{"id":"W3036976462","doi":"10.24908/pceea.vi0.14133","title":"PEP – USING SMARTPHONE AND RUBRICS TO EVALUATE LARGE GROUPS IN PROJECT-BASED COURSE","year":2020,"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":"Polytechnique Montréal","funders":"Polytechnique Montréal","keywords":"Rubric; Laptop; Computer science; Course (navigation); Event (particle physics); Process (computing); Multimedia; Software engineering; World Wide Web; Operating system; Engineering; Mathematics education","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":[],"consensus_categories":[],"category_scores_codex":[0.000359034,0.0001888867,0.0001990374,0.0002804715,0.00007279398,0.00007886614,0.0001966576,0.0001214024,0.000008544172],"category_scores_gemma":[0.0006093803,0.0002176664,0.00004418707,0.0009232342,0.000007595556,0.0001708508,0.00002913602,0.0002933711,0.000004921955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001875001,"about_ca_system_score_gemma":0.0003212083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001256816,"about_ca_topic_score_gemma":0.001113963,"domain_scores_codex":[0.9988576,0.000006370701,0.000273272,0.0001963034,0.0002801473,0.000386341],"domain_scores_gemma":[0.9993861,0.00003320818,0.00008931175,0.00007275379,0.0001644921,0.0002540891],"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":[0.00000935506,0.00009415616,0.2352892,0.001213894,0.0001314049,7.535118e-7,0.01029453,0.6377625,0.09663675,0.00166941,0.01628993,0.0006080785],"study_design_scores_gemma":[0.000552903,0.00003295324,0.1147899,0.0003105332,0.0000494987,0.000002765595,0.0007100708,0.8619602,0.008599029,0.000008966257,0.0124631,0.0005200775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955345,0.0002340911,0.0002485361,0.001828015,0.0007423354,0.0005339744,0.00002370098,0.0001879426,0.0006668378],"genre_scores_gemma":[0.9954722,0.000004457065,0.003923767,0.0003020696,0.000121515,0.00005172613,0.000005764983,0.00006843048,0.00005006716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2241977,"threshold_uncertainty_score":0.8876179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045679002744758,"score_gpt":0.2342989585078625,"score_spread":0.223842168480415,"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."}}