{"id":"W2792239552","doi":"10.3406/stice.2007.958","title":"Apprentissage machine pour la prédiction de la réaction émotionnelle de l’apprenant","year":2007,"lang":"en","type":"article","venue":"Sciences et Technologies de l Information et de la Communication pour l Éducation et la Formation","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Diction; TUTOR; Psychology; Action (physics); Process (computing); Computer science; Cognition; Artificial intelligence; Cognitive science; Cognitive psychology; Mathematics education; Linguistics","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":[0.002084886,0.001336478,0.00174495,0.0009529124,0.0004491816,0.0009980011,0.001029082,0.001647481,0.003131245],"category_scores_gemma":[0.006902694,0.0004060148,0.001154434,0.0007841782,0.0004530924,0.00101617,0.0007207044,0.002329935,0.001922126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004611052,"about_ca_system_score_gemma":0.000668018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008200156,"about_ca_topic_score_gemma":0.003957147,"domain_scores_codex":[0.9986752,0.0005661379,0.0000864507,0.0003281788,0.0002189437,0.0001250936],"domain_scores_gemma":[0.9956514,0.003463655,0.0001834951,0.0002075068,0.0004263057,0.00006759657],"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":[0.0006253698,0.0005535961,0.01036781,0.0001915615,0.000276985,0.0002977641,0.0002974209,0.2748153,0.009986826,0.003672321,0.005151587,0.6937636],"study_design_scores_gemma":[0.000007997301,0.00003448772,0.001048908,0.000005560241,0.000008268822,0.00003794202,0.0000121307,0.9966048,0.0009169908,0.0008209116,0.0004951823,0.00000694277],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08120258,0.001049589,0.9122224,0.0003843849,0.0001611116,0.0001455983,0.000313431,0.003427187,0.001093669],"genre_scores_gemma":[0.7637467,0.0004791962,0.2292366,0.0001493935,0.0001847103,0.0005170154,0.0006738244,0.0001401156,0.00487227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008200156,"threshold_uncertainty_score":0.01630485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02507765377434162,"score_gpt":0.329702914382876,"score_spread":0.3046252606085343,"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."}}