{"id":"https://openalex.org/W7124854551","doi":"https://doi.org/10.1109/te.2025.3637343","title":"Predicting Student Performance Using a Cost-Sensitive Deep Learning Network With Sparse Attention Mechanism","display_name":"Predicting Student Performance Using a Cost-Sensitive Deep Learning Network With Sparse Attention Mechanism","publication_year":2026,"publication_date":"2026-01-20","ids":{"openalex":"https://openalex.org/W7124854551","doi":"https://doi.org/10.1109/te.2025.3637343"},"language":null,"primary_location":{"id":"doi:10.1109/te.2025.3637343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/te.2025.3637343","pdf_url":null,"source":{"id":"https://openalex.org/S207741693","display_name":"IEEE Transactions on Education","issn_l":"0018-9359","issn":["0018-9359","1557-9638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310322189","host_organization_name":"IEEE Education Society","host_organization_lineage":["https://openalex.org/P4310322189","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Education Society","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 Transactions on Education","raw_type":"journal-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/A5114138350","display_name":"Hongkun QIU","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongkun Qiu","raw_affiliation_strings":["School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123411691","display_name":"Haojie Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haojie Zhu","raw_affiliation_strings":["School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":"https://orcid.org/0009-0006-2288-8620","affiliations":[{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaoyong Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyong Hu","raw_affiliation_strings":["School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029622984","display_name":"Yiting Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajie Wang","raw_affiliation_strings":["School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123444457","display_name":"Yue Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Xu","raw_affiliation_strings":["School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087889964","display_name":"W. Li","orcid":null},"institutions":[{"id":"https://openalex.org/I125904092","display_name":"Shenyang Aerospace University","ror":"https://ror.org/02423gm04","country_code":"CN","type":"education","lineage":["https://openalex.org/I125904092"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenlong Li","raw_affiliation_strings":["School of Computer Science, Shenyang Aerospace University, Shenyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shenyang Aerospace University, Shenyang, China","institution_ids":["https://openalex.org/I125904092"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I125904092"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04491742,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"69","issue":"1","first_page":"10","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.821399986743927,"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":0.821399986743927,"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":0.06920000165700912,"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/T14025","display_name":"Educational Technology and Assessment","score":0.012299999594688416,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/deep-learning","display_name":"Deep learning","score":0.722599983215332},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.5898000001907349},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5764999985694885},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.5742999911308289},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5659999847412109},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.49810001254081726},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4740999937057495},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.46810001134872437},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.44200000166893005},{"id":"https://openalex.org/keywords/educational-data-mining","display_name":"Educational data mining","score":0.44179999828338623}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7700999975204468},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7486000061035156},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.722599983215332},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6859999895095825},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.5898000001907349},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.5742999911308289},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5659999847412109},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.49810001254081726},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4740999937057495},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.46810001134872437},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.44200000166893005},{"id":"https://openalex.org/C2777598771","wikidata":"https://www.wikidata.org/wiki/Q5341279","display_name":"Educational data mining","level":2,"score":0.44179999828338623},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43790000677108765},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4368000030517578},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.430400013923645},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.40950000286102295},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31779998540878296},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.31220000982284546},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3001999855041504},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.28859999775886536},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.2847000062465668},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2644999921321869},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/te.2025.3637343","is_oa":false,"landing_page_url":"https://doi.org/10.1109/te.2025.3637343","pdf_url":null,"source":{"id":"https://openalex.org/S207741693","display_name":"IEEE Transactions on Education","issn_l":"0018-9359","issn":["0018-9359","1557-9638"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310322189","host_organization_name":"IEEE Education Society","host_organization_lineage":["https://openalex.org/P4310322189","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Education Society","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 Transactions on Education","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6573662757873535,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"<italic":[0],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[1,40,104,129,187],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Contribution:</i>":[2],"This":[3],"study":[4],"proposes":[5],"a":[6,66,109,123,163,169,199],"student":[7,133],"performance":[8],"prediction":[9,184],"model":[10,197,228],"that":[11,194],"leverages":[12],"real-world":[13,244],"data":[14,53,113,134,237],"on":[15,222],"students\u2019":[16],"daily":[17],"academic":[18,23,116,126],"activities":[19],"to":[20,31,71,148,179,219],"forecast":[21],"their":[22,97],"outcomes.":[24,81],"The":[25,190,227],"predicted":[26],"results":[27,192],"can":[28,119],"be":[29],"used":[30,178],"provide":[32],"students":[33],"with":[34,92,208,231],"personalized":[35,63],"and":[36,61,74,80,236],"adaptive":[37],"support.":[38],"<p":[39,103,128,186],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[41,105,130,188],"<i>Background:</i>":[42],"Traditional":[43],"educational":[44,245],"assessment":[45,101],"systems":[46],"face":[47],"systemic":[48],"challenges":[49],"such":[50],"as":[51],"weak":[52],"support,":[54],"high":[55],"subjectivity,":[56],"lack":[57],"of":[58,111,202],"scientific":[59],"rigor,":[60],"insufficient":[62],"guidance.":[64],"As":[65],"result,":[67],"these":[68],"assessments":[69],"struggle":[70],"objectively,":[72],"comprehensively,":[73],"accurately":[75,121],"reflect":[76],"the":[77,138,151,181,195,223],"learning":[78,157],"process":[79],"In":[82,206],"contrast,":[83],"artificial":[84],"intelligence":[85],"(AI)":[86],"technologies":[87],"have":[88],"demonstrated":[89],"significant":[90],"potential,":[91],"empirical":[93],"research":[94],"preliminarily":[95],"validating":[96],"advantages":[98],"in":[99,243],"enhancing":[100],"effectiveness.":[102],"<i>Research":[106],"Question:</i>":[107],"Given":[108],"range":[110],"student-related":[112],"collected":[114],"through":[115],"management":[117],"systems,":[118],"we":[120],"predict":[122],"student\u2019s":[124],"future":[125],"performance?":[127],"<i>Method:</i>":[131],"Diverse":[132],"are":[135,159],"fed":[136],"into":[137],"proposed":[139,196],"model.":[140],"A":[141,174],"sparse":[142],"attention":[143],"mechanism":[144],"is":[145,177,229],"then":[146],"employed":[147],"automatically":[149],"identify":[150],"most":[152],"discriminative":[153],"features.":[154],"These":[155],"extracted":[156],"features":[158],"further":[160],"processed":[161],"by":[162],"deep":[164],"neural":[165],"network,":[166],"which":[167],"incorporates":[168],"Cost-Sensitive":[170],"Combined":[171],"Loss":[172],"function.":[173],"multilayer":[175],"perceptron":[176],"derive":[180],"final":[182],"grade":[183],"results.":[185],"<i>Findings:</i>":[189],"experimental":[191],"show":[193],"attains":[198],"recall":[200,214],"rate":[201],"74.52%":[203],"across":[204],"tasks.":[205],"comparison":[207],"selected":[209],"baseline":[210],"models,":[211],"it":[212],"yields":[213],"improvements":[215],"ranging":[216],"from":[217],"3%":[218],"20%,":[220],"depending":[221],"specific":[224],"evaluation":[225],"setting.":[226],"designed":[230],"considerations":[232],"for":[233],"deployment":[234],"adaptability":[235],"privacy,":[238],"indicating":[239],"its":[240],"potential":[241],"applicability":[242],"scenarios.":[246]},"counts_by_year":[],"updated_date":"2026-02-24T06:16:03.338239","created_date":"2026-01-21T00:00:00"}