{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002445173,0.00009177072,0.0001061114,0.00004554052,0.0001972501,0.0001991307,0.0002981614,0.00003300878,0.000003615399],"category_scores_gemma":[0.00000497389,0.00007875569,0.00006542051,0.00008610912,0.000004985306,0.0002003501,0.0000753846,0.00005896911,0.000003487683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006007362,"about_ca_system_score_gemma":0.00002712654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002359646,"about_ca_topic_score_gemma":0.00009637672,"domain_scores_codex":[0.9991695,0.00001947065,0.000203716,0.0002314936,0.0001269485,0.0002488192],"domain_scores_gemma":[0.9996312,0.00004145718,0.00004679637,0.000178591,0.00007319393,0.00002875916],"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":[5.863795e-7,0.00001033793,0.0006527695,0.000002951593,0.000004731581,0.00000113389,0.00005017525,0.7577201,0.0001208849,0.2405321,0.0000518242,0.0008524622],"study_design_scores_gemma":[0.00006892781,0.00001904462,0.00004530136,0.0000245953,0.000002625709,0.000003415017,0.00004405209,0.9952805,0.0001463093,0.0006245191,0.003628389,0.0001123105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009981515,0.0001439427,0.9875948,0.00005854549,0.0003610415,0.0001467314,1.071843e-7,0.0001341094,0.001579193],"genre_scores_gemma":[0.8848767,6.436213e-7,0.1103423,0.00004773954,0.0004079523,0.00000737362,5.060692e-7,0.000009004496,0.004307847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8772525,"threshold_uncertainty_score":0.3567094,"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."}}