{"id":"W3083588286","doi":"10.1145/3411170.3411232","title":"Features Exploration for Grades Prediction using Machine Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Key (lock); Plan (archaeology); Machine learning; Selection (genetic algorithm); Artificial intelligence; Data science; Data mining; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000081869,0.00005933265,0.0000667531,0.00003106101,0.0001411062,0.0001184454,0.0001386966,0.00002810453,0.000002697606],"category_scores_gemma":[0.0001055248,0.00005097923,0.00003859035,0.0001755518,0.000006589701,0.0003959855,0.00004207668,0.000113108,0.000004204086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008815265,"about_ca_system_score_gemma":0.00001669017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001323681,"about_ca_topic_score_gemma":0.000002639255,"domain_scores_codex":[0.999513,0.00002635163,0.00009125788,0.0001729446,0.00009865096,0.00009777526],"domain_scores_gemma":[0.9997663,0.00003223696,0.00004241443,0.00006909546,0.00004042773,0.00004952592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006306715,0.0001599924,0.02178465,0.0002260358,0.0001258553,0.00001097301,0.0104669,0.6007006,0.04755112,0.1741891,0.006114952,0.1386067],"study_design_scores_gemma":[0.0001352095,0.000119031,0.000140768,0.000005352857,0.000006151563,0.000001915824,0.00006316847,0.9928635,0.001296776,0.001184992,0.004125633,0.00005755384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004346248,0.00005320275,0.9810991,0.01390235,0.00007681335,0.00006072413,0.000001459464,0.0003209935,0.0001391615],"genre_scores_gemma":[0.804128,0.00001082372,0.1945831,0.0004751015,0.0002874222,0.000002505026,0.0000193027,0.000007644607,0.0004861258],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7997817,"threshold_uncertainty_score":0.2078873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06428963824488679,"score_gpt":0.2909155143726314,"score_spread":0.2266258761277446,"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."}}