{"id":"W2015646334","doi":"10.1109/mis.2009.59","title":"Improving the Behavior of Intelligent Tutoring Agents with Data Mining","year":2009,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Université de Montpellier","keywords":"Computer science; Association rule learning; Data mining; K-optimal pattern discovery; Intelligent agent; Artificial intelligence; Sequential Pattern Mining; Intelligent decision support system; Machine learning; Knowledge extraction; Data science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007065115,0.0001977805,0.0002423509,0.00009363542,0.0001790584,0.0003095792,0.003257734,0.00004982678,0.000004015174],"category_scores_gemma":[0.00003078236,0.0001307505,0.000043791,0.000438241,0.00005776079,0.0004767117,0.0002909436,0.0001498367,0.00003048334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005473687,"about_ca_system_score_gemma":0.00007603156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004277548,"about_ca_topic_score_gemma":0.00001074491,"domain_scores_codex":[0.997987,0.00005647676,0.0005652282,0.0005880087,0.0004793532,0.0003239068],"domain_scores_gemma":[0.996826,0.0001151827,0.0003413293,0.002491694,0.0001262249,0.00009956653],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009164105,0.0003887145,0.001065169,0.00006948677,0.0000835808,0.00003640037,0.003073851,0.001259801,0.004235934,0.005056784,0.003607459,0.9811137],"study_design_scores_gemma":[0.0002055601,0.0004194329,0.001120072,0.0004994102,0.0001059406,0.0001895882,0.001785212,0.9322829,0.03347087,0.00002860369,0.02930423,0.0005882283],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03789819,0.0003843765,0.9592495,0.0001193911,0.001059766,0.0007612372,0.00004728819,0.0001286972,0.0003515586],"genre_scores_gemma":[0.9746692,0.0000358801,0.02444479,0.00006197579,0.0002390538,0.0001230593,0.00002749632,0.00001610772,0.0003824042],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9805254,"threshold_uncertainty_score":0.6053734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08249325113046038,"score_gpt":0.3094061991097688,"score_spread":0.2269129479793084,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}