{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003180642,0.00133012,0.001015359,0.004063077,0.000429434,0.001690119,0.0003534896,0.001038063,0.0008499969],"category_scores_gemma":[0.03403416,0.0003136936,0.000788651,0.00153187,0.0003262059,0.001827335,0.0009810546,0.0008341653,0.0004475709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005108629,"about_ca_system_score_gemma":0.0004410767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0040184,"about_ca_topic_score_gemma":0.005431735,"domain_scores_codex":[0.9974842,0.0008195839,0.000302357,0.0004526885,0.0007104694,0.0002305818],"domain_scores_gemma":[0.9620067,0.02918736,0.003913956,0.001136758,0.002918068,0.000837199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004460009,0.0009411027,0.5924256,0.001069548,0.0005287295,0.0006128494,0.005152845,0.01227579,0.1009189,0.0003102757,0.002406462,0.2788979],"study_design_scores_gemma":[0.00005981354,0.002429267,0.7795416,0.0001591603,0.0002321918,0.00103543,0.0014939,0.1854356,0.02661286,0.001387843,0.001366265,0.0002460275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691671,0.0005995333,0.0276417,0.00008125525,0.00003725849,0.0001262981,0.0008240449,0.000777533,0.0007452929],"genre_scores_gemma":[0.9810939,0.0001906456,0.0174657,0.00002727872,0.0000193848,0.00007867237,0.0007912319,0.00004685482,0.0002862281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004063077,"threshold_uncertainty_score":0.01682103,"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."}}