{"id":"https://openalex.org/W3139777886","doi":"https://doi.org/10.1145/3448139.3448182","title":"Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions","display_name":"Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions","publication_year":2021,"publication_date":"2021-04-05","ids":{"openalex":"https://openalex.org/W3139777886","doi":"https://doi.org/10.1145/3448139.3448182","mag":"3139777886"},"language":"en","primary_location":{"id":"doi:10.1145/3448139.3448182","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3448139.3448182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"LAK21: 11th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002703058","display_name":"Jeffrey Matayoshi","orcid":"https://orcid.org/0000-0003-1321-8159"},"institutions":[{"id":"https://openalex.org/I4210126214","display_name":"McGraw-Hill Education (United States)","ror":"https://ror.org/034x1ve75","country_code":"US","type":"company","lineage":["https://openalex.org/I4210126214"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jeffrey Matayoshi","raw_affiliation_strings":["McGraw Hill ALEKS, United States"],"affiliations":[{"raw_affiliation_string":"McGraw Hill ALEKS, United States","institution_ids":["https://openalex.org/I4210126214"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083171731","display_name":"Shamya Karumbaiah","orcid":"https://orcid.org/0000-0002-7920-4510"},"institutions":[{"id":"https://openalex.org/I36788626","display_name":"California University of Pennsylvania","ror":"https://ror.org/01spssf70","country_code":"US","type":"education","lineage":["https://openalex.org/I36788626"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shamya Karumbaiah","raw_affiliation_strings":["University of Pennsylvania, United States"],"affiliations":[{"raw_affiliation_string":"University of Pennsylvania, United States","institution_ids":["https://openalex.org/I36788626"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5002703058"],"corresponding_institution_ids":["https://openalex.org/I4210126214"],"apc_list":null,"apc_paid":null,"fwci":0.6798,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.74655352,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"449","last_page":"455"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9837999939918518,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9837999939918518,"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/T11236","display_name":"Control Systems and Identification","score":0.9749000072479248,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.9685999751091003,"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/computer-science","display_name":"Computer science","score":0.6593413352966309},{"id":"https://openalex.org/keywords/transition","display_name":"Transition (genetics)","score":0.5824365615844727},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.540776789188385},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.470889151096344},{"id":"https://openalex.org/keywords/marginal-model","display_name":"Marginal model","score":0.42270803451538086},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4140665829181671},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3433944284915924},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33598461747169495},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1726933717727661},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.11545106768608093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6593413352966309},{"id":"https://openalex.org/C194232998","wikidata":"https://www.wikidata.org/wiki/Q1606712","display_name":"Transition (genetics)","level":3,"score":0.5824365615844727},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.540776789188385},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.470889151096344},{"id":"https://openalex.org/C197656967","wikidata":"https://www.wikidata.org/wiki/Q17058458","display_name":"Marginal model","level":3,"score":0.42270803451538086},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4140665829181671},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3433944284915924},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33598461747169495},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1726933717727661},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.11545106768608093},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3448139.3448182","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3448139.3448182","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"LAK21: 11th International Learning Analytics and Knowledge Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.550000011920929,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W312173419","https://openalex.org/W1596515083","https://openalex.org/W1995864528","https://openalex.org/W2058064543","https://openalex.org/W2099873065","https://openalex.org/W2110065044","https://openalex.org/W2110776215","https://openalex.org/W2142635246","https://openalex.org/W2149860264","https://openalex.org/W2158211513","https://openalex.org/W2181523240","https://openalex.org/W2269494466","https://openalex.org/W2295843391","https://openalex.org/W2631996795","https://openalex.org/W2774925224","https://openalex.org/W2775105814","https://openalex.org/W2796696901","https://openalex.org/W2971763496","https://openalex.org/W3128913340","https://openalex.org/W4230728097","https://openalex.org/W4247415745"],"related_works":["https://openalex.org/W4255837520","https://openalex.org/W2387011115","https://openalex.org/W4234808182","https://openalex.org/W2382043075","https://openalex.org/W2809151339","https://openalex.org/W2360673138","https://openalex.org/W2809370583","https://openalex.org/W4301447905","https://openalex.org/W2333722679","https://openalex.org/W4255628145"],"abstract_inverted_index":{"Many":[0],"areas":[1],"of":[2,8,42,51,102,154,162,201,204],"educational":[3],"research":[4],"require":[5],"the":[6,26,35,40,49,100,120,127,138,149,152,160,181,188,202],"analysis":[7],"data":[9],"that":[10,123,145,180],"have":[11],"an":[12,142],"inherent":[13],"sequential":[14],"or":[15],"temporal":[16],"ordering.":[17],"In":[18],"certain":[19],"cases,":[20],"researchers":[21],"are":[22,124],"specifically":[23],"interested":[24],"in":[25,111,116,191,197],"transitions":[27,59,205],"between":[28,60,206],"different":[29],"states\u2014or":[30],"events\u2014in":[31],"these":[32,43,80,84,103,130],"sequences,":[33],"with":[34,72,129,151],"goal":[36],"being":[37],"to":[38,56,76,86,147,173],"understand":[39],"significance":[41,203],"transitions;":[44],"one":[45],"notable":[46],"example":[47],"is":[48],"study":[50,66,113],"affect":[52],"dynamics,":[53],"which":[54],"aims":[55],"identify":[57],"important":[58],"affective":[61],"states.":[62,207],"Unfortunately,":[63],"a":[64,69,134],"recent":[65],"has":[67],"revealed":[68],"statistical":[70],"bias":[71,128,189],"several":[73],"metrics":[74,85],"used":[75],"measure":[77],"and":[78,89,169],"compare":[79],"transitions,":[81],"possibly":[82],"causing":[83],"return":[87],"unexpected":[88],"inflated":[90],"values.":[91],"This":[92],"issue":[93],"then":[94,158],"causes":[95],"extra":[96],"difficulties":[97],"when":[98],"interpreting":[99],"results":[101,178],"transition":[104,193],"metrics.":[105,131],"Building":[106],"on":[107],"this":[108,112,163],"previous":[109],"work,":[110],"we":[114,140],"look":[115],"more":[117,198],"detail":[118],"at":[119],"specific":[121],"mechanisms":[122],"responsible":[125],"for":[126,137,187],"After":[132],"giving":[133],"theoretical":[135],"explanation":[136],"issue,":[139],"present":[141],"alternative":[143],"procedure":[144,184],"attempts":[146],"address":[148],"problem":[150],"use":[153],"marginal":[155,182],"models.":[156],"We":[157],"analyze":[159],"effectiveness":[161],"procedure,":[164],"both":[165],"by":[166,170],"running":[167],"simulations":[168],"applying":[171],"it":[172],"actual":[174],"student":[175],"data.":[176],"The":[177],"indicate":[179],"model":[183],"seemingly":[185],"compensates":[186],"observed":[190],"other":[192],"metrics,":[194],"thus":[195],"resulting":[196],"accurate":[199],"estimates":[200]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
