PREDICTING STUDENT SUCCESS IN PUBLIC ECONOMICS

Year 2018, Volume: 10 Issue: 2, 57 - 75, 31.12.2018

Z Robinson

Abstract

Student performance in public economics and consequently microeconomics as
pre-requisite can improve with the correct pedagogic intervention. This paper
proposes an empirical model that investigates the factors or predictors that may
best explain the success rate in the subject field. The model has been designed
according to existing studies and adjusted to support the discussion behind the
success rate of public economics students at third year level. The dependent
variable is effectively the final mark reached, whilst using a dummy variable to
indicate pass or failure. The coefficients or explanatory variables include the age
of the student, the assignment marks, whether studying full-time or not, gender,
home language, the final mark of the pre-requisite microeconomics first and
second-year-level together with the number of repeats of the latter. The
methodology supports an ordinary least squares regression analysis, but because
of binary data, a binary logit model is also investigated. The results suggest that
the final course mark of first year level and especially second year level have a
significant impact on the final mark of third year Public Economics. This was to
be expected in the sense that the Public Economics content is Microeconomic
based. A higher mark for the assignments during the year also usually results in a
higher final mark for the student. Studying in the home language tends to benefit
the student, although a third-year student tends to be more senior and mature in their studies. Age seems to become a factor because a significant gap between
second and third year studies tends to develop, and potentially has a negative
impact on the final results. Part-time students tend to perform better, with the
student possibly more resourceful in terms of facilities and time management. It
was found that the more the student repeated Public Economics in previous years
of study, the probability to pass Public Economics decreased. The more they
repeated second-year Microeconomics, their probability of passing Public
Economics also got lower. This coincides with the final marks variable as
dependent variable. The results may, amongst others, promote a more efficient,
effective and economic e-learning environment, and may further assist in guiding
other tertiary institutions with the challenges arising within the open distance
learning arena.

Keywords

Public economics, microeconomics, student success, pedagogic intervention

References

• Athey, S.; Katz, L.E.; Krueger, A.B.; Levitt, S. and Poterba, J. (2007). What does performance in graduate school predict? Graduate Economics education and student outcomes. AEA papers and proceedings, 97(2):512-518.
• Bisschoff, C. A. (2005). A prelimary model to identify low-risk MBA applicants. SAJEMS, 8(3), 300–309.
• Chen, J. and Lin, T. (2012). Do supplemental online recorded lecturers help students learn microeconomics? International Review of Economics Education, 11(1): 6-15.
• Cho, M-H. and Heron, M.L. (2015). Self-regulated learning: the role of motivation, emotion, and use of learning strategies in students’ learning experiences in a self-paced online mathematics course. Distance Education, 36(1): 80-99.
• Conradie, P. W. (2014). Supporting Self-Directed Learning by Connectivism and Personal Learning Environments. International Journal of Information and Education Technology, 4(3), 254–259. http://doi.org/10.7763/IJIET.2014.V4.408.
• De Hart, K.; Doussy, E.; Swanepoel, A. ; Van Dyk, M. ; De Clerq, B. and Venter, J. (2011). Increasing throughput: Factors affecting performance of entrylevel undergraduate taxation students at an ODL institution in South Africa. Progressio, 33(1): 171-188.
• Du Plessis, A.; Müller, H. and Prinsloo, P. (2005). Determining the profile of a successful first-year accounting student. SAJHE, 19(4):684-698.
• Hase, S., & Kenyon, C. (2000). From andragogy to heutagogy. In UltiBase Articles. Retrieved from http://www.psy.gla.ac.uk/~steve/pr/Heutagogy.html.
• Keeve, A.; Naude, L. & Esterhuyse, K. (2012). Some predictors of academic performance of first-year students in three- and four-year curricula. Acta Academica, 44(1): 121-158.
• Kizito, R. N. (2016). Connectivism in learning activity design: Implications for pedagogically-based technology adoption in African higher education contexts. International Review of Research in Open and Distance Learning, 17(2), 19–39.
• Peters, O. (2010). Distance education in transition, Developments and issues. (5th ed), (pp. 11 – 93). Oldenburg, Germany: BIS verlag der Carl von Ossietzky Universität Oldenburg.
• Pretorius, A.M.; Prinsloo, P. and Uys, M.D. (2009). Student performance in Introductory Microeconomics at an African open distance learning institution. Africa Education Review, 6(1): 140-158.
• Risenga, A. (2010). Attributes of students’ success and failure in typical ODL institutions. Progressio, 32(2): 85-101.
• Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology & Distance Learning, 2(1).
• Smith, L. and Edwards, L. (2007). A multivariate evaluation of mainstream and academic development courses in first-year microeconomics. South African Journal of Economics, 75(1): 99-117.
• Smith, L.C. (2009). An analysis of the impact of pedagogic interventions in first-year academic development and mainstream courses in microeconomics. South African Journal of Economics, 77(1): 162-178.
• Smith, L.C. and Ranchhod, V. (2012). Measuring the impact of educational inteventions on the academic performance second-year Microeconomics. South African Journal of Economics, 80(3), 431–448.
• Tait, A. (2015). Student success in open, distance and e-learning. The ICDE Report Series. ICDE: Norway.
• Wagemans, L.J., Valcke, M. and Dochy, F. (1991). Learning Economics at a distance: Quality and impact of expertise. A study with Open University students. Distance Education, 12(2).