{"id":"https://openalex.org/W7131208491","doi":"https://doi.org/10.1145/3785022.3785104","title":"Is Log-Traced Engagement Enough? Extending Reading Analytics With Trait-Level Flow and Reading Strategy Metrics","display_name":"Is Log-Traced Engagement Enough? Extending Reading Analytics With Trait-Level Flow and Reading Strategy Metrics","publication_year":2026,"publication_date":"2026-04-25","ids":{"openalex":"https://openalex.org/W7131208491","doi":"https://doi.org/10.1145/3785022.3785104"},"language":null,"primary_location":{"id":"doi:10.1145/3785022.3785104","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785022.3785104","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3785022.3785104","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028068649","display_name":"Erwin Daniel L\u00f3pez Zapata","orcid":"https://orcid.org/0000-0003-3793-9524"},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Erwin Daniel L\u00f3pez Zapata","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3793-9524","affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5126462892","display_name":"Atsushi Shimada","orcid":null},"institutions":[{"id":"https://openalex.org/I135598925","display_name":"Kyushu University","ror":"https://ror.org/00p4k0j84","country_code":"JP","type":"education","lineage":["https://openalex.org/I135598925"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsushi Shimada","raw_affiliation_strings":["Kyushu University, Fukuoka, Japan"],"raw_orcid":"https://orcid.org/0000-0002-3635-9336","affiliations":[{"raw_affiliation_string":"Kyushu University, Fukuoka, Japan","institution_ids":["https://openalex.org/I135598925"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5028068649"],"corresponding_institution_ids":["https://openalex.org/I135598925"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34832877,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"503","last_page":"513"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13353","display_name":"Flow Experience in Various Fields","score":0.4368000030517578,"subfield":{"id":"https://openalex.org/subfields/3204","display_name":"Developmental and Educational Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13353","display_name":"Flow Experience in Various Fields","score":0.4368000030517578,"subfield":{"id":"https://openalex.org/subfields/3204","display_name":"Developmental and Educational Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.29120001196861267,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.048700001090765,"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/operationalization","display_name":"Operationalization","score":0.864300012588501},{"id":"https://openalex.org/keywords/reading","display_name":"Reading (process)","score":0.714900016784668},{"id":"https://openalex.org/keywords/learning-analytics","display_name":"Learning analytics","score":0.6646999716758728},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6503000259399414},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.44690001010894775},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.3869999945163727},{"id":"https://openalex.org/keywords/reading-comprehension","display_name":"Reading comprehension","score":0.36329999566078186},{"id":"https://openalex.org/keywords/student-engagement","display_name":"Student engagement","score":0.3619999885559082}],"concepts":[{"id":"https://openalex.org/C9354725","wikidata":"https://www.wikidata.org/wiki/Q286017","display_name":"Operationalization","level":2,"score":0.864300012588501},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.714900016784668},{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.6646999716758728},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6503000259399414},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.5460000038146973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4948999881744385},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.44690001010894775},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.40119999647140503},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C2778780117","wikidata":"https://www.wikidata.org/wiki/Q3269423","display_name":"Reading comprehension","level":3,"score":0.36329999566078186},{"id":"https://openalex.org/C194519906","wikidata":"https://www.wikidata.org/wiki/Q7627827","display_name":"Student engagement","level":2,"score":0.3619999885559082},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C2781040141","wikidata":"https://www.wikidata.org/wiki/Q7300602","display_name":"Reading motivation","level":3,"score":0.328000009059906},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.32269999384880066},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.32100000977516174},{"id":"https://openalex.org/C547764534","wikidata":"https://www.wikidata.org/wiki/Q8236","display_name":"Literacy","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.3111000061035156},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.28349998593330383},{"id":"https://openalex.org/C75630572","wikidata":"https://www.wikidata.org/wiki/Q538904","display_name":"Applied psychology","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C53059260","wikidata":"https://www.wikidata.org/wiki/Q374758","display_name":"Multilevel model","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C84653758","wikidata":"https://www.wikidata.org/wiki/Q5575175","display_name":"Goal orientation","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C2781009140","wikidata":"https://www.wikidata.org/wiki/Q7170389","display_name":"Persistence (discontinuity)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/3785022.3785104","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785022.3785104","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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"},{"id":"pmh:doi:10.48550/arxiv.2602.19616","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:arXiv.org:2602.19616","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.19616","pdf_url":"https://arxiv.org/pdf/2602.19616","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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"}],"best_oa_location":{"id":"doi:10.1145/3785022.3785104","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3785022.3785104","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8947741389274597}],"awards":[{"id":"https://openalex.org/G2358816916","display_name":null,"funder_award_id":"JPMJCR22D1","funder_id":"https://openalex.org/F4320338075","funder_display_name":"Core Research for Evolutional Science and Technology"},{"id":"https://openalex.org/G6718509927","display_name":null,"funder_award_id":"CREST","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G7548823080","display_name":null,"funder_award_id":"JP22H00551","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Student":[0],"engagement":[1,57,103],"is":[2,11],"a":[3,151],"central":[4],"construct":[5],"in":[6,59,99,125],"Learning":[7],"Analytics,":[8],"yet":[9],"it":[10],"often":[12],"operationalized":[13],"through":[14],"persistence":[15],"indicators":[16,58,128],"derived":[17,49],"from":[18,50,66],"logs,":[19,29],"overlooking":[20],"affective\u2013cognitive":[21],"states.":[22],"Focusing":[23],"on":[24],"the":[25,37,108,115,143],"analysis":[26,144],"of":[27,46,76,92,145,166],"reading":[28,47,80,93,118,146],"this":[30],"study":[31],"examines":[32],"how":[33,126],"trait-level":[34],"flow\u2014operationalized":[35],"as":[36],"tendency":[38],"to":[39,136],"experience":[40],"Deep":[41],"Effortless":[42],"Concentration":[43],"(DEC)\u2014and":[44],"traces":[45,91],"strategies":[48,94],"e-book":[51],"interaction":[52],"data":[53,65],"can":[54],"extend":[55],"traditional":[56],"explaining":[60],"learning":[61],"outcomes.":[62],"We":[63],"collected":[64],"100":[67],"students":[68],"across":[69],"two":[70],"engineering":[71],"courses,":[72],"combining":[73],"questionnaire":[74],"measures":[75,165],"DEC":[77,89,113],"with":[78,158],"fine-grained":[79],"logs.":[81],"Correlation":[82],"and":[83,90,111,120,140,154,163],"regression":[84],"analyses":[85],"show":[86],"that":[87,160],"(1)":[88],"explain":[95],"substantial":[96],"additional":[97],"variance":[98],"grades":[100],"beyond":[101,150],"log-traced":[102],"(\u0394R2":[104],"=":[105],"21.3%":[106],"over":[107],"baseline":[109],"25.5%),":[110],"(2)":[112],"moderates":[114],"relationship":[116],"between":[117],"behaviors":[119],"outcomes,":[121],"indicating":[122],"trait-sensitive":[123],"differences":[124],"log-derived":[127],"translate":[129],"into":[130],"performance.":[131],"These":[132],"findings":[133],"suggest":[134],"that,":[135],"support":[137],"more":[138],"equitable":[139],"personalized":[141],"interventions,":[142],"logs":[147],"should":[148],"move":[149],"one-size-fits-all":[152],"interpretation":[153],"integrate":[155],"personal":[156],"traits":[157],"metrics":[159],"include":[161],"behavioral":[162],"strategic":[164],"reading.":[167]},"counts_by_year":[],"updated_date":"2026-04-30T09:15:22.047038","created_date":"2026-02-25T00:00:00"}