{"id":"W2108798585","doi":"10.1109/icalt.2007.176","title":"Learning Mechanisms for a Tutoring Cognitive Agent","year":2007,"lang":"en","type":"article","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Cognition; Human–computer interaction; Cognitive systems; Cognitive science; Artificial intelligence; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009604114,0.0005232961,0.0003843963,0.0004915567,0.0006225663,0.002171717,0.002071233,0.001542161,0.005490277],"category_scores_gemma":[0.003079539,0.000295453,0.0005457245,0.0002321601,0.001114282,0.002740812,0.001213683,0.001291548,0.001145351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007432912,"about_ca_system_score_gemma":0.0008025048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00155332,"about_ca_topic_score_gemma":0.0009236825,"domain_scores_codex":[0.9994969,0.0001019171,0.00005145341,0.0001105451,0.0001780539,0.00006107051],"domain_scores_gemma":[0.9987634,0.0004831731,0.0001322977,0.000192415,0.0002933432,0.0001353847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001497056,0.0001397587,0.001229842,0.0003780636,0.0001299248,0.0003676804,0.001155524,0.07269697,0.03131754,0.7746958,0.003235591,0.1145036],"study_design_scores_gemma":[0.0002238985,0.0004147936,0.00073775,0.0001096223,0.0002044317,0.0004672123,0.0001991645,0.5231605,0.03158256,0.3830111,0.05979459,0.00009438186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02855517,0.0006302082,0.9535116,0.0007033995,0.0001290522,0.0001498623,0.00005341909,0.00238856,0.01387869],"genre_scores_gemma":[0.5769273,0.0005538324,0.4097188,0.0003453167,0.0001304293,0.0004588643,0.00009431969,0.00009676254,0.0116743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005490277,"threshold_uncertainty_score":0.01836681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02818693408599757,"score_gpt":0.279942703400082,"score_spread":0.2517557693140844,"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."}}