{"id":"W4408842724","doi":"10.33050/mentari.v3i2.746","title":"Leveraging Big Data for Student Success and Institutional Growth","year":2025,"lang":"en","type":"article","venue":"Jurnal Mentari Manajemen Pendidikan dan Teknologi Informasi","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Learning Partnership","funders":"","keywords":"Big data; Data science; Computer science; Mathematics education; Political science; Psychology; Data mining","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":[0.0006746859,0.0002331739,0.000261664,0.0003339425,0.0006221186,0.0005335818,0.00233668,0.0000782111,0.000002095604],"category_scores_gemma":[0.0001079878,0.0002053031,0.00005750023,0.0003421463,0.0001225798,0.001185441,0.002359236,0.0003369855,0.000002796231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001493301,"about_ca_system_score_gemma":0.0002545303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000037846,"about_ca_topic_score_gemma":0.00006667436,"domain_scores_codex":[0.9981757,0.00003487618,0.0004980764,0.0004964085,0.000346781,0.0004481491],"domain_scores_gemma":[0.9987452,0.0001117446,0.0001916148,0.000743938,0.0001003161,0.0001071693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004091845,0.0002731461,0.1631488,0.0002428797,0.0004794052,0.0000548672,0.0007627412,0.0003313455,0.00006813944,0.62058,0.004169796,0.2098479],"study_design_scores_gemma":[0.005597319,0.0007050364,0.1969066,0.0003735766,0.0002722423,0.0001646574,0.001943532,0.1411581,0.0009331354,0.01749003,0.6332156,0.00124019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3375878,0.0009332345,0.5845194,0.04328249,0.004814506,0.001631152,0.00005949154,0.0008491815,0.02632277],"genre_scores_gemma":[0.9684258,0.0001900867,0.02685337,0.001983079,0.000215997,0.00002848229,0.0002705332,0.00001004995,0.002022559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6308381,"threshold_uncertainty_score":0.837202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0422132477794523,"score_gpt":0.3148942475971928,"score_spread":0.2726809998177405,"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."}}