Misconception Detection
Misconception Detection is a diagnostic process in Educational Data Mining that identifies persistent, context-specific student misunderstandings by analyzing patterns in learning behaviors and performance data to distinguish them from temporary errors or lack of knowledge.
649
Publications
31K
Citations
1.6K
Authors
545
Institutions
Publications per year
2017–2026
224
Parents
Children
Adaptive RemediationConcept Inventory AnalysisConcept Prerequisite ModelingConceptual Change AnalysisDifficult Concept IdentificationExplainable Misconception DetectionFeature-based Misconception DetectionFine-grained Knowledge State ModelingMisconception ClassificationMulti-label Error Diagnosis
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Indonesia University of Education
Bandung, Indonesia
12 h-index
42 publications
1.2K citations
State University of Malang
Malang, Indonesia
10 h-index
26 publications
521 citations
Carnegie Mellon University
Pittsburgh, United States
9 h-index
23 publications
1.7K citations
University of Pennsylvania
Philadelphia, United States
9 h-index
10 publications
259 citations
University of Washington
Seattle, United States
7 h-index
9 publications
1.3K citations
Purdue University West Lafayette
West Lafayette, United States
7 h-index
25 publications
568 citations
New York University
New York, United States
6 h-index
11 publications
677 citations
Virginia Tech
Blacksburg, United States
6 h-index
13 publications
582 citations
1–8 of 545
21 publications · 532 citations
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12 publications · 251 citations
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IJSE · 10 publications · 1.6K citations
10
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7 publications · 205 citations
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6 publications · 391 citations
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