{"id":"W1885054444","doi":"10.24908/pceea.v0i0.3613","title":"BEYOND BLOOM’S: USEFUL CONSTRUCTS FOR DEVELOPING GRADUATE ATTRIBUTE INDICATORS","year":2011,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Educational Assessment and Pedagogy","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Categorization; Taxonomy (biology); Process (computing); Concept learning; Cognition; Cognitive dimensions of notations; Dimension (graph theory); Domain (mathematical analysis); Computer science; Concept map; Psychology; Cognitive science; Artificial intelligence; Mathematics education; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008625065,0.0001143215,0.0001366901,0.0003351137,0.0005051754,0.00008592928,0.000357775,0.0001475021,0.00004704477],"category_scores_gemma":[0.001276983,0.0001201898,0.0000766502,0.0006774652,0.00005080529,0.0002795592,0.00001600054,0.0001390107,0.000005843326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002010127,"about_ca_system_score_gemma":0.004129483,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01801937,"about_ca_topic_score_gemma":0.028198,"domain_scores_codex":[0.9988461,0.0000109385,0.0002566783,0.0001685374,0.0003517661,0.0003660071],"domain_scores_gemma":[0.9983636,0.0001074765,0.0003995129,0.00005907181,0.0008712625,0.0001990337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000001728015,0.00003554161,0.5554407,0.00007949607,0.00007306186,1.606699e-8,0.01598666,0.000002613369,0.00009004653,0.4032141,0.02438901,0.0006870044],"study_design_scores_gemma":[0.0002408247,0.00001671482,0.8111069,0.0001119689,0.00009568015,9.693414e-7,0.008287252,0.00002668574,0.002003315,0.01073948,0.1669578,0.0004123341],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9552987,0.00005974094,0.00004188271,0.01398996,0.003967726,0.0008191756,0.00005991098,0.00008362277,0.02567931],"genre_scores_gemma":[0.992109,0.00001683697,0.003980447,0.0003663691,0.0003706204,0.0001055824,0.00001640647,0.00001986073,0.003014878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3924746,"threshold_uncertainty_score":0.9895349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05491754598583849,"score_gpt":0.29507635780069,"score_spread":0.2401588118148515,"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."}}