{"id":"W3179772339","doi":"10.24908/pceea.vi0.14874","title":"AN EXPERIENTIAL LEARNING APPROACH FOR DEVELOPING MENTAL MODELS IN ENGINEERING DESIGN EDUCATION","year":2021,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Design Education and Practice","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Experiential learning; Psychology; Mental model; Leverage (statistics); Knowledge management; Mental mapping; Mental representation; Computer science; Cognition; Cognitive science; Mathematics education; Cognitive psychology; Artificial intelligence","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":[0.006656352,0.0009300272,0.0002885674,0.001774589,0.001529369,0.005760665,0.002818075,0.001514615,0.008368745],"category_scores_gemma":[0.01129383,0.0004891827,0.0008980497,0.001332458,0.008426433,0.006427594,0.00554105,0.003359952,0.000864577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004047714,"about_ca_system_score_gemma":0.003755681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001584554,"about_ca_topic_score_gemma":0.002577767,"domain_scores_codex":[0.99585,0.003056551,0.0001659716,0.0002756429,0.0004982569,0.0001536196],"domain_scores_gemma":[0.993214,0.005183036,0.0002003408,0.0006754684,0.0004289777,0.0002981527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004162424,0.0004741085,0.001063821,0.0004015059,0.00002195766,0.0002339443,0.03223745,0.003503956,0.001742981,0.8341718,0.002253229,0.1238536],"study_design_scores_gemma":[0.00009023633,0.0002938484,0.001004711,0.0007765742,0.00003497303,0.0005788698,0.01826633,0.02430647,0.005012279,0.8304722,0.1190779,0.00008562034],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02161448,0.0004541148,0.8984562,0.003437996,0.00009914018,0.0005466224,0.00008155827,0.0002486173,0.0750613],"genre_scores_gemma":[0.2936237,0.0006690685,0.6928229,0.0005824793,0.00003132431,0.0014291,0.0001261991,0.00006360786,0.01065166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008368745,"threshold_uncertainty_score":0.03520256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614288579097544,"score_gpt":0.2341230542976313,"score_spread":0.2179801685066559,"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."}}