{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:10:06Z","timestamp":1760058606205,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T00:00:00Z","timestamp":1744416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>College enrollment has long been recognized as a critical pathway to better employment prospects and improved economic outcomes. However, the overall enrollment rates have declined in recent years, and students with a lower socioeconomic status (SES) or those from disadvantaged backgrounds remain significantly underrepresented in higher education. To investigate the factors influencing college enrollment among low-SES high school students, this study analyzed data from the High School Longitudinal Study of 2009 (HSLS:09) using five widely used machine learning algorithms. The sample included 5223 ninth-grade students from lower socioeconomic backgrounds (51% female; Mage = 14.59) whose biological parents or stepparents completed a parental questionnaire. The results showed that, among all five classifiers, the random forest algorithm achieved the highest classification accuracy at 67.73%. Additionally, the top three predictors of enrollment in 2-year or 4-year colleges were students\u2019 overall high school GPA, parental educational expectations, and the number of close friends planning to attend a 4-year college. Conversely, the most important predictors of non-enrollment were high school GPA, parental educational expectations, and the number of close friends who had dropped out of high school. These findings advance our understanding of the factors shaping college enrollment for low-SES students and highlight two important factors for intervention: improving students\u2019 academic performance and fostering future-oriented goals among their peers and parents.<\/jats:p>","DOI":"10.3390\/bdcc9040099","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T08:11:39Z","timestamp":1744704699000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting College Enrollment for Low-Socioeconomic-Status Students Using Machine Learning Approaches"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9859-9749","authenticated-orcid":false,"given":"Surina","family":"He","sequence":"first","affiliation":[{"name":"Measurement, Evaluation and Data Science (MEDS), University of Alberta, Edmonton, AB T6G 2R3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8191-4143","authenticated-orcid":false,"given":"Mehrdad","family":"Yousefpoori-Naeim","sequence":"additional","affiliation":[{"name":"Measurement, Evaluation and Data Science (MEDS), University of Alberta, Edmonton, AB T6G 2R3, Canada"}]},{"given":"Ying","family":"Cui","sequence":"additional","affiliation":[{"name":"Centre for Research in Applied Measurement and Evaluation (CRAME), University of Alberta, Edmonton, AB T6G 2R3, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2475-9647","authenticated-orcid":false,"given":"Maria","family":"Cutumisu","sequence":"additional","affiliation":[{"name":"Department of Educational and Counselling Psychology, Faculty of Education, McGill University, Montreal, QC H3A 1Y2, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s12552-014-9126-1","article-title":"Do expectations make the difference? A look at the effect of educational expectations and academic performance on enrollment in post-secondary education","volume":"6","author":"Bates","year":"2014","journal-title":"Race Soc. Probl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1177\/0272431603260919","article-title":"The relation of early adolescents\u2019 college plans and both academic ability and task-value beliefs to subsequent college enrollment","volume":"24","author":"Eccles","year":"2004","journal-title":"J. Early Adolesc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1177\/0734282920903168","article-title":"Predicting postsecondary enrollment with secondary student engagement data","volume":"38","author":"Fraysier","year":"2020","journal-title":"J. Psychoeduc. Assess."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.econedurev.2012.09.011","article-title":"The effects of family college savings on postsecondary school enrollment rates of students with disabilities","volume":"33","author":"Cheatham","year":"2013","journal-title":"Econ. Educ. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.appdev.2017.03.007","article-title":"Who makes the cut? Parental involvement and math trajectories predicting college enrollment","volume":"50","author":"Degol","year":"2017","journal-title":"J. Appl. Dev. Psychol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1177\/21651434211067425","article-title":"Parent expectations, deaf youth expectations, and transition goals as predictors of postsecondary education enrollment","volume":"45","author":"Johnson","year":"2022","journal-title":"Career Dev. Transit. Except. Individ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1037\/dev0001003","article-title":"Developmental changes in the frequency and functions of school-related communication with friends and family across high school: Effects on college enrollment","volume":"58","author":"Lessard","year":"2022","journal-title":"Dev. Psychol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1177\/0013124506291783","article-title":"Peer influences on the college-going decisions of low socioeconomic status urban youth","volume":"39","author":"Sokatch","year":"2006","journal-title":"Educ. Urban Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s11162-013-9297-4","article-title":"Creating college opportunity: School counselors and their influence on postsecondary enrollment","volume":"54","author":"Belasco","year":"2013","journal-title":"Res. High. Educ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1007\/s11162-009-9150-y","article-title":"Examining the effects of high school contexts on postsecondary enrollment","volume":"51","author":"Engberg","year":"2010","journal-title":"Res. High. Educ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.econedurev.2017.08.002","article-title":"Intended college enrollment and educational inequality: Do students lack information?","volume":"60","author":"Peter","year":"2017","journal-title":"Econ. Educ. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1037\/1045-3830.23.2.199","article-title":"Predictors of educational attainment in the Chicago Longitudinal Study","volume":"23","author":"Ou","year":"2008","journal-title":"Sch. Psychol. Q."},{"key":"ref_13","unstructured":"Bureau of Labor Statistics, U.S. Department of Labor (2025, March 06). The Economics Daily: High School Graduates with No College had Unemployment Rate of 4.5 Percent in February 2022, Available online: https:\/\/www.bls.gov\/opub\/ted\/2022\/high-school-graduates-with-no-college-had-unemployment-rate-of-4-5-percent-in-february-2022.htm."},{"key":"ref_14","unstructured":"Wolla, S.A., and Sullivan, J. (2025, March 06). Education, Income, and Wealth. Page One Economics. Available online: https:\/\/www.stlouisfed.org\/publications\/page-one-economics\/2017\/01\/03\/education-income-and-wealth."},{"key":"ref_15","unstructured":"Shrider, E.A., Kollar, M., Chen, F., and Semega, J. (2021). Income and Poverty in the United States: 2020."},{"key":"ref_16","unstructured":"Guzman, G., and Kollar, M. (2025, March 06). Income in the United States: 2023 (Current Population Reports, P60\u2013282), Available online: https:\/\/www.census.gov\/library\/publications\/2024\/demo\/p60-282.html."},{"key":"ref_17","unstructured":"Juszkiewicz, J. (2020). Trends in Community College Enrollment and Completion Data."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/00461520.2022.2062597","article-title":"Equity in online learning","volume":"57","author":"Tate","year":"2022","journal-title":"Educ. Psychol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1177\/0894845320946397","article-title":"Motivation and postsecondary enrollment among high school students whose parents did not go to college","volume":"49","author":"Tsai","year":"2022","journal-title":"J. Career Dev."},{"key":"ref_20","first-page":"1","article-title":"School belonging: A review of the history, current trends, and future directions","volume":"33","author":"Slaten","year":"2016","journal-title":"Educ. Dev. Psychol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1177\/1538192708317620","article-title":"Understanding Latina and Latino college choice: A social capital and chain migration analysis","volume":"7","author":"Perez","year":"2008","journal-title":"J. Hisp. High. Educ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1111\/j.1467-9620.2006.00655.x","article-title":"Student planning and information problems in different college structures","volume":"108","author":"Person","year":"2006","journal-title":"Teach. Coll. Rec."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1080\/00221546.2005.11772296","article-title":"The relationship between parental involvement as social capital and college enrollment: An examination of racial\/ethnic group differences","volume":"76","author":"Perna","year":"2005","journal-title":"J. High. Educ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1353\/jhe.0.0020","article-title":"Contextual influences on parental involvement in college going: Variations by socioeconomic class","volume":"79","author":"Bell","year":"2008","journal-title":"J. High. Educ."},{"key":"ref_25","first-page":"34","article-title":"Examining social capital as a predictor of enrollment in four-year institutions of postsecondary education for low socioeconomic status students","volume":"4","author":"Stimpson","year":"2010","journal-title":"Enroll. Manag. J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"200","DOI":"10.3102\/0162373712462624","article-title":"Can high schools reduce college enrollment gaps with a new counseling model?","volume":"35","author":"Stephan","year":"2013","journal-title":"Educ. Eval. Policy Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2156759X1101400","DOI":"10.5330\/PSC.n.2011-14.231","article-title":"Post-secondary expectations and educational attainment","volume":"14","author":"Sciarra","year":"2011","journal-title":"Prof. Sch. Couns."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1177\/1478210319860987","article-title":"Broadening conceptions of a \u201ccollege-going culture\u201d: The role of high school climate factors in college enrollment and persistence","volume":"18","author":"Knight","year":"2020","journal-title":"Policy Futures Educ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1162\/rest.2006.88.1.126","article-title":"Financial aid packages and college enrollment decisions: An econometric case study","volume":"88","author":"Linsenmeier","year":"2006","journal-title":"Rev. Econ. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1257\/pol.2.2.185","article-title":"Estimating the effect of student aid on college enrollment: Evidence from a government grant policy reform","volume":"2","author":"Nielsen","year":"2010","journal-title":"Am. Econ. J. Econ. Policy"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ragab, A.H.M., Noaman, A.Y., Al-Ghamdi, A.S., and Madbouly, A.I. (2014, January 9). A comparative analysis of classification algorithms for students college enrollment approval using data mining. Proceedings of the IDEE \u201914: Interaction Design in Educational Environments, Albacete, Spain.","DOI":"10.1145\/2643604.2643631"},{"key":"ref_32","unstructured":"Slim, A., Hush, D., Ojah, T., and Babbitt, T. (2018, January 15\u201318). Predicting student enrollment based on student and college characteristics. Proceedings of the 11th International Conference on Educational Data Mining, EDM, Buffalo, NY, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11853","DOI":"10.1007\/s11227-021-03763-y","article-title":"Predicting freshmen enrollment based on machine learning","volume":"77","author":"Yang","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ujkani, B., Minkovska, D., and Stoyanova, L. (2021, January 15\u201317). A machine learning approach for predicting student enrollment in the university. Proceedings of the 2021 International Scientific Conference Electronics (ET), Sozopol, Bulgaria.","DOI":"10.1109\/ET52713.2021.9579795"},{"key":"ref_35","unstructured":"Bronfenbrenner, U. (1992). Ecological Systems Theory, Jessica Kingsley Publishers."},{"key":"ref_36","unstructured":"Bronfenbrenner, U. (2009). The Ecology of Human Development, Harvard University Press."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/00405841.2014.947216","article-title":"Ecological theory: Preventing youth bullying, aggression, and victimization","volume":"53","author":"Espelage","year":"2014","journal-title":"Theory Into Pract."},{"key":"ref_38","first-page":"44","article-title":"DARPA\u2019s explainable artificial intelligence (XAI) program","volume":"40","author":"Gunning","year":"2019","journal-title":"AI Mag."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pasquale, F. (2015). The Black Box Society: The Secret Algorithms that Control Money and information, Harvard University Press.","DOI":"10.4159\/harvard.9780674736061"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s44217-024-00209-4","article-title":"Enhancing High-School Dropout Identification: A Collaborative Approach Integrating Human and Machine Insights","volume":"3","author":"Bulut","year":"2024","journal-title":"Discov. Educ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"124990","DOI":"10.1109\/ACCESS.2024.3444483","article-title":"From sample poverty to rich feature learning: A new metric learning method for few-shot classification","volume":"12","author":"Zhang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1007\/s10462-021-10088-y","article-title":"Explainable artificial intelligence: A comprehensive review","volume":"55","author":"Minh","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_44","unstructured":"Bowers, A.J. (2021). Early warning systems and indicators of dropping out of upper secondary school: The emerging role of digital technologies. OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots, OECD Publishing."},{"key":"ref_45","first-page":"347","article-title":"High school counselor contacts as predictors of college enrollment","volume":"9","author":"Tang","year":"2019","journal-title":"Prof. Couns."},{"key":"ref_46","unstructured":"Fonti, V., and Belitser, E. (2017). Feature Selection Using Lasso, Vrije Universiteit Amsterdam. Available online: https:\/\/www.researchgate.net\/profile\/David-Booth-7\/post\/Regression-of-pairwise-trait-similarity-on-similarity-in-personal-attributes\/attachment\/5b18368d4cde260d15e3a4e3\/AS%3A634606906785793%401528313485788\/download\/werkstuk-fonti_tcm235-836234.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/10485252.2017.1404598","article-title":"Multiple predicting K-fold cross-validation for model selection","volume":"30","author":"Jung","year":"2018","journal-title":"J. Nonparamet. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1540023","DOI":"10.1142\/S0218213015400230","article-title":"Performance-estimation properties of cross-validation-based protocols with simultaneous hyper-parameter optimization","volume":"24","author":"Tsamardinos","year":"2015","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_50","first-page":"39","article-title":"Evaluation of classification models in machine learning","volume":"7","author":"Milica","year":"2017","journal-title":"Theory Appl. Math. Comput. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Linardatos, P., Papastefanopoulos, V., and Kotsiantis, S. (2020). Explainable ai: A review of machine learning interpretability methods. Entropy, 23.","DOI":"10.3390\/e23010018"},{"key":"ref_52","unstructured":"Lundberg, S.M., and Lee, S.I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Available online: https:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1300\/J002v42n02_06","article-title":"Who is more important for early adolescents\u2019 developmental choices? Peers or parents?","volume":"42","author":"Wang","year":"2007","journal-title":"Marriage Fam. Rev."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1023\/B:EDPR.0000012343.96370.39","article-title":"A model of future-oriented motivation and self-regulation","volume":"16","author":"Miller","year":"2004","journal-title":"Educ. Psychol. Rev."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/99\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:13:31Z","timestamp":1760030011000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/4\/99"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,12]]},"references-count":54,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["bdcc9040099"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9040099","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2025,4,12]]}}}