{"id":"https://openalex.org/W7123338813","doi":"https://doi.org/10.1109/access.2026.3652353","title":"Item Response Time Analysis Using Ex-Gaussian Distribution for Disengagement Detection in Online Low-Stakes Tests","display_name":"Item Response Time Analysis Using Ex-Gaussian Distribution for Disengagement Detection in Online Low-Stakes Tests","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123338813","doi":"https://doi.org/10.1109/access.2026.3652353"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3652353","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652353","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3652353","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122875691","display_name":"N. Chotikakamthorn","orcid":null},"institutions":[{"id":"https://openalex.org/I91538806","display_name":"King Mongkut's Institute of Technology Ladkrabang","ror":"https://ror.org/055mf0v62","country_code":"TH","type":"education","lineage":["https://openalex.org/I91538806"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"N. Chotikakamthorn","raw_affiliation_strings":["School of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand"],"affiliations":[{"raw_affiliation_string":"School of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand","institution_ids":["https://openalex.org/I91538806"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5122875691"],"corresponding_institution_ids":["https://openalex.org/I91538806"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07382603,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"7860","last_page":"7878"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.5460000038146973,"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"}},"topics":[{"id":"https://openalex.org/T10467","display_name":"Psychometric Methodologies and Testing","score":0.5460000038146973,"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":0.07320000231266022,"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":0.04569999873638153,"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/disengagement-theory","display_name":"Disengagement theory","score":0.7269999980926514},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.582099974155426},{"id":"https://openalex.org/keywords/item-response-theory","display_name":"Item response theory","score":0.4700999855995178},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.46889999508857727},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.44760000705718994},{"id":"https://openalex.org/keywords/log-normal-distribution","display_name":"Log-normal distribution","score":0.4345000088214874},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3862999975681305},{"id":"https://openalex.org/keywords/response-time","display_name":"Response time","score":0.37209999561309814},{"id":"https://openalex.org/keywords/test-statistic","display_name":"Test statistic","score":0.3594000041484833}],"concepts":[{"id":"https://openalex.org/C25740722","wikidata":"https://www.wikidata.org/wiki/Q3044915","display_name":"Disengagement theory","level":2,"score":0.7269999980926514},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6456999778747559},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.582099974155426},{"id":"https://openalex.org/C19875794","wikidata":"https://www.wikidata.org/wiki/Q1207340","display_name":"Item response theory","level":3,"score":0.4700999855995178},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.46889999508857727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4657000005245209},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4546999931335449},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.44760000705718994},{"id":"https://openalex.org/C151620405","wikidata":"https://www.wikidata.org/wiki/Q826116","display_name":"Log-normal distribution","level":2,"score":0.4345000088214874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41830000281333923},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3862999975681305},{"id":"https://openalex.org/C19012869","wikidata":"https://www.wikidata.org/wiki/Q578372","display_name":"Response time","level":2,"score":0.37209999561309814},{"id":"https://openalex.org/C169857963","wikidata":"https://www.wikidata.org/wiki/Q1461038","display_name":"Test statistic","level":3,"score":0.3594000041484833},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C39896193","wikidata":"https://www.wikidata.org/wiki/Q380344","display_name":"Descriptive statistics","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.3255999982357025},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.3043999969959259},{"id":"https://openalex.org/C159447121","wikidata":"https://www.wikidata.org/wiki/Q490535","display_name":"Response bias","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29170000553131104},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.27160000801086426},{"id":"https://openalex.org/C102094743","wikidata":"https://www.wikidata.org/wiki/Q133871","display_name":"Normal distribution","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.2621999979019165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2567000091075897},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.25619998574256897},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C2778263599","wikidata":"https://www.wikidata.org/wiki/Q1669245","display_name":"Item analysis","level":3,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3652353","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652353","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3652353","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3652353","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9089317321777344,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G4163857706","display_name":null,"funder_award_id":"256109A1180054","funder_id":"https://openalex.org/F4320324172","funder_display_name":"King Mongkut's Institute of Technology Ladkrabang"},{"id":"https://openalex.org/G7005324380","display_name":null,"funder_award_id":"A1180361056","funder_id":"https://openalex.org/F4320324172","funder_display_name":"King Mongkut's Institute of Technology Ladkrabang"},{"id":"https://openalex.org/G7690748432","display_name":null,"funder_award_id":"256109A1180054","funder_id":"https://openalex.org/F4320322817","funder_display_name":"National Research Council of Thailand"},{"id":"https://openalex.org/G8036871569","display_name":null,"funder_award_id":"A118-0361-056","funder_id":"https://openalex.org/F4320324172","funder_display_name":"King Mongkut's Institute of Technology Ladkrabang"}],"funders":[{"id":"https://openalex.org/F4320322817","display_name":"National Research Council of Thailand","ror":"https://ror.org/018wfhg78"},{"id":"https://openalex.org/F4320324172","display_name":"King Mongkut's Institute of Technology Ladkrabang","ror":"https://ror.org/055mf0v62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1968779470","https://openalex.org/W1968938713","https://openalex.org/W1993710471","https://openalex.org/W1994312853","https://openalex.org/W2011130275","https://openalex.org/W2051338293","https://openalex.org/W2057917450","https://openalex.org/W2057959895","https://openalex.org/W2091856951","https://openalex.org/W2113912089","https://openalex.org/W2130506309","https://openalex.org/W2131709143","https://openalex.org/W2140411235","https://openalex.org/W2153998488","https://openalex.org/W2154372487","https://openalex.org/W2265729613","https://openalex.org/W2272807018","https://openalex.org/W2340793802","https://openalex.org/W2532353081","https://openalex.org/W2541518829","https://openalex.org/W2708872728","https://openalex.org/W2747769621","https://openalex.org/W2767944276","https://openalex.org/W2795669339","https://openalex.org/W2890544186","https://openalex.org/W2913875100","https://openalex.org/W3001495527","https://openalex.org/W3016441286","https://openalex.org/W3030235465","https://openalex.org/W3033100679","https://openalex.org/W3094685058","https://openalex.org/W3192913530","https://openalex.org/W3204145574","https://openalex.org/W4213251304","https://openalex.org/W4232734994","https://openalex.org/W4367395182","https://openalex.org/W4386438896","https://openalex.org/W4388221536","https://openalex.org/W4403674400"],"related_works":[],"abstract_inverted_index":{"This":[0],"study":[1],"addresses":[2],"the":[3,38,47,55,63,79,86,100,133,136,146,176,230,252,267,271,281],"problem":[4],"of":[5,28,67,135,138,145,185,238,245,270],"detecting":[6],"disengagement":[7,127],"in":[8,13,261,264],"online":[9],"low-stakes":[10],"tests":[11],"used":[12,76],"blended":[14],"learning":[15],"within":[16],"higher":[17,243],"education.":[18],"The":[19,35,51,71,199,226],"detection":[20,128,154],"method":[21,36,179],"was":[22,59,75,129,202],"developed":[23],"based":[24,131],"on":[25,99,132,142],"an":[26,235],"analysis":[27],"item":[29,90,172,186],"responses":[30,91],"and":[31,94,106,115,120,161,171,207,210,216,241,258,273],"associated":[32,149],"response":[33,43,64,96,262],"times.":[34],"applied":[37],"ex-Gaussian":[39],"mixture":[40,52,73],"model":[41],"to":[42,61,77,111,158,255,266],"times,":[44,263],"rather":[45],"than":[46],"conventional":[48],"lognormal":[49],"model.":[50],"component":[53,74],"with":[54,150,234],"smallest":[56],"Gaussian":[57],"mean":[58],"chosen":[60],"represent":[62],"times":[65],"distribution":[66,134],"early":[68,93,139],"correct":[69],"responses.":[70,140,247],"selected":[72],"obtain":[78],"model\u2019s":[80],"mode,":[81],"which":[82],"then":[83],"served":[84],"as":[85],"threshold":[87],"for":[88,126,168,212],"classifying":[89],"into":[92,118],"subsequent":[95],"groups.":[97],"Based":[98],"two":[101,153],"classified":[102],"groups,":[103],"descriptive":[104],"statistics":[105],"graphical":[107],"visualizations":[108],"were":[109,156,220],"introduced":[110],"support":[112],"manual":[113],"inspection":[114],"provide":[116],"insight":[117],"item-":[119],"person-level":[121,268],"characteristics.":[122,198],"A":[123],"test":[124,224,239],"statistic":[125],"formulated":[130],"number":[137,237],"Drawing":[141],"prior":[143,183],"knowledge":[144,184],"success":[147],"probabilities":[148],"disengaged":[151,246],"responses,":[152],"boundaries":[155],"defined":[157],"classify":[159],"item-preknowledge":[160],"rapid-guessing":[162],"behaviors.":[163],"Unlike":[164],"existing":[165],"model-based":[166],"methods":[167],"rapid":[169],"guessing":[170],"preknowledge":[173],"behavior":[174],"detections,":[175],"proposed":[177],"non-parametric":[178],"does":[180,191],"not":[181],"require":[182],"or":[187,195],"person":[188],"parameters,":[189],"nor":[190],"it":[192],"involve":[193],"modeling":[194],"estimating":[196],"such":[197],"method\u2019s":[200,231,253],"performance":[201,232,277],"assessed":[203],"using":[204],"both":[205],"real":[206],"simulated":[208],"data,":[209],"results":[211,249],"true":[213],"positive":[214,218],"rates":[215,219],"false":[217],"reported":[221],"under":[222,280],"various":[223],"conditions.":[225,283],"findings":[227],"indicate":[228],"that":[229],"improves":[233],"increasing":[236],"items":[240],"a":[242],"proportion":[244],"Simulation":[248],"further":[250],"demonstrated":[251],"robustness":[254],"measurement":[256],"error":[257],"small":[259],"variations":[260],"contrast":[265],"adaptation":[269],"NT10":[272],"CUMP":[274],"methods,":[275],"whose":[276],"varied":[278],"significantly":[279],"same":[282]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-14T00:00:00"}