{"id":"W3094467965","doi":"10.1145/3382507.3418884","title":"Eye-Tracking to Predict User Cognitive Abilities and Performance for User-Adaptive Narrative Visualizations","year":2020,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Eye tracking; Leverage (statistics); Human–computer interaction; Narrative; Cognition; Visualization; User interface; Comprehension; User modeling; Task (project management); Cognitive load; Multimedia; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009900626,0.0005726825,0.0003157652,0.0008409751,0.0001640978,0.0007674774,0.0002370532,0.0005662547,0.001764729],"category_scores_gemma":[0.01345773,0.0001715307,0.0002892361,0.0004933681,0.0001222243,0.0008074393,0.0004381257,0.0004520008,0.0005262475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002077179,"about_ca_system_score_gemma":0.0002169549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004850724,"about_ca_topic_score_gemma":0.007003945,"domain_scores_codex":[0.9996073,0.0001142925,0.0000324163,0.0001110462,0.00009067556,0.00004428028],"domain_scores_gemma":[0.9934192,0.004165427,0.0009985191,0.000488248,0.0006716136,0.0002569246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002808099,0.001276402,0.6148634,0.0005897851,0.0004725503,0.000270358,0.003685964,0.02964467,0.1345986,0.0006054211,0.002614727,0.2085699],"study_design_scores_gemma":[0.00006632743,0.001523566,0.8283573,0.00005209387,0.0001493689,0.0002800239,0.0005602273,0.1436238,0.02315632,0.001026576,0.0010978,0.0001065868],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887384,0.000104748,0.009004595,0.00004171548,0.000006553031,0.00005621901,0.0007895993,0.0002880853,0.0009700231],"genre_scores_gemma":[0.9928411,0.0000550711,0.006176047,0.00001897843,0.000004842138,0.00005012789,0.0004321219,0.00002211477,0.0003996053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004850724,"threshold_uncertainty_score":0.009644985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04514664073143106,"score_gpt":0.334546095878952,"score_spread":0.289399455147521,"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."}}