{"id":"W4367314076","doi":"10.3390/educsci13050457","title":"Personalized Learning in Virtual Learning Environments Using Students’ Behavior Analysis","year":2023,"lang":"en","type":"article","venue":"Education Sciences","topic":"Learning Styles and Cognitive Differences","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Synchronous learning; Personalized learning; Active learning (machine learning); Educational technology; Style (visual arts); Computer science; Experiential learning; Learning styles; Quarter (Canadian coin); Virtual learning environment; Collaborative learning; Open learning; Instructional simulation; Cooperative learning; Mathematics education; Artificial intelligence; Psychology; Multimedia; Teaching method; Knowledge management","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0009963737,0.0004082717,0.0004775585,0.001681253,0.0002106182,0.001118748,0.000406577,0.0003472518,0.000965692],"category_scores_gemma":[0.003405529,0.0001718222,0.0004183257,0.0008356689,0.0001576463,0.0008301419,0.0005022345,0.0003608063,0.0003030867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000440986,"about_ca_system_score_gemma":0.000245896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002154121,"about_ca_topic_score_gemma":0.003125943,"domain_scores_codex":[0.9992167,0.000260473,0.00005530758,0.0002051855,0.0002011428,0.00006114709],"domain_scores_gemma":[0.9983926,0.0007754435,0.0002704889,0.0001735842,0.0002095395,0.0001781778],"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":[0.0005960129,0.00192377,0.3799225,0.000187371,0.0003640285,0.0001876554,0.001314966,0.0581735,0.01414808,0.001006499,0.001202556,0.5409732],"study_design_scores_gemma":[0.00003963361,0.001125572,0.3745759,0.00003439686,0.0001746303,0.0001926309,0.001055591,0.6061159,0.01115891,0.003124647,0.002283768,0.0001183529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9281396,0.0001206619,0.06858295,0.00007847683,0.00001288157,0.0001347175,0.0002625006,0.0006086164,0.00205974],"genre_scores_gemma":[0.9784166,0.00005425304,0.02060027,0.00001332662,0.000005696354,0.00006412457,0.0001820921,0.00001606046,0.0006475926],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002154121,"threshold_uncertainty_score":0.005269408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07635137550816308,"score_gpt":0.4432628704125534,"score_spread":0.3669114949043903,"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."}}