{"id":"https://openalex.org/W2962800828","doi":"https://doi.org/10.48550/arxiv.1604.02336","title":"Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation","display_name":"Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation","publication_year":2016,"publication_date":"2016-04-08","ids":{"openalex":"https://openalex.org/W2962800828","doi":"https://doi.org/10.48550/arxiv.1604.02336","mag":"2962800828"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1604.02336","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1604.02336","pdf_url":"https://arxiv.org/pdf/1604.02336","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"parent_works":[],"lineage":["https://openalex.org/W2962800828"],"child_works_count":0,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1604.02336","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022112403","display_name":"Kevin H. Wilson","orcid":"https://orcid.org/0000-0001-8920-7152","observed_orcids":["https://orcid.org/0000-0001-8920-7152"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wilson, Kevin H.","raw_affiliation_strings":["Knewton, Inc. New York , NY"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Knewton, Inc. New York , NY","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052058309","display_name":"Yan Karklin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I1344073410","display_name":"Howard Hughes Medical Institute","ror":"https://ror.org/006w34k90","country_code":"US","type":"facility","lineage":["https://openalex.org/I1344073410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karklin, Yan","raw_affiliation_strings":["Knewton, Inc. New York , NY","Howard Hughes Medical Institute, Chevy Chase, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Knewton, Inc. New York , NY","institution_ids":[]},{"raw_affiliation_string":"Howard Hughes Medical Institute, Chevy Chase, United States","institution_ids":["https://openalex.org/I1344073410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006864150","display_name":"Bojian Han","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Han, Bojian","raw_affiliation_strings":["Carnegie Mellon University. Pittsburgh , PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University. Pittsburgh , PA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079641101","display_name":"Chaitanya Ekanadham","orcid":"https://orcid.org/0009-0006-8029-5284","observed_orcids":["https://orcid.org/0009-0006-8029-5284"]},"institutions":[{"id":"https://openalex.org/I57206974","display_name":"New York University","ror":"https://ror.org/0190ak572","country_code":"US","type":"education","lineage":["https://openalex.org/I57206974"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ekanadham, Chaitanya","raw_affiliation_strings":["Knewton, Inc. New York , NY","New York University, New York, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Knewton, Inc. New York , NY","institution_ids":[]},{"raw_affiliation_string":"New York University, New York, United States","institution_ids":["https://openalex.org/I57206974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":98,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"539","last_page":"544"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9883000254631042,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9883000254631042,"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/T11122","display_name":"Online Learning and Analytics","score":0.007499999832361937,"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/T10467","display_name":"Psychometric Methodologies and Testing","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/item-response-theory","display_name":"item response theory","score":0.9430000185966492},{"id":"https://openalex.org/keywords/deep-knowledge-tracing","display_name":"deep knowledge tracing","score":0.8460000157356262},{"id":"https://openalex.org/keywords/recurrent-neural-networks","display_name":"recurrent neural networks","score":0.8149999976158142},{"id":"https://openalex.org/keywords/student-response-prediction","display_name":"student response prediction","score":0.6919999718666077},{"id":"https://openalex.org/keywords/computer-based-learning-systems","display_name":"computer-based learning systems","score":0.6769999861717224},{"id":"https://openalex.org/keywords/bayesian-probabilistic-model","display_name":"Bayesian probabilistic model","score":0.6769999861717224},{"id":"https://openalex.org/keywords/interpretability","display_name":"interpretability","score":0.7860000133514404}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8211338520050049},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7510343790054321},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6625880002975464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6129528880119324},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5392862558364868},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.50534588098526},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.45721837878227234},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.450314998626709},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.44975441694259644},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3493604063987732},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1723599135875702},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11168643832206726},{"id":"https://openalex.org/C171606756","wikidata":"https://www.wikidata.org/wiki/Q506132","display_name":"Psychometrics","level":2,"score":0.08096197247505188}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1604.02336","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1604.02336","pdf_url":"https://arxiv.org/pdf/1604.02336","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1604.02336","doi":"https://doi.org/10.48550/arxiv.1604.02336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1604.02336","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,"listed_in":[],"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"},{"id":"mag:2962800828","doi":null,"is_oa":false,"landing_page_url":"https://files.eric.ed.gov/fulltext/ED592649.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306418235","display_name":"Educational Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Educational Data Mining","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1604.02336","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1604.02336","pdf_url":"https://arxiv.org/pdf/1604.02336","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality education","score":0.8278999924659729}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2962800828.pdf","grobid_xml":"https://content.openalex.org/works/W2962800828.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W571028990","https://openalex.org/W1265790443","https://openalex.org/W1483702380","https://openalex.org/W1540164127","https://openalex.org/W1850742715","https://openalex.org/W1869752048","https://openalex.org/W1897162008","https://openalex.org/W2015040676","https://openalex.org/W2066816090","https://openalex.org/W2095705004","https://openalex.org/W2117122404","https://openalex.org/W2160815625","https://openalex.org/W2163644476","https://openalex.org/W2184045248","https://openalex.org/W2950489292","https://openalex.org/W2963069010","https://openalex.org/W2964195932","https://openalex.org/W3216769429"],"related_works":["https://openalex.org/W3125752750","https://openalex.org/W6894172245","https://openalex.org/W2588697749","https://openalex.org/W1787332917","https://openalex.org/W1482178462","https://openalex.org/W2126910927","https://openalex.org/W7101163899","https://openalex.org/W6948786110","https://openalex.org/W3192889988","https://openalex.org/W3205711883"],"abstract_inverted_index":{"Estimating":[0],"student":[1,110,144],"proficiency":[2,18],"is":[3],"an":[4],"important":[5],"task":[6],"for":[7,84],"computer":[8],"based":[9],"learning":[10],"systems.":[11],"We":[12,37,60,129],"compare":[13],"a":[14,26,44,113,135],"family":[15],"of":[16,78,93,116,143],"IRT-based":[17,63,132],"estimation":[19],"methods":[20,64],"to":[21,86,139,155],"Deep":[22],"Knowledge":[23],"Tracing":[24],"(DKT),":[25],"recently":[27],"proposed":[28],"recurrent":[29],"neural":[30],"network":[31],"model":[32,42],"with":[33],"promising":[34],"initial":[35],"results.":[36],"evaluate":[38],"how":[39],"well":[40],"each":[41],"predicts":[43],"student's":[45],"future":[46],"response":[47,111],"given":[48],"previous":[49],"responses":[50],"using":[51],"two":[52],"publicly":[53],"available":[54],"and":[55,152],"one":[56],"proprietary":[57],"data":[58,72,104,146],"set.":[59],"find":[61],"that":[62,81,95,131],"consistently":[65],"matched":[66],"or":[67],"outperformed":[68],"DKT":[69],"across":[70],"all":[71],"sets":[73,105],"at":[74],"the":[75,124],"finest":[76],"level":[77],"content":[79],"granularity":[80],"was":[82],"tractable":[83],"them":[85],"be":[87],"trained":[88],"on.":[89],"A":[90],"hierarchical":[91],"extension":[92,115],"IRT":[94,117,122],"captured":[96],"item":[97],"grouping":[98],"structure":[99],"performed":[100],"best":[101],"overall.":[102],"When":[103],"included":[106],"non-trivial":[107],"autocorrelations":[108],"in":[109],"patterns,":[112],"temporal":[114],"improved":[118],"performance":[119],"over":[120],"standard":[121],"while":[123,147],"RNN-based":[125,141],"method":[126],"did":[127],"not.":[128],"conclude":[130],"models":[133,142],"provide":[134],"simpler,":[136],"better-performing":[137],"alternative":[138],"existing":[140],"interaction":[145],"also":[148],"affording":[149],"more":[150],"interpretability":[151],"guarantees":[153],"due":[154],"their":[156],"formulation":[157],"as":[158],"Bayesian":[159],"probabilistic":[160],"models.":[161]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":18},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":3}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2019-07-30T00:00:00"}
