{"id":"https://openalex.org/W2535589986","doi":"https://doi.org/10.13016/m2k649w59","title":"On The K-Nearest Neighbor Approach To The Generation Of Fuzzy Rules For College Student Performance Prediction","display_name":"On The K-Nearest Neighbor Approach To The Generation Of Fuzzy Rules For College Student Performance Prediction","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2535589986","doi":"https://doi.org/10.13016/m2k649w59","mag":"2535589986"},"language":"en","primary_location":{"id":"pmh:oai:mdsoar.org:11603/10630","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11603/10630","pdf_url":null,"source":{"id":"https://openalex.org/S4306402556","display_name":"Maryland Shared Open Access Repository (USMAI Consortium)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},"parent_works":[],"lineage":["https://openalex.org/W2535589986"],"child_works_count":0,"type":"article","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.13016/m2k649w59","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027078573","display_name":"Clarence Yandell Weston","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Weston, Clarence Yandell","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5027078573"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3456,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.71131209,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"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":0.7529000043869019,"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":0.7529000043869019,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.07739999890327454,"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/T13559","display_name":"Edcuational Technology Systems","score":0.06270000338554382,"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/k-nearest-neighbors","display_name":"k-nearest neighbors","score":0.7639999985694885},{"id":"https://openalex.org/keywords/fuzzy-rules","display_name":"fuzzy rules","score":0.7670000195503235},{"id":"https://openalex.org/keywords/fuzzy-logic-neural-networks","display_name":"fuzzy logic neural network","score":0.8040000200271606},{"id":"https://openalex.org/keywords/rule-extraction","display_name":"rule extraction","score":0.781000018119812},{"id":"https://openalex.org/keywords/rule-minimization","display_name":"rule minimization","score":0.5460000038146973},{"id":"https://openalex.org/keywords/student-retention","display_name":"student retention","score":0.5049999952316284},{"id":"https://openalex.org/keywords/university-admission","display_name":"university admission","score":0.5659999847412109},{"id":"https://openalex.org/keywords/student-diversity","display_name":"student diversity","score":0.3540000021457672},{"id":"https://openalex.org/keywords/clustering","display_name":"clustering","score":0.38499999046325684}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.493722528219223},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.49169808626174927},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46576905250549316},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.45803216099739075},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4498986303806305}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:mdsoar.org:11603/10630","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11603/10630","pdf_url":null,"source":{"id":"https://openalex.org/S4306402556","display_name":"Maryland Shared Open Access Repository (USMAI Consortium)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"doi:10.13016/m2k649w59","doi":"https://doi.org/10.13016/m2k649w59","is_oa":true,"landing_page_url":"https://doi.org/10.13016/m2k649w59","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Collection"},{"id":"mag:2535589986","doi":null,"is_oa":false,"landing_page_url":"https://mdsoar.org/handle/11603/10630","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.13016/m2k649w59","doi":"https://doi.org/10.13016/m2k649w59","is_oa":true,"landing_page_url":"https://doi.org/10.13016/m2k649w59","pdf_url":null,"source":{"id":"https://openalex.org/S4306402644","display_name":"Digital Repository at the University of Maryland (University of Maryland College Park)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I66946132","host_organization_name":"University of Maryland, College Park","host_organization_lineage":["https://openalex.org/I66946132"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Collection"},"sustainable_development_goals":[{"display_name":"Quality education","score":0.6164000034332275,"id":"https://metadata.un.org/sdg/4"}],"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/W6922497448","https://openalex.org/W2912096545","https://openalex.org/W2998689133","https://openalex.org/W2324219773","https://openalex.org/W2049829621","https://openalex.org/W2737927392","https://openalex.org/W2136398397","https://openalex.org/W2888509550","https://openalex.org/W2121916842","https://openalex.org/W3005758552"],"abstract_inverted_index":{"Prediction":[0],"of":[1,10,132,156,159,203,254,326],"college":[2,38,91,124,140,195,284,293],"student":[3,23,64,174,196,285,294,314,339,345],"performance":[4,141,160],"has":[5,69,85],"long":[6],"been":[7,57],"a":[8,108,168,204],"topic":[9],"research":[11],"for":[12,99,139,144,172],"College":[13],"Board":[14],"administrators":[15],"and":[16,25,29,103,123,178,272,288],"educators":[17],"concerned":[18],"with":[19,51,119,186,238,259],"university":[20,40],"admittance":[21],"standards,":[22],"development,":[24],"retention.":[26],"Predictive":[27],"indicators":[28,143,161,287],"composite":[30],"rules":[31,218,229,255,280,309,334],"made":[32],"from":[33,116,265,283,336],"high":[34,117],"school":[35],"GPA":[36,43],"(HSGPA),":[37],"or":[39],"first":[41],"year":[42],"(CFYGPA),":[44],"standardized":[45],"tests":[46],"scores":[47],"(SAT/ACT),":[48],"in":[49,59,150,201,212],"conjunction":[50],"individual":[52,146],"subject":[53],"grades":[54],"have":[55],"all":[56],"studied":[58],"an":[60,87,133,157],"attempt":[61,93],"to":[62,73,94,135,167,214,250,291,308,351],"forecast":[63],"academic":[65],"performance.":[66,295],"Each":[67],"indicator":[68],"its":[70],"shortcomings":[71],"leading":[72],"less":[74],"informative":[75],"predictive":[76,170],"studies.":[77],"To":[78,191],"complicate":[79],"things,":[80],"diversity":[81],"among":[82],"incoming":[83],"freshmen":[84],"become":[86],"increasing":[88],"concern":[89],"as":[90,341],"officials":[92],"make":[95,352],"the":[96,130,154,173,187,194,221,227,239,252,301,312,343],"scholastic":[97,121],"experience":[98],"students":[100,113,180,185,237],"more":[101],"culturally":[102],"ethnically":[104],"diverse.":[105],"This":[106],"creates":[107],"situation":[109],"where":[110],"by":[111],"design,":[112],"are":[114,305],"selected":[115],"schools":[118],"different":[120],"metrics":[122],"preparatory":[125],"practices":[126],"thus":[127],"eliminating":[128],"even":[129],"possibility":[131],"\u2018apples":[134],"apples'":[136],"selection":[137,155,230],"criterion":[138,231],"prediction":[142,197,217,228,279,327],"each":[145],"student.":[147],"The":[148,296],"task":[149],"this":[151,192,269],"case":[152],"is":[153,199,232,248,318,321,328,349],"aggregate":[158],"(prediction":[162],"rules)":[163],"that":[164,262,323],"will":[165],"lead":[166],"valid":[169],"study":[171],"while":[175,257],"simultaneously":[176],"grouping":[177],"evaluating":[179],"based":[181,234],"only":[182,235],"on":[183,236],"other":[184],"most":[188,240],"similar":[189,241,324],"background.":[190],"end,":[193],"problem":[198],"formulated":[200],"terms":[202],"modified":[205],"Fuzzy":[206],"Logic":[207],"Neural":[208],"Network":[209],"inference":[210,276],"system":[211],"order":[213],"dynamically":[215],"extract":[216],"generated":[219,310,335],"using":[220,300,330],"K-Nearest":[222,302],"Neighbor":[223,303],"training":[224],"samples.":[225],"Thus,":[226],"intuitively":[233],"backgrounds.":[242],"Multi-Valued":[243],"Sequential":[244],"Interactive":[245],"Synthesis":[246],"(MVSIS)":[247],"used":[249],"keep":[251],"number":[253,333],"manageable":[256],"dealing":[258],"any":[260],"non-determinism":[261],"may":[263],"result":[264],"conflicting":[266],"rules.":[267],"Using":[268],"hybrid":[270],"clustering":[271],"fuzzy":[273],"neural":[274],"network":[275],"approach,":[277],"meaningful":[278],"were":[281],"extracted":[282],"grading":[286],"applied":[289],"effectively":[290],"predict":[292],"minimized":[297],"rule":[298],"set":[299,317,348],"samples":[304],"then":[306],"compared":[307],"when":[311,342],"entire":[313,344],"data":[315,340,346],"sample":[316,347],"used.":[319],"It":[320],"shown":[322],"accuracy":[325],"attained":[329],"relatively":[331],"small":[332],"nearest":[337],"neighbor":[338],"utilized":[350],"predictions.":[353]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-11-14T11:04:58.165337","created_date":"2016-10-28T00:00:00"}
