{"id":"W4321438061","doi":"10.19173/irrodl.v24i1.6589","title":"Using Survival Analysis to Identify Populations of Learners at Risk of Withdrawal: Conceptualization and Impact of Demographics","year":2023,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Psychological intervention; Dropout (neural networks); Conceptualization; Psychology; Demographics; Intervention (counseling); Identification (biology); At-risk students; Medical education; Higher education; Mathematics education; Computer science; Medicine; Demography; Sociology; Political science","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":[0.004776064,0.0000656944,0.0003268163,0.0005374483,0.00009331818,0.00004649506,0.0006662301,0.00002664652,0.00001255719],"category_scores_gemma":[0.002658991,0.00004847544,0.000100953,0.003908807,0.0001778864,0.0001500439,0.0008439736,0.0002132821,5.099468e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004034068,"about_ca_system_score_gemma":0.00009809409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001993385,"about_ca_topic_score_gemma":0.00009842152,"domain_scores_codex":[0.9979469,0.0006665814,0.0004585088,0.000174003,0.0006236552,0.0001304031],"domain_scores_gemma":[0.9981412,0.00066966,0.0003780414,0.0002035425,0.0005558308,0.00005169069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001942687,0.00002480746,0.9134871,0.0002171104,0.000222474,8.612497e-7,0.0002645162,0.0782537,0.0002747525,0.005542981,0.00003582621,0.001656466],"study_design_scores_gemma":[0.0002740103,0.0001107944,0.5806892,0.001703068,0.00009269841,0.0000015853,0.0008858018,0.4150288,0.00004728428,0.001019858,0.00007757115,0.00006929566],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9837598,0.001226372,0.01384265,0.0008084477,0.00001966157,0.0001892373,0.00007203151,0.000006229755,0.00007561261],"genre_scores_gemma":[0.9927861,0.006182176,0.0008802215,0.000004276244,0.000005745004,0.00000226074,0.0001011121,0.000003816735,0.0000342891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3367752,"threshold_uncertainty_score":0.3183253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1746378960091158,"score_gpt":0.5292195005070426,"score_spread":0.3545816044979269,"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."}}