Predicting Computer Engineering students' dropout in Cuban Higher Education with pre-enrollment and early performance data

  1. Niurys Lázaro Alvarez 1
  2. Zoraida Callejas 1
  3. David Griol 2
  1. 1 1Universidad de las Ciencias Informáticas (Cuba)
  2. 2 University of Granada (Spain)
Revista:
JOTSE

ISSN: 2013-6374

Año de publicación: 2020

Volumen: 10

Número: 2

Páginas: 241-258

Tipo: Artículo

DOI: 10.3926/JOTSE.922 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: JOTSE

Resumen

We present an educational data analytics case study aimed at the early detection of potential dropout in Computer Engineering studies in Cuba. We have employed institutional data of 456 students and performed several experiments for predicting their permanency into three (promotion, repetition, and dropout) or two classes (promoting, not promoting). We have also tested a combination of classification features for training and testing decision trees and neural networks; including information obtained at the time of enrollment, after the first semester and after the first academic year. Our results show a considerable accuracy using all features (96.71%). Using only the features available at the time of enrolment and after the first semester we obtain very positive results (68.86% and 93.85% accuracy respectively) with a high recall of non-promoting students. Thus, it is possible to obtain an early assessment of the risk of dropout that can help defining prevention policies.

Referencias bibliográficas

  • Ahmed, N., Kloot, B., & Collier-Reed, B.I. (2015). Why students leave engineering and built environment programmes when they are academically eligible to continue. European Journal of Engineering Education, 40(2), 128-144. https://doi.org/10.1080/03043797.2014.928670
  • Alkan, N. (2014). Humor, Loneliness and Acceptance: Predictors of University Drop-out Intentions. Procedia - Social and Behavioral Sciences, 152, 1079-1086. https://doi.org/10.1016/j.sbspro.2014.09.278
  • 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, 7(49), 647-664. https://doi.org/10.1007/s11162-008-9098-3
  • Alzen, J.L., Langdon, L.S., & Otero, V.K. (2018). A logistic regression investigation of the relationship between the Learning Assistant model and failure rates in introductory STEM courses. International Journal of STEM Education, 5(1), 56. https://doi.org/10.1186/s40594-018-0152-1
  • Amaya, Y., Barrientos, E., & Heredia, D. (2015). Student Dropout Predictive Model Using Data Mining Techniques. IEEE Latin American Transactions, 13(9), 3127-3134. https://doi.org/10.1109/TLA.2015.7350068
  • Araque, F., Roldán, C., & Salguero, A. (2009). Factors influencing university drop out rates. Computers & Education, 53(3), 563-574. https://doi.org/10.1016/j.compedu.2009.03.013
  • Badr, G., Algobail, A., Almutairi, H., & Almutery, M. (2016). Predicting Students ’ Performance in University Courses: A Case Study and Tool in KSU Mathematics Department. Procedia - Procedia Computer Science, 82(March), 80-89. https://doi.org/10.1016/j.procs.2016.04.012
  • Baradwaj, B.K., & Pal, S. (2011). Mining Educational Data to Analyze Students’ Performance. International Journal of Advanced Computer Science and Applications (IJACSA), 2(6), 63-69. https://doi.org/10.14569/IJACSA.2011.020609
  • Barra, C., & Zotti, R. (2017). What we can learn from the use of student data in efficiency analysis within the context of higher education? Tertiary Education and Management, 23(3), 276-303. https://doi.org/10.1080/13583883.2017.1329450
  • 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.
  • Braxton, J., & Hirschy, A.S. (2005). Theoretical Developments in College Student Departure. In College Student Retention: Formula for Student Success (67-87). Greenwood Press.
  • Burgos, C., Campanario, M.L., Pena, D. de la, Lara, J.A., Lizcano, D., & Martinez, M.A. (2018). Data mining for modeling students’ performance: A tutoring action plan to prevent academic dropout. Computers and Electrical Engineering, 66, 541-556. https://doi.org/10.1016/j.compeleceng.2017.03.005
  • Cabrera, A.F., Nora, A., Terenzini, P.T., Pascarella, E., & Hagedorn, L.S. (1999). Campus Racial Climate and the Adjustment of Students to College: A Comparison between White Students and African‑American Students. The Journal of Higher Education, 70(2), 134-160. https://doi.org/10.1080/00221546.1999.11780759
  • Canedo, E.D., Santos, G.A., & Leite, L.L. (2018). An Assessment of the Teaching-Learning Methodologies Used in the Introductory Programming Courses at a Brazilian University. Informatics in Education, 17(1), 45-59. https://doi.org/10.15388/infedu.2018.03
  • Chies, L., Graziosi, G., & Pauli, F. (2014). Job Opportunities and Academic Dropout: The Case of the University of Trieste. Procedia Economics and Finance, 17, 63-70. https://doi.org/10.1016/S2212-5671(14)00879-X
  • Chung, J.Y., & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346-353. https://doi.org/10.1016/j.childyouth.2018.11.030
  • Costa, F.J.D, Bispo, M.D.S., & Pereira, R.D.C.D.F. (2018). Dropout and retention of undergraduate students in management: A study at a Brazilian Federal University. RAUSP Management Journal, 53(1), 74‑85. https://doi.org/10.1016/j.rauspm.2017.12.007
  • Costa, E.B., Fonseca, B., Santana, M.A., & de Araújo, F.F. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students ’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247-256. https://doi.org/10.1016/j.chb.2017.01.047
  • Delgado, B., María, J., & Quijada-González, M. (2012). Los sistemas de acceso, normativas de permanencia, y estrategias de tutoría y retención de estudiantes de educación superior en Cuba. Informe Nacional. Proyecto ACCEDES.
  • Donoso, S., & Schiefelbein, E. (2007). Análisis de los modelos explicativos de retención de estudiantes en la Universidad: Una visión desde la desigualdad social. Estudios Pedagógicos, XXXIII(1), 7-27. https://doi.org/10.4067/S0718-07052007000100001
  • Dužević, I. (2015). A conceptual framework for analysing the impact of influences on student engagement and learning. Tertiary Education and Management, 21(1), 66-79. https://doi.org/10.1080/13583883.2014.1000368
  • Elster, D. (2014). First-Year Students’ Priorities and Choices in STEM Studies–IRIS Findings from Germany and Austria. Science Education International, 25(1), 52-59.
  • Ethington, C.A. (1990). A psychologycal model of student persistence. Research in Higher Education, 31(3), 266-269. https://doi.org/10.1007/BF00992313
  • Fonseca, G., & García, F. (2016). Permanencia y abandono de estudios en estudiantes universitarios: Un análisis desde la teoría organizacional. Revista de la Educación Superior, 45(179), 25-39. https://doi.org/10.1016/j.resu.2016.06.004
  • Freeman, T.M., Anderman, L.H., & Jensen, J.M. (2007). Sense of belonging in college freshmen at the classroom and campus levels. The Journal of Experimental Education, 3(75), 203-220. https://doi.org/10.3200/JEXE.75.3.203-220
  • Graffigna, A.M., Hidalgo, L., Jofré, A., Berenguer, M.D.C., Moyano, A., & Esteybar, I. (2014). Tutorial Practice as a Strategy of Retention at the School of Engineering. Procedia - Social and Behavioral Sciences, 116, 2489-2493. https://doi.org/10.1016/j.sbspro.2014.01.598
  • Gregori, P., Martínez, V., & Moyano-Fernández, J.J. (2018). Basic actions to reduce dropout rates in distance learning. Evaluation and Program Planning, 66, 48-52. https://doi.org/10.1016/j.evalprogplan.2017.10.004
  • Hausmann, L.R., Schofield, J.W., & Woods, R.L. (2008). Sense of belonging as a predictor of intentions to persist among African American and White first-year college students. Research in Higher Education, 7(48), 803-839. https://doi.org/10.1007/s11162-007-9052-9
  • IESALC-UNESCO (2007). Informe sobre la Educación Superior en América Latina y el Caribe 2000-2005. Instituto Internacional de la UNESCO para la Educación Superior en América Latina y el Caribe (IESALC). Available at: http://www.oei.es/salactsi/informe_educacion_superiorAL2007.pdf
  • Itzhaki, Y., Itzhaky, H., & Yablon, Y.B. (2018). The contribution of parental and societal conditional regard to adjustment of high school dropouts. Journal of Adolescence, 62, 151-161. https://doi.org/10.1016/j.adolescence.2017.11.014
  • Kerby, M.B. (2015). Toward a New Predictive Model of Student Retention in Higher Education. Journal of College Student Retention: Research, Theory & Practice, 17(2), 138-161. https://doi.org/10.1177/1521025115578229
  • Kori, K., Pedaste, M., Niitsoo, M., Kuusik, R., Altin, H., Tõnisson, E. et al. (2015). Why do Students Choose to Study Information and Communications Technology? Procedia - Social and Behavioral Sciences, 191, 2867-2872. https://doi.org/10.1016/j.sbspro.2015.04.249
  • Lacave, C., Molina, A.I., & Cruz-Lemus, J.A. (2018). Learning Analytics to identify dropout factors of Computer Science studies through Bayesian networks. Behaviour & Information Technology, 37(10-11), 993‑1007. https://doi.org/10.1080/0144929X.2018.1485053
  • Lansford, J.E., Dodge, K.A., Pettit, G.S., & Bates, J.E. (2016). A Public Health Perspective on School Dropout and Adult Outcomes: A Prospective Study of Risk and Protective Factors From Age 5 to 27 Years. Journal of Adolescent Health, 58, 652-658. https://doi.org/10.1016/j.jadohealth.2016.01.014
  • Lázaro, N., Callejas, Z., & Griol, D. (2017). La deserción estudiantil en educación superior: S.O.S. en carreras de Ingeniería Informática. VII CLABES. Available at: http://revistas.utp.ac.pa/index.php/clabes/article/view/1674
  • López, I., Marín, G., & García, M.E. (2012). Deserción escolar en el primer año de la carrera de Medicina. Revista de Educación Médica Superior, 26(1), 45-52.
  • López, S., Carpeño, A., Arriaga, J., & Ruiz, M. (2016). Experiencias para el Fomento de las Vocaciones Tecnológicas entre Estudiantes de Enseñanza Secundaria. In TICAI 2016: TICs para el Aprendizaje de la Ingeniería (89-96). IEEE.
  • Lotkowski, V.A., Robbins, S.B., & Noeth, R.J. (2004). The Role of Academic and Non-Academic Factors in Improving College Retention. ACT Policy Report. American College Testing ACT Inc, 7(48), 803-839. https://doi.org/10.1037/e420492008-001
  • Migali, G., & Zucchelli, E. (2017). Personality traits, forgone health care and high school dropout: Evidence from US adolescents. Journal of Economic Psychology, 62, 98-119. https://doi.org/10.1016/j.joep.2017.06.007
  • Miranda, M.A., & Guzmán, J. (2017). Análisis de la deserción de estudiantes universitarios usando técnicas de minería de datos. Formacion Universitaria, 10(3), 61-68. https://doi.org/10.4067/S0718-50062017000300007
  • Mohamed, M.H., & Waguih, H.M. (2018). A proposed academic advisor model based on data mining classification techniques. International Journal of Advanced Computer Research, 8(36), 129-136. https://doi.org/10.19101/IJACR.2018.836003
  • Munizaga-Mellado, F R., Cifuentes-Orellana, M.B., & Beltrán-Gabrie, A.J. (2018). Retención y Abandono Estudiantil en la Educación Superior Universitaria en América Latina y el Caribe: Una Revisión Sistemática. Education Policy Analysis Archives, 26, 61. https://doi.org/10.14507/epaa.26.3348
  • Mussida, C., Sciulli, D., & Signorelli, M. (2018). Secondary school dropout and work outcomes in ten developing countries. Journal of Policy Modeling. https://doi.org/10.1016/j.jpolmod.2018.06.005
  • Nagy, M., & Molontay, R. (2018). Predicting Dropout in Higher Education based on Secondary School Performance. 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES), 389-394.https://doi.org/10.1109/INES.2018.8523888
  • Nandeshwar, A., Menzies, T., & Nelson, A. (2011). Learning patterns of university student retention. Expert Systems with Applications, 38(12), 14984-14996. https://doi.org/10.1016/j.eswa.2011.05.048
  • Paimin, A.N., Prpic, J.K., Hadgraft, R.G., & Alias, M. (2017). Understanding student’s learning experiences in higher education. Proceedings of INTED2017 Conference, 6670-6676. https://doi.org/10.21125/inted.2017.1543
  • Pascarella, E.T., & Terenzini, P.T. (1980). Predicting Freshman Persistence and Voluntary Dropout Decisions from a Theoretical Model. The Journal of Higher Education, 51(1), 60-75. https://doi.org/10.2307/1981125
  • Peña-Calvo, J.V., Inda-Caro, M., Rodríguez-Menéndez, C., & Fernández-García, C. (2016). Perceived Supports and Barriers for Career Development for Second-Year STEM Students. Journal of Engineering Education, 105(2), 341-365. https://doi.org/10.1002/jee.20115
  • Pernas-Gómez, M., Sierra-Figueredo, S., Fernández-Sacasas, J.A., Miralles-Aguilera, E., & Diego-Cobelo, J.M. (2009). Principios estratégicos de la educación en Ciencias de la salud en Cuba (II): La pertinencia. Revista Cubana de Educacion Medica Superior, 23(2), 1-12.
  • Pritchard, M.E., & Wilson, G.S. (2003). Using emotional and social factors to predict student success. Journal of College Student Development, 1(44), 18-28. https://doi.org/10.1353/csd.2003.0008
  • Ramsdal, G., Gurgens, R., & Wynn, R. (2013). Dropout and early unemployment. International Journal of Educational Research, 62, 75-86. https://doi.org/10.1016/j.ijer.2013.06.011
  • Reason, R.D. (2003). Student variables that predict retention: Recent research and new developments. Naspa Journal, 4(40), 598-616. https://doi.org/10.2202/0027-6014.1286
  • Robbins, S.B., Allen, J., Casillas, A., Peterson, C.H., & Lee, H. (2006). Unraveling the differential effects of motivational and skills, social, and self-management measures from traditional predictors of college outcomes. Journal of College Student Development, 3(98), 598-616. https://doi.org/10.1037/0022-0663.98.3.598
  • Robison, S., Jaggers, J., Rhodes, J., Blackmon, B.J., & Church, W. (2017). Correlates of educational success: Predictors of school dropout and graduation for urban students in the Deep South. Children and Youth Services Review, 73, 37-46. https://doi.org/10.1016/j.childyouth.2016.11.031
  • Rodríguez-Gómez, D., Feixas, M., Gairín, J., & Muñoz, J.L. (2012). Understanding Catalan University Dropout from a Comparative Approach. Procedia - Social and Behavioral Sciences, 46, 1424-1429. https://doi.org/10.1016/j.sbspro.2012.05.314
  • Spady, W.G. (1970). Dropouts from Higher Education: An Interdisciplinary Review and Synthesis. Interchange, 1(1), 64-85. https://doi.org/10.1007/BF02214313
  • Suárez-Montes, N., & Díaz-Subieta, L.B. (2015). Estrés académico, deserción y estrategias de retención de estudiantes en la educación superior. Revista de Salud Pública, 17(2), 300-313. https://doi.org/10.15446/rsap.v17n2.52891
  • Teixeira de Mello, S.P., de Melo, P.A., & de Mello Filho, R.T. (2015). Study of the Dropout in Technological Education in a Higher Education Institution in Southern Brazil. Eccos-Revista Cientifica, 37, 181-196. https://doi.org/10.5585/eccos.n37.4890
  • Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research Winter, 45(1), 89-125. https://doi.org/10.3102/00346543045001089
  • Tinto, V. (1982). Defining dropout: A matter of perspective. New Directions for Institutional Research, 1982(36), 3-15. https://doi.org/10.1002/ir.37019823603
  • Tinto, V. (2010). From theory to action: Exploring the institutional conditions for student retention. Higher education: Handbook of theory and research. https://doi.org/10.1007/978-90-481-8598-6_2
  • Ullah, M.A., Alam, M.M., Mahiuddin, Md., & Rahman, M.M. (2019). Predicting Factors of Students Dissatisfaction for Retention. In Abraham, A., Dutta, P., Mandal, J. K., Bhattacharya, A., & Dutta, S. (Eds.), Advances in Intelligent Systems and Computing Proceedings of IEMIS 2018 (755, 501-510). Springer. https://doi.org/10.1007/978-981-13-1951-8_45
  • United Nations (2015). Transforming our world: The 2030 Agenda for Sustainable Development (A/RES/70/1). United Nations. Available at: https://sustainabledevelopment.un.org/post2015/transformingourworld
  • Vila, D., Cisneros, S., Granda, P., & Ortega, C. (2019). Detection of Desertion Patterns in University Students Using Data Mining Techniques: A Case Study. In Botto-Tobar, M., Pizarro, G., Zúñiga-Prieto, M., D’Armas, M., & Sánchez, M.Z. (Eds.), Technology Trends. CITT 2018. Communications in Computer and Information Science (895, 420-429). Springer, Cham. https://doi.org/10.1007/978-3-030-05532-5_31
  • Villanueva, E., Bentancur, N., Lacerda, M. de, & González, M.D. (2008). Reformas de la educación superior: 25 propuestas para la educación superior en América Latina y el Caribe. IESALC-UNESCO, 241-295.
  • Villwock, R., Appio, A., & Andreta, A.A. (2015). Educational Data Mining with Focus on Dropout Rates. IJCSNS International Journal of Computer Science and Network Security, 15(3), 17-23.
  • Williams, B., Wankat, P.C., & Neto, P. (2018). Not so global: A bibliometric look at engineering education research. European Journal of Engineering Education, 43(2), 190-200. https://doi.org/10.1080/03043797.2016.1153043
  • Zając, T.Z., & Komendant-Brodowska, A. (2018). Premeditated, dismissed and disenchanted: Higher education dropouts in Poland. Tertiary Education and Management, 25(1), 1-16. https://doi.org/10.1007/s11233-018-09010-z
  • Zehetmeier, D., Axel, B., Bruggemann-Klein, A., Thurner, V., & Schlierkamp, K. (2015). Finding Competence Characteristics among First Semester Students in Computer Science. 45th Annual Frontiers in Education Conference, 1079-1087. https://doi.org/10.1109/FIE.2015.7344201
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