{"id":"W2220126485","doi":"","title":"Exploring the Effects of Errors in Assessment and Time Requirements of Learning Objects in a Peer-Based Intelligent Tutoring System","year":2011,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Selection (genetic algorithm); Representation (politics); Learning object; Artificial intelligence; Object (grammar); Value (mathematics); Intelligent tutoring system; Human–computer interaction; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009818224,0.0001745205,0.0003327219,0.0001881538,0.000269969,0.00007218657,0.0009817204,0.00005456506,0.00000240725],"category_scores_gemma":[0.0003810621,0.0001145446,0.0001275058,0.001021871,0.0001851401,0.0003720396,0.0006035581,0.001067868,0.000005327347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004379381,"about_ca_system_score_gemma":0.0002047195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130196,"about_ca_topic_score_gemma":0.0000121718,"domain_scores_codex":[0.9960038,0.001213297,0.0005225372,0.0003828558,0.001283814,0.0005937176],"domain_scores_gemma":[0.9976074,0.001273277,0.0001781414,0.0005381551,0.0003374377,0.00006555434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001880303,0.0006276722,0.1682668,0.006937293,0.000443085,0.0001562821,0.4921845,0.01819546,0.07896548,0.2164962,0.0001494808,0.01738975],"study_design_scores_gemma":[0.002353487,0.002405053,0.1619011,0.01215106,0.00003614389,0.00001466839,0.06577122,0.4703838,0.2820726,0.0007377078,0.001272793,0.000900341],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852943,0.0001895835,0.01240863,0.0001379464,0.0004755702,0.0007819272,3.719525e-7,0.00004609835,0.0006655117],"genre_scores_gemma":[0.9982409,0.0000454852,0.001043804,0.000008729713,0.00006703255,0.0001525934,3.940899e-7,0.00001692824,0.0004241727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4521884,"threshold_uncertainty_score":0.4670992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1727841942316676,"score_gpt":0.3592314250504457,"score_spread":0.1864472308187781,"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."}}