Academic performance in economics degrees: comparative analysis by levels of education in a Bolivian public university

Authors

DOI:

https://doi.org/10.70208/3007.8245.v6.n1.428

Keywords:

academic performance, higher education, academic trajectories, descriptive analysis, educational equity

Abstract

This study analyzes academic performance among students enrolled in five economics-related programs at a public university in Bolivia during the 2024 academic term. A quantitative, non-experimental, cross-sectional, and descriptive-comparative design was employed. The study was based on a census of 41,736 academic records, analyzed through descriptive statistics, dispersion measures, and boxplot diagrams according to academic level. The results reveal a general trend of progressive improvement in students’ academic performance throughout their university trajectory, with a critical decline observed during the second semester and a gradual reduction in variability at advanced levels. Nevertheless, low outliers persist across all levels, suggesting the existence of internal performance gaps. The findings indicate that academic performance reflects a process of progressive adaptation to the university environment; however, early and sustained institutional interventions are required to ensure greater equity in learning outcomes.

References

Astin, A. W. (1993). What matters in college? Four critical years revisited. Jossey-Bass.

Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. En J. A. Larusson & B. White (Eds.), Learning analytics (pp. 61–75). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4

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

Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Wiley. https://doi.org/10.1002/0471746096

Credé, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance. Perspectives on Psychological Science, 3(6), 425–453. https://doi.org/10.1111/j.1745-6924.2008.00089.x

DeMars, C. (2010). Item response theory. Oxford University Press.

Diseth, Å. (2011). Self-efficacy, goal orientations and learning strategies as mediators between preceding and subsequent academic achievement. Learning and Individual Differences, 21(2), 191–195. https://doi.org/10.1016/j.lindif.2011.01.003

Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Lawrence Erlbaum Associates.

Goldstein, H. (2011). Multilevel statistical models (4th ed.). Wiley.

Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

Komarraju, M., Karau, S. J., & Schmeck, R. R. (2009). Role of the Big Five personality traits in predicting college students’ academic motivation and achievement. Learning and Individual Differences, 19(1), 47–52. https://doi.org/10.1016/j.lindif.2008.07.001

Kuh, G. D., Kinzie, J., Schuh, J. H., & Whitt, E. J. (2008). Student success in college: Creating conditions that matter. Jossey-Bass.

Marginson, S. (2016). The worldwide trend to high participation higher education: Dynamics of social stratification in inclusive systems. Higher Education, 72, 413–434. https://doi.org/10.1007/s10734-016-0016-x

McNeish, D., & Stapleton, L. M. (2016). The effect of small sample size on two-level model estimates: A review and illustration. Educational Psychology Review, 28, 295–314. https://doi.org/10.1007/s10648-014-9287-x

Ministerio de Educación de Bolivia. (2019). Estadísticas educativas.

National Center for Education Statistics. (2021). Digest of education statistics. https://nces.ed.gov

Organisation for Economic Co-operation and Development. (2022). Education at a glance 2022: OECD indicators. OECD Publishing. https://doi.org/10.1787/3197152b-en

Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). Jossey-Bass.

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Danish Institute for Educational Research.

Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838

Schumacker, R. E., & Lomax, R. G. (2016). A beginner’s guide to structural equation modeling (4th ed.). Routledge.

Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford University Press.

Thomas, L. (2012). Building student engagement and belonging in higher education. Higher Education Academy. https://doi.org/10.13140/RG.2.1.5056.7843

Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). University of Chicago Press.

Tinto, V. (1997). Classrooms as communities: Exploring the educational character of student persistence. Journal of Higher Education, 68(6), 599–623. https://doi.org/10.1080/00221546.1997.11779003

Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.

York, T. T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical Assessment, Research, and Evaluation, 20(5), 1–20. https://doi.org/10.7275/hz5x-tx03

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

Published

2026-05-22

How to Cite

Forest Herrera, W. (2026). Academic performance in economics degrees: comparative analysis by levels of education in a Bolivian public university. Horizonte Academico, 6(1), 1753–1775. https://doi.org/10.70208/3007.8245.v6.n1.428

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