{"id":"W2405263560","doi":"","title":"Personalized presentation of multimedia objects for home healthcare environments: a peer-based intelligent tutoring approach.","year":2012,"lang":"en","type":"article","venue":"International Conference on User Modeling, Adaptation, and Personalization","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Presentation (obstetrics); Multimedia; Curriculum; Value (mathematics); Health care; Intelligent tutoring system; Personalized learning; Human–computer interaction; Order (exchange); World Wide Web; Teaching method; Open learning; Cooperative learning; Machine learning; Mathematics education; 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":[0.002256572,0.0007695566,0.0007157091,0.0009258484,0.0006350048,0.002229386,0.0027498,0.001849421,0.002687562],"category_scores_gemma":[0.01017774,0.0004319279,0.0007312619,0.000493055,0.000737379,0.003279646,0.001949828,0.0008950458,0.0009674564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006854825,"about_ca_system_score_gemma":0.0007535933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001409466,"about_ca_topic_score_gemma":0.002282585,"domain_scores_codex":[0.9980797,0.0008362222,0.0001156949,0.0004036753,0.0004853114,0.00007951701],"domain_scores_gemma":[0.9962391,0.001689528,0.0003702476,0.0007226228,0.00069283,0.0002856567],"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":[0.0006211416,0.001020042,0.01491901,0.0007734568,0.0004280852,0.001477579,0.005561695,0.1911561,0.04623039,0.03919941,0.008340021,0.690273],"study_design_scores_gemma":[0.0000538481,0.0003520709,0.002887916,0.00007512426,0.0001569707,0.0008377819,0.0009716458,0.9182166,0.01627827,0.04067039,0.01941608,0.00008331825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03194833,0.0002664596,0.9597049,0.0004200989,0.00002994856,0.0002739689,0.00009939117,0.002642359,0.004614495],"genre_scores_gemma":[0.5965346,0.0002767718,0.3986354,0.0001264044,0.00005786786,0.0002006643,0.0002599254,0.0001661853,0.003742198],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0027498,"threshold_uncertainty_score":0.01193404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1333079331296142,"score_gpt":0.318181196506369,"score_spread":0.1848732633767548,"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."}}