Explainable Misconception Detection
Explainable Misconception Detection is an academic method within the field of educational data mining that not only identifies learners' misconceptions but also provides transparent, interpretable, and human-understandable justifications for the detected misconceptions, thereby distinguishing it from black-box approaches to misconception detection.
27
Publications
1.1K
Citations
79
Authors
30
Institutions
Publications per year
2017–2026
6
Parents
27
1–5 of 27
79
1
h-index
1
h-index
1
h-index
1
h-index
1
h-index
1–5 of 79
30
Indonesia University of Education
Bandung, Indonesia
2 h-index
11 publications
228 citations
University of Pennsylvania
Philadelphia, United States
2 h-index
3 publications
98 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
23 citations
Open University of Israel
Raanana, Israel
1 h-index
1 publications
47 citations
Queen Mary University of London
London, United Kingdom
1 h-index
1 publications
23 citations
Palo Alto University
Palo Alto, United States
1 h-index
1 publications
23 citations
University of California, Los Angeles
Los Angeles, United States
1 h-index
3 publications
105 citations

Universidad Nacional de La Plata
La Plata, Argentina
1 h-index
4 publications
64 citations
1–8 of 30
2 publications · 132 citations
2
h-index
2 publications · 28 citations
2
h-index
ETRD · 1 publications · 10 citations
1
h-index
CJES · 1 publications · 20 citations
1
h-index
1 publications · 26 citations
1
h-index
1–5