{"id":"W2558234735","doi":"10.1177/0735633116678995","title":"Person-Oriented Approaches to Profiling Learners in Technology-Rich Learning Environments for Ecological Learner Modeling","year":2016,"lang":"en","type":"article","venue":"Journal of Educational Computing Research","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Wilson Centre; University Health Network; McGill University; Institute for Christian Studies; University of Toronto","funders":"","keywords":"Metacognition; Profiling (computer programming); Cognition; Computer science; Cluster analysis; Mathematics education; Psychology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01439502,0.0001510289,0.0002996567,0.001591811,0.0004030207,0.00003411337,0.0004128266,0.0001931565,0.0001432456],"category_scores_gemma":[0.009081021,0.0001093499,0.00008432492,0.0009994793,0.0001556703,0.0000841332,0.0001057987,0.001855399,0.00005738093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004836535,"about_ca_system_score_gemma":0.0002698335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007897276,"about_ca_topic_score_gemma":5.479101e-7,"domain_scores_codex":[0.9960749,0.001724191,0.0005748808,0.0004128713,0.0005325989,0.000680516],"domain_scores_gemma":[0.9956602,0.003507994,0.0002521745,0.0001646812,0.0002983297,0.0001166393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001033418,0.001571077,0.7579458,0.00004496475,0.0002179449,0.00001295554,0.01625151,0.05725054,0.01040299,0.06154285,0.0009173258,0.09280864],"study_design_scores_gemma":[0.01551591,0.01273431,0.5498004,0.002944005,0.0001030423,0.00070731,0.217979,0.1263426,0.00297561,0.03636787,0.03177109,0.002758875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9067977,0.0001213203,0.07843182,0.01255408,0.0003935264,0.0002974456,6.621176e-7,0.00001302926,0.001390431],"genre_scores_gemma":[0.9385938,0.000001987389,0.05735808,0.00003386099,0.0005324777,0.0000309538,0.000002494685,0.00002956693,0.003416754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2081454,"threshold_uncertainty_score":0.9992659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3855538752664576,"score_gpt":0.4800233049336837,"score_spread":0.09446942966722616,"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."}}