{"id":"https://openalex.org/W7225627716","doi":"https://doi.org/10.5281/zenodo.24082123","title":"A Comparison of Deep Learning Techniques for Predicting Students' Academic Performance","display_name":"A Comparison of Deep Learning Techniques for Predicting Students' Academic Performance","publication_year":2026,"publication_date":"2026-11-09","ids":{"openalex":"https://openalex.org/W7225627716","doi":"https://doi.org/10.5281/zenodo.24082123"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24082123","doi":"https://doi.org/10.5281/zenodo.24082123","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24082123","pdf_url":null,"source":{"id":"https://openalex.org/S7407079774","display_name":"GPH-International Journal of Computer Science and Engineering","issn_l":"3050-9661","issn":["3050-9661"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.5281/zenodo.24082123","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161345838","display_name":"Mohamed Aboh Hamdalla Elbashir","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I77072003","display_name":"University of Kordofan","ror":"https://ror.org/04zfq3f98","country_code":"SD","type":"education","lineage":["https://openalex.org/I77072003"]}],"countries":["SD"],"is_corresponding":false,"raw_author_name":"Mohamed Aboh Hamdalla Elbashir","raw_affiliation_strings":["Department of Management Information Systems, Faculty of Economics & Commercial Studies, University of Kordofan, Sudan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Management Information Systems, Faculty of Economics & Commercial Studies, University of Kordofan, Sudan","institution_ids":["https://openalex.org/I77072003"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5161340823","display_name":"Mohamed Hamouda Karboos","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I77072003","display_name":"University of Kordofan","ror":"https://ror.org/04zfq3f98","country_code":"SD","type":"education","lineage":["https://openalex.org/I77072003"]}],"countries":["SD"],"is_corresponding":false,"raw_author_name":"Mohamed Hamouda Karboos","raw_affiliation_strings":["Department of Information Technology, Faculty of computer studies & statistics, University of Kordofan, Sudan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Technology, Faculty of computer studies & statistics, University of Kordofan, Sudan","institution_ids":["https://openalex.org/I77072003"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I77072003"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T14414","display_name":"Artificial Intelligence in Education","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/academic-performance-prediction","display_name":"student performance prediction","score":0.9440000057220459},{"id":"https://openalex.org/keywords/at-risk-student-identification","display_name":"at-risk student identification","score":0.6700000166893005},{"id":"https://openalex.org/keywords/deep-learning-model-comparison","display_name":"deep learning model comparison","score":0.7549999952316284},{"id":"https://openalex.org/keywords/lstm","display_name":"LSTM","score":0.9739999771118164},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.7089999914169312},{"id":"https://openalex.org/keywords/mlp","display_name":"MLP","score":0.7570000290870667},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.8700000047683716},{"id":"https://openalex.org/keywords/prior-academic-achievement","display_name":"prior academic achievement","score":0.8240000009536743},{"id":"https://openalex.org/keywords/gender-differences","display_name":"gender differences","score":0.7670000195503235},{"id":"https://openalex.org/keywords/field-of-study","display_name":"field of study","score":0.8650000095367432},{"id":"https://openalex.org/keywords/higher-education","display_name":"higher education","score":0.75},{"id":"https://openalex.org/keywords/educational-data-mining","display_name":"educational data mining","score":0.7889999747276306}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6757000088691711},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6669999957084656},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6477000117301941},{"id":"https://openalex.org/C2781206393","wikidata":"https://www.wikidata.org/wiki/Q2748419","display_name":"Academic achievement","level":2,"score":0.6172000169754028},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5759999752044678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5670999884605408},{"id":"https://openalex.org/C120912362","wikidata":"https://www.wikidata.org/wiki/Q136822","display_name":"Higher education","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C2777598771","wikidata":"https://www.wikidata.org/wiki/Q5341279","display_name":"Educational data mining","level":2,"score":0.4318999946117401},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.3720000088214874},{"id":"https://openalex.org/C10676486","wikidata":"https://www.wikidata.org/wiki/Q17067409","display_name":"Learning development","level":3,"score":0.36719998717308044},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.335999995470047},{"id":"https://openalex.org/C20685875","wikidata":"https://www.wikidata.org/wiki/Q7239678","display_name":"Predictive validity","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.30410000681877136},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29739999771118164},{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C83209312","wikidata":"https://www.wikidata.org/wiki/Q1053367","display_name":"Predictive analytics","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C2779479571","wikidata":"https://www.wikidata.org/wiki/Q4812273","display_name":"At-risk students","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.2597000002861023}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24082123","doi":"https://doi.org/10.5281/zenodo.24082123","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24082123","pdf_url":null,"source":{"id":"https://openalex.org/S7407079774","display_name":"GPH-International Journal of Computer Science and Engineering","issn_l":"3050-9661","issn":["3050-9661"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},{"id":"doi:10.5281/zenodo.24082124","doi":"https://doi.org/10.5281/zenodo.24082124","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24082124","pdf_url":null,"source":{"id":"https://openalex.org/S7407079774","display_name":"GPH-International Journal of Computer Science and Engineering","issn_l":"3050-9661","issn":["3050-9661"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24082123","doi":"https://doi.org/10.5281/zenodo.24082123","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24082123","pdf_url":null,"source":{"id":"https://openalex.org/S7407079774","display_name":"GPH-International Journal of Computer Science and Engineering","issn_l":"3050-9661","issn":["3050-9661"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"JournalArticle"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.9235000014305115,"display_name":"Quality education"},{"id":"https://metadata.un.org/sdg/10","score":0.5,"display_name":"Reduced inequalities"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4404689177","https://openalex.org/W4409251948","https://openalex.org/W4388704683","https://openalex.org/W7168165426","https://openalex.org/W4408239615","https://openalex.org/W4412824555","https://openalex.org/W7164880723","https://openalex.org/W4416241762","https://openalex.org/W4411204694","https://openalex.org/W4414231463"],"abstract_inverted_index":{"Abstract":[0],"Higher":[1],"educational":[2,22],"institutions":[3,197],"are":[4],"undergoing":[5],"a":[6,189],"significant":[7],"digital":[8],"transformation,":[9],"driven":[10],"by":[11],"rapid":[12],"advances":[13],"in":[14,198,233],"artificial":[15],"intelligence":[16],"and":[17,51,74,83,89,124,207,230,241],"the":[18,41,60,78,97,112,130,134,141,157],"growing":[19],"availability":[20],"of":[21,44,48,62,101,106,138,161],"data.":[23],"Predicting":[24],"students\u2019":[25,69],"academic":[26,49,70,73,90,98,113,150,163,180,201,235],"performance":[27,61,71,85,249],"has":[28],"become":[29],"an":[30],"important":[31],"analytical":[32],"task":[33],"for":[34,67,94,204],"higher":[35,195],"education":[36,196],"institutions,":[37],"as":[38],"it":[39],"enables":[40],"early":[42,205],"identification":[43,206],"students":[45,103],"at":[46],"risk":[47],"underperformance":[50],"supports":[52],"timely":[53],"intervention.":[54,208],"This":[55],"study":[56,95,187],"aims":[57],"to":[58,213,224],"compare":[59],"four":[63],"deep":[64,117,217],"learning":[65,118,218,252],"models":[66,119],"predicting":[68],"using":[72],"demographic":[75],"data,":[76,236],"identify":[77],"most":[79,158],"influential":[80,159],"predictive":[81,136,175,239],"features,":[82],"examine":[84],"differences":[86,178],"across":[87,179],"gender":[88,170],"majors.":[91],"The":[92,126,165],"dataset":[93],"is":[96,211],"achievement":[99],"data":[100,246],"2,878":[102],"from":[104],"Faculty":[105],"Economics":[107],"&":[108],"Commercial":[109],"Studies":[110],"during":[111],"year":[114],"2017-2019.":[115],"Four":[116],"were":[120,182],"evaluated":[121],"(MLP,":[122],"CNN,LSTM":[123],"DNN).":[125],"results":[127],"demonstrated":[128],"that":[129,148,169,192],"LSTM":[131],"model":[132],"achieved":[133],"highest":[135],"accuracy":[137,240],"97.14%,":[139],"outperforming":[140],"other":[142],"models.":[143],"Feature-importance":[144],"analysis":[145],"further":[146],"revealed":[147],"prior":[149],"achievement,":[151],"particularly":[152,220],"second-semester":[153],"GPA":[154],"(GPA2),":[155],"was":[156],"predictor":[160],"subsequent":[162],"performance.":[164],"findings":[166],"also":[167],"indicated":[168],"had":[171],"limited":[172],"influence":[173],"on":[174],"performance,":[176],"whereas":[177],"majors":[181],"more":[183,215],"pronounced.":[184],"Overall,":[185],"this":[186],"provides":[188],"methodological":[190],"framework":[191],"can":[193],"support":[194],"developing":[199],"data-driven":[200],"prediction":[202],"systems":[203],"Future":[209],"research":[210],"recommended":[212],"investigate":[214],"advanced":[216],"architectures,":[219],"hybrid":[221],"CNN\u2013LSTM":[222],"models,":[223],"leverage":[225],"both":[226],"local":[227],"feature":[228],"patterns":[229],"temporal":[231],"dependencies":[232],"sequential":[234],"potentially":[237],"improving":[238],"computational":[242],"efficiency.":[243],"Keywords:":[244],"Educational":[245],"mining,":[247],"Academic":[248],"prediction,":[250],"Deep":[251],"Algorithms.":[253]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-10T00:00:00"}
