{"id":"W4221122218","doi":"10.53730/ijhs.v6ns1.4841","title":"How big data is used as a key element for hybrid university education","year":2022,"lang":"en","type":"article","venue":"International Journal of Health Sciences","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Hybrid learning; Blended learning; Scopus; Big data; Online learning; Key (lock); Computer science; Element (criminal law); Face (sociological concept); Mathematics education; Psychology; Artificial intelligence; Multimedia; Educational technology; Sociology; Political science; Data mining","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":[0.01228329,0.0004364721,0.0005768286,0.004367403,0.001866071,0.01444908,0.00103052,0.001520662,0.004063393],"category_scores_gemma":[0.02130208,0.0002785972,0.0009314553,0.005926975,0.003628895,0.01044556,0.005615157,0.002096185,0.0007335529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002851416,"about_ca_system_score_gemma":0.005426812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009793162,"about_ca_topic_score_gemma":0.002077909,"domain_scores_codex":[0.9845367,0.009731081,0.001163251,0.0008454772,0.003025712,0.0006978587],"domain_scores_gemma":[0.9685655,0.02428622,0.002503971,0.001461672,0.002171252,0.00101136],"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":[0.0001717955,0.0003246347,0.03306814,0.03037489,0.0007894504,0.001553015,0.04488492,0.002009326,0.004483427,0.2957992,0.01488545,0.5716558],"study_design_scores_gemma":[0.00004524024,0.0004934057,0.03566704,0.02977205,0.0006556151,0.002465052,0.1212855,0.001913686,0.009538561,0.1703957,0.6275412,0.0002269352],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2107927,0.134767,0.2467613,0.08071901,0.002774818,0.002600602,0.001912204,0.0006956575,0.3189766],"genre_scores_gemma":[0.8754879,0.03843345,0.07204248,0.005100712,0.0003548196,0.0009287683,0.0004176975,0.0001180126,0.007116246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01444908,"threshold_uncertainty_score":0.06496108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09618275036082491,"score_gpt":0.3817992156184544,"score_spread":0.2856164652576295,"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."}}