{"id":"W2574075402","doi":"","title":"Predicting confusion in information visualization from eye tracking and interaction data","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Confusion; Visualization; Computer science; Focus (optics); Human–computer interaction; Information visualization; Data visualization; Eye tracking; Random forest; User satisfaction; Tracking (education); Data science; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004431077,0.0001543582,0.0001445514,0.0003771779,0.00007562419,0.0006325255,0.0008946952,0.00007405107,0.000208992],"category_scores_gemma":[0.0008249981,0.000126851,0.00002181222,0.0002417654,0.00006270061,0.004431931,0.0004825903,0.000119569,0.0001892423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009293894,"about_ca_system_score_gemma":0.00006629489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002488962,"about_ca_topic_score_gemma":0.0002601773,"domain_scores_codex":[0.9981412,0.0000839143,0.0006970858,0.0004536488,0.000452779,0.0001713614],"domain_scores_gemma":[0.9986892,0.0001705051,0.0002927083,0.0004662681,0.0003072979,0.00007398229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002966678,0.0000794708,0.002419821,0.000004002813,0.00001216581,0.000003821779,0.0008844788,0.0001109268,0.005402787,0.509456,0.00005737057,0.4815395],"study_design_scores_gemma":[0.0001025562,0.00004968177,0.003598081,0.0003906449,0.00000377009,0.000002688887,0.0004663022,0.9539047,0.01416005,0.02573994,0.001378432,0.0002031238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04679868,0.000004470409,0.9470658,0.003719497,0.0008735662,0.000136498,0.00009096036,0.0001070216,0.001203472],"genre_scores_gemma":[0.9973936,0.0001637758,0.001591005,0.0004712204,0.0001001005,0.000006328521,0.0002392143,0.000006233603,0.00002851955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9537938,"threshold_uncertainty_score":0.6099458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1565846372061156,"score_gpt":0.3893210314244875,"score_spread":0.232736394218372,"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."}}