{"id":"W4308802348","doi":"10.24908/pceea.vi.15857","title":"Achieving Deep Learning through Integration of 360° Virtual Reality Tour, Hands-on Experience, and Simulation-Based Design in a Project-Based Laboratory","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; Government of Ontario","keywords":"Process (computing); Engineering design process; Virtual reality; Instructional simulation; Flexibility (engineering); Computer science; Project-based learning; Experiential learning; Component (thermodynamics); Systems engineering; Engineering management; Engineering; Human–computer interaction","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":[],"consensus_categories":[],"category_scores_codex":[0.0005484613,0.0001880161,0.0002079616,0.0004693998,0.0001857727,0.00005091662,0.000199587,0.0001007373,0.00001592117],"category_scores_gemma":[0.001272938,0.0002169909,0.00004971802,0.0009319385,0.00002042375,0.0002241837,0.00002159017,0.0004890235,4.16248e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00259724,"about_ca_system_score_gemma":0.0003176004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001620207,"about_ca_topic_score_gemma":0.0003822617,"domain_scores_codex":[0.9987477,0.00003760459,0.0003732857,0.0002023351,0.000376732,0.0002623238],"domain_scores_gemma":[0.9991641,0.0002609123,0.0002275123,0.00009617404,0.0001811061,0.00007019374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007858267,0.00003049276,0.009935936,0.00008214257,0.00001325435,7.351942e-8,0.00404954,0.9727114,0.0123768,0.00031673,0.0001799582,0.0002958482],"study_design_scores_gemma":[0.0003739488,0.0001062376,0.01389894,0.0001701117,0.0000130138,3.156563e-7,0.001640907,0.9689338,0.01300219,0.000007572882,0.001608607,0.000244309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939133,0.0001036194,0.003874016,0.0001720985,0.0005485362,0.0006198866,0.00001682142,0.000181486,0.0005702673],"genre_scores_gemma":[0.9972542,0.000002666289,0.002304017,0.00005641852,0.00003934317,0.0002186944,0.00001417828,0.00005099709,0.0000594952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003963002,"threshold_uncertainty_score":0.8848632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01200290582250308,"score_gpt":0.2444000384503319,"score_spread":0.2323971326278288,"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."}}