{"id":"https://openalex.org/W7155612677","doi":"https://doi.org/10.1145/3785022.3785056","title":"A Theory Driven Supervised Learning Approach For Math Skill Proficiency Prediction Using Assessment and Practice Data","display_name":"A Theory Driven Supervised Learning Approach For Math Skill Proficiency Prediction Using Assessment and Practice Data","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7155612677","doi":"https://doi.org/10.1145/3785022.3785056"},"language":null,"primary_location":{"id":"doi:10.1145/3785022.3785056","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785022.3785056","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3785022.3785056","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047337343","display_name":"Zoheb Borbora","orcid":null},"institutions":[{"id":"https://openalex.org/I3131949601","display_name":"Renaissance University","ror":"https://ror.org/020w9wy05","country_code":"NG","type":"education","lineage":["https://openalex.org/I3131949601"]}],"countries":["NG"],"is_corresponding":true,"raw_author_name":"Zoheb Hassan Borbora","raw_affiliation_strings":["Renaissance Learning, Wisconsin Rapids, Wisconsin, USA"],"raw_orcid":"https://orcid.org/0000-0003-0538-9732","affiliations":[{"raw_affiliation_string":"Renaissance Learning, Wisconsin Rapids, Wisconsin, USA","institution_ids":["https://openalex.org/I3131949601"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134602843","display_name":"John Bielinski","orcid":"https://orcid.org/0009-0004-9285-1941"},"institutions":[{"id":"https://openalex.org/I3131949601","display_name":"Renaissance University","ror":"https://ror.org/020w9wy05","country_code":"NG","type":"education","lineage":["https://openalex.org/I3131949601"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"John Bielinski","raw_affiliation_strings":["Renaissance Learning, Wisconsin Rapids, Wisconsin, USA"],"raw_orcid":"https://orcid.org/0009-0004-9285-1941","affiliations":[{"raw_affiliation_string":"Renaissance Learning, Wisconsin Rapids, Wisconsin, USA","institution_ids":["https://openalex.org/I3131949601"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019787987","display_name":"BRUCE BRAY","orcid":null},"institutions":[{"id":"https://openalex.org/I3131949601","display_name":"Renaissance University","ror":"https://ror.org/020w9wy05","country_code":"NG","type":"education","lineage":["https://openalex.org/I3131949601"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Bruce Bray","raw_affiliation_strings":["Renaissance Learning, Wisconsin Rapids, Wisconsin, USA"],"raw_orcid":"https://orcid.org/0009-0002-7541-1288","affiliations":[{"raw_affiliation_string":"Renaissance Learning, Wisconsin Rapids, Wisconsin, USA","institution_ids":["https://openalex.org/I3131949601"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047337343"],"corresponding_institution_ids":["https://openalex.org/I3131949601"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.96166528,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"239","last_page":"249"},"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":0.4812000095844269,"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":0.4812000095844269,"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/T10467","display_name":"Psychometric Methodologies and Testing","score":0.2531000077724457,"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"}},{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.06689999997615814,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5551999807357788},{"id":"https://openalex.org/keywords/item-response-theory","display_name":"Item response theory","score":0.5299999713897705},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.3490000069141388},{"id":"https://openalex.org/keywords/meta-learning","display_name":"Meta learning (computer science)","score":0.33149999380111694},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3246000111103058},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.3125999867916107},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.3018999993801117}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6323999762535095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6011999845504761},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5889000296592712},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5551999807357788},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.5299999713897705},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3490000069141388},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.33149999380111694},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2930999994277954},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C132758656","wikidata":"https://www.wikidata.org/wiki/Q5307365","display_name":"Dreyfus model of skill acquisition","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3785022.3785056","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785022.3785056","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3785022.3785056","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785022.3785056","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the LAK26: 16th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.8713616132736206,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1985824294","https://openalex.org/W2008056655","https://openalex.org/W2015040676","https://openalex.org/W2295598076","https://openalex.org/W2505213109","https://openalex.org/W2919115771","https://openalex.org/W2949109425","https://openalex.org/W3004891940","https://openalex.org/W3009218590","https://openalex.org/W3017131514","https://openalex.org/W3096180801","https://openalex.org/W4236137412","https://openalex.org/W4280651558","https://openalex.org/W4366262984","https://openalex.org/W4386021815","https://openalex.org/W4399619126","https://openalex.org/W4405468173"],"related_works":[],"abstract_inverted_index":{"Skill":[0],"proficiency":[1,20,177],"prediction":[2,21,122,178],"is":[3,23,133,146],"an":[4,155],"important":[5],"problem":[6,51],"in":[7,120,196],"the":[8,43,71,104,121,127,159,175,187,197],"personalized":[9],"learning":[10,16,50,109],"space.":[11],"We":[12,41,111],"describe":[13],"a":[14,46],"machine":[15],"approach":[17,44,156,182],"to":[18,31,61,143],"skill":[19,83,170,176],"that":[22,64,70,77,106,126,157,162],"highly":[24],"interpretable,":[25],"efficient,":[26],"and":[27,37,85,97,117,137,189],"can":[28],"be":[29],"used":[30,55],"incorporate":[32],"data":[33,113,140],"from":[34,57,114],"assessment,":[35],"practice":[36,118,138,150],"potentially,":[38],"other":[39],"sources.":[40],"formulated":[42],"as":[45,141,172],"theory":[47],"driven":[48],"supervised":[49],"-":[52],"specifically,":[53],"we":[54,153],"concepts":[56],"Item":[58,92],"Response":[59,93],"Theory":[60],"construct":[62],"features":[63,74],"provide":[65],"model":[66,105,128],"interpretability.":[67],"Results":[68,124],"indicate":[69,125],"most":[72],"predictive":[73],"are":[75,78],"those":[76],"estimates":[79],"of":[80,169,193],"student":[81,139],"ability,":[82],"difficulty":[84],"ability-difficulty":[86],"difference,":[87],"thus":[88],"aligning":[89],"well":[90],"with":[91,108],"Theory.":[94],"Model":[95],"diagnostics":[96],"error":[98],"analysis":[99],"provided":[100],"several":[101],"insights":[102],"into":[103],"align":[107],"theories.":[110],"incorporated":[112],"both":[115,134],"assessment":[116,136,148],"products":[119],"model.":[123],"performs":[129],"better":[130],"when":[131,144],"there":[132,145],"prior":[135],"opposed":[142],"just":[147],"or":[149],"data.":[151],"Finally,":[152],"proposed":[154,181],"recommends":[158],"instructional":[160],"category":[161],"best":[163],"represents":[164],"each":[165],"student\u2019s":[166],"current":[167],"level":[168],"development":[171],"determined":[173],"by":[174],"value.":[179],"The":[180],"provides":[183],"confidence":[184],"metrics":[185],"for":[186,191],"recommendations":[188],"optimizes":[190],"percentage":[192],"students":[194],"placed":[195],"categories.":[198]},"counts_by_year":[],"updated_date":"2026-04-26T06:07:20.044499","created_date":"2026-04-26T00:00:00"}
