{"id":"https://openalex.org/W3021330828","doi":"https://doi.org/10.3390/sym12050728","title":"An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning","display_name":"An Ensemble Prediction Model for Potential Student Recommendation Using Machine Learning","publication_year":2020,"publication_date":"2020-05-03","ids":{"openalex":"https://openalex.org/W3021330828","doi":"https://doi.org/10.3390/sym12050728","mag":"3021330828"},"language":"en","primary_location":{"id":"doi:10.3390/sym12050728","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12050728","pdf_url":"https://www.mdpi.com/2073-8994/12/5/728/pdf?version=1590482604","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2073-8994/12/5/728/pdf?version=1590482604","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051174076","display_name":"Lijuan Yan","orcid":"https://orcid.org/0000-0002-2889-4662"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lijuan Yan","raw_affiliation_strings":["Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China","National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China"],"raw_orcid":"https://orcid.org/0000-0002-2889-4662","affiliations":[{"raw_affiliation_string":"Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China","institution_ids":["https://openalex.org/I40963666"]},{"raw_affiliation_string":"National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044688428","display_name":"Yanshen Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanshen Liu","raw_affiliation_strings":["Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China","National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei Research Center for Educational Informationization, Central China Normal University, Wuhan 430079, China","institution_ids":["https://openalex.org/I40963666"]},{"raw_affiliation_string":"National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China","institution_ids":["https://openalex.org/I40963666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5051174076"],"corresponding_institution_ids":["https://openalex.org/I40963666"],"apc_list":{"value":2000,"currency":"CHF","value_usd":2227},"apc_paid":{"value":2000,"currency":"CHF","value_usd":2227},"fwci":6.6173,"has_fulltext":true,"cited_by_count":48,"citation_normalized_percentile":{"value":0.96364082,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"12","issue":"5","first_page":"728","last_page":"728"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9984999895095825,"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":0.9984999895095825,"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/T14025","display_name":"Educational Technology and Assessment","score":0.9623000025749207,"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"}},{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9257000088691711,"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/interpretability","display_name":"Interpretability","score":0.7701402902603149},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7378161549568176},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7362866997718811},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.714932918548584},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.666748583316803},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6638768911361694},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.6564038395881653},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6459777355194092},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5451556444168091},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.4729616045951843},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4667765200138092},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.41453033685684204},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.41027164459228516},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.401790976524353}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7701402902603149},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7378161549568176},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7362866997718811},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.714932918548584},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.666748583316803},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6638768911361694},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.6564038395881653},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6459777355194092},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5451556444168091},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.4729616045951843},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4667765200138092},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.41453033685684204},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.41027164459228516},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.401790976524353}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/sym12050728","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12050728","pdf_url":"https://www.mdpi.com/2073-8994/12/5/728/pdf?version=1590482604","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:00a00559da6c4a30976069588b5f2d69","is_oa":true,"landing_page_url":"https://doaj.org/article/00a00559da6c4a30976069588b5f2d69","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symmetry, Vol 12, Iss 5, p 728 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2073-8994/12/5/728/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/sym12050728","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Symmetry","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/sym12050728","is_oa":true,"landing_page_url":"https://doi.org/10.3390/sym12050728","pdf_url":"https://www.mdpi.com/2073-8994/12/5/728/pdf?version=1590482604","source":{"id":"https://openalex.org/S190787756","display_name":"Symmetry","issn_l":"2073-8994","issn":["2073-8994"],"is_oa":true,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Symmetry","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15","score":0.47999998927116394}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323173","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3021330828.pdf","grobid_xml":"https://content.openalex.org/works/W3021330828.grobid-xml"},"referenced_works_count":40,"referenced_works":["https://openalex.org/W1486785378","https://openalex.org/W1538739073","https://openalex.org/W1563088657","https://openalex.org/W1573845792","https://openalex.org/W1615500924","https://openalex.org/W1680392829","https://openalex.org/W1977137874","https://openalex.org/W2016320097","https://openalex.org/W2033820684","https://openalex.org/W2034159055","https://openalex.org/W2038749650","https://openalex.org/W2055260418","https://openalex.org/W2069253414","https://openalex.org/W2076198619","https://openalex.org/W2081777295","https://openalex.org/W2087347434","https://openalex.org/W2100654536","https://openalex.org/W2103672843","https://openalex.org/W2110023503","https://openalex.org/W2123636872","https://openalex.org/W2139212933","https://openalex.org/W2145445683","https://openalex.org/W2148603752","https://openalex.org/W2158698691","https://openalex.org/W2162720347","https://openalex.org/W2171562468","https://openalex.org/W2324200563","https://openalex.org/W2526186559","https://openalex.org/W2558331558","https://openalex.org/W2562986337","https://openalex.org/W2593653544","https://openalex.org/W2614528177","https://openalex.org/W2617342978","https://openalex.org/W2768611774","https://openalex.org/W2802381512","https://openalex.org/W2811385791","https://openalex.org/W2911964244","https://openalex.org/W4235028801","https://openalex.org/W6637386731","https://openalex.org/W6751587928"],"related_works":["https://openalex.org/W4382315444","https://openalex.org/W4233259193","https://openalex.org/W2600353413","https://openalex.org/W4390916549","https://openalex.org/W2915435096","https://openalex.org/W4381298925","https://openalex.org/W4285213578","https://openalex.org/W2097856925","https://openalex.org/W1997565450","https://openalex.org/W4298012357"],"abstract_inverted_index":{"Student":[0],"performance":[1,45,53],"prediction":[2,13,26],"has":[3],"become":[4],"a":[5,18,36],"hot":[6],"research":[7],"topic.":[8],"Most":[9],"of":[10],"the":[11,80,128,139,153],"existing":[12],"models":[14],"are":[15,23,77,155],"built":[16],"by":[17,86],"machine":[19,65,71],"learning":[20,66],"method.":[21],"They":[22],"interested":[24],"in":[25,46,79,110,136],"accuracy":[27],"but":[28],"pay":[29],"less":[30],"attention":[31],"to":[32,40,97,114,163],"interpretability.":[33],"We":[34],"propose":[35],"stacking":[37],"ensemble":[38],"model":[39,141],"predict":[41],"and":[42,75,83,125],"analyze":[43],"student":[44,52],"academic":[47,108],"competition.":[48],"In":[49],"this":[50,137],"model,":[51],"is":[54],"classified":[55],"into":[56],"two":[57],"symmetrical":[58],"categorical":[59],"classes.":[60],"To":[61],"improve":[62],"accuracy,":[63],"three":[64],"algorithms,":[67],"including":[68],"support":[69],"vector":[70],"(SVM),":[72],"random":[73],"forest,":[74],"AdaBoost":[76],"established":[78],"first":[81],"level":[82],"then":[84],"integrated":[85],"logistic":[87],"regression":[88],"via":[89],"stacking.":[90],"A":[91],"feature":[92],"importance":[93],"analysis":[94,154],"was":[95],"applied":[96],"identify":[98],"important":[99,149],"variables.":[100],"The":[101,148],"experimental":[102],"data":[103],"were":[104],"collected":[105],"from":[106,152],"four":[107],"years":[109],"Hankou":[111],"University.":[112],"According":[113],"comparative":[115],"studies":[116],"on":[117],"five":[118],"evaluation":[119],"metrics":[120],"(precision,":[121],"recall,":[122],"F1,":[123],"error,":[124],"area":[126],"under":[127],"receiver":[129],"operating":[130],"characteristic":[131],"curve":[132],"(":[133],"AUC":[134],")":[135],"analysis,":[138],"proposed":[140],"generally":[142],"performs":[143],"better":[144],"than":[145],"compared":[146],"models.":[147],"variables":[150],"identified":[151],"interpretable,":[156],"they":[157],"can":[158],"be":[159],"used":[160],"as":[161],"guidance":[162],"select":[164],"potential":[165],"students.":[166]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}
