{"id":"W197955839","doi":"","title":"Incorporating an Affective Behavior Model into an Educational Game","year":2009,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Educational game; Affect (linguistics); Computer science; Component (thermodynamics); Intelligent tutoring system; Affective computing; Human–computer interaction; Game based learning; Artificial intelligence; Multimedia; 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":[0.001052112,0.0008008559,0.0003642588,0.0003023369,0.0002419969,0.0009721696,0.0007935313,0.0006179567,0.001669316],"category_scores_gemma":[0.002833159,0.0003129179,0.0004637343,0.0001094927,0.0003375172,0.001060012,0.0005917137,0.0008041786,0.0003777874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006347335,"about_ca_system_score_gemma":0.0005124955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001933606,"about_ca_topic_score_gemma":0.002467868,"domain_scores_codex":[0.9994202,0.0002394187,0.00004523008,0.0001044849,0.0001424713,0.00004818024],"domain_scores_gemma":[0.9991022,0.000542558,0.000079297,0.00005494021,0.0001503092,0.0000707145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007296895,0.002219409,0.01226472,0.0004717101,0.0002737192,0.0004299491,0.001439435,0.614013,0.07404625,0.04976947,0.003581125,0.2407616],"study_design_scores_gemma":[0.00003811347,0.0002532814,0.000923867,0.00001431144,0.00003845071,0.00004655372,0.00002941541,0.9886819,0.002944857,0.004901027,0.00210276,0.00002556123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1158656,0.00009719038,0.8723481,0.0005189118,0.00009393012,0.0005985862,0.00008814138,0.001456886,0.008932672],"genre_scores_gemma":[0.7600163,0.00009117463,0.2349572,0.0002230827,0.00002717462,0.0005213136,0.00007960284,0.00006060186,0.004023556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001933606,"threshold_uncertainty_score":0.005584419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08394541589574217,"score_gpt":0.4139218161868768,"score_spread":0.3299764002911346,"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."}}