Model-Based Hint Generation is a hint generation approach that belongs to the class of methods utilizing an explicit computational or cognitive model of the learner, domain, or problem-solving process to systematically derive and provide feedback, distinguishing it from heuristic or rule-based strategies by its reliance on structured representations of knowledge or behavior.
4
Publications
378
Citations
7
Authors
5
Institutions
Publications per year
2017–2026
0
Parents
Publications
4
Learning from hints in neural networks
Yaser S. Abu‐Mostafa · Journal of Complexity · 1990 · 243 citations
Model-based matching and hinting of fonts
Roger D. Hersch, Claude Bétrisey · 1991 · 22 citations · Full text
Model-based matching and hinting of fonts
Roger D. Hersch, Claude Bétrisey · ACM SIGGRAPH Computer Graphics · 1991 · 10 citations · Full text
1–5 of 4
Authors
7
- RD
2
h-index
- CB
2
h-index
- JS
1
h-index
- MJMarvin J. Croy
University of North Carolina at Charlotte, United States
1 publications · 103 citations
1
h-index
- TB
1
h-index
1–5 of 7
Institutions
5
École Polytechnique Fédérale de Lausanne
Lausanne, Switzerland
2 h-index
4 publications
64 citations
Human Computer Interaction (Switzerland)
Zurich, Switzerland
1 h-index
4 publications
412 citations
University of North Carolina at Charlotte
Charlotte, United States
1 h-index
3 publications
309 citations
California Institute of Technology
Pasadena, United States
1 h-index
1 publications
243 citations
Carnegie Mellon University
Pittsburgh, United States
1 h-index
1 publications
103 citations
1–5 of 5
Venues
- International Journal of Artificial Intelligence in Education
1 publications · 103 citations
1
h-index
- Journal of Complexity
1 publications · 243 citations
1
h-index
- ACM SIGGRAPH Computer Graphics
1 publications · 10 citations
1
h-index
- Revista de Investigação e Divulgação em Educação Matemática
RIDEMA · 1 publications · 11 citations
1–4