{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006756776,0.0001683407,0.0002938264,0.0002212525,0.0003870695,0.000032437,0.0008883103,0.0003862405,0.0101865],"category_scores_gemma":[0.0009389911,0.0001853236,0.0001096432,0.0002280896,0.0003420702,0.00007201189,0.0002469107,0.0001810483,0.0012745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001468348,"about_ca_system_score_gemma":0.0007683553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005913108,"about_ca_topic_score_gemma":0.001545143,"domain_scores_codex":[0.9981207,0.0003297324,0.0004643355,0.0004377275,0.0004328597,0.0002146921],"domain_scores_gemma":[0.9975166,0.0003434962,0.0004752606,0.0007782589,0.0008076943,0.00007866999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001384982,0.007135859,0.0128203,0.0002781239,0.0005028535,1.618237e-7,0.06625363,0.002184107,0.00004462303,0.1299033,0.7700629,0.0106757],"study_design_scores_gemma":[0.0008834681,0.0002255247,0.00738269,0.0001233896,0.00001603447,4.194167e-7,0.004032888,0.0002514518,0.00006350307,0.003681057,0.9829959,0.0003436384],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05017121,0.0005840125,0.678511,0.012146,0.01740765,0.01675722,0.0005376755,0.0007127374,0.2231724],"genre_scores_gemma":[0.8672097,0.0001430338,0.007179992,0.0001353093,0.001139303,0.001041719,0.0006341547,0.00004554876,0.1224712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8170385,"threshold_uncertainty_score":0.9995031,"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."}}