{"id":"W2013974720","doi":"10.1145/964442.964446","title":"Building and evaluating an intelligent pedagogical agent to improve the effectiveness of an educational game","year":2004,"lang":"en","type":"article","venue":"","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Computer science; Educational game; Prime (order theory); Probabilistic logic; Test (biology); Prime time; Game based learning; Artificial intelligence; Human–computer interaction; Mathematics education; 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":[0.003865364,0.001054547,0.0008108527,0.0004957552,0.0004711515,0.001907549,0.001617654,0.001578366,0.001362591],"category_scores_gemma":[0.01209936,0.000370415,0.0003243338,0.0001978693,0.0006822382,0.001568688,0.0008632935,0.0009171749,0.0003788558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008397688,"about_ca_system_score_gemma":0.001224248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001648194,"about_ca_topic_score_gemma":0.001873489,"domain_scores_codex":[0.9982992,0.0007259501,0.0001926288,0.0002744871,0.0003914011,0.0001163238],"domain_scores_gemma":[0.9941624,0.003700818,0.0004415628,0.0004371603,0.0009004968,0.0003575369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003856647,0.02252915,0.05060515,0.002960624,0.0008105134,0.0007177464,0.002997706,0.2631163,0.147306,0.01182048,0.003345226,0.4899344],"study_design_scores_gemma":[0.001259574,0.01063694,0.009677496,0.0001212747,0.0005998894,0.0002878394,0.0004937476,0.8644586,0.09838451,0.001741567,0.01222091,0.0001176995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8790673,0.0003127383,0.1111612,0.0002899567,0.00008724083,0.0023311,0.0001151085,0.001450446,0.005184815],"genre_scores_gemma":[0.7559516,0.0002044264,0.2404254,0.0001380956,0.00001660399,0.0009003236,0.0001956526,0.00005646674,0.002111409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003865364,"threshold_uncertainty_score":0.02044225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08058104714846731,"score_gpt":0.3938329504119589,"score_spread":0.3132519032634916,"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."}}