{"id":"https://openalex.org/W2951559695","doi":"https://doi.org/10.1145/3325917.3325919","title":"Student Performance Prediction using Multi-Layers Artificial Neural Networks","display_name":"Student Performance Prediction using Multi-Layers Artificial Neural Networks","publication_year":2019,"publication_date":"2019-04-06","ids":{"openalex":"https://openalex.org/W2951559695","doi":"https://doi.org/10.1145/3325917.3325919","mag":"2951559695"},"language":"en","primary_location":{"id":"doi:10.1145/3325917.3325919","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3325917.3325919","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Information System and Data Mining","raw_type":"proceedings-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/A5043160131","display_name":"Saud Altaf","orcid":"https://orcid.org/0000-0002-3455-6854"},"institutions":[{"id":"https://openalex.org/I200749728","display_name":"Pir Mehr Ali Shah Arid Agriculture University","ror":"https://ror.org/035zn2q74","country_code":"PK","type":"education","lineage":["https://openalex.org/I200749728"]}],"countries":["PK"],"is_corresponding":true,"raw_author_name":"Saud Altaf","raw_affiliation_strings":["Pir Mehr Ali Shah Arid, Agriculture University, Rawalpindi, Pakistan"],"affiliations":[{"raw_affiliation_string":"Pir Mehr Ali Shah Arid, Agriculture University, Rawalpindi, Pakistan","institution_ids":["https://openalex.org/I200749728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027673259","display_name":"Waseem Soomro","orcid":null},"institutions":[{"id":"https://openalex.org/I125931852","display_name":"Manukau Institute of Technology","ror":"https://ror.org/002r67t24","country_code":"NZ","type":"education","lineage":["https://openalex.org/I125931852","https://openalex.org/I4387152882"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Waseem Soomro","raw_affiliation_strings":["Manukau Institute of Technology, New Zealand"],"affiliations":[{"raw_affiliation_string":"Manukau Institute of Technology, New Zealand","institution_ids":["https://openalex.org/I125931852"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018303123","display_name":"Mohd Izani Mohamed Rawi","orcid":null},"institutions":[{"id":"https://openalex.org/I82724352","display_name":"Universiti Teknologi MARA","ror":"https://ror.org/05n8tts92","country_code":"MY","type":"education","lineage":["https://openalex.org/I82724352"]}],"countries":["MY"],"is_corresponding":false,"raw_author_name":"Mohd Izani Mohamed Rawi","raw_affiliation_strings":["Universiti Teknologi MARA, Malaysia"],"affiliations":[{"raw_affiliation_string":"Universiti Teknologi MARA, Malaysia","institution_ids":["https://openalex.org/I82724352"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5043160131"],"corresponding_institution_ids":["https://openalex.org/I200749728"],"apc_list":null,"apc_paid":null,"fwci":5.4167,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.95462487,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"59","last_page":"64"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9998000264167786,"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":0.9998000264167786,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8004786968231201},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.7767469882965088},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6862823963165283},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6769999265670776},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6034280061721802},{"id":"https://openalex.org/keywords/identifier","display_name":"Identifier","score":0.6021180152893066},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4475725293159485},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.43530434370040894},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41754698753356934}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8004786968231201},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.7767469882965088},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6862823963165283},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6769999265670776},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6034280061721802},{"id":"https://openalex.org/C154504017","wikidata":"https://www.wikidata.org/wiki/Q853614","display_name":"Identifier","level":2,"score":0.6021180152893066},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4475725293159485},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.43530434370040894},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41754698753356934},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3325917.3325919","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3325917.3325919","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 3rd International Conference on Information System and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1190947359","https://openalex.org/W1548739117","https://openalex.org/W1909216329","https://openalex.org/W1975127290","https://openalex.org/W2018146476","https://openalex.org/W2100525561","https://openalex.org/W2290063373","https://openalex.org/W2563138197","https://openalex.org/W2617300785","https://openalex.org/W2724733028","https://openalex.org/W4389515286"],"related_works":["https://openalex.org/W4378651134","https://openalex.org/W1979633005","https://openalex.org/W2981877337","https://openalex.org/W3203938600","https://openalex.org/W4286910063","https://openalex.org/W2163707935","https://openalex.org/W83146503","https://openalex.org/W2169074127","https://openalex.org/W202723009","https://openalex.org/W2188612292"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"Neural":[3,78,129,182],"Network":[4,130,183],"(NN)":[5],"has":[6,262],"seen":[7],"widespread":[8],"and":[9,106,124,139,265],"successful":[10,229],"implementations":[11],"in":[12,135,147,168,193,255,259],"a":[13,33,58],"wide":[14],"range":[15,107],"of":[16,29,77,103,137,149,177,203,213,277],"data":[17,64,98,110,189,247],"mining":[18],"applications,":[19],"often":[20],"surpassing":[21],"other":[22],"classifiers.":[23,179,210],"This":[24,171],"study":[25],"plans":[26],"to":[27,36,47,118,190,228,251,270],"research":[28],"NN":[30],"that":[31,201,234],"are":[32],"fitting":[34],"classifier":[35],"foresee":[37],"understudy":[38],"execution":[39],"from":[40,96,108],"Learning":[41],"Management":[42],"System":[43],"information":[44],"with":[45,143,222,242],"regards":[46],"Educational":[48],"Data":[49],"Mining.":[50],"The":[51,90,198,211],"dataset":[52],"utilized":[53],"for":[54,93,121,217,274],"this":[55,88],"examination":[56],"is":[57,140,184],"Moodle":[59],"log":[60,63],"document":[61],"containing":[62],"around":[65],"900":[66],"understudies":[67],"more":[68],"than":[69],"10":[70],"college":[71],"classes.":[72],"To":[73],"assess":[74,191],"the":[75,101,128,144,154,165,169,178,181,235,243,245,252],"applicability":[76],"Networks,":[79],"two":[80],"case":[81],"studies":[82],"compare":[83],"their":[84],"predictive":[85,160,175],"performance":[86,161,176,195,257],"on":[87,114,141,159,186],"dataset.":[89],"features":[91],"used":[92,216],"training":[94],"originate":[95],"LMS":[97],"obtained":[99,120,224],"during":[100],"length":[102],"each":[104,115],"course,":[105],"usage":[109],"like":[111],"time":[112],"spent":[113],"course":[116,122,156,166,188,205],"page,":[117],"grades":[119,225],"assignments":[123],"quizzes.":[125],"After":[126],"training,":[127],"outperforms":[131],"all":[132],"six":[133],"classifiers":[134,146,206],"terms":[136,148],"accuracy":[138,254],"par":[142],"best":[145],"recall.":[150],"We":[151,231],"also":[152,220],"assessed":[153],"effect":[155],"predictors":[157,215],"have":[158],"by":[162],"leaving":[163],"out":[164],"identifiers":[167],"data.":[170],"does":[172],"not":[173],"affect":[174],"Furthermore,":[180],"trained":[185,209],"individual":[187,214],"difference":[192],"classification":[194,218,269],"between":[196],"courses.":[197],"results":[199],"show":[200],"half":[202],"these":[204],"better":[207,272],"generally":[208],"importance":[212],"was":[219],"investigated,":[221],"previously":[223],"contributing":[226],"most":[227],"predictions.":[230],"can":[232],"conclude":[233],"proposed":[236],"neural":[237],"network":[238],"architecture":[239],"works":[240],"well":[241],"selecting":[244],"feature":[246,260],"sets.":[248],"It":[249],"seems":[250],"results,":[253],"student":[256,278],"prediction":[258,276],"vector":[261],"been":[263],"achieved":[264],"satisfactory":[266],"through":[267],"appropriate":[268],"take":[271],"decision":[273],"efficient":[275],"performance.":[279]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}