{"id":"W3091469317","doi":"10.3390/electronics9101613","title":"IoT System for School Dropout Prediction Using Machine Learning Techniques Based on Socioeconomic Data","year":2020,"lang":"en","type":"article","venue":"Electronics","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; King Saud University","keywords":"Machine learning; Dropout (neural networks); Computer science; Artificial intelligence; Decision tree; Support vector machine; Context (archaeology); Precision and recall; Multilayer perceptron; Process (computing); Naive Bayes classifier; Socioeconomic status; Modalities; Artificial neural network","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":[],"consensus_categories":[],"category_scores_codex":[0.0004184211,0.0001352779,0.0001744652,0.00006401011,0.0002314513,0.0001418471,0.0007898326,0.00007623064,0.000003875606],"category_scores_gemma":[0.0001280597,0.0001380341,0.00005779588,0.0001286872,0.00001264046,0.0001595299,0.0001437727,0.0004732813,0.00002239122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002798701,"about_ca_system_score_gemma":0.0003506417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006485671,"about_ca_topic_score_gemma":0.000003044119,"domain_scores_codex":[0.9988003,0.00007551437,0.0002137737,0.000468556,0.0001344175,0.0003074156],"domain_scores_gemma":[0.9991733,0.00007594076,0.0001390491,0.0004726286,0.0000408405,0.00009817392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004908427,0.0006811185,0.03160719,0.001896709,0.0006248853,0.00004256122,0.0009383609,0.6408641,0.07172291,0.05285678,0.008051822,0.1902227],"study_design_scores_gemma":[0.0002332827,0.0004669622,0.000006404888,0.00003389032,0.00002106028,0.000002930783,0.0000119416,0.966197,0.003544201,0.00007430294,0.02928209,0.0001259069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009927402,0.0005005592,0.9829426,0.00506146,0.0001069925,0.0002586519,0.00003864396,0.00105735,0.0001062825],"genre_scores_gemma":[0.8758308,0.00003475665,0.1224923,0.0007556984,0.0005830797,0.000008657407,0.0001639962,0.00004108875,0.00008966623],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8659034,"threshold_uncertainty_score":0.5628867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0265834733912835,"score_gpt":0.2824811232976079,"score_spread":0.2558976499063244,"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."}}