{"id":"W2588240791","doi":"10.24059/olj.v20i2.802","title":"Using Learning Analytics to Identify Medical Student Misconceptions in an Online Virtual Patient Environment","year":2015,"lang":"en","type":"article","venue":"Online Learning","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; McGill University; University Health Network","funders":"","keywords":"Formative assessment; Computer science; Learning analytics; Analytics; Domain (mathematical analysis); Inclusion (mineral); Chart; Data science; Machine learning; Psychology; Mathematics education; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001040019,0.0003332194,0.0004439451,0.0004837452,0.0002340035,0.0002560057,0.001041344,0.0001856678,0.00005952572],"category_scores_gemma":[0.0008441801,0.0003409834,0.0001091797,0.0008124202,0.00007722773,0.0004674067,0.0009237081,0.001657929,0.0001069814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666624,"about_ca_system_score_gemma":0.0002959083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000114299,"about_ca_topic_score_gemma":0.0001400609,"domain_scores_codex":[0.9955423,0.0006600941,0.0007753209,0.0008196388,0.001533328,0.0006692759],"domain_scores_gemma":[0.9980437,0.0001298274,0.0002509874,0.0005359738,0.0001355753,0.0009039061],"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":[0.000008066248,0.0008727665,0.06432194,0.000004180456,0.00002112468,0.0001470838,0.004473879,0.8770479,0.0003468197,0.0002703603,0.00001954719,0.05246636],"study_design_scores_gemma":[0.0007485219,0.001085726,0.01662229,0.0001422346,0.0000241407,0.00003121456,0.007209076,0.9667394,0.00001317013,0.00007114161,0.006864588,0.0004484494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8686434,0.00007535748,0.128315,0.002367916,0.000224424,0.0001323853,0.000004384722,0.0001930492,0.00004412412],"genre_scores_gemma":[0.9377886,0.00003228907,0.06068665,0.000487555,0.0003568819,0.000004206232,0.0001124198,0.00004334684,0.0004880694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08969156,"threshold_uncertainty_score":0.9999042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0817676672790888,"score_gpt":0.3954793453496462,"score_spread":0.3137116780705574,"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."}}