{"id":"https://openalex.org/W7083673341","doi":"https://doi.org/10.48550/arxiv.2509.21514","title":"Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing","display_name":"Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing","publication_year":2025,"publication_date":"2025-09-25","ids":{"openalex":"https://openalex.org/W7083673341","doi":"https://doi.org/10.48550/arxiv.2509.21514"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2509.21514","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.21514","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2509.21514","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Mitton, Joshua","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mitton, Joshua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Bhattacharyya, Prarthana","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhattacharyya, Prarthana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Abboud, Ralph","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abboud, Ralph","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Woodhead, Simon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Woodhead, Simon","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":true,"primary_topic":{"id":"https://openalex.org/T12364","display_name":"Archaeological Research and Protection","score":0.18330000340938568,"subfield":{"id":"https://openalex.org/subfields/1912","display_name":"Space and Planetary Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12364","display_name":"Archaeological Research and Protection","score":0.18330000340938568,"subfield":{"id":"https://openalex.org/subfields/1912","display_name":"Space and Planetary Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.08250000327825546,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.05739999935030937,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.5910999774932861},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5523999929428101},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.3977000117301941},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.30160000920295715},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.28119999170303345}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.70169997215271},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.5910999774932861},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5523999929428101},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5038999915122986},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4706999957561493},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31450000405311584},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2509.21514","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.21514","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2509.21514","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.21514","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8248088359832764}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Research":[0],"on":[1,8,65],"Knowledge":[2],"Tracing":[3],"(KT)":[4],"models":[5,17,39],"traditionally":[6],"focuses":[7],"improving":[9],"predictive":[10],"accuracy.":[11],"However,":[12],"responsible":[13,233],"real-world":[14],"deployment":[15],"requires":[16],"to":[18,21,25,45,75,82,90,108,207,237],"know":[19],"when":[20],"defer":[22],"uncertain":[23,69],"predictions":[24,70],"a":[26,143,154,201,229,246],"human":[27],"teacher.":[28],"We":[29,48],"introduce":[30],"an":[31],"intrinsic":[32],"selective":[33],"prediction":[34,222],"layer":[35],"for":[36,248],"existing":[37],"KT":[38,234],"using":[40,59],"Monte":[41],"Carlo":[42],"Dropout":[43],"(MC-Dropout)":[44],"quantify":[46],"uncertainty.":[47],"evaluate":[49],"this":[50,119],"approach":[51],"across":[52,129],"three":[53],"architectures":[54],"(DKT,":[55],"SAKT,":[56],"and":[57,86,126,181,195,216,240],"AKT)":[58],"the":[60,66,103,111,115,139,161,168,190],"Eedi":[61],"mathematics":[62],"dataset.":[63],"Abstaining":[64],"20\\%":[67],"most":[68,197],"lifts":[71],"accuracy":[72],"by":[73,80,88],"2.3":[74],"3.0":[76],"percentage":[77,84,92],"points,":[78],"AUC":[79,140],"1.9":[81],"2.4":[83],"points":[85,93],"F1":[87],"1.4":[89],"4.3":[91],"without":[94],"any":[95],"retraining.":[96],"This":[97,204],"abstention":[98],"strategy":[99],"is":[100,227],"highly":[101],"targeted:":[102],"deferred":[104],"set":[105],"exhibits":[106],"1.45":[107],"1.60":[109],"times":[110,138],"error":[112],"rate":[113],"of":[114,142,160,189,232],"kept":[116],"set.":[117],"Furthermore,":[118],"targeting":[120],"holds":[121],"within":[122],"every":[123],"question-difficulty":[124],"quartile":[125],"remains":[127],"fair":[128],"student-ability":[130],"levels.":[131],"Importantly,":[132],"MC-Dropout":[133,214],"variance":[134,158],"gives":[135],"roughly":[136],"five":[137],"lift":[141],"calibrated":[144],"two-parameter":[145],"logistic":[146],"(2PL)":[147],"Item":[148],"Response":[149],"Theory":[150],"(IRT)":[151],"baseline":[152],"as":[153,209],"selective-prediction":[155],"signal.":[156],"A":[157],"decomposition":[159],"model's":[162],"epistemic":[163,211,225],"uncertainty":[164,226],"(BALD)":[165],"reveals":[166],"that":[167,213],"entire":[169],"classical":[170],"psychometric":[171],"stack,":[172],"comprising":[173],"question":[174],"difficulty,":[175],"student":[176],"ability,":[177],"IRT-style":[178],"outcome":[179],"ambiguity,":[180],"historical":[182],"curriculum":[183],"coverage,":[184],"explains":[185],"less":[186],"than":[187,245],"4\\%":[188],"signal":[191],"under":[192],"linear":[193],"modeling":[194],"at":[196],"23\\%":[198],"even":[199],"with":[200,223],"non-linear":[202],"regressor.":[203],"leaves":[205],"77\\%":[206],"90\\%":[208],"architecture-specific":[210],"content":[212],"surfaces":[215],"simpler":[217],"proxies":[218],"cannot":[219],"recover.":[220],"Selective":[221],"model-native":[224],"therefore":[228],"necessary":[230],"component":[231],"deployment,":[235],"complementary":[236],"subgroup-fairness":[238],"audits":[239],"downstream":[241],"classroom":[242],"evaluation":[243],"rather":[244],"substitute":[247],"them.":[249]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2025-10-10T00:00:00"}
