{"id":"https://openalex.org/W7154111251","doi":"https://doi.org/10.48550/arxiv.2604.08870","title":"Temporal Dropout Risk in Learning Analytics: A Harmonized Survival Benchmark Across Dynamic and Early-Window Representations","display_name":"Temporal Dropout Risk in Learning Analytics: A Harmonized Survival Benchmark Across Dynamic and Early-Window Representations","publication_year":2026,"publication_date":"2026-04-10","ids":{"openalex":"https://openalex.org/W7154111251","doi":"https://doi.org/10.48550/arxiv.2604.08870"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08870","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08870","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.08870","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133492768","display_name":"Rafael da Silva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"da Silva, Rafael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133479678","display_name":"Jeff Eicher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eicher, Jeff","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133475814","display_name":"Gregory Longo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Longo, Gregory","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.6658999919891357,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.6658999919891357,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.2685000002384186,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.006399999838322401,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/brier-score","display_name":"Brier score","score":0.8549000024795532},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7462000250816345},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7204999923706055},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.6916999816894531},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5623999834060669},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.49900001287460327},{"id":"https://openalex.org/keywords/jackknife-resampling","display_name":"Jackknife resampling","score":0.4756999909877777},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.4708000123500824},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.38429999351501465},{"id":"https://openalex.org/keywords/recursive-partitioning","display_name":"Recursive partitioning","score":0.3646000027656555}],"concepts":[{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.8549000024795532},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7462000250816345},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7204999923706055},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.6916999816894531},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.651199996471405},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6345000267028809},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5914999842643738},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5623999834060669},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C81790035","wikidata":"https://www.wikidata.org/wiki/Q847158","display_name":"Jackknife resampling","level":3,"score":0.4756999909877777},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.4708000123500824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39570000767707825},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C137345334","wikidata":"https://www.wikidata.org/wiki/Q7303350","display_name":"Recursive partitioning","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.31790000200271606},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.31540000438690186},{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2944999933242798},{"id":"https://openalex.org/C207609745","wikidata":"https://www.wikidata.org/wiki/Q4944086","display_name":"Bootstrapping (finance)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28040000796318054},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.26179999113082886},{"id":"https://openalex.org/C21308566","wikidata":"https://www.wikidata.org/wiki/Q7169365","display_name":"Permutation (music)","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08870","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08870","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.08870","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08870","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6824501156806946}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Student":[0],"dropout":[1,34,210],"is":[2,102],"a":[3,29,50,60,98,133,160,201,213,218],"persistent":[4],"concern":[5],"in":[6,56,114,181,205],"Learning":[7,41,206],"Analytics,":[8],"yet":[9],"comparative":[10],"studies":[11],"frequently":[12],"evaluate":[13],"predictive":[14,84,165],"models":[15,55],"under":[16],"heterogeneous":[17],"protocols,":[18],"prioritizing":[19],"discrimination":[20,115],"over":[21],"temporal":[22,33,174],"interpretability":[23],"and":[24,59,74,88,116,152,175,208],"calibration.":[25,89],"This":[26],"study":[27],"introduces":[28],"survival-oriented":[30],"benchmark":[31,204],"for":[32],"risk":[35,211],"modelling":[36],"using":[37],"the":[38,107,121,163,182,186,198],"Open":[39],"University":[40],"Analytics":[42,207],"Dataset":[43],"(OULAD).":[44],"Two":[45],"harmonized":[46],"arms":[47],"are":[48,91],"compared:":[49],"dynamic":[51,122],"weekly":[52],"arm,":[53,63,109,123],"with":[54,64,185],"person-period":[57],"representation,":[58],"comparable":[61,108],"continuous-time":[62],"an":[65],"expanded":[66],"roster":[67],"of":[68,188,200,220],"families":[69],"--":[70],"tree-based":[71],"survival,":[72],"parametric,":[73],"neural":[75],"models.":[76],"The":[77],"evaluation":[78],"protocol":[79],"integrates":[80],"four":[81],"analytical":[82],"layers:":[83],"performance,":[85],"ablation,":[86],"explainability,":[87],"Results":[90],"reported":[92],"within":[93,120,132],"each":[94],"arm":[95],"separately,":[96],"as":[97,144,212],"single":[99],"cross-arm":[100],"ranking":[101],"not":[103,168],"methodologically":[104],"warranted.":[105],"Within":[106],"Random":[110],"Survival":[111],"Forest":[112],"leads":[113,126],"horizon-specific":[117],"Brier":[118,130],"scores;":[119],"Poisson":[124],"Piecewise-Exponential":[125],"narrowly":[127],"on":[128,159],"integrated":[129],"score":[131],"tight":[134],"five-family":[135],"cluster.":[136],"No-refit":[137],"bootstrap":[138],"sampling":[139],"variability":[140],"qualifies":[141],"these":[142],"positions":[143],"directional":[145],"signals":[146],"rather":[147,216],"than":[148,217],"absolute":[149],"superiority.":[150],"Ablation":[151],"explainability":[153],"analyses":[154],"converged,":[155],"across":[156],"all":[157],"families,":[158],"shared":[161],"finding:":[162],"dominant":[164],"signal":[166],"was":[167],"primarily":[169],"demographic":[170],"or":[171],"structural,":[172],"but":[173],"behavioral.":[176],"Calibration":[177],"corroborated":[178],"this":[179],"pattern":[180],"better-discriminating":[183],"models,":[184],"exception":[187],"XGBoost":[189],"AFT,":[190],"which":[191],"exhibited":[192],"systematic":[193],"bias.":[194],"These":[195],"results":[196],"support":[197],"value":[199],"harmonized,":[202],"multi-dimensional":[203],"situate":[209],"temporal-behavioral":[214],"process":[215],"function":[219],"static":[221],"background":[222],"attributes.":[223]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-14T00:00:00"}
