{"id":"W2317376037","doi":"10.14288/1.0166864","title":"Eye-tracking as a source of information for automatically predicting user learning with MetaTutor, an intelligent tutoring system to support self-regulated learning","year":2014,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Tracking (education); Eye tracking; Intelligent tutoring system; Human–computer interaction; Artificial intelligence; Multimedia; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105974,0.0004800782,0.0005028551,0.002197279,0.0001957366,0.00067301,0.0003361242,0.0005761464,0.0009505411],"category_scores_gemma":[0.005470177,0.0002116225,0.000341261,0.00093584,0.0001178819,0.0007782243,0.0004136841,0.0005005558,0.0006391488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003187447,"about_ca_system_score_gemma":0.0002734997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003265367,"about_ca_topic_score_gemma":0.007071612,"domain_scores_codex":[0.999331,0.0001797955,0.00004617896,0.0001802347,0.0002160239,0.0000467793],"domain_scores_gemma":[0.9953917,0.002701914,0.000667835,0.0002942943,0.0007974932,0.0001467912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001060508,0.0006987397,0.328828,0.0003913128,0.0002471375,0.0002184355,0.001029944,0.016603,0.08663898,0.000429271,0.005194547,0.5586601],"study_design_scores_gemma":[0.000049787,0.001127181,0.5607139,0.00009270388,0.0001855251,0.0003847768,0.0004289164,0.3744093,0.0585961,0.0005755931,0.003333987,0.000102235],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9434927,0.0003872676,0.04989202,0.0001502876,0.00003607838,0.0001076065,0.002511055,0.001638995,0.00178404],"genre_scores_gemma":[0.9590428,0.0002241247,0.03802615,0.00003293384,0.00002308328,0.00007499881,0.001551388,0.00005488568,0.0009694805],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003265367,"threshold_uncertainty_score":0.006492734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006591483274655736,"score_gpt":0.1943418476888416,"score_spread":0.1877503644141858,"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."}}