Qingdao Academy of Intelligent Industries
Qingdao, China
1 h-index
2 publications
62 citations
University of California San Diego
San Diego, United States
1 h-index
1 publications
18 citations
1–8 of 42
2 publications · 51 citations
2
h-index
2 publications · 66 citations
2
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1 publications · 209 citations
1
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1 publications · 84 citations
1
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1 publications · 215 citations
1
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