Cold-start Learner Modeling
Cold-Start Learner Modeling is a learner modeling approach that addresses the challenge of initializing personalized instructional systems for new learners with little or no prior interaction data by leveraging external knowledge sources, demographic information, or pre-assessments to generate initial learner profiles.
6
Publications
178
Citations
19
Authors
13
Institutions
Publications per year
2017–2026
6
Parents
6
Sequential Scenario-Specific Meta Learner for Online Recommendation
Zhengxiao Du, Xiaowei Wang, Hongxia Yang et al. · 2019 · 105 citations
Cold Start Knowledge Tracing with Attentive Neural Turing Machine
Jinjin Zhao, Shreyansh Bhatt, Candace Thille et al. · 2020 · 15 citations
Sequential Scenario-Specific Meta Learner for Online Recommendation
Zhengxiao Du, Xiaowei Wang, Hongxia Yang et al. · arXiv (Cornell University) · 2019 · 12 citations · Full text
1–5 of 6
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1–5 of 19
13
Tsinghua University
Beijing, China
2 h-index
4 publications
234 citations

Alibaba Group (China)
Hangzhou, China
2 h-index
5 publications
339 citations

École Normale Supérieure Paris-Saclay
Gif-sur-Yvette, France
1 h-index
1 publications
11 citations

Philadelphia University
Philadelphia, United States
1 h-index
1 publications
16 citations
Laboratoire de Recherche en Informatique
Gif-sur-Yvette, France
1 h-index
2 publications
22 citations
Association for Computing Machinery
New York, United States
1 h-index
1 publications
12 citations
University of Pennsylvania
Philadelphia, United States
1 h-index
1 publications
16 citations
Columbia University
New York, United States
1 h-index
1 publications
16 citations
1–8 of 13
Conference · 1 publications · 16 citations
1
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1 publications · 19 citations
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Repository · 1 publications · 12 citations
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1 publications · 11 citations
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