{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003706871,0.001268342,0.0007690559,0.001869973,0.0002922094,0.001435984,0.0006140539,0.001132169,0.0008294872],"category_scores_gemma":[0.04407139,0.0003516375,0.0007587022,0.0006988166,0.0003177832,0.001844756,0.0007168637,0.001138812,0.0006068916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008065174,"about_ca_system_score_gemma":0.0004772516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009350468,"about_ca_topic_score_gemma":0.006045573,"domain_scores_codex":[0.9986908,0.0005187843,0.0001031596,0.0003045177,0.0002417155,0.000141123],"domain_scores_gemma":[0.9636704,0.0273336,0.001995307,0.003326173,0.002757439,0.0009170774],"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":[0.002686902,0.001405213,0.3236521,0.0004605273,0.0003977229,0.0002904515,0.002546164,0.316874,0.0180583,0.001351604,0.005158234,0.3271187],"study_design_scores_gemma":[0.00001427479,0.0003724071,0.05997999,0.00002485728,0.00003308495,0.00008282056,0.0001535633,0.9332305,0.004901043,0.0007904957,0.0003610654,0.00005596523],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9461493,0.0002404808,0.04969115,0.0001714453,0.00001979506,0.0001215114,0.0006469206,0.00177613,0.001183109],"genre_scores_gemma":[0.9885793,0.00007253348,0.01041346,0.00001656878,0.000003873829,0.00005013612,0.0004824037,0.00005401856,0.0003276915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009350468,"threshold_uncertainty_score":0.01960403,"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."}}