{"id":"W3002675797","doi":"10.24908/pceea.vi0.13767","title":"ENGAGING PROSPECTIVE ENGINEERS IN MATH EDUCATION THROUGH ROBOTICS AND KNOWLEDGE BUILDING","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Technology-Enhanced Education Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Robotics; Artificial intelligence; Educational robotics; Multidisciplinary approach; Competition (biology); Mathematics education; Gateway (web page); Math education; Computer science; Engineering; Knowledge management; Robot; Mathematics; Sociology; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.001823658,0.0003784885,0.0002407748,0.0005760048,0.001413282,0.003139793,0.0007906789,0.0007524273,0.01000738],"category_scores_gemma":[0.004018781,0.0001832336,0.0003671553,0.0004709586,0.001070552,0.001233648,0.00379584,0.000877613,0.002893707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006240993,"about_ca_system_score_gemma":0.002100069,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006091197,"about_ca_topic_score_gemma":0.003025238,"domain_scores_codex":[0.9987238,0.0005089293,0.00004266429,0.0001834263,0.0002484163,0.000292721],"domain_scores_gemma":[0.9964792,0.001494962,0.0004139909,0.000272466,0.0002762027,0.001063257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004059614,0.0239092,0.09767396,0.001160327,0.00005884505,0.0009653366,0.05883987,0.001716211,0.03320664,0.02874883,0.007815165,0.7454997],"study_design_scores_gemma":[0.0003688975,0.01742231,0.3509192,0.001414624,0.0002175593,0.004366395,0.1285079,0.007776575,0.07397638,0.04723376,0.3674739,0.0003225914],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9100363,0.0003953829,0.01667901,0.001028723,0.0000530669,0.0003445184,0.00004996278,0.00009478413,0.07131819],"genre_scores_gemma":[0.9432608,0.0005934369,0.02437131,0.0004132361,0.00002540902,0.000307876,0.00009564377,0.00001482436,0.03091741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9993909,"threshold_uncertainty_score":0.03347802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006229813079011871,"score_gpt":0.2574125149371229,"score_spread":0.251182701858111,"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."}}