{"id":"W3211073716","doi":"10.1007/s10639-021-10772-0","title":"Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns","year":2021,"lang":"en","type":"article","venue":"Education and Information Technologies","topic":"Clinical Reasoning and Diagnostic Skills","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Context (archaeology); Virtual patient; Metacognition; Educational technology; Computer science; Psychological intervention; Medical education; Psychology; Artificial intelligence; Mathematics education; Machine learning; Medicine; Cognition","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":[0.001470942,0.0004364087,0.0004573613,0.003203176,0.0002759852,0.001141691,0.0006456474,0.0005106846,0.0009016254],"category_scores_gemma":[0.01127985,0.0001909047,0.0005065275,0.001611821,0.0002332664,0.0009949333,0.0007896082,0.000456272,0.0003681883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003825845,"about_ca_system_score_gemma":0.0006408604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002407256,"about_ca_topic_score_gemma":0.003167669,"domain_scores_codex":[0.9985573,0.0004861395,0.0002087061,0.0003122553,0.0003030459,0.0001326256],"domain_scores_gemma":[0.9885744,0.008003894,0.001058833,0.0005991459,0.001152227,0.0006114474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007274533,0.001255866,0.8190426,0.0001675432,0.0002913116,0.0003843176,0.001155303,0.007079614,0.006159707,0.0003015226,0.0007021895,0.1627324],"study_design_scores_gemma":[0.00004263359,0.001658297,0.7851366,0.00007627451,0.0004088262,0.001340523,0.003450065,0.1899886,0.01296332,0.002543579,0.002307794,0.00008350659],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888725,0.00008351698,0.009889854,0.00007409617,0.000005370796,0.00004910113,0.0004603999,0.0000858994,0.00047929],"genre_scores_gemma":[0.991511,0.00006699091,0.007435385,0.00001505795,0.000005629331,0.00002891521,0.0006293936,0.00001158572,0.000295928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003203176,"threshold_uncertainty_score":0.007779181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01238854284235095,"score_gpt":0.3143649913959002,"score_spread":0.3019764485535493,"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."}}