KT-IDEM: Introducing item difficulty to the knowledge tracing model

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6787 LNCS, Issue , 2011, Pages 243-254

  Pardos, Zachary A ^a   Heffernan, Neil T ^a  

^a Worcester Polytechnic Institute   (United States)

Author keywords

Bayesian Networks; Data Mining; Item Difficulty; Knowledge Tracing; User Modeling

Indexed keywords

COMPUTER EDUCATION; DATA SETS; DIAGNOSTIC ABILITY; EFFECT MODEL; FEATURE GENERATION; INTELLIGENT TUTORING SYSTEM; ITEM DIFFICULTY; KNOWLEDGE TRACING; PERFORMANCE GAIN; PREDICTION ACCURACY; RESEARCH QUESTIONS; SKILL LEVELS; SLIP MODEL; TRACING MODEL; USER MODELING; VALIDATION TESTING;

BAYESIAN NETWORKS; CAPILLARY FLOW; COMPUTER AIDED INSTRUCTION; ENGINEERING RESEARCH; INFERENCE ENGINES; MATHEMATICAL MODELS;

TOPOLOGY;

EID: 79960334337     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-22362-4_21     Document Type: Conference Paper

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