{"id":"W1501816783","doi":"10.1007/978-3-642-13388-6_17","title":"Discovering and Recognizing Student Interaction Patterns in Exploratory Learning Environments","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Association rule learning; Class (philosophy); Classifier (UML); Artificial intelligence; Machine learning; Exploratory analysis; Learning environment; Exploratory research; Human–computer interaction; Data science; Mathematics education","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":[0.0004534188,0.0004868667,0.0003970537,0.002018263,0.0003914525,0.001286245,0.0006591392,0.0008123202,0.0009945882],"category_scores_gemma":[0.002702893,0.0003288265,0.0005284232,0.001346529,0.0002344875,0.001467214,0.001133613,0.0005616648,0.0007761269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00019293,"about_ca_system_score_gemma":0.000276165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001330506,"about_ca_topic_score_gemma":0.003675946,"domain_scores_codex":[0.9994809,0.0001060641,0.00003856352,0.0001713732,0.0001285915,0.00007457996],"domain_scores_gemma":[0.9980631,0.001305221,0.0002256143,0.0001264157,0.0001463777,0.0001332547],"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":[0.0008515623,0.0008741062,0.1814331,0.0003712327,0.0002079091,0.0006745126,0.003775952,0.008476747,0.06562733,0.001244686,0.004964223,0.7314986],"study_design_scores_gemma":[0.00008143488,0.00107457,0.332297,0.0001782582,0.0003063124,0.002578509,0.006720861,0.5870124,0.04622756,0.0127554,0.01063742,0.0001302694],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8364131,0.000549898,0.1573824,0.0001484133,0.000034827,0.0001111325,0.0009528944,0.001408095,0.002999335],"genre_scores_gemma":[0.8991827,0.00032611,0.0963428,0.00003653978,0.00003156102,0.0001137608,0.001480181,0.00009595858,0.002390491],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002018263,"threshold_uncertainty_score":0.00332725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01574873754056265,"score_gpt":0.2642184239812123,"score_spread":0.2484696864406497,"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."}}