{"id":"https://openalex.org/W3175774972","doi":"https://doi.org/10.1145/3450613.3456834","title":"Data-Driven Modeling of Learners\u2019 Individual Differences for Predicting Engagement and Success in Online Learning","display_name":"Data-Driven Modeling of Learners\u2019 Individual Differences for Predicting Engagement and Success in Online Learning","publication_year":2021,"publication_date":"2021-06-21","ids":{"openalex":"https://openalex.org/W3175774972","doi":"https://doi.org/10.1145/3450613.3456834","mag":"3175774972"},"language":"en","primary_location":{"id":"doi:10.1145/3450613.3456834","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3450613.3456834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"},"type":"conference-paper","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/A5028915960","display_name":"Kamil Akh\u00fcseyino\u011flu","orcid":"https://orcid.org/0000-0002-7761-9755"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kamil Akhuseyinoglu","raw_affiliation_strings":["University of Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037674585","display_name":"Peter Brusilovsky","orcid":"https://orcid.org/0000-0002-1902-1464"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peter Brusilovsky","raw_affiliation_strings":["University of Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, USA","institution_ids":["https://openalex.org/I170201317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170201317"],"apc_list":null,"apc_paid":null,"fwci":3.2008,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.92496794,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"201","last_page":"212"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":1.0,"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/T10162","display_name":"Online and Blended Learning","score":0.9914000034332275,"subfield":{"id":"https://openalex.org/subfields/3304","display_name":"Education"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.989799976348877,"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/learning-analytics","display_name":"Learning analytics","score":0.7276268601417542},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7013338804244995},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5799410939216614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48737412691116333},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48495176434516907},{"id":"https://openalex.org/keywords/educational-data-mining","display_name":"Educational data mining","score":0.45818793773651123},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.42289724946022034},{"id":"https://openalex.org/keywords/personality","display_name":"Personality","score":0.4220007061958313},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.37489083409309387},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.1699458658695221}],"concepts":[{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.7276268601417542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7013338804244995},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5799410939216614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48737412691116333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48495176434516907},{"id":"https://openalex.org/C2777598771","wikidata":"https://www.wikidata.org/wiki/Q5341279","display_name":"Educational data mining","level":2,"score":0.45818793773651123},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.42289724946022034},{"id":"https://openalex.org/C187288502","wikidata":"https://www.wikidata.org/wiki/Q641118","display_name":"Personality","level":2,"score":0.4220007061958313},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.37489083409309387},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.1699458658695221},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3450613.3456834","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3450613.3456834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6000000238418579}],"awards":[{"id":"https://openalex.org/G495183660","display_name":"Collaborative Research: Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education","funder_award_id":"1740775","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W595197191","https://openalex.org/W1511367625","https://openalex.org/W1528273163","https://openalex.org/W1602437292","https://openalex.org/W1969975705","https://openalex.org/W1982210139","https://openalex.org/W1983412528","https://openalex.org/W1987623438","https://openalex.org/W1987971958","https://openalex.org/W1994740530","https://openalex.org/W2013811491","https://openalex.org/W2016463819","https://openalex.org/W2032780221","https://openalex.org/W2039840987","https://openalex.org/W2051963836","https://openalex.org/W2068383400","https://openalex.org/W2071949631","https://openalex.org/W2072967044","https://openalex.org/W2090550551","https://openalex.org/W2117753904","https://openalex.org/W2121543297","https://openalex.org/W2146036244","https://openalex.org/W2155648052","https://openalex.org/W2158826560","https://openalex.org/W2169211232","https://openalex.org/W2187089797","https://openalex.org/W2201401714","https://openalex.org/W2216374816","https://openalex.org/W2282066221","https://openalex.org/W2342637743","https://openalex.org/W2469783852","https://openalex.org/W2507635762","https://openalex.org/W2592323421","https://openalex.org/W2605961645","https://openalex.org/W2609751240","https://openalex.org/W2732606502","https://openalex.org/W2752444842","https://openalex.org/W2769764771","https://openalex.org/W2796407591","https://openalex.org/W2808852657","https://openalex.org/W3036459948","https://openalex.org/W4246762826","https://openalex.org/W4302335325","https://openalex.org/W7029788801"],"related_works":["https://openalex.org/W3061763893","https://openalex.org/W2529601000","https://openalex.org/W3157139468","https://openalex.org/W2819101378","https://openalex.org/W3021163624","https://openalex.org/W123076107","https://openalex.org/W2911048623","https://openalex.org/W1898103075","https://openalex.org/W2481267635","https://openalex.org/W3114046518"],"abstract_inverted_index":{"Individual":[0],"differences":[1,24,143,163,173],"have":[2],"been":[3],"recognized":[4],"as":[5,183],"an":[6,27,105],"important":[7,28,176],"factor":[8],"in":[9,18,25,36,144,174],"the":[10,40,49,179],"learning":[11,43,53,107,114,180],"process.":[12],"However,":[13,116],"there":[14],"are":[15],"few":[16],"successes":[17],"using":[19,126],"known":[20],"dimensions":[21],"of":[22,30,52,78,100,112,118,161,171,178],"individual":[23,142,162,172],"solving":[26],"problem":[29],"predicting":[31,175],"student":[32,65,79,113],"performance":[33,131],"and":[34,68,185],"engagement":[35],"online":[37,106],"learning.":[38],"At":[39],"same":[41],"time,":[42],"analytics":[44],"research":[45,88,155],"has":[46],"demonstrated":[47],"that":[48,157],"large":[50,98],"volume":[51,99],"data":[54,102],"collected":[55,103],"by":[56,104],"modern":[57,120],"e-learning":[58],"systems":[59],"could":[60,69],"be":[61,70],"used":[62,71],"to":[63,72,84,96,136],"recognize":[64],"behavior":[66,123,128,147],"patterns":[67,75],"connect":[73],"these":[74,86],"with":[76],"measures":[77],"performance.":[80],"Our":[81,154],"paper":[82],"attempts":[83],"bridge":[85],"two":[87],"directions.":[89],"By":[90],"applying":[91],"a":[92,97,149],"sequence":[93],"mining":[94,124],"approach":[95],"learner":[101],"system,":[108],"we":[109,134],"build":[110],"models":[111,170],"behavior.":[115],"instead":[117],"following":[119],"work":[121,139],"on":[122,140,148],"(i.e.,":[125],"this":[127,146,158],"directly":[129],"for":[130],"prediction":[132],"tasks),":[133],"attempt":[135],"follow":[137],"traditional":[138,169],"modeling":[141],"quantifying":[145],"latent":[150],"data-driven":[151,159],"personality":[152],"scale.":[153],"shows":[156],"model":[160],"performs":[164],"significantly":[165],"better":[166],"than":[167],"several":[168],"parameters":[177],"process,":[181],"such":[182],"success":[184],"engagement.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
