{"id":"W2134513264","doi":"10.1109/icalt.2006.1652614","title":"TIDES - Using Bayesian Networks for Student Modeling","year":2006,"lang":"en","type":"article","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Montréal","funders":"","keywords":"Bayesian network; Computer science; Imperfect; TUTOR; Intelligent tutoring system; Bayesian probability; Machine learning; Artificial intelligence; Plan (archaeology); Order (exchange)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004460077,0.001338134,0.001316012,0.002326775,0.001018268,0.003403876,0.002211287,0.002290088,0.007117768],"category_scores_gemma":[0.02082437,0.001246588,0.001697857,0.001811313,0.001249972,0.005158407,0.002263522,0.002184699,0.002008106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00221621,"about_ca_system_score_gemma":0.001871357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02514323,"about_ca_topic_score_gemma":0.02840857,"domain_scores_codex":[0.996845,0.001633462,0.0001640749,0.0006097345,0.0005800397,0.0001677231],"domain_scores_gemma":[0.9932321,0.005192545,0.0004679043,0.0003980442,0.0005296055,0.0001797955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001420291,0.00008092959,0.00248353,0.0001041014,0.0001635063,0.0001301093,0.0002974371,0.8283521,0.0004875101,0.08859787,0.002224424,0.0769364],"study_design_scores_gemma":[0.00001237747,0.000008334978,0.000124041,0.00001182319,0.00001856412,0.0000150899,0.00001388523,0.9548782,0.0001671132,0.04336368,0.001374683,0.00001222513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003083862,0.0001840055,0.9935847,0.0002846555,0.00003241176,0.00005502207,0.0002014248,0.000475184,0.002098676],"genre_scores_gemma":[0.3721211,0.001223849,0.6116357,0.0003196904,0.0001799752,0.000718797,0.00120762,0.0003420414,0.01225126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02514323,"threshold_uncertainty_score":0.04999375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03380853087708886,"score_gpt":0.2835701662748935,"score_spread":0.2497616353978046,"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."}}