{"id":"W1518092398","doi":"10.1007/978-3-642-13437-1_46","title":"Using Emotional Coping Strategies in Intelligent Tutoring Systems","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Emotional Intelligence and Performance","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Coping (psychology); Computer science; Anxiety; Intelligent tutoring system; Cognitive psychology; Human–computer interaction; Artificial intelligence; Psychology; Clinical 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":[0.0004088795,0.0003751599,0.0002317571,0.0001723703,0.0001311912,0.001045616,0.0004316142,0.0004550725,0.002892361],"category_scores_gemma":[0.001333575,0.00009642755,0.0002108547,0.0002014376,0.0002252118,0.000618901,0.0004353547,0.000451262,0.0005100063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001363316,"about_ca_system_score_gemma":0.0001181014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001100217,"about_ca_topic_score_gemma":0.000168927,"domain_scores_codex":[0.9998245,0.00008729001,0.000009425298,0.00001975707,0.00004093007,0.00001824427],"domain_scores_gemma":[0.9997031,0.000222135,0.00001528303,0.00001656068,0.00002624753,0.0000167342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000198034,0.0004649832,0.001559537,0.0003969526,0.00009345064,0.0002369756,0.001407277,0.00794795,0.02855472,0.01142322,0.005066834,0.9426501],"study_design_scores_gemma":[0.0007680357,0.004815063,0.06566496,0.002289769,0.001531841,0.004364708,0.007696482,0.2980398,0.1453971,0.2145787,0.254542,0.0003116903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4421536,0.02850694,0.3575298,0.002304243,0.0008632329,0.0003208598,0.00009879294,0.001704199,0.1665183],"genre_scores_gemma":[0.9235423,0.005067099,0.05351997,0.0002889031,0.0001633877,0.0001876289,0.00008211413,0.00006637215,0.01708218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002892361,"threshold_uncertainty_score":0.00967598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09127022621146984,"score_gpt":0.3584669991288086,"score_spread":0.2671967729173388,"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."}}