{"id":"W3173649727","doi":"10.24908/pceea.vi0.14945","title":"COVID-19: A MOTIVATOR FOR CHANGE IN ENGINEERING EDUCATION?","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Coronavirus disease 2019 (COVID-19); Best practice; Faculty development; Set (abstract data type); Medical education; Psychology; Instructional design; Pedagogy; Professional development; Medicine; Political science; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0003436285,0.0002515719,0.0002553134,0.0004907171,0.00009989963,0.0001012513,0.0003058093,0.0002174128,0.00004794083],"category_scores_gemma":[0.00331547,0.0003060708,0.0001207307,0.0009486001,0.000009247779,0.0003270336,0.00003079754,0.0003303048,0.000005225598],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006668177,"about_ca_system_score_gemma":0.001612167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002400686,"about_ca_topic_score_gemma":0.002262104,"domain_scores_codex":[0.998587,0.000005377184,0.0003962027,0.0002625053,0.000261457,0.0004874792],"domain_scores_gemma":[0.9987736,0.0001094624,0.0001283196,0.0001568127,0.0003393692,0.0004923895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001108407,0.0005445022,0.264397,0.01278775,0.0005840121,0.000001890776,0.0164843,0.4295773,0.1283819,0.06516166,0.07840656,0.003662033],"study_design_scores_gemma":[0.001413851,0.00004487067,0.1621136,0.001634122,0.0001311218,0.00004659155,0.001987651,0.2162596,0.05207821,0.0003441036,0.5618895,0.002056788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.941554,0.006493131,0.001475075,0.0152047,0.02054249,0.004543107,0.0002599803,0.001861637,0.00806592],"genre_scores_gemma":[0.9915493,0.00003101318,0.005718259,0.0005378023,0.0004420948,0.0008022459,0.00003766548,0.0001184544,0.0007631902],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.483483,"threshold_uncertainty_score":0.9999391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020573509133503,"score_gpt":0.2458599555668874,"score_spread":0.2356542204755524,"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."}}