Effects of practices and teaching methods on different measures of the educational output: the case of Spanish universities
Abstract This article analyses the relationships between the educational resources applied in higher education and two types of measures of educational output: average grade obtained by students and the contribution of studies to the development of diverse professional competencies. The relationships are modelled using multi-level production function equations, with the corresponding output measure as the dependent variable. The explanatory variables are the prevalence of various teaching/learning modes and a set of variables that control for the behaviour of students during their studies and for their personal attributes. Estimates, using data from European project Reflex, show significant relationships between the teaching and learning methods used and the alternative measures of educational output considered. The results show that attending lectures has the greatest impact on average grades; nonetheless, more proactive learning methods such as problem-based learning, internships and work provision, and practical knowledge are most influential for the development of professional competencies.
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ANGRIST, J. D. y LAVY, V. (2002) New evidence on classroom computers and pupil learning. The Economic Journal, 112, 735-765.
http://dx.doi.org/10.1111/1468-0297.00068
BARROW, L. y ROUSE, C. E. (2004) Using market valuation to assess the importance and efficiency of public school spending. Journal of Public Economics, 88, 1747-1769.
http://dx.doi.org/10.1016/S0047-2727(03)00024-0
BEATTIE, K. y JAMES, R. (1997) Flexible coursework delivery to Australian postgraduates: how effective is the teaching and learning. Higher Education, 33, 177-194.
http://dx.doi.org/10.1023/A:1002991406703
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http://dx.doi.org/10.1023/A:1018736125097
BELFIELD, C. R. y FIELDING, A. (2001) Measuring the relationship between resources and outcomes in higher education in the UK. Economics of Education Review, 20, 589-602.
http://dx.doi.org/10.1016/S0272-7757(00)00037-6
Bryk, A. S. y Raudembush, S. W. (1992) Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage Publications.
DE LEEUW, J. y KREFT, I. G. G. (1986) Random coefficient models for multilevel analysis. Journal of Educational Statistics, 11, 57-85.
http://dx.doi.org/10.2307/1164848
Dolton, P. y Makepeace, G. M. (1990) Graduate earnings after six years: who are the winners? Studies in Higher Education, 15 (1), 313-355.
http://dx.doi.org/10.1080/03075079012331377581
DOLTON, P.; MARCENARO, O. D. y NAVARRO, L. (2003) The effective use of student time: a stochastic frontier production function case study. Economics of Education Review, 22, 547-560.
http://dx.doi.org/10.1016/S0272-7757(03)00027-X
HANUSHEK, E. A.; RIVKIN, S. G. y KAIN, J. F. (2005) Teachers, schools, and academic achievement. Econometrica, 73, 417-458.
http://dx.doi.org/10.1111/j.1468-0262.2005.00584.x
HARTOG, J. (2001) On human capital and individual capabilities. Review of Income and Wealth, 47 (4), 515-540.
http://dx.doi.org/10.1111/1475-4991.00032
HOXBY, C. M. (2000) The effects of class size on student achievement: new evidence from population variation. Quarterly Journal of Economics, 115, 1239-1285.
http://dx.doi.org/10.1162/003355300555060
Jacob, B. A. y Lefgren, L. (2004a) The impact of teacher training on student achievement: quasi-experimental evidence from school reform efforts in Chicago. Journal of Human Resources, 39, 50-79.
http://dx.doi.org/10.2307/3559005
— (2004b) Remedial education and student achievement: a regression-discontinuity analysis. Review of Economics and Statistics, 86, 226-244.
http://dx.doi.org/10.1162/003465304323023778
James, E.; Alsalam, N.; Conaty, J. C. y To, D. L. (1989) College quality and future earnings: where should you send your child to college? American Economic Review, 79 (2), 247-252.
KRUEGER, A. B. (2003) Economic considerations and class size. The Economic Journal, 113, F34-F63.
http://dx.doi.org/10.1111/1468-0297.00098
Longford, N. T. (1993) Random coefficient models. Oxford, GB: Clarendon Press.
MENG, C. y HEIKE, H. (2005) Student time allocation, the learning environment, and the acquisition of competencies. ROA Research Memorandum ROA-RM2005/1E, Maastricht University.
MOULTON, B. R. (1989) Alternative Tests of the Error Components Model. Econometrica, 57 (3), 685-693.
http://dx.doi.org/10.2307/1911059
— (1990) An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units. The Review of Economics and Statistics, 72 (2), 334-338.
http://dx.doi.org/10.2307/2109724
Pescarella, E. T.; Smart, J. C. y Smylie, M. A. (1992) College tuition costs and early career socio-economic achievement: do you get what you pay for? Higher Education, 24 (3), 275-290.
http://dx.doi.org/10.1007/BF00128447
PISCHKE, J. (2003) The impact of length of school year on student performance and earnings: evidence from the German short school years. National Bureau of Economic Research Working paper, n.º 9964.NBER.
PRITCHETT, L. y FILMER, D. (1999) What education production functions really show: A positive theory of education expenditure. Economics of Education Review, 18, 223-239.
http://dx.doi.org/10.1016/S0272-7757(98)00034-X
ROUSE, C. E.; KRUEGER, A. B. y MARKMAN, L. (2004) Putting computerized instruction to the test: a randomized evaluation of a 'scientifically-based' reading program. Economics of Education Review, 23, 323-338.
http://dx.doi.org/10.1016/j.econedurev.2003.10.005
RYAN, M.; DELANEY, L. y HARMON, C. (2010) Micro-level determinants of lecture attendance and additional Study-Hours. UCD Centre for economic research Working paper series 10/25. Dublin, Irlanda: University College.
TODD, P. E. y WOLPIN, D. I. (2003) On the specification and estimation of the production function for cognitive achievement. The Economic Journal, 113, F3-F33.
http://dx.doi.org/10.1111/1468-0297.00097
Vila, L. E.; Pérez, P. J. y Morillas, F. G. (2012) Higher education and the development of competencies for innovation in the workplace. Management Decision, 50 (9), 1634-1648.
http://dx.doi.org/10.1108/00251741211266723
WORTHINGTON, A. C. (2001) An empirical survey of frontier efficiency measurement techniques in education. Education Economics, 9 (3), 245-268.
http://dx.doi.org/10.1080/09645290110086126
http://dx.doi.org/10.1111/1468-0297.00068
BARROW, L. y ROUSE, C. E. (2004) Using market valuation to assess the importance and efficiency of public school spending. Journal of Public Economics, 88, 1747-1769.
http://dx.doi.org/10.1016/S0047-2727(03)00024-0
BEATTIE, K. y JAMES, R. (1997) Flexible coursework delivery to Australian postgraduates: how effective is the teaching and learning. Higher Education, 33, 177-194.
http://dx.doi.org/10.1023/A:1002991406703
BELFIELD, C. R.; BULLOCK, A. D. y FIELDING, A. (1999) Graduates' view on the contribution of their higher education to their general development: a retrospective evaluation for the United Kingdom. Research in Higher Education, 40 (4), 409-438.
http://dx.doi.org/10.1023/A:1018736125097
BELFIELD, C. R. y FIELDING, A. (2001) Measuring the relationship between resources and outcomes in higher education in the UK. Economics of Education Review, 20, 589-602.
http://dx.doi.org/10.1016/S0272-7757(00)00037-6
Bryk, A. S. y Raudembush, S. W. (1992) Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage Publications.
DE LEEUW, J. y KREFT, I. G. G. (1986) Random coefficient models for multilevel analysis. Journal of Educational Statistics, 11, 57-85.
http://dx.doi.org/10.2307/1164848
Dolton, P. y Makepeace, G. M. (1990) Graduate earnings after six years: who are the winners? Studies in Higher Education, 15 (1), 313-355.
http://dx.doi.org/10.1080/03075079012331377581
DOLTON, P.; MARCENARO, O. D. y NAVARRO, L. (2003) The effective use of student time: a stochastic frontier production function case study. Economics of Education Review, 22, 547-560.
http://dx.doi.org/10.1016/S0272-7757(03)00027-X
HANUSHEK, E. A.; RIVKIN, S. G. y KAIN, J. F. (2005) Teachers, schools, and academic achievement. Econometrica, 73, 417-458.
http://dx.doi.org/10.1111/j.1468-0262.2005.00584.x
HARTOG, J. (2001) On human capital and individual capabilities. Review of Income and Wealth, 47 (4), 515-540.
http://dx.doi.org/10.1111/1475-4991.00032
HOXBY, C. M. (2000) The effects of class size on student achievement: new evidence from population variation. Quarterly Journal of Economics, 115, 1239-1285.
http://dx.doi.org/10.1162/003355300555060
Jacob, B. A. y Lefgren, L. (2004a) The impact of teacher training on student achievement: quasi-experimental evidence from school reform efforts in Chicago. Journal of Human Resources, 39, 50-79.
http://dx.doi.org/10.2307/3559005
— (2004b) Remedial education and student achievement: a regression-discontinuity analysis. Review of Economics and Statistics, 86, 226-244.
http://dx.doi.org/10.1162/003465304323023778
James, E.; Alsalam, N.; Conaty, J. C. y To, D. L. (1989) College quality and future earnings: where should you send your child to college? American Economic Review, 79 (2), 247-252.
KRUEGER, A. B. (2003) Economic considerations and class size. The Economic Journal, 113, F34-F63.
http://dx.doi.org/10.1111/1468-0297.00098
Longford, N. T. (1993) Random coefficient models. Oxford, GB: Clarendon Press.
MENG, C. y HEIKE, H. (2005) Student time allocation, the learning environment, and the acquisition of competencies. ROA Research Memorandum ROA-RM2005/1E, Maastricht University.
MOULTON, B. R. (1989) Alternative Tests of the Error Components Model. Econometrica, 57 (3), 685-693.
http://dx.doi.org/10.2307/1911059
— (1990) An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units. The Review of Economics and Statistics, 72 (2), 334-338.
http://dx.doi.org/10.2307/2109724
Pescarella, E. T.; Smart, J. C. y Smylie, M. A. (1992) College tuition costs and early career socio-economic achievement: do you get what you pay for? Higher Education, 24 (3), 275-290.
http://dx.doi.org/10.1007/BF00128447
PISCHKE, J. (2003) The impact of length of school year on student performance and earnings: evidence from the German short school years. National Bureau of Economic Research Working paper, n.º 9964.NBER.
PRITCHETT, L. y FILMER, D. (1999) What education production functions really show: A positive theory of education expenditure. Economics of Education Review, 18, 223-239.
http://dx.doi.org/10.1016/S0272-7757(98)00034-X
ROUSE, C. E.; KRUEGER, A. B. y MARKMAN, L. (2004) Putting computerized instruction to the test: a randomized evaluation of a 'scientifically-based' reading program. Economics of Education Review, 23, 323-338.
http://dx.doi.org/10.1016/j.econedurev.2003.10.005
RYAN, M.; DELANEY, L. y HARMON, C. (2010) Micro-level determinants of lecture attendance and additional Study-Hours. UCD Centre for economic research Working paper series 10/25. Dublin, Irlanda: University College.
TODD, P. E. y WOLPIN, D. I. (2003) On the specification and estimation of the production function for cognitive achievement. The Economic Journal, 113, F3-F33.
http://dx.doi.org/10.1111/1468-0297.00097
Vila, L. E.; Pérez, P. J. y Morillas, F. G. (2012) Higher education and the development of competencies for innovation in the workplace. Management Decision, 50 (9), 1634-1648.
http://dx.doi.org/10.1108/00251741211266723
WORTHINGTON, A. C. (2001) An empirical survey of frontier efficiency measurement techniques in education. Education Economics, 9 (3), 245-268.
http://dx.doi.org/10.1080/09645290110086126
Pérez Vázquez, P. J., & Vila Lladosa, L. E. (2016). Effects of practices and teaching methods on different measures of the educational output: the case of Spanish universities. Aula, 19, 95–110. https://doi.org/10.14201/14688
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