{"id":"W1967952393","doi":"10.1145/2678025.2701376","title":"Prediction of Users' Learning Curves for Adaptation while Using an Information Visualization","year":2015,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Leverage (statistics); Human–computer interaction; Learning curve; Visualization; User interface; User modeling; Adaptation (eye); User interface design; Task (project management); Eye tracking; Data visualization; Multi-task learning; User experience design; Interface (matter); Machine learning; Artificial intelligence","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":[0.0004267165,0.00005540176,0.00007471448,0.0001360418,0.00005907097,0.00009301217,0.0001489394,0.00003271845,0.000004905271],"category_scores_gemma":[0.0001953551,0.00005492458,0.00001872558,0.0003467771,0.00001043585,0.004575594,0.00003318124,0.00002215919,0.000003042344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002865397,"about_ca_system_score_gemma":0.00009154221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003137045,"about_ca_topic_score_gemma":0.000005671453,"domain_scores_codex":[0.9992856,0.00005040558,0.0002671773,0.00009290451,0.0002263858,0.00007749292],"domain_scores_gemma":[0.9990616,0.00001881492,0.0001861021,0.0001326489,0.0005416487,0.00005917892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003506782,0.0002481553,0.004495678,0.0005217498,0.00004310464,1.521569e-7,0.01970475,0.5138952,0.001035778,0.4211946,0.00782978,0.03099599],"study_design_scores_gemma":[0.0002848376,0.0001432165,0.0001024299,0.00003676627,0.000008562932,8.541161e-7,0.0009211818,0.9922534,0.0007496622,0.0002779423,0.005166269,0.00005489806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002956868,0.00001220118,0.996228,0.00004391608,0.0001275778,0.0001529114,0.00001132775,0.0001271779,0.0003400324],"genre_scores_gemma":[0.8890103,0.00004355221,0.108533,0.000457043,0.0000862912,0.0000105558,0.001698209,0.00001069213,0.0001503658],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.887695,"threshold_uncertainty_score":0.3317195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1494905630247925,"score_gpt":0.3379030994939007,"score_spread":0.1884125364691082,"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."}}