Feedback Optimization
Feedback Optimization is a methodological framework within adaptive feedback systems that systematically enhances feedback quality and delivery by dynamically adjusting feedback parameters based on learner performance, engagement, and contextual data to maximize learning outcomes.
71
Publications
2.9K
Citations
222
Authors
95
Institutions
Publications per year
2017–2026
28
Parents
71
1–5 of 71
222
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h-index
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95
ETH Zurich
Zurich, Switzerland
9 h-index
37 publications
1.3K citations
Purdue University West Lafayette
West Lafayette, United States
4 h-index
10 publications
362 citations
Harvard University
Cambridge, United States
4 h-index
7 publications
169 citations
Tsinghua University
Beijing, China
2 h-index
5 publications
204 citations

Sichuan University
Chengdu, China
2 h-index
2 publications
106 citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
6 publications
213 citations
University of Illinois Urbana-Champaign
Urbana, United States
2 h-index
3 publications
40 citations
Freie Universität Berlin
Berlin, Germany
2 h-index
7 publications
716 citations
1–8 of 95
6 publications · 116 citations
6
h-index
4 publications · 125 citations
3
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2 publications · 108 citations
2
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2 publications · 55 citations
2
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2 publications · 103 citations
2
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1–5