{"id":"W3153870390","doi":"10.18260/1-2--35025","title":"Overcoming Non-numerical Challenges in an Engineering Numerical Methods Course","year":2020,"lang":"en","type":"article","venue":"2020 ASEE Virtual Annual Conference Content Access Proceedings","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"","keywords":"Course (navigation); Computer science; MATLAB; Syllabus; Numerical analysis; Population; Curriculum; Numerical integration; Mathematics education; Calculus (dental); Mathematics; Programming language; Engineering; Pedagogy","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003357787,0.0006458722,0.0007912621,0.0001623607,0.00006916347,0.0003817811,0.001059438,0.0002518142,0.0001051618],"category_scores_gemma":[0.0003528707,0.0007001666,0.0001259848,0.0006528923,0.00005526814,0.002485926,0.0003864908,0.001116691,0.00004652796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001608688,"about_ca_system_score_gemma":0.00004419604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002042297,"about_ca_topic_score_gemma":0.00000141644,"domain_scores_codex":[0.9971763,0.00003249004,0.0006914503,0.0008248899,0.000443163,0.0008317141],"domain_scores_gemma":[0.9987206,0.0001240163,0.00009639846,0.0001825356,0.0002336636,0.0006427767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005215167,0.0006601396,0.02064678,0.001553615,0.0005182988,0.0002762267,0.06316692,0.2701002,0.5284669,0.01127007,0.001676214,0.1011431],"study_design_scores_gemma":[0.0008992232,0.0006185116,0.01401482,0.0002414266,0.00003624797,0.00001961588,0.007048098,0.9576368,0.01539106,0.00001901498,0.002906757,0.001168365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8578424,0.00245687,0.1308954,0.00193816,0.000992522,0.0009304355,0.00004304945,0.00256728,0.002333783],"genre_scores_gemma":[0.9897138,0.000236837,0.009128686,0.0002021359,0.0003872527,0.0001459113,0.00001645629,0.0001535806,0.00001536709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6875366,"threshold_uncertainty_score":0.9995449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09000448607356092,"score_gpt":0.3284667839233373,"score_spread":0.2384622978497764,"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."}}