{"id":"W2932449923","doi":"10.1007/978-3-030-11932-4_51","title":"Experiential Learning Approaches in Automotive Engineering: Implementing Real World Experiences","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Educational Games and Gamification","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Experiential learning; Automotive industry; Computer science; Curriculum; CarSim; Process (computing); Experiential education; Knowledge management; Engineering management; Human–computer interaction; Engineering; Mathematics education; Artificial intelligence; Psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009149958,0.0005588809,0.000148738,0.0002719093,0.000508095,0.002409519,0.001357081,0.0007509596,0.009268136],"category_scores_gemma":[0.001419395,0.0001288197,0.0002045521,0.0002975142,0.0009498001,0.001657512,0.002237572,0.000910937,0.0009953186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007050218,"about_ca_system_score_gemma":0.0006337158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006545931,"about_ca_topic_score_gemma":0.001447035,"domain_scores_codex":[0.9995608,0.0002134656,0.00001073014,0.00003755809,0.0001217701,0.00005569062],"domain_scores_gemma":[0.9995098,0.0003016787,0.00001242235,0.00004103842,0.00005481888,0.00008023951],"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":[0.0001692876,0.002426486,0.001196019,0.0007771433,0.00003404979,0.0005670837,0.02647994,0.01315754,0.01502454,0.2342787,0.01313375,0.6927555],"study_design_scores_gemma":[0.000199723,0.001957141,0.00809522,0.001273137,0.00008309394,0.001785624,0.03896127,0.05856779,0.03287137,0.2054609,0.6506172,0.0001275068],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2231592,0.002175565,0.3389069,0.001850129,0.0003951963,0.000379469,0.00006449467,0.0006931728,0.4323758],"genre_scores_gemma":[0.7181382,0.00206868,0.1200855,0.0003836714,0.00003937647,0.000360141,0.0001132275,0.0001307739,0.1586804],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009268136,"threshold_uncertainty_score":0.03100502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04530382913170844,"score_gpt":0.3270847234350659,"score_spread":0.2817808943033575,"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."}}