{"id":"https://openalex.org/W2901328873","doi":null,"title":"Aiming for Success: Evaluating Statistical and Machine Learning Methods to Predict High School Student Performance and Improve Early Warning Systems","display_name":"Aiming for Success: Evaluating Statistical and Machine Learning Methods to Predict High School Student Performance and Improve Early Warning Systems","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2901328873","mag":"2901328873"},"language":"en","primary_location":{"id":"pmh:oai:opencommons.uconn.edu:dissertations-8260","doi":null,"is_oa":true,"landing_page_url":"https://opencommons.uconn.edu/dissertations/1982","pdf_url":"https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=8260&context=dissertations","source":{"id":"https://openalex.org/S4306401180","display_name":"OpenCommons at University of Connecticut (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=8260&context=dissertations","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072691903","display_name":"David Alexandro","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"David Alexandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5072691903"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":null,"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":0.9965999722480774,"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":0.9965999722480774,"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/T13929","display_name":"Educational Outcomes and Influences","score":0.002400000113993883,"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"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0003000000142492354,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/early-warning-systems","display_name":"early warning systems","score":0.9150000214576721},{"id":"https://openalex.org/keywords/high-school-dropout","display_name":"high school dropout","score":0.8970000147819519},{"id":"https://openalex.org/keywords/academic-performance-prediction","display_name":"student performance prediction","score":0.8429999947547913},{"id":"https://openalex.org/keywords/at-risk-students","display_name":"at-risk students","score":0.7829999923706055},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.9779999852180481},{"id":"https://openalex.org/keywords/classification-and-regression-trees","display_name":"classification and regression trees","score":0.8370000123977661},{"id":"https://openalex.org/keywords/elastic-net","display_name":"elastic net","score":0.6800000071525574},{"id":"https://openalex.org/keywords/oversampling","display_name":"oversampling","score":0.9380000233650208},{"id":"https://openalex.org/keywords/youden-index","display_name":"Youden index","score":0.921999990940094},{"id":"https://openalex.org/keywords/feature-importance","display_name":"feature importance","score":0.8460000157356262},{"id":"https://openalex.org/keywords/special-education","display_name":"special education","score":0.671999990940094},{"id":"https://openalex.org/keywords/students-with-disabilities","display_name":"students with disabilities","score":0.9769999980926514},{"id":"https://openalex.org/keywords/standardized-test-scores","display_name":"standardized test scores","score":0.8209999799728394},{"id":"https://openalex.org/keywords/student-attendance","display_name":"student attendance","score":0.765999972820282},{"id":"https://openalex.org/keywords/course-performance","display_name":"course performance","score":0.7979999780654907}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5435142517089844},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4821869432926178},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.45520877838134766},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.4146159887313843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.402663916349411},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.3812798857688904},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3194723129272461},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:opencommons.uconn.edu:dissertations-8260","doi":null,"is_oa":true,"landing_page_url":"https://opencommons.uconn.edu/dissertations/1982","pdf_url":"https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=8260&context=dissertations","source":{"id":"https://openalex.org/S4306401180","display_name":"OpenCommons at University of Connecticut (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},{"id":"pmh:oai:digitalcommons.lib.uconn.edu:dissertations-8260","doi":null,"is_oa":false,"landing_page_url":"https://digitalcommons.lib.uconn.edu/dissertations/1982","pdf_url":null,"source":{"id":"https://openalex.org/S4377196438","display_name":"OpenCommons - UConn (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},{"id":"mag:2901328873","doi":null,"is_oa":false,"landing_page_url":"https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=8260&context=dissertations","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":"pmh:oai:opencommons.uconn.edu:dissertations-8260","doi":null,"is_oa":true,"landing_page_url":"https://opencommons.uconn.edu/dissertations/1982","pdf_url":"https://opencommons.uconn.edu/cgi/viewcontent.cgi?article=8260&context=dissertations","source":{"id":"https://openalex.org/S4306401180","display_name":"OpenCommons at University of Connecticut (University of Connecticut)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I140172145","host_organization_name":"University of Connecticut","host_organization_lineage":["https://openalex.org/I140172145"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Doctoral Dissertations","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Quality education","id":"https://metadata.un.org/sdg/4","score":0.9235000014305115}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2901328873.pdf","grobid_xml":"https://content.openalex.org/works/W2901328873.grobid-xml"},"referenced_works_count":110,"referenced_works":["https://openalex.org/W14585722","https://openalex.org/W31913789","https://openalex.org/W55483720","https://openalex.org/W118296737","https://openalex.org/W299484544","https://openalex.org/W408024275","https://openalex.org/W429766147","https://openalex.org/W564104565","https://openalex.org/W565319145","https://openalex.org/W639324889","https://openalex.org/W1492095792","https://openalex.org/W1560593431","https://openalex.org/W1606301240","https://openalex.org/W1616654592","https://openalex.org/W1964942722","https://openalex.org/W1966326540","https://openalex.org/W1966602737","https://openalex.org/W1973948212","https://openalex.org/W1980614089","https://openalex.org/W1987748769","https://openalex.org/W1988790447","https://openalex.org/W1988934215","https://openalex.org/W1989155212","https://openalex.org/W1990114608","https://openalex.org/W1990830572","https://openalex.org/W1998979050","https://openalex.org/W1999318832","https://openalex.org/W1999795754","https://openalex.org/W2007488439","https://openalex.org/W2011109801","https://openalex.org/W2017564691","https://openalex.org/W2017665047","https://openalex.org/W2022451761","https://openalex.org/W2022696841","https://openalex.org/W2024311320","https://openalex.org/W2032549039","https://openalex.org/W2033055257","https://openalex.org/W2034213054","https://openalex.org/W2037655921","https://openalex.org/W2040745621","https://openalex.org/W2041394251","https://openalex.org/W2041909880","https://openalex.org/W2046289026","https://openalex.org/W2047028564","https://openalex.org/W2047104567","https://openalex.org/W2057946468","https://openalex.org/W2067782908","https://openalex.org/W2069240093","https://openalex.org/W2069497179","https://openalex.org/W2069992838","https://openalex.org/W2070094080","https://openalex.org/W2073984073","https://openalex.org/W2076524433","https://openalex.org/W2080058845","https://openalex.org/W2084829927","https://openalex.org/W2086144929","https://openalex.org/W2093525795","https://openalex.org/W2100931415","https://openalex.org/W2102461707","https://openalex.org/W2103919309","https://openalex.org/W2106393550","https://openalex.org/W2116554310","https://openalex.org/W2122825543","https://openalex.org/W2128931910","https://openalex.org/W2135046866","https://openalex.org/W2142644047","https://openalex.org/W2145946706","https://openalex.org/W2148143831","https://openalex.org/W2152575748","https://openalex.org/W2155094374","https://openalex.org/W2162068690","https://openalex.org/W2163848058","https://openalex.org/W2169570446","https://openalex.org/W2186375577","https://openalex.org/W2290214234","https://openalex.org/W2291342904","https://openalex.org/W2295396604","https://openalex.org/W2323557332","https://openalex.org/W2335107214","https://openalex.org/W2337553400","https://openalex.org/W2340351150","https://openalex.org/W2346848267","https://openalex.org/W2468460137","https://openalex.org/W2475548568","https://openalex.org/W2508031730","https://openalex.org/W2518642784","https://openalex.org/W2520190240","https://openalex.org/W2525065922","https://openalex.org/W2528942084","https://openalex.org/W2562319768","https://openalex.org/W2563418004","https://openalex.org/W2595391037","https://openalex.org/W2601799947","https://openalex.org/W2606059411","https://openalex.org/W2617588148","https://openalex.org/W2623062402","https://openalex.org/W2728486967","https://openalex.org/W2730483724","https://openalex.org/W2732599124","https://openalex.org/W2743594598","https://openalex.org/W2761522480","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2917993905","https://openalex.org/W2966207845","https://openalex.org/W3005910007","https://openalex.org/W3085162807","https://openalex.org/W3175417087","https://openalex.org/W4239510810","https://openalex.org/W6958141423"],"related_works":["https://openalex.org/W2901400593","https://openalex.org/W2955886184","https://openalex.org/W7154740517","https://openalex.org/W3214631808","https://openalex.org/W2965075997","https://openalex.org/W7134856569","https://openalex.org/W4411550415","https://openalex.org/W2896629844","https://openalex.org/W6892846335","https://openalex.org/W3003555941"],"abstract_inverted_index":{"In":[0,252],"response":[1],"to":[2,23,36,44,60,110,118,127,247,260,277,295,308,318,327],"the":[3,87,91,157,185,233,279],"high":[4,46],"school":[5,47],"dropout":[6],"crisis,":[7],"which":[8,190],"comes":[9],"with":[10,267],"great":[11],"economic":[12],"and":[13,26,40,50,70,107,137,144,149,154,202,229,317],"social":[14],"costs,":[15],"early":[16],"warning":[17],"systems":[18],"(EWSs)":[19],"have":[20],"been":[21],"developed":[22,108,176],"systematically":[24],"predict":[25,45,111],"improve":[27,51,261,328],"student":[28,48],"outcomes.":[29,330],"The":[30,100,219],"purpose":[31],"of":[32,67,93,96,115,156,188,236,281],"this":[33,57],"study":[34,58,85,101,170,269],"is":[35,270,302],"evaluate":[37],"different":[38],"statistical":[39],"machine":[41],"learning":[42],"methods":[43,89],"performance":[49,155,231],"EWSs.":[52],"By":[53],"improving":[54],"education":[55,276],"EWSs,":[56],"aims":[59],"better":[61],"identify":[62],"those":[63],"students":[64,79,191,266,313],"in":[65,90,275,324],"need":[66],"targeted":[68],"support":[69,278],"inform":[71],"on-the-ground":[72],"practitioners":[73,307],"who":[74,314],"may":[75,80],"intervene":[76],"long":[77],"before":[78],"be":[81,288],"dropping":[82],"out.\\nThe":[83],"current":[84],"explores":[86],"aforementioned":[88],"context":[92],"a":[94,128,162,178,216,272],"cohort":[95],"40,008":[97],"Connecticut":[98],"students.":[99],"utilized":[102],"more":[103,282],"than":[104,213],"100":[105],"predictors":[106,235,258],"models":[109,158,175,196,243],"each":[112],"student\u2019s":[113,129],"probability":[114],"being":[116,237],"on-track":[117],"graduate":[119],"within":[120],"four":[121],"years":[122],"using":[123,177,209],"data":[124],"collected":[125],"prior":[126],"entry":[130],"into":[131],"9th":[132],"grade.":[133],"Random":[134],"forests,":[135],"classification":[136,167,262],"regression":[138],"tree":[139],"(CART,":[140],"or":[141],"decision":[142],"tree),":[143],"regularized":[145],"logistic":[146],"regression\u2014ridge,":[147],"lasso,":[148],"elastic":[150,241],"net\u2014models":[151],"were":[152,207,232],"developed,":[153],"was":[159],"evaluated":[160],"on":[161],"validation":[163],"dataset":[164],"by":[165,182,290],"comparing":[166],"accuracy":[168],"measures.\\nThe":[169],"revealed":[171],"that":[172,224],"random":[173,249],"forests":[174,250],"training":[179],"set":[180],"balanced":[181],"oversampling":[183],"did":[184],"best":[186,204],"job":[187],"identifying":[189],"are":[192,315],"at":[193,215,321],"risk.":[194],"These":[195],"captured":[197],"complex":[198],"interactions":[199],"among":[200],"covariates":[201],"performed":[203],"when":[205],"thresholds":[206],"optimized":[208],"Youden\u2019s":[210],"index":[211],"rather":[212],"defaulted":[214],"0.5":[217],"cut-off.":[218],"variable":[220],"importance":[221],"rankings":[222],"showed":[223],"standardized":[225],"test":[226,319],"scores,":[227],"attendance,":[228],"course":[230],"top-ranking":[234],"on-track.":[238],"Coefficients":[239],"from":[240],"net":[242],"provided":[244],"nuanced":[245],"information":[246],"complement":[248],"results.":[251],"addition,":[253],"incorporating":[254],"detailed":[255],"special":[256],"education-related":[257],"served":[259],"accuracy,":[263],"especially":[264],"for":[265,306],"disabilities.\\nThis":[268],"filling":[271],"practical":[273],"void":[274],"development":[280],"sophisticated":[283],"predictive":[284],"models.":[285],"This":[286],"will":[287],"usable":[289],"researchers":[291],"as":[292],"an":[293,304,325],"approach":[294],"ensure":[296],"future":[297],"EWSs":[298],"work":[299],"optimally.":[300],"It":[301],"also":[303],"opportunity":[305],"leverage":[309],"new":[310],"knowledge":[311],"about":[312],"at-risk,":[316],"interventions":[320],"many":[322],"levels":[323],"attempt":[326],"graduation":[329]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2018-11-29T00:00:00"}