ISEG Undergraduate Students: Determinants of Academic Performance

Author(s):

Graça Leão FernandesMargarida Chagas Lopes

Conference:

ECER 2008

Network:

22. Research in Higher Education

Format:

Paper

Session Information

22 SES 08D, Teaching and Learning in Higher Education (Part 7)

Paper Session

Time:

2008-09-12

08:30-10:00

Room:

B1 136

Chair:

Coral Mary Pepper

Contribution

Over the last three decades there have been in Portugal a quite huge public investment in the school system and the implementation of an effective compulsory education. These changes came along with the democratization of school experience. Moreover the increase in the number of students in higher education is a national goal that have been pursued by education policy. However, in spite of all the government and parents investment in children education, school failure is a major problem that affects all levels of education. At university level school failure affects mainly undergraduate students in their first year of studies . This problem is a major concern for those involved in higher education political definition and this is why it becomes a research field of much interest. This paper intends to shed some light on the determinants of academic performance of undergraduate students of the School of Economics and Management in their first year of studies. Following Dollado and Morales (2007) we will measure success in academic performance using the exam´s marks in three 1st year subjects with decreasing degrees of mathematical complexity, Maths, Economy 1 and Management and Economic History. In this paper we go further than Dollado and Morales (2007) since, besides type of school, specialization track at high school and marks obtained at the university entry-exam, we study the role of individual characteristics, family socio-economic background and pre-university school trajectory on the academic performance. We put an emphasis on the effect of transitions from public\private or private\public schools and from in-home to out-home.

Method

We use a data base of almost 530 first year undergraduate students who entered the School of Economics and Management on 2007\2008 school year. Our theoretical background will consist in an educational production function: Mi=F(Si, Xi, PUi, Ti, vi) Where Mi - Average of the marks on the three subjects; Si - Individual characteristics; Xi - Family socio-economic background variables; PUi - Pre-university trajectory variables; Ti - Transition variables; vi - effect of unobserved variables. We have information on individual characteristics (sex, age, place of birth, civil-status, nojob\part time job\full-time job), parents socio-economic background (mother and father school level, situation towards occupation and employment, number of siblings), pre-university school trajectory (number of failures, gaps and changing school in the basic and secondary levels, changes from public to private or private to public schools in the secondary level, specialization track, university entry-exam’s marks, school where the student completed the secondary level) and transitions due to university frequency (in to out home transition). We also have individual data on the marks obtained on the subjects chosen. As we have a continuous support of the dependent variable (numerical marks in a 0 to 20 scale) we run OLS regressions to estimate the above mentioned production function. As the marks are censored at the lower and upper end a standard Tobit model is also estimated. To analyse the hypothesis of different impacts of the exogenous variables at different points of the dependent variable distribution we also run quantile regressions.

Expected Outcomes

We hope to find answers for questions related to academic performance such as: - Is there a gender determination? - How big is the effect of family socio-economic background? - What is the effect of specialization track? - Do pre-university school trajectory matters? - What is the impact of transitions? Previous studies showed the influence upon success at university level of a set of conventional determinants as individual characteristics, socio-economic status of the family of origin, own prior scholar performance, kind of study at high school (for upper secondary’s). We also expect to assess if pre-university trajectory characteristics, mainly transitions between different types of school, and transitions from in home to out home situation have some impact on academic performance at university level.

References

Ammermueller et al. (2003), Schooling Quality in Eastern Europe: Educational Production During Transition, IZA DP nº 746, March. Dollado, J. and Morales, E. (2007), Which Factors Determine Academic Performance of Undergraduate Students in Economics? Some Spanish Evidence, Documento de Trabalho 2007-23, Serie Capital Humano y Empleo, Fedea. Eide and Showalter (1998), “The effect of School Quality on Student Performance: A Quantile Regression Approach”, Economic Letters, Vol.58, pp.345-350. Ermisch, J. and Francesconi, M. (2001), “Family Matters: Impact of Family Background on Educational Attainment”, Economica, May, 137-156. Figlio, D.N. and Stone J. (1999), “Are Private Schools Really Better?”, Research in Labor Economics, 115-140. Gibson, A. and Asthana, S. (1998), “Schools, Pupils and Exam Results:Contextualising School’ Performance”, British Educational Research Journal, Vol.24, pp.269-282. Hanushek, E. A. (1979), “Conceptual and Empirical Issues in the Estimation of Educational Production Functions”, Journal of Human Ressources, 14(3), pp.351-388. Haveman, R. and Wolfe, B. (1995), “The Determinants of Children’s Attainment: A Review of Methods and Findings”, Journal of Economic Literature, 33(4), 1829-1878. Kalb, G. and Maani, S. (2007), The Importance of Observing Early School Leaving and Usually Unobserved Background and Peer Characteristics in Analysing Academic Performance, WP nº 5/2007, Melbourne Institute of Applied Economic and Social Research. Koenker, R. and Basset, G. (1978), “Regression Quantiles”, Econometrica, 46, 33-50. Krueger, A. (1999), “Experimental Estimates of Education Production Functions”, Quaterly Journal of Economics 114 (2): 497-532. Lazear, E. (2001), “Educational Production”, Quaterly Journal of Economics 113 (3): 777-803. Lorenzo, C. (2004), High School Types, Academic Performance and Early Labour Market Outcomes, IZA DP nº 1048, March. Ludger, W. (2004), How Equal Are Educational Opportunities? Family Background and Student Achievement in Europe and the United States, IZA DP nº1284, September. Maani, S. and Kalb, G. (2007), “Academic Performance, Childhood Economic Resources, and the Choice to Leave School at the Age Sixteen”, Economics of Education Review, 26(2). Miller, P. and Volker, P. (1989), “Socio-economic Influences on Educational Attainment: Evidence and Implications for the Tertiary Education Finance Debate”, Australian Journal of Statistics, 31A, 47-70. Neal, D. (1997), “The Effects of Catholic Secondary Schooling on Educational Achievement”, Journal of Labor Economics, 15(1), 98-123. OCES 2004– O sistema de ensino superior em Portugal 1993-2003 http://www.estatisticas.gpeari.mctes.pt/index.php?id_categoria=47&id_item=95451 Rothstein, JM (2004), “College Performance Predictions and the SAT”, Journal of Econometrics, 121, 297-317. Rudd, E. (1984), “A Comparison Between the Results Achieved by Women and Men Studying for First Degrees in British Universities”, Studies in Higher Education, Vol9, pp.47-57. Smith, J. and Naylor, R. (2001), “Determinants of Degree Performance in U.K. Universities: A Statistical Analysis of the 1993 Student Cohort”, Oxford Bulletin of Economics and Statistics, 63, 29-60.

Author Information

Graça Leão Fernandes

School of Economics and Management,Technical University of Lisbon

Mathematics

Lisboa

174

Margarida Chagas Lopes

SOCIUS (Research Center in Economic and Organizational Sociology)

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