{"id":"W2485519559","doi":"10.4018/978-1-60960-165-2.ch010","title":"A Decision-Theoretic Tutor for Analogical Problem Solving","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"TUTOR; Selection (genetic algorithm); Computer science; Mechanism (biology); Probabilistic logic; Artificial intelligence; Process (computing); Decision problem; Bayesian network; Machine learning; Management science; Engineering; Algorithm","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":[0.001191223,0.0009372646,0.0005234765,0.0005269824,0.0004273753,0.002266179,0.002248568,0.001881774,0.02227393],"category_scores_gemma":[0.003414429,0.0003136443,0.0005367274,0.0007302312,0.001053933,0.003160886,0.001442507,0.00230905,0.007442428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001338801,"about_ca_system_score_gemma":0.001200543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005338284,"about_ca_topic_score_gemma":0.0006325895,"domain_scores_codex":[0.9990946,0.0003743713,0.00003936185,0.0001464139,0.0002994236,0.0000458403],"domain_scores_gemma":[0.9989564,0.0006477522,0.0000426693,0.0001252257,0.0001125829,0.0001153504],"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":[0.0001295238,0.0005818495,0.001006012,0.0009155549,0.00004137884,0.0002366663,0.001299423,0.03080068,0.00737479,0.4421869,0.03948655,0.4759407],"study_design_scores_gemma":[0.0001280994,0.0003651266,0.0006858428,0.0003795253,0.0000394178,0.001280076,0.0002783353,0.2026166,0.01015172,0.2559311,0.528075,0.00006917385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007922384,0.003995514,0.9116614,0.002034808,0.0002547709,0.0003002067,0.0001933576,0.002230688,0.07140683],"genre_scores_gemma":[0.1282836,0.002782212,0.7957432,0.001173222,0.0001710921,0.0005056833,0.0004880464,0.0003091852,0.07054366],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02227393,"threshold_uncertainty_score":0.07451373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03296746507902201,"score_gpt":0.2542457350228952,"score_spread":0.2212782699438731,"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."}}