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This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

The purpose of this chapter is to investigate students’ qualitatively different ways of understanding the learning object in three undergraduate courses in the discipline of accounting. The theoretical framework of variation theory, a general learning theory, is applied. The lecturers chose a learning object which is investigated under two different teaching conditions – the conventional lecture model and the variational method. Two student groups were identified as a comparison group and a target group, comparable in various relevant parameters. All students took three required accounting courses. In the comparison group, the lecturers used the conventional lecture model and in the target group the variational model. The results indicated significant differences between the two groups’ examination results in the three courses, with students in the target group performing much better. The educational implications and limitations of the study, and areas for further research, are discussed.

This contribution discusses a continuum model of large discrete networks in planar domains. For this model, the Kirchhoff law, boundary conditions and capacity constraints lead in a system optimisation approach to a infinite dimensional constrained optimisation problem and to “mixed” variational inequalities. Mixed finite element methods can be formulated for these variational inequalities such that computable discretizations of the continuum problem are obtained.

Studies in higher education tend to use different methods and methodologies, from documentary analysis to auto/biographical and observational studies. Most studies are either qualitative or qualitative. A mixed-methods approach has emerged in recent years, in which the qualitative approach generally plays an important role. The purpose of this chapter is to show the potential of a new methodology that is also appropriate for higher education research and widely used in the social sciences: probabilistic topic models. A probabilistic method can be used to analyse and categorise thousands of words. After collecting large sets of texts, content analysis is used to deeply analyse the meaning of these words. The huge number of texts published today pushes researchers to employ new techniques in their search for hidden structures built upon a set of core ideas. These methods are called topic modelling algorithms, with Latent Dirichlet Allocation being the basic probabilistic topic model. The application of these new techniques to the field of higher education is extremely useful, for two reasons: (1) studies in this area deal in some cases with a great volume of data and (2) these techniques allow one to devise models in a way that is unsupervised by humans (even when researchers operate on the resulting model); thus they are less subjective than other types of analyses and methods used for qualitative purposes. This chapter shows the foundations and recent applications of the technique in the higher education field, as well as challenges related to this new technique.