{"id":"W2739911667","doi":"","title":"Qualitative, quantitative, and data mining methods for analyzing log data to characterize students' learning strategies and behaviors","year":2010,"lang":"en","type":"article","venue":"International Conference of Learning Sciences","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Educational data mining; Data science; Learning analytics; Qualitative property; Quantitative research; Quantitative analysis (chemistry); Cognition; Artificial intelligence; Machine learning; Psychology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005582758,0.0001597467,0.000263849,0.0003186125,0.0003702173,0.00152449,0.003710337,0.0000526742,0.00001344418],"category_scores_gemma":[0.003834281,0.0001453,0.00002102793,0.0003374698,0.0004334454,0.002535389,0.002129573,0.0003986183,0.000001272348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005860894,"about_ca_system_score_gemma":0.0002037322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001034815,"about_ca_topic_score_gemma":0.00006540169,"domain_scores_codex":[0.9977419,0.0003252767,0.0003730607,0.0008751153,0.0004436567,0.000240944],"domain_scores_gemma":[0.9972379,0.001494482,0.0003644617,0.0004348329,0.0003637395,0.0001045451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005594685,0.0001815065,0.21825,0.00009438716,0.0002097437,0.000006136033,0.04768411,0.001193199,0.1597122,0.308142,0.00004836628,0.2644224],"study_design_scores_gemma":[0.0003775253,0.0006466518,0.03521574,0.0001799466,0.00003898038,0.00001443484,0.03535381,0.9220245,0.0002227503,0.002299675,0.00321012,0.0004158782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6422578,0.00004276805,0.3552263,0.001992209,0.0002174655,0.00008032109,0.00002061004,0.0000422154,0.0001202903],"genre_scores_gemma":[0.5692838,0.0000239214,0.4304971,0.00001547244,0.00002448879,0.000002850253,0.00004156429,0.000004326964,0.0001065353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9208313,"threshold_uncertainty_score":0.999512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3193778205409949,"score_gpt":0.5534960642674233,"score_spread":0.2341182437264284,"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."}}