Dropout and Graduation in Higher Education: CHAID Analysis

Yıl 2024, Cilt: 20 Sayı: 1, 107 - 121, 30.06.2024

Nesrin Hark Söylemez

https://doi.org/10.17244/eku.1287393

Öz

This study aims to investigate the socioeconomic variables and their order of importance that have a significant effect on the dropout and graduation of higher education students. Relational survey model was used in the study. In the study, the "Students Dropout and Academic Success Dataset," was utilized. The dataset, created by the Polytechnic Institute of Portalegre, consists of 4424 records. CHAID decision tree algorithm was used to analyze the data. With this method, the independent variables that demonstrate the maximum variation in the dependent variable have been identified hierarchically. It is found that, 49.93% of the students are “graduate”, 32.12% are “dropout”, and 17.948% are “enrolled”. Obtained findings show that the graduation rates of the students are not at the desired level. “Tuition fees up to date” was found as the best variable that explains the students’ school completion. 86.55% of students with not up to date tuition fees were found as dropout and 55.95% of students with up-to-date tuition fees were found as graduate. “Scholarship holder” was found as the variable that best explains the clusters formed by variable “tuition fees up to date”. 89.00% of the students that don’t have their tuition fees up to date and don’t hold a scholarship dropout the school, while 78.44% of students that have their tuition fees up to date and holding a scholarship are graduate. Building on the results obtained from the study, several suggestions were proposed for coping with dropout problem and further guiding research on dropout.

Anahtar Kelimeler

Higher education, dropout, graduation, decision trees, CHAID analysis

Yükseköğretimde Okul Terki ve Mezuniyet: CHAID Analizi

Öz

Bu araştırmada, yükseköğretim öğrencilerinin okul terki ve mezuniyet durumları üzerinde anlamlı etkisi olan sosyoekonomik değişkenlerin tespit edilmesi ve önem sırasının belirlenmesi amaçlanmıştır. Araştırmada ilişkisel tarama modeli kullanılmıştır. Çalışmada “Students Dropout and Academic Success Dataset” veri seti kullanılmıştır. Polytechnic Institute of Portalegre tarafından oluşturulan veri seti, 4424 kayıt içermektedir. Verilerin analiz edilmesinde CHAID karar ağacı algoritması kullanılmıştır. Bu sayede bağımlı değişkende en fazla farklılaşmayı gösteren bağımsız değişkenler hiyerarşik olarak tespit edilmiştir. Araştırmada öğrencilerin %49.93’ünün okulu tamamlama durumlarının “mezun”, %32.12’sinin “terk”, %17.94’ünün “devam eden” olduğu görülmektedir. Elde edilen bulgular öğrencilerin mezuniyet oranlarının istenilen düzeyde olmadığını göstermektedir. Öğrencilerin okul bitirme durumlarını en iyi açıklayan değişkenin “üniversite harç borcu” olduğu bulunmuştur. Harç borcu olan öğrencilerin %86.55'i okulu terk etmiş, harç borcu olmayan öğrencilerin %55.95'i mezun olmuştur. “Üniversite harç borcu” değişkeninin oluşturduğu kümeyi en iyi açıklayan değişken “burs sahibi” olarak bulunmuştur. Üniversite harç borcu olan ve burslu olmayan öğrencilerin %89.00’u okulu terk ederken, harç borcu olmayan ve burslu öğrencilerin %78.44'ü mezun olmuştur. Araştırmadan elde edilen sonuçlardan yola çıkılarak, okul terki sorunuyla başa çıkmak ve okul terkiyle ilgili daha fazla araştırmayı yönlendirmek için çeşitli önerilerde bulunulmuştur.

Anahtar Kelimeler

Yükseköğretim, okul terki, mezuniyet, karar ağaçları, CHAID analizi

Kaynakça

• Aina, C. (2013). Parental background and university dropout in Italy. Higher Education, 65(4), 437–456. https://doi.org/10.1007/s10734-012-9554-z
• Allen, J., Robbins, S. B., Casillas, A., & Oh, I. S. (2008). Third-year college retention and transfer: Effects of academic performance, motivation, and social connectedness. Research in Higher Education, 49(7), 647–664. https://doi.org/10.1007/s11162-008-9098-3
• Araque, F., Roldán, C., & Salguero, A. (2009). Factors influencing university drop out rates. Computers and Education, 53(3), 563–574. https://doi.org/10.1016/j.compedu.2009.03.013
• Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education, 12(2), 155–187. https://doi.org/10.1007/BF00976194
• Bean, J. P. (1983). The Application of a Model of Turnover in Work Organizations to the Student Attrition Process. The Review of Higher Education, 6(2), 129–148. https://doi.org/10.1353/rhe.1983.0026
• Behr, A., Giese, M., Teguim Kamdjou, H. D., & Theune, K. (2021). Motives for dropping out from higher education—An analysis of bachelor’s degree students in Germany. European Journal of Education, 56(2), 325–343. https://doi.org/10.1111/ejed.12433
• Belfield, C. R., & Levin, H. M. (2007). The Price We Pay: Economic and Social Consequences of Inadequate Education. Brookings Institution Press.
• Belloc, F., Maruotti, A., & Petrella, L. (2010). University drop-out: An Italian experience. Higher Education, 60(2), 127–138. https://doi.org/10.1007/s10734-009-9290-1
• Bennett, R. (2003). Determinants of undergraduate student drop out rates in a University Business Studies Department. Journal of Further and Higher Education, 27(2), 123–141. https://doi.org/10.1080/030987703200065154
• Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241–258). https://doi.org/10.4324/9781315775357
• Bruinsma, M. (2003). Effectiveness of higher education : Factors that determine outcomes of university education [University of Groningen]. https://hdl.handle.net/11370/cfad7159-79c0-4b94-8da5-6e7edf31bca9
• Bülbül, T. (2012). Yükseköğretimde Okul Terki: Nedenler ve Çözümler. Eğitim ve Bilim. http://egitimvebilim.ted.org.tr/index.php/EB/article/view/1490
• Chan, F., Cheing, G., Chan, J. Y. C., Rosenthal, D. A., & Chronister, J. (2006). Predicting employment outcomes of rehabilitation clients with orthopedic disabilities: A CHAID analysis. Disability and Rehabilitation, 28(5), 257–270. https://doi.org/10.1080/09638280500158307
• Chen, R. (2008). Financial Aid and Student Dropout in Higher Education: A Heterogeneous Research Approach. In Higher education: Handbook of theory and research (pp. 209–239). New York: Springer Science + Business Media B.V. https://doi.org/10.1007/978-1-4020-6959-8_7
• Chien, C. F., & Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications, 34(1), 280–290. https://doi.org/10.1016/j.eswa.2006.09.003
• Cohen, L., Manion, L., & Morrison, K. (2007). Research Methods in Education. In Research Methods in Education (6th ed.). https://doi.org/10.4324/9780203029053
• Cullen, B. (2000). Evaluating integrated responses to educational disadvantage: Vol. Children’s.
• Daud, A., Lytras, M. D., Aljohani, N. R., Abbas, F., Abbasi, R. A., & Alowibdi, J. S. (2017). Predicting student performance using advanced learning analytics. 26th International World Wide Web Conference 2017, WWW 2017 Companion, 415–421. https://doi.org/10.1145/3041021.3054164
• Dekkers, H., & Claassen, A. (2001). Dropouts - disadvantaged by definition? A study of the perspective of very early school leavers. Studies in Educational Evaluation, 27(4), 341–354. https://doi.org/10.1016/S0191-491X(01)00034-7
• DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (1999). An event history model of student departure. Economics of Education Review, 18(3), 375–390. https://doi.org/10.1016/s0272-7757(98)00049-1
• EACEA. (2015). Tackling early leaving from education and training in Europe : strategies, policies and measures. Publications Office. https://doi.org/10.2797/33979
• Eurostat. (2022). Early leavers from education and training - Statistics Explained. Early Leavers from Education and Training. https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Early_leavers_from_education_and_training
• Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to Design and Evaluate Research in Education. New York: McGraw-Hill.
• Graeff-Martins, A. S., Oswald, S., Obst Comassetto, J., Kieling, C., Rocha Gonçalves, R., & Rohde, L. A. (2006). A package of interventions to reduce school dropout in public schools in a developing country. European Child & Adolescent Psychiatry, 15(8), 442–449. https://doi.org/10.1007/s00787-006-0555-2
• Gury, N. (2011). Dropping out of higher education in France: A micro-economic approach using survival analysis. Education Economics, 19(1), 51–64. https://doi.org/10.1080/09645290902796357
• HEFCE. (2013). Higher education and beyond: Outcomes from full-time first degree study (Issue July). https://dera.ioe.ac.uk//17941/
• Himmetoğlu, B., Yılmaz, G., Sunar, S., Cengizoğlu, S., & Oğuz Balıktay, S. (2022). Bir Bakışta Eğitim 2022: Türkiye Üzerine Değerlendirme ve Öneriler. https://tedmem.org/download/bir-bakista-egitim-2022?wpdmdl=4048&refresh=6399a76a6bdef1671014250
• Hovdhaugen, E. (2009). Transfer and dropout: Different forms of student departure in Norway. Studies in Higher Education, 34(1), 1–17. https://doi.org/10.1080/03075070802457009
• Kass, G. V. (1980). An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics, 29(2), 119. https://doi.org/10.2307/2986296
• Kayri, M., Elkonca, F., Şevgin, H., & Ceyhan, G. (2014). Ortaokul Öğrencilerinin Fen ve Teknoloji Dersine Yönelik Tutumlarının CHAID Analizi ile İncelenmesi. Eğitim Bilimleri Araştırmaları Dergisi, 4(1), 301–316.
• Lassibille, G., & Gómez, L. N. (2008). Why do higher education students drop out? Evidence from Spain. Education Economics, 16(1), 89–105. https://doi.org/10.1080/09645290701523267
• Linoff, G. S., & Berry, M. J. A. (2011). Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (3rd ed.).
• Liu, X., Zhou, Y. H. A., Liu, Z., Alison, Y. H., & Zhou, A. (2009). Who Drops out? A Study of Secondary School Dropouts in Connecticut. NERA Conference Proceedings , 4. http://digitalcommons.uconn.edu/nera_2009http://digitalcommons.uconn.edu/nera_2009/4
• Manona, W. (2015). An Empirical Assessment of Dropout Rate of Learners at Selected High Schools in King William’s Town, South Africa. Africa’s Public Service Delivery and Performance Review, 3(4), 164. https://doi.org/10.4102/apsdpr.v3i4.102
• Martins, M. V., Tolledo, D., Machado, J., Baptista, L. M. T., & Realinho, V. (2021). Early Prediction of student’s Performance in Higher Education: A Case Study. Advances in Intelligent Systems and Computing, 1365 AIST, 166–175. https://doi.org/10.1007/978-3-030-72657-7_16
• McCubbin, I. (2003). An Examination of Criticisms made of Tinto’s 1975 Student Integration Model of Attrition. Integration The Vlsi Journal, February, 1–12.
• McCulloch, A. (2014). Learning from futuretrack: dropout from higher education (Issue 168). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/287689/bis-14-641-learning-from-futuretrack-dropout-from-higher-education-bis-research-paper-168.pdf
• Napoli, A. R., & Wortman, P. M. (1998). Psychosocial factors related to retention and early departure of two-year community college students. Research in Higher Education, 39(4), 419–455. https://doi.org/10.1023/A:1018789320129
• OECD. (2022). Education at a Glance 2022: OECD Indicators. OECD Publishing, Paris. https://doi.org/10.1787/3197152b-en
• Orr, D., Wespel, J., & Usher, A. (2014). Do changes in cost-sharing have an impact on the behaviour of students and higher education institutions ? Evidence from nine case studies VOLUME I: Comparative Report and Volume II National reports.
• Quinn, J. (2013). Drop-out and Completion in Higher Education in Europe among students from under-represented groups (Issue October). https://doi.org/10.13140/RG.2.1.4274.1360
• Realinho, V., Machado, J., Baptista, L., & Martins, M. V. (2022). Predicting Student Dropout and Academic Success. Data, 7(11), 146. https://doi.org/10.3390/data7110146
• Rumberger, R. W. (2020). The economics of high school dropouts. In Entry for the Encyclopedia of the Economics of Education (2nd ed., pp. 149–158). Elsevier. https://doi.org/10.1016/B978-0-12-815391-8.00012-4
• Saa, A. A., Al-Emran, M., & Shaalan, K. (2020). Mining Student Information System Records to Predict Students’ Academic Performance. Advances in Intelligent Systems and Computing, 921, 229–239. https://doi.org/10.1007/978-3-030-14118-9_23
• Şahin, B., & Uysal, M. (2017). A Survey of the Open High School in Terms of the Participation Issue in Adult Education. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 50(1), 127–159. https://doi.org/10.1501/Egifak_0000001390
• Schargel, F., & Smink, J. (2001). Strategies to help solve our school dropout problem (1st ed.). Routledge, New York. https://doi.org/10.4324/9781315854090
• Smith, J. P., & Naylor, R. A. (2001). Dropping out of university: A statistical analysis of the probability of withdrawal for UK university students. Journal of the Royal Statistical Society. Series A: Statistics in Society, 164(2), 389–405. https://doi.org/10.1111/1467-985X.00209
• Spady, W. G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64–85. https://doi.org/10.1007/BF02214313
• Spady, W. G. (1971). Dropouts from higher education: Toward an empirical model. Interchange, 2(3), 38–62. https://doi.org/10.1007/BF02282469
• St. John, E. P., & Starkey, J. B. (1995). The Influence of Prices on the Persistence of Adult Undergraduates. Journal of Student Financial Aid, 25(2). https://doi.org/10.55504/0884-9153.1135
• Sullivan, W. (2017). Machine Learning Beginners - Guide Algorithms: Supervised & Unsupervised Learning, Decision Tree & Random Forest Introduction. Springer International Publisher.
• Thomas, E., & Quinn, J. (2007). First generation entry into higher education: An international study. In Learning and Teaching in Higher Education (Issue 3). Society for Research into Higher Education.
• Thomas, L. (2002). Student retention in higher education: The role of institutional habitus. Journal of Education Policy, 17(4). https://doi.org/10.1007/s10734-012-9554-z
• Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research, 45(1), 89–125. https://doi.org/10.3102/00346543045001089
• Tinto, V. (1993). Leaving College: Rethinking the Causes and Cures of Student Attrition. In Academe (2nd ed., Vol. 73, Issue 6). Chicago: The University of Chicago Press. https://doi.org/10.2307/40250027
• Tinto, V. (1997). Classrooms as communities: Exploring the educational character of student persistence. Journal of Higher Education, 68(6). https://eric.ed.gov/?id=EJ555722
• Tinto, V. (2017). Through the Eyes of Students. Journal of College Student Retention: Research, Theory & Practice, 19(3), 254–269. https://doi.org/10.1177/1521025115621917
• van Diepen, M., & Franses, P. H. (2006). Evaluating chi-squared automatic interaction detection. Information Systems, 31(8), 814–831. https://doi.org/10.1016/j.is.2005.03.002
• White, S., & Kelly, F. (2010). The School Counselor’s Role in School Dropout Prevention. Journal of Counseling and Development, 88(2), 227–235. https://doi.org/10.1002/j.1556-6678.2010.tb00014.x