{"id":"W2114184575","doi":"10.1007/s11409-007-9016-7","title":"Examining trace data to explore self-regulated learning","year":2007,"lang":"en","type":"article","venue":"Metacognition and Learning","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":274,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of Victoria","funders":"Simon Fraser University; Canada Research Chairs","keywords":"Metacognition; Self-regulated learning; Construct (python library); TRACE (psycholinguistics); Psychology; Adaptation (eye); Exploratory research; Computer science; Cognitive psychology; Mathematics education; Cognition","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0127975,0.0002776704,0.0003446791,0.0004787734,0.0008189039,0.000112177,0.000262422,0.0001865661,0.0009525183],"category_scores_gemma":[0.002106503,0.0002798811,0.00003743873,0.0007692839,0.00005772241,0.0002318691,0.0001806031,0.002063557,0.000383974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003073532,"about_ca_system_score_gemma":0.00001693475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004770667,"about_ca_topic_score_gemma":0.000003617572,"domain_scores_codex":[0.9952044,0.002621409,0.0004636994,0.0008133564,0.0002827675,0.000614334],"domain_scores_gemma":[0.9979988,0.001048133,0.0002219669,0.0003819642,0.0001479509,0.0002012096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002714481,0.0001741693,0.02730491,0.00003972359,0.0003511915,0.0001054727,0.1021909,0.0003093957,0.01843091,0.0005732779,0.00077533,0.8494732],"study_design_scores_gemma":[0.001896268,0.0008162694,0.3857615,0.0001631524,0.0002363696,0.0001584952,0.06830381,0.001580724,0.0005459015,0.00006122245,0.5395515,0.0009247381],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.85364,0.0003136065,0.1191671,0.0001584163,0.00039382,0.0001827657,0.000001337695,0.0007595869,0.02538335],"genre_scores_gemma":[0.9609823,0.000005919351,0.03070846,0.0003688087,0.0003534088,0.00001302375,0.0001504625,0.00007046372,0.007347128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8485485,"threshold_uncertainty_score":0.9999653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2898974300896617,"score_gpt":0.4333952759236632,"score_spread":0.1434978458340015,"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."}}