{"id":"W1907204175","doi":"10.24908/pceea.v0i0.3733","title":"IMPLEMENTING A RUBRIC-BASED ASSESSMENT SCHEME INTO THE MULTIDISCIPLINARY DESIGN STREAM AT QUEEN'S UNIVERSITY","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council of Canada; Queen's University","funders":"","keywords":"Rubric; Deliverable; Checklist; Engineering education; Scheme (mathematics); Computer science; Peer assessment; Process (computing); Multidisciplinary approach; Engineering management; Mathematics education; Engineering; Psychology; Systems engineering","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":[0.03403083,0.0008080682,0.0006861996,0.00454455,0.002397219,0.004475594,0.002812324,0.001127822,0.007382048],"category_scores_gemma":[0.06670307,0.0006860674,0.0006138406,0.002369489,0.001248933,0.002296782,0.004069861,0.002804153,0.004658193],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006277733,"about_ca_system_score_gemma":0.02399353,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009042616,"about_ca_topic_score_gemma":0.03221902,"domain_scores_codex":[0.9730518,0.01395005,0.003185957,0.001639983,0.007340421,0.0008317917],"domain_scores_gemma":[0.8915314,0.01981322,0.004234642,0.01484427,0.05855762,0.01101882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001923021,0.001803609,0.01334926,0.0004738576,0.0000159751,0.0001885483,0.006437097,0.004413838,0.02206047,0.004774713,0.0152582,0.9310321],"study_design_scores_gemma":[0.0008465542,0.01083765,0.130437,0.002577747,0.0001243112,0.00189741,0.01491579,0.1221214,0.12356,0.02856462,0.5626829,0.001434709],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1498596,0.000412757,0.7947126,0.003307256,0.000960439,0.008842577,0.0004563497,0.009521637,0.03192671],"genre_scores_gemma":[0.09791767,0.0001632547,0.8843729,0.000357511,0.00004934515,0.001957183,0.0003527208,0.0004683166,0.01436109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9937223,"threshold_uncertainty_score":0.1799744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00850940012465861,"score_gpt":0.1986383695072864,"score_spread":0.1901289693826277,"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."}}