{"id":"W2796082891","doi":"10.29007/mcs1","title":"User Study of Emotional Visualization Dashboard for Educational Software","year":2018,"lang":"en","type":"paratext","venue":"EasyChair preprint","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Industry Canada","keywords":"Dashboard; Visualization; Computer science; Learning analytics; Human–computer interaction; Eye tracking; Data visualization; Software; World Wide Web; Multimedia; Data science; 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":[0.003560609,0.001239297,0.000688757,0.0009450912,0.0008132852,0.001216836,0.0008250775,0.001015794,0.004538985],"category_scores_gemma":[0.01658365,0.0003387047,0.0005475061,0.0004917061,0.0005116338,0.001268956,0.000927161,0.001016554,0.0008106116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002920286,"about_ca_system_score_gemma":0.0001789908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001093117,"about_ca_topic_score_gemma":0.001398638,"domain_scores_codex":[0.998179,0.001091383,0.0001427852,0.0002065714,0.0002776188,0.0001026601],"domain_scores_gemma":[0.972473,0.0219428,0.000489108,0.001721753,0.002494432,0.0008790056],"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":[0.007884673,0.008451264,0.09694583,0.004249002,0.0007506171,0.01439155,0.1171442,0.008619948,0.2854798,0.002512931,0.03414847,0.4194219],"study_design_scores_gemma":[0.001129859,0.02850911,0.3460693,0.001019313,0.000899466,0.01169687,0.04836482,0.173863,0.2461562,0.003310496,0.1380104,0.0009711968],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678762,0.0001515503,0.02538165,0.0003701434,0.00007099036,0.0002272927,0.0005383521,0.003650426,0.001733493],"genre_scores_gemma":[0.9666354,0.0001303562,0.02766829,0.0002420833,0.00003975319,0.0002779107,0.0009069131,0.0006870835,0.003412243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004538985,"threshold_uncertainty_score":0.01883048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05133651636026367,"score_gpt":0.4143333608727857,"score_spread":0.362996844512522,"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."}}