{"id":"W2133329934","doi":"","title":"Pedagogical Agents for Personalized Multi-user Virtual Environments*","year":2009,"lang":"en","type":"article","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Personalization; Computer science; Human–computer interaction; Key (lock); Multimedia; User modeling; Virtual machine; Personalized learning; World Wide Web; User interface; Teaching method","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"not_applicable","genre":"other","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002126101,0.00013139,0.0001420112,0.00004048711,0.0001490848,0.0001018263,0.0004191271,0.00005516631,0.00008944028],"category_scores_gemma":[0.00003111864,0.0001046366,0.00011773,0.00005503628,0.00001642512,0.0002232382,0.00005586939,0.00008678841,0.0001886134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004388878,"about_ca_system_score_gemma":0.00001294926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006094177,"about_ca_topic_score_gemma":3.7131e-7,"domain_scores_codex":[0.9989116,0.0000414574,0.0001799361,0.0003515497,0.0002275822,0.0002879364],"domain_scores_gemma":[0.9995706,0.00004930613,0.00005366954,0.0002208726,0.00001794321,0.00008760756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000597841,0.00007817192,0.0001255128,0.000001225037,0.000008729386,0.000004261939,0.0007812756,0.0001464986,0.001619829,0.9906599,0.000527284,0.006041339],"study_design_scores_gemma":[0.0005105995,0.0002367918,0.001267084,0.000009400245,0.000002728506,0.00000430824,0.00016661,0.002748732,0.0009877278,0.0001181587,0.9937905,0.0001573746],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.003211844,0.00003257655,0.9947391,0.0003849143,0.000256373,0.0002233389,8.620793e-7,0.0001045296,0.001046456],"genre_scores_gemma":[0.05040231,0.00000408865,0.03549432,0.0007550566,0.0001228929,0.00001340687,0.000001848063,0.000006277983,0.9131998],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9932632,"threshold_uncertainty_score":0.4266956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1158711810789673,"score_gpt":0.3376808143130361,"score_spread":0.2218096332340688,"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."}}