{"id":"W2647154366","doi":"10.1007/978-3-319-61425-0_10","title":"Personalized Tag-Based Knowledge Diagnosis to Predict the Quality of Answers in a Community of Learners","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Ranging; Quality (philosophy); Mean squared error; Field (mathematics); Sample (material); Naive Bayes classifier; Artificial intelligence; Machine learning; Data science; Statistics; Mathematics","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":[0.001351096,0.0005939283,0.0009717877,0.00409787,0.0005390029,0.001130704,0.001347293,0.001759541,0.001931746],"category_scores_gemma":[0.005585659,0.0001717504,0.0005979295,0.002226606,0.0002619505,0.001948047,0.001156666,0.0008516228,0.001512804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006215193,"about_ca_system_score_gemma":0.0007860051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005437769,"about_ca_topic_score_gemma":0.01010348,"domain_scores_codex":[0.9989122,0.0002298722,0.0000785997,0.0003409226,0.0002969078,0.0001415876],"domain_scores_gemma":[0.9952756,0.002813676,0.0003310897,0.0003356126,0.0009419975,0.0003018884],"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":[0.002351739,0.003055609,0.1476955,0.000341224,0.0005569527,0.0005156625,0.0009486927,0.03113044,0.02449645,0.001848654,0.01214714,0.7749119],"study_design_scores_gemma":[0.00008494437,0.000675689,0.030746,0.00004591088,0.0002889826,0.0003095315,0.0004467652,0.947681,0.01126271,0.006512247,0.001885511,0.00006073614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8205934,0.00219902,0.1678035,0.0004789483,0.0001822211,0.0002378241,0.002508451,0.002030075,0.003966492],"genre_scores_gemma":[0.953452,0.0002133797,0.04043784,0.0001022661,0.000112706,0.00008692984,0.002473855,0.00003730101,0.003083769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005437769,"threshold_uncertainty_score":0.01081222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06173944265229001,"score_gpt":0.3258761021413216,"score_spread":0.2641366594890315,"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."}}