{"id":"W2883325838","doi":"10.1109/access.2018.2851790","title":"E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Field (mathematics); Data science; Process (computing); Analytics; Machine learning; Learning analytics; Artificial intelligence; Data analysis; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002490678,0.0001625872,0.0002034046,0.0004126518,0.0004876322,0.000910224,0.004230802,0.00008759506,0.00001770021],"category_scores_gemma":[0.0005810396,0.000157011,0.00001514776,0.0003897063,0.0003876764,0.002439043,0.005071987,0.0005654565,0.00001645382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000341802,"about_ca_system_score_gemma":0.000107016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004289531,"about_ca_topic_score_gemma":0.0002436106,"domain_scores_codex":[0.9976805,0.0003966266,0.0002257054,0.0007148125,0.0005318447,0.0004504815],"domain_scores_gemma":[0.9974015,0.000348876,0.0001279766,0.001682532,0.000299899,0.0001392418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004363296,0.0001994479,0.01339303,0.000245663,0.000176743,0.0002014871,0.009009839,0.0000751431,0.002364084,0.01746268,0.01197437,0.9448539],"study_design_scores_gemma":[0.0003673861,0.000600029,0.002129568,0.0003240494,0.00003695244,0.0001663586,0.0006236313,0.600414,0.004400692,0.007948929,0.3821548,0.0008336542],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5158455,0.005591664,0.4459077,0.004870474,0.0009868244,0.0006340509,0.00008826319,0.002795991,0.02327958],"genre_scores_gemma":[0.9095415,0.004310037,0.08487226,0.00008404307,0.0004006532,0.000006411239,0.00006408065,0.00003776282,0.0006832232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9440202,"threshold_uncertainty_score":0.8777311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.699501726311832,"score_gpt":0.4858837748751497,"score_spread":0.2136179514366823,"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."}}