{"id":"https://openalex.org/W7163576081","doi":"https://doi.org/10.48550/arxiv.2606.04161","title":"When Offline Selectors Cannot Beat the Best Single Model: A Diagnostic Study on edX Dropout Prediction","display_name":"When Offline Selectors Cannot Beat the Best Single Model: A Diagnostic Study on edX Dropout Prediction","publication_year":2026,"publication_date":"2026-06-02","ids":{"openalex":"https://openalex.org/W7163576081","doi":"https://doi.org/10.48550/arxiv.2606.04161"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.04161","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04161","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.04161","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128073670","display_name":"Tyler Crosse","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Crosse, Tyler","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137914769","display_name":"Alan Nadelsticher Ruvalcaba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruvalcaba, Alan Nadelsticher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137857678","display_name":"Dustin Khang LeDuc","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"LeDuc, Dustin Khang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137887479","display_name":"Thomas Trask","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Trask, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047923729","display_name":"Nicholas Lytle","orcid":"https://orcid.org/0000-0001-7009-9905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lytle, Nicholas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137900212","display_name":"David Joyner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Joyner, David","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/T12535","display_name":"Machine Learning and Data Classification","score":0.2321999967098236,"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"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.2321999967098236,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.08919999748468399,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.08320000022649765,"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/bottleneck","display_name":"Bottleneck","score":0.6309999823570251},{"id":"https://openalex.org/keywords/oracle","display_name":"Oracle","score":0.6033999919891357},{"id":"https://openalex.org/keywords/offset","display_name":"Offset (computer science)","score":0.45170000195503235},{"id":"https://openalex.org/keywords/smoothing","display_name":"Smoothing","score":0.42080000042915344},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.39489999413490295},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.3885999917984009},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.3774000108242035},{"id":"https://openalex.org/keywords/beat","display_name":"Beat (acoustics)","score":0.35370001196861267}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7035999894142151},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6309999823570251},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.6033999919891357},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48500001430511475},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.45170000195503235},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42989999055862427},{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.42080000042915344},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.39489999413490295},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C189809214","wikidata":"https://www.wikidata.org/wiki/Q829522","display_name":"Beat (acoustics)","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.33799999952316284},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.32100000977516174},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.303600013256073},{"id":"https://openalex.org/C2778855872","wikidata":"https://www.wikidata.org/wiki/Q7286559","display_name":"Rake","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2808000147342682},{"id":"https://openalex.org/C99821215","wikidata":"https://www.wikidata.org/wiki/Q1136583","display_name":"Swap (finance)","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.04161","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04161","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.04161","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04161","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Different":[0],"predictors":[1],"often":[2],"excel":[3],"on":[4,70,79,146,165,219],"different":[5],"inputs,":[6],"so":[7],"picking":[8],"the":[9,34,106,122,128,131,153,156,174,198,215,229,237],"best":[10],"one":[11],"per":[12],"instance":[13],"promises":[14],"higher":[15],"accuracy":[16,163,221],"than":[17],"committing":[18],"to":[19,32,109,121,140,181],"a":[20,48,51,71,76,182,202],"single":[21,36,158],"model.":[22],"In":[23],"practice,":[24],"selectors":[25],"trained":[26],"from":[27,82],"logged":[28],"data":[29],"routinely":[30],"fail":[31],"beat":[33],"strongest":[35,157],"predictor.":[37],"Three":[38],"causes":[39],"typically":[40],"go":[41],"unseparated":[42],"before":[43],"more":[44],"tuning":[45,127],"is":[46,192],"applied:":[47],"mismatched":[49],"learner,":[50,129],"state":[52,108,230],"that":[53,102],"does":[54],"not":[55,235],"predict":[56],"which":[57],"model":[58,160],"wins,":[59],"or":[60,133,231],"buffer-to-deployment":[61],"label":[62,84],"shift.":[63],"A":[64],"three-stage":[65],"diagnostic":[66],"rules":[67],"them":[68],"out":[69],"shared":[72],"buffer.":[73],"Stage~1":[74],"estimates":[75],"local":[77,193],"ceiling":[78],"oracle":[80,154],"recovery":[81],"$k$-NN":[83],"consistency.":[85],"Stage~2":[86],"asks":[87],"whether":[88,111],"paired":[89],"BC":[90],"and":[91,96,170,186,214],"offline-RL":[92],"learners":[93,211,217],"(BC,":[94],"DQN,":[95,169],"CQL":[97,171,196],"across":[98,210],"penalty":[99],"weights)":[100],"reach":[101],"ceiling.":[103],"Stage~3":[104],"ablates":[105],"selector":[107],"test":[110,220],"richer":[112],"features":[113],"would":[114],"raise":[115],"it.":[116],"The":[117,190,224],"combined":[118],"verdict":[119],"points":[120,164],"most":[123],"promising":[124],"next":[125,225],"step:":[126],"redesigning":[130],"state,":[132],"collecting":[134],"new":[135,233],"data.":[136,149],"We":[137],"apply":[138],"it":[139,179],"selecting":[141],"among":[142],"five":[143],"dropout-prediction":[144],"models":[145],"edX":[147],"clickstream":[148],"Across":[150],"16":[151],"windows,":[152],"beats":[155],"base":[159],"by":[161],"9.7":[162],"average,":[166],"yet":[167],"BC,":[168],"land":[172],"in":[173],"same":[175],"test-accuracy":[176],"band":[177],"below":[178],"(robust":[180],"tenfold":[183],"buffer":[184],"sweep":[185],"$N{=}2{,}000$":[187],"held-out":[188],"examples).":[189],"bottleneck":[191],"representational":[194],"ambiguity:":[195],"closes":[197],"imitation":[199],"gap":[200],"without":[201],"deployment":[203],"gain":[204],"(not":[205,212,222],"conservatism),":[206],"regret":[207],"clusters":[208],"tightly":[209],"tie-breaking),":[213],"three":[216],"converge":[218],"shift).":[223],"iteration":[226],"should":[227],"change":[228],"collect":[232],"data,":[234],"tune":[236],"offline":[238],"learner":[239],"further.":[240]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-05T00:00:00"}