{"id":"W4385218009","doi":"10.3390/psych5030050","title":"An Introduction to Bayesian Knowledge Tracing with pyBKT","year":2023,"lang":"en","type":"article","venue":"Psych","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton; University of Alberta","funders":"","keywords":"Python (programming language); Computer science; Tracing; Probabilistic logic; Bayesian probability; Machine learning; Statistical model; Data mining; Artificial intelligence; Data science; Programming language","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":[0.0003341165,0.00008653237,0.00008777648,0.0001659538,0.0001322863,0.0001358699,0.0003114125,0.00002455448,0.00000998083],"category_scores_gemma":[0.00001116329,0.00007251077,0.00001972203,0.0007880933,0.000006415172,0.0002694667,0.00002995163,0.00009701907,0.0005388678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002864225,"about_ca_system_score_gemma":0.00002085502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002617093,"about_ca_topic_score_gemma":0.0000236639,"domain_scores_codex":[0.999091,0.00004930508,0.0001106044,0.0003786993,0.000136517,0.0002338528],"domain_scores_gemma":[0.9994154,0.00001616713,0.00002896701,0.0003822522,0.00006693866,0.00009025294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003955135,0.0002067199,0.005780106,0.00009100764,0.0000558969,0.00004458774,0.03624913,0.02458403,0.01847163,0.6073914,0.02716115,0.2799248],"study_design_scores_gemma":[0.0003534565,0.001169729,0.02186454,0.0001693902,0.0000078951,0.00006933445,0.001644968,0.09253186,0.008575397,0.0008215799,0.872036,0.0007558853],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0504694,0.00001480325,0.9424106,0.0029909,0.001294452,0.0001223469,2.180683e-7,0.0005171348,0.002180104],"genre_scores_gemma":[0.9781708,6.436202e-7,0.009582173,0.00004521062,0.001430501,0.0000174171,0.000001395312,0.00001368539,0.01073825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9328285,"threshold_uncertainty_score":0.6926236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02006556139158163,"score_gpt":0.2958401694424989,"score_spread":0.2757746080509173,"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."}}