{"id":"W2948014945","doi":"","title":"Predicting Learners' Performance Using EEG and Eye Tracking Features.","year":2019,"lang":"en","type":"article","venue":"The Florida AI Research Society","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Electroencephalography; Computer science; Eye tracking; Artificial intelligence; Tracking (education); Speech recognition; Computer vision; Psychology; Neuroscience","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":[0.002084439,0.000152581,0.0001592326,0.00004538942,0.0009744324,0.0004176176,0.0006295242,0.00009539325,0.00003797318],"category_scores_gemma":[0.000232478,0.0001024348,0.00009962339,0.0004746201,0.0004451202,0.0004987401,0.0005358979,0.001430158,0.00004633192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008216719,"about_ca_system_score_gemma":0.00008368526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000647902,"about_ca_topic_score_gemma":0.000002123316,"domain_scores_codex":[0.9974696,0.0003117646,0.0001652459,0.0004937424,0.0008450099,0.0007146041],"domain_scores_gemma":[0.9985578,0.0007963413,0.00004993317,0.0004033086,0.00009577629,0.00009684808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007649284,0.0000431565,0.05099352,0.0002785685,0.00003520621,0.000007504288,0.02830074,0.002681565,0.905122,0.0004297924,0.004528752,0.007502748],"study_design_scores_gemma":[0.001004051,0.0003793862,0.02837063,0.0003850592,0.00001498873,0.0000973407,0.006717871,0.5515129,0.403824,0.00031561,0.00691529,0.0004629397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960729,0.0001940031,0.00005060486,0.001485573,0.0004367814,0.000369893,0.000003602302,0.00008509536,0.001301502],"genre_scores_gemma":[0.9964393,0.0001899474,0.0001514107,0.0006559732,0.0003731259,0.000007127915,5.453126e-7,0.00002500873,0.002157611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5488313,"threshold_uncertainty_score":0.7494645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07147097187794674,"score_gpt":0.3714961869799181,"score_spread":0.3000252151019713,"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."}}