{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T16:39:25Z","timestamp":1775234365555,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031643118","type":"print"},{"value":"9783031643125","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-64312-5_40","type":"book-chapter","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T10:04:39Z","timestamp":1719828279000},"page":"334-341","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advancing High School Dropout Predictions Using Machine Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7656-9976","authenticated-orcid":false,"given":"Anika","family":"Alam","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6079-5456","authenticated-orcid":false,"given":"A. Brooks","family":"Bowden","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"40_CR1","volume-title":"Are Gpas an inconsistent measure of college readiness across high schools?","author":"EM Allensworth","year":"2019","unstructured":"Allensworth, E.M., Clark, K.: Are Gpas an inconsistent measure of college readiness across high schools? University of Chicago Consortium on School Research, Examining Assumptions About Grades Versus Standardized Test Scores (2019)"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Balfanz, R., Herzog, L., Mac Iver, D.J.: Preventing student disengagement and keeping students on the graduation path in urban middle-grades schools: early identification and effective interventions. Educ. Psychol. 42(4) (2007)","DOI":"10.1080\/00461520701621079"},{"key":"40_CR3","unstructured":"Belfield, C.R. Levin, H.M. (eds.):The price we pay: Economic and social consequences of inadequate education. Brookings Institution Press (2007)"},{"issue":"3","key":"40_CR4","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1080\/00220670903382970","volume":"103","author":"AJ Bowers","year":"2010","unstructured":"Bowers, A.J.: Grades and graduation: a longitudinal risk perspective to identify student dropouts. J. Educ. Res. 103(3), 191\u2013207 (2010)","journal-title":"J. Educ. Res."},{"issue":"3","key":"40_CR5","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1080\/10824669.2012.692071","volume":"17","author":"AJ Bowers","year":"2012","unstructured":"Bowers, A.J., Sprott, R.: Why tenth graders fail to finish high school: a dropout typology latent class analysis. J. Educ. Stud. Placed Risk 17(3), 129\u2013148 (2012)","journal-title":"J. Educ. Stud. Placed Risk"},{"issue":"3","key":"40_CR6","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1080\/00220671.2011.552075","volume":"105","author":"AJ Bowers","year":"2012","unstructured":"Bowers, A.J., Sprott, R.: Examining the multiple trajectories associated with dropping out of high school: a growth mixture model analysis. J. Educ. Res. 105(3), 176\u2013195 (2012)","journal-title":"J. Educ. Res."},{"issue":"1","key":"40_CR7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/10824669.2018.1523734","volume":"24","author":"AJ Bowers","year":"2019","unstructured":"Bowers, A.J., Zhou, X.: Receiver operating characteristic (ROC) area under the curve (AUC): a diagnostic measure for evaluating the accuracy of predictors of education outcomes. J. Educ. Stud. Placed Risk 24(1), 20\u201346 (2019)","journal-title":"J. Educ. Stud. Placed Risk"},{"key":"40_CR8","unstructured":"Burke, A.: Early Identification of high school graduation outcomes in oregon leadership network schools. Rel 2015\u2013079. Regional Educational Laboratory Northwest (2015)"},{"key":"40_CR9","unstructured":"Butler, M.A.: Rural-urban continuum codes for metro and nonmetro counties. US Department of Agriculture, Economic Research Service, Agriculture And . . (1990)"},{"key":"40_CR10","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artific. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artific. Intell. Res."},{"issue":"1","key":"40_CR11","first-page":"131","volume":"20","author":"J Cook","year":"2020","unstructured":"Cook, J., Ramadas, V.: When to consult precision-recall curves. Stand. Genomic Sci. 20(1), 131\u2013148 (2020)","journal-title":"Stand. Genomic Sci."},{"issue":"9\u201310","key":"40_CR12","doi-asserted-by":"publisher","first-page":"1697","DOI":"10.1016\/j.jpubeco.2003.11.002","volume":"88","author":"TS Dee","year":"2004","unstructured":"Dee, T.S.: Are there civic returns to education? J. Public Econ. 88(9\u201310), 1697\u20131720 (2004)","journal-title":"J. Public Econ."},{"key":"40_CR13","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez, A., Garcia, S., Herrera, F., Chawla, N.V.: SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artific. Intell. Res. 61, 863\u2013905 (2018)","journal-title":"J. Artific. Intell. Res."},{"issue":"2","key":"40_CR14","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/10824669.2014.962696","volume":"19","author":"MA Gottfried","year":"2014","unstructured":"Gottfried, M.A.: Chronic absenteeism and its effects on students\u2019 academic and socioemotional outcomes. J. Educ. Stud. Placed Risk 19(2), 53\u201375 (2014)","journal-title":"J. Educ. Stud. Placed Risk"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Jackson, C.K.:\u00a0The effects of an incentive-based high-school intervention on college outcomes\u00a0(No. w15722). National Bureau of Economic Research (2010)","DOI":"10.3386\/w15722"},{"key":"40_CR16","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-031-38747-0_2","volume-title":"An Introduction to Statistical Learning: with Applications in Python","author":"G James","year":"2023","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R., Taylor, J.: Statistical learning. In: An Introduction to Statistical Learning: with Applications in Python, pp. 15\u201367. Springer International Publishing, Cham (2023)"},{"key":"40_CR17","unstructured":"Kahlenberg, R.D.: All together now: creating middle-class schools through public school choice. Rowman & Littlefield (2004)"},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Kroese, D.P., Botev, Z., Taimre, T., Vaisman, R.: Data science and machine learning: mathematical and statistical methods. CRC Press (2019)","DOI":"10.1201\/9780367816971"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Kruger, J.G.C., De Souza Britto Jr, A., Barddal, J.P.: An explainable machine learning approach for student dropout prediction. Expert Syst. Appl. 233, 120933 (2023)","DOI":"10.1016\/j.eswa.2023.120933"},{"issue":"15","key":"40_CR20","doi-asserted-by":"publisher","first-page":"3093","DOI":"10.3390\/app9153093","volume":"9","author":"S Lee","year":"2019","unstructured":"Lee, S., Chung, J.Y.: The machine learning-based dropout early warning system for improving the performance of dropout prediction. Appl. Sci. 9(15), 3093 (2019)","journal-title":"Appl. Sci."},{"issue":"1","key":"40_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-018-0151-6","volume":"5","author":"JL Leevy","year":"2018","unstructured":"Leevy, J.L., Khoshgoftaar, T.M., Bauder, R.A., Seliya, N.: A survey on addressing high-class imbalance in big data. J. Big Data 5(1), 1\u201330 (2018)","journal-title":"J. Big Data"},{"key":"40_CR22","unstructured":"Losen, D., Orfield, G., Balfanz, R.: Confronting the graduation rate crisis in Texas. Civil Rights Project at Harvard University (2006)"},{"key":"40_CR23","doi-asserted-by":"publisher","DOI":"10.1201\/9780429170140","volume-title":"ROC analysis for classification and prediction in practice","author":"C Nakas","year":"2023","unstructured":"Nakas, C., Bantis, L., Gatsonis, C.: ROC analysis for classification and prediction in practice. CRC Press (2023)"},{"key":"40_CR24","unstructured":"NCES. Common Core of Data Public Elementary\/Secondary School Universe Survey(2023)"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"P\u00e9rez Fern\u00e1ndez, S., Mart\u00ednez Camblor, P., Filzmoser, P., Corral Blanco, N.O.: nsROC: an R package for non-standard ROC curve analysis.\u00a0R J. 10(2) (2018)","DOI":"10.32614\/RJ-2018-043"},{"issue":"4","key":"40_CR26","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1086\/657114","volume":"116","author":"SF Reardon","year":"2011","unstructured":"Reardon, S.F., Bischoff, K.: Income inequality and income segregation. Am. J. Sociol. 116(4), 1092\u20131153 (2011)","journal-title":"Am. J. Sociol."},{"issue":"2","key":"40_CR27","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1111\/obes.12277","volume":"81","author":"D Sansone","year":"2019","unstructured":"Sansone, D.: Beyond early warning indicators: high school dropout and machine learning. Oxford Bull. Econ. Stat. 81(2), 456\u2013485 (2019)","journal-title":"Oxford Bull. Econ. Stat."},{"issue":"3","key":"40_CR28","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1177\/0013161X18799439","volume":"55","author":"LC Sorensen","year":"2019","unstructured":"Sorensen, L.C.: \u201cBig Data\u201d in educational administration: an application for predicting school dropout risk. Educ. Adm. Q. 55(3), 404\u2013446 (2019)","journal-title":"Educ. Adm. Q."},{"key":"40_CR29","unstructured":"Weissman, A.: Friend Or Foe? the role of machine learning in education policy research [Doctoral Dissertation] (2022)"},{"key":"40_CR30","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1007\/s10648-016-9363-5","volume":"29","author":"JF Zaff","year":"2017","unstructured":"Zaff, J.F., Donlan, A., Gunning, A., Anderson, S.E., Mcdermott, E., Sedaca, M.: Factors that promote high school graduation: a review of the literature. Educ. Psychol. Rev. 29, 447\u2013476 (2017)","journal-title":"Educ. Psychol. Rev."}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-64312-5_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T07:05:40Z","timestamp":1734159940000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-64312-5_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031643118","9783031643125"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-64312-5_40","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"2 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Recife","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aied2024.cesar.school\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}