{"id":"W224927278","doi":"10.1007/978-3-642-38844-6_18","title":"Comparing and Combining Eye Gaze and Interface Actions for Determining User Learning with an Interactive Simulation","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Gaze; Human–computer interaction; Java applet; User interface; Eye tracking; Interface (matter); Machine learning; Artificial intelligence; Classifier (UML); Java; Programming language","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005903865,0.0004404099,0.0004953659,0.0005061697,0.0005893677,0.00122122,0.0006469049,0.00016626,0.000003976067],"category_scores_gemma":[0.00009810972,0.0003908887,0.00004282115,0.0001694692,0.0002738793,0.00166588,0.0005843702,0.0008677018,0.000005209998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001652644,"about_ca_system_score_gemma":0.00009734082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004074616,"about_ca_topic_score_gemma":0.00004085778,"domain_scores_codex":[0.9975256,0.00005966259,0.000386352,0.001205734,0.0003551116,0.0004675615],"domain_scores_gemma":[0.9978159,0.0009406869,0.000387044,0.0004018293,0.0003017829,0.0001527656],"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":[0.00002713132,0.00001615392,0.002241343,0.000074926,0.00003134676,0.000008986179,0.006382176,0.7498982,0.0003335566,0.01164809,5.583852e-7,0.2293375],"study_design_scores_gemma":[0.0003181037,0.0006582279,0.0005990891,0.001210639,0.0000115119,0.00003859037,0.0000134545,0.9927755,0.000356956,0.001744902,0.001767834,0.0005051658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01931502,0.0001090742,0.9789672,0.0000569793,0.0005006594,0.0004923674,7.464009e-7,0.0001418094,0.0004160776],"genre_scores_gemma":[0.825657,0.000005712791,0.1732869,0.000063014,0.0001569949,0.00001568933,0.000002231365,0.00003451328,0.0007778896],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.806342,"threshold_uncertainty_score":0.9998543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04010015664911529,"score_gpt":0.2983857984901774,"score_spread":0.2582856418410622,"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."}}