{"id":"W2561340722","doi":"10.5539/mas.v11n1p264","title":"Improving Teaching and Learning Outcomes – A Novel Cognitive Science Approach","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Behaviorism; Computer science; Cognition; Taxonomy (biology); Learning sciences; Curriculum; Constructivism (international relations); Process (computing); Artificial intelligence; Learning theory; Mathematics education; Management science; Cognitive science; Experiential learning; Psychology; Pedagogy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.005316776,0.0002398501,0.0002384289,0.0004124231,0.002746449,0.0008986544,0.001537037,0.00004377207,0.000001131835],"category_scores_gemma":[0.0009848302,0.0001591558,0.00003794163,0.0006707466,0.001158595,0.00172078,0.001205518,0.0005015912,0.00002191561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001695839,"about_ca_system_score_gemma":0.0003059207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006460414,"about_ca_topic_score_gemma":6.797719e-7,"domain_scores_codex":[0.996387,0.00004953256,0.0002659344,0.001345412,0.00109308,0.0008590562],"domain_scores_gemma":[0.9986227,0.0003447777,0.0001992649,0.000396809,0.0001902895,0.0002462293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002293301,0.00002554129,0.001164432,0.00000706184,0.000002848821,9.245877e-7,0.002669385,0.00005213171,0.6070122,0.2186711,1.946093e-7,0.1703919],"study_design_scores_gemma":[0.001482971,0.0001718026,0.02308411,0.0002596855,0.00001510684,0.0000948679,0.002207926,0.928207,0.03959173,0.002556324,0.000931667,0.001396749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.103106,0.00001825969,0.8843574,0.00008644182,0.0001599079,0.0002143628,5.975629e-7,0.0002283474,0.01182869],"genre_scores_gemma":[0.9251497,7.745526e-7,0.07294882,0.000103796,0.00004354536,0.00002760221,9.030808e-8,0.00001236267,0.001713348],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9281549,"threshold_uncertainty_score":0.9985518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063552371005028,"score_gpt":0.2477447507625584,"score_spread":0.2271092270525081,"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."}}