{"id":"W2592139278","doi":"10.24908/pceea.v0i0.6456","title":"EDUCATIONAL DATA MINING APPROACH FOR ENGINEERING GRADUATE ATTRIBUTES ANALYSIS","year":2017,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Educational Technology and Assessment","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Université de Moncton","funders":"National Research Council Canada; Université de Moncton","keywords":"Curriculum; Process (computing); Computer science; Field (mathematics); Set (abstract data type); Data science; Concept map; Educational data mining; Data mining; Artificial intelligence; Mathematics education; Psychology; Mathematics; Pedagogy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.008408973,0.001233754,0.001710958,0.01127918,0.001194908,0.003578009,0.00226703,0.001281643,0.002567361],"category_scores_gemma":[0.0236296,0.0005390699,0.002657953,0.01129459,0.0006064634,0.002011173,0.002176157,0.002689158,0.001324008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001345359,"about_ca_system_score_gemma":0.003096918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005054619,"about_ca_topic_score_gemma":0.004893231,"domain_scores_codex":[0.9935581,0.002116555,0.001226266,0.001236135,0.001611725,0.0002512239],"domain_scores_gemma":[0.9857149,0.008745587,0.0009894477,0.00171325,0.00252256,0.0003143323],"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":[0.0004396622,0.00138042,0.04534042,0.001347337,0.0009404476,0.0008693157,0.001555971,0.06434914,0.003906916,0.03478319,0.01536459,0.8297226],"study_design_scores_gemma":[0.0001395701,0.0003738128,0.02408752,0.0005048769,0.0003647549,0.0009190656,0.002916274,0.7699226,0.009495679,0.1304927,0.06059361,0.0001896426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01533917,0.000531173,0.9731294,0.0007450706,0.00008421009,0.000860257,0.004841443,0.002201367,0.002267961],"genre_scores_gemma":[0.105785,0.0003994069,0.8821118,0.000163862,0.00007071006,0.00182684,0.008500807,0.00009154873,0.00104999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01127918,"threshold_uncertainty_score":0.04447144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04673410479826912,"score_gpt":0.2837450732654153,"score_spread":0.2370109684671462,"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."}}