{"id":"W2504410277","doi":"10.4018/978-1-59140-500-9.ch008","title":"A Multiagent Framework for an Adaptive E-Learning Systems","year":2005,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Personalization; Adaptation (eye); Computer science; Process (computing); Adaptive learning; Architecture; Multi-agent system; Perception; Artificial intelligence; Human–computer interaction; World Wide Web; Psychology; Geography","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":[0.0008278555,0.001222079,0.0006592765,0.000746078,0.001275971,0.003776168,0.002491136,0.002984351,0.0110298],"category_scores_gemma":[0.0007966369,0.000553078,0.0009266267,0.0007563363,0.001512837,0.003054828,0.002417342,0.003547454,0.003645282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376881,"about_ca_system_score_gemma":0.001173542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002788033,"about_ca_topic_score_gemma":0.003090122,"domain_scores_codex":[0.9992482,0.0002685891,0.00006022401,0.0001202486,0.000252739,0.00004983237],"domain_scores_gemma":[0.9997692,0.00008742964,0.00001494483,0.00003601259,0.00004797447,0.00004451595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000215519,0.0000564749,0.00008726682,0.0003243433,0.00004531956,0.0003588142,0.0004981279,0.02491407,0.002990832,0.893317,0.01274337,0.06464276],"study_design_scores_gemma":[0.00004059781,0.00007375265,0.0001416091,0.0002907032,0.0000404807,0.0004822624,0.0001590933,0.1174734,0.001539935,0.3402706,0.5394461,0.00004153412],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001385506,0.01272613,0.9106735,0.002621196,0.0006408478,0.0002429671,0.0001033412,0.001026918,0.07057963],"genre_scores_gemma":[0.09709854,0.01329014,0.7943101,0.001112945,0.000830283,0.001048962,0.0003104363,0.0002637164,0.091735],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0110298,"threshold_uncertainty_score":0.03689837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04578712783866092,"score_gpt":0.2819991440758031,"score_spread":0.2362120162371421,"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."}}