{"id":"W3036341584","doi":"10.24908/pceea.vi0.14136","title":"MACRO-COGNITIVE ANALYSIS OF DESIGN SKILLS FOR SUPPORTING ENGINEERING DESIGN EDUCATION","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Design Education and Practice","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Relevance (law); Cognition; Macro; Computer science; Fluency; Sensemaking; Process (computing); Engineering design process; Frame (networking); Design process; Domain (mathematical analysis); Field (mathematics); Cognitive psychology; Psychology; Human–computer interaction; Engineering; Work in process; Mathematics education","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002695212,0.0007325773,0.0003224835,0.003766763,0.0006180059,0.003250245,0.0005222773,0.0004696663,0.004983329],"category_scores_gemma":[0.01590774,0.0002254213,0.0004993513,0.001420185,0.001685391,0.002956879,0.001423932,0.0008653087,0.0005676781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001853041,"about_ca_system_score_gemma":0.001885884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00402041,"about_ca_topic_score_gemma":0.006669689,"domain_scores_codex":[0.9984916,0.0006781633,0.00009853813,0.000201551,0.000414477,0.0001156046],"domain_scores_gemma":[0.9863573,0.009534772,0.001156998,0.001061553,0.001415062,0.0004741923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003696659,0.001282757,0.1706033,0.001879456,0.0001652228,0.0003471388,0.05232657,0.01341738,0.02104544,0.09265668,0.003343087,0.6425632],"study_design_scores_gemma":[0.00007192161,0.0008374705,0.4909871,0.0009478234,0.0001771423,0.0006363446,0.04866456,0.1101849,0.01657196,0.28035,0.05035698,0.0002138281],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.700848,0.0007734944,0.2260697,0.001209846,0.0000451052,0.000541937,0.0005237939,0.0007455277,0.06924261],"genre_scores_gemma":[0.9443777,0.0001816792,0.05360023,0.00007290641,0.000009002753,0.0001237548,0.0002023732,0.00004192617,0.001390364],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004983329,"threshold_uncertainty_score":0.01667094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01297843661519944,"score_gpt":0.2390207219665783,"score_spread":0.2260422853513789,"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."}}