{"id":"W2463958247","doi":"","title":"Analyzing Student Inquiry Data Using Process Discovery and Sequence Classification.","year":2015,"lang":"en","type":"article","venue":"NPARC","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Process (computing); Process mining; Sequential Pattern Mining; Knowledge extraction; Sequence (biology); Set (abstract data type); Data mining; Data science; Business process discovery; Machine learning; Artificial intelligence; Work in process; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01075291,0.001303881,0.001733626,0.01949034,0.001022007,0.002962943,0.001764692,0.001330425,0.001411727],"category_scores_gemma":[0.03317527,0.0003710488,0.002299594,0.01461304,0.0007077398,0.002365608,0.001769325,0.001793291,0.001225141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001487836,"about_ca_system_score_gemma":0.003075722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008046167,"about_ca_topic_score_gemma":0.008214987,"domain_scores_codex":[0.9867917,0.004611693,0.001720174,0.001906215,0.004571881,0.0003982789],"domain_scores_gemma":[0.9512475,0.03157138,0.004814236,0.005203894,0.006449042,0.000713935],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000521833,0.001400109,0.118255,0.00174031,0.0006890275,0.0003592222,0.002338296,0.02454472,0.007540992,0.007567371,0.003478749,0.8315644],"study_design_scores_gemma":[0.0001349696,0.001109831,0.08950378,0.0005891999,0.0004710238,0.001378267,0.002844888,0.7984487,0.03632247,0.03647165,0.03247292,0.0002522662],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1215794,0.001186426,0.8584324,0.0006379049,0.000129631,0.003098356,0.007312141,0.003377457,0.004246288],"genre_scores_gemma":[0.2710129,0.0006685754,0.7140931,0.0001100781,0.00007062018,0.002328205,0.01026078,0.0001055007,0.001350196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01949034,"threshold_uncertainty_score":0.05686748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2918215355759158,"score_gpt":0.4145837869204557,"score_spread":0.1227622513445399,"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."}}