{"id":"W2942615504","doi":"10.5539/cis.v12n2p103","title":"Unsupervised Characterization and Visualization of Students’ Academic Performance Features","year":2019,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Silhouette; Computer science; Class (philosophy); Cluster analysis; Metric (unit); k-means clustering; Cluster (spacecraft); Feature (linguistics); Mathematics education; Artificial intelligence; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004268129,0.00006697035,0.00009175242,0.0002527947,0.000105775,0.0002466717,0.0004299168,0.00003315985,0.000001564755],"category_scores_gemma":[0.00001461186,0.0000572484,0.000009168019,0.000607858,0.00008841937,0.007069385,0.0002840928,0.0000769517,0.00000992551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009033831,"about_ca_system_score_gemma":0.00004366983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001005147,"about_ca_topic_score_gemma":1.937353e-8,"domain_scores_codex":[0.9991479,0.00001333171,0.0002168214,0.0001328568,0.0003755948,0.0001135112],"domain_scores_gemma":[0.9994766,0.00001795664,0.0001316377,0.0001512375,0.0001685951,0.00005398172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000009304642,0.00002910497,0.4772849,0.0002499851,0.000007562106,1.170671e-7,0.00957439,0.001101781,0.00845538,0.08993243,0.00003262856,0.4133224],"study_design_scores_gemma":[0.0001557213,0.0000628418,0.5011011,0.00003170978,0.000001059219,0.000004651667,0.00001499028,0.4973767,0.0006103251,0.00002176694,0.0005622463,0.00005683834],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7938124,0.00001004642,0.2056594,0.0001569502,0.0001367605,0.00007452648,6.575267e-7,0.00003291247,0.0001164063],"genre_scores_gemma":[0.9962242,0.0001939582,0.00312276,0.0003895275,0.00001981155,7.589545e-7,0.000008388634,0.000001353346,0.00003926599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4962749,"threshold_uncertainty_score":0.5125133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007415503979852132,"score_gpt":0.272789192338405,"score_spread":0.2653736883585529,"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."}}