{"id":"W3002875345","doi":"10.24908/pceea.vi0.13769","title":"DEVELOPING A FRAMEWORK TO EVALUATE INDIVIDUAL LEARNING IN ENGINEERING DESIGN PROBLEMS – PART 2: ASSESSMENT OF INDIVIDUAL LEARNING IN TEAM ENVIRONMENTS","year":2019,"lang":"en","type":"article","venue":"Proceedings of the Canadian Engineering Education Association (CEEA)","topic":"Engineering Education and Curriculum Development","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Accreditation; Competence (human resources); Team-based learning; Dilemma; Computer science; Set (abstract data type); Outcome (game theory); Curriculum; Project-based learning; Function (biology); Knowledge management; Engineering management; Mathematics education; Engineering; Psychology; Medical education; Pedagogy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001690789,0.0002964283,0.0003633063,0.001003785,0.00005690673,0.00009463185,0.0004601667,0.0002470092,0.00003559625],"category_scores_gemma":[0.0009676138,0.0003396268,0.00006501639,0.001342323,0.000007095973,0.0002419011,0.00006502075,0.0008398158,0.00001295891],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004070833,"about_ca_system_score_gemma":0.0008021474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003380206,"about_ca_topic_score_gemma":0.0002725724,"domain_scores_codex":[0.9977214,0.00002391851,0.0006903903,0.0002957098,0.0006949298,0.0005736024],"domain_scores_gemma":[0.9991736,0.0001400326,0.0002436865,0.0001168884,0.0001274853,0.0001983141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[6.604612e-7,0.00003164261,0.2005999,0.0003070121,0.00006289268,5.218675e-8,0.002652463,0.7932993,0.00120477,0.001000781,0.0003237097,0.0005167401],"study_design_scores_gemma":[0.0004273269,0.00004498981,0.9052256,0.002051515,0.0000370104,0.000002299995,0.0009479267,0.07695653,0.002329651,0.00007028446,0.01123136,0.0006755321],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925319,0.0001346613,0.002574957,0.0006497391,0.001908476,0.00131734,0.000008116771,0.0001746949,0.0007001465],"genre_scores_gemma":[0.9813435,0.00002991231,0.01780847,0.00005858193,0.00006148491,0.0002705571,0.0000180042,0.00007655393,0.0003329083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7163428,"threshold_uncertainty_score":0.9999056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01158993156493036,"score_gpt":0.2306812639887513,"score_spread":0.219091332423821,"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."}}