{"id":"W3136224172","doi":"10.1109/iv51561.2020.00083","title":"Using Machine Learning to Explore the Relation Between Student Engagement and Student Performance","year":2020,"lang":"en","type":"article","venue":"2020 24th International Conference Information Visualisation (IV)","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Student engagement; Learning analytics; Mathematics education; Computer science; Active learning (machine learning); Relation (database); Psychology; Artificial intelligence; Data science","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":[0.0006878556,0.000170242,0.0001521302,0.0001373871,0.0003689143,0.0007905512,0.0006808346,0.00004873492,0.00006091994],"category_scores_gemma":[0.0002301136,0.0001424129,0.00004033851,0.0003500574,0.00002878559,0.00202291,0.0004763768,0.000381492,0.0001278519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032623,"about_ca_system_score_gemma":0.00007815648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001945218,"about_ca_topic_score_gemma":0.000001609616,"domain_scores_codex":[0.9980167,0.0001636548,0.0005770905,0.0002288231,0.0008447594,0.0001689811],"domain_scores_gemma":[0.9988874,0.00009534544,0.0003405417,0.0001596247,0.0003783079,0.000138814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006080689,0.0000910478,0.4428878,0.0001045681,0.0003058335,0.000002745787,0.2542415,0.1411774,0.0006255199,0.08847595,0.0006759248,0.07135087],"study_design_scores_gemma":[0.0003242437,0.0001480131,0.09175429,0.00003774944,0.00001585522,0.000002306951,0.00344143,0.8919942,0.00009372171,0.00009585993,0.01191939,0.0001729737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.683192,0.00001015312,0.2950896,0.01978103,0.0002623042,0.000321686,0.000008533648,0.0001587243,0.001175973],"genre_scores_gemma":[0.9930877,0.0000490393,0.004618065,0.001813842,0.0002217184,0.0000174422,0.000128002,0.000007082743,0.00005709661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7508168,"threshold_uncertainty_score":0.7623303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1801124621072724,"score_gpt":0.3913535851259982,"score_spread":0.2112411230187258,"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."}}