Interdisciplinarity in design education: understanding the undergraduate student experience

Interdisciplinarity is becoming a critical issue of concern for design schools across East Asia in their attempts to provide industry graduates with the skills and competences to make creative contributions in a global economy. As a result, East Asian higher education institutions are aggressively endeavouring to provide interdisciplinary undergraduate curriculum that combine traditional designerly skills with engineering knowledge and methods. The current study takes an interdisciplinary undergraduate course as case-study to examine how the pedagogic strategy of team teaching influences student learning experience. Two surveys of student learning were conducted for this research purpose. The first provided an indication of the holistic student learning experience, while the second explored the conditions of team and non-team teaching as influence upon learning experiences specifically. Results showed how students taught by a single instructor provided a more positive overall opinion of course quality and experienced significantly more encouragement to participate compared to team taught respondents. However, findings also indicated how team teaching significantly increased the students’ experience of a balanced contribution from different disciplinary perspectives and how the team teaching approach was significantly more effective in providing students with greater opportunities to understand the relevance of the different disciplines to the course subject. We conclude with a discussion of results in terms of the effective use of team teaching at undergraduate level as strategy for interdisciplinary learning experiences.     

Editors’ introduction transitional economies: Progress and pitfalls

Many Asian countries are undergoing transition from centrally planned to market-oriented economies. The People's Republic of China (PRC) and Viet Nam have been going through this transition since the 1980s, while Mongolia and the former Soviet republics of Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan began their transition in the 1990s with the dissolution of the Soviet Union in 1991. On the other hand, Cambodia, the Lao People's Democratic Republic, and Myanmar, while they have begun reforms, are still in the early stages of transition. North Korea has probably made the least progress in this regard. This Special Issue on Transitional Economies aims to provide valuable insights for Asia's developing countries still in transition and to provide them with policy recommendations for avoiding the pitfalls of transition experienced in the 1990s.     

Efficiency of the OLSE for regressions on two-dimensional grids with sinusoidal regressors and spatially correlated errors

For spatial regressions with sinusoidal surfaces, the ordinary least squares estimator (OLSE) is shown to be asymptotically as efficient as the generalized least squares estimator (GLSE) in that the covariance matrices of the two estimators have the same nontrivial limit under the same normalization.     

A semiparametric cointegrating regression: Investigating the effects of age distributions on consumption and saving

We consider a semiparametric cointegrating regression model, for which the disequilibrium error is further explained nonparametrically by a functional of distributions changing over time. The paper develops the statistical theories of the model. We propose an efficient econometric estimator and obtain its asymptotic distribution. A specification test for the model is also investigated. The model and methodology are applied to analyze how an aging population in the US influences the consumption level and the savings rate. We find that the impact of age distribution on the consumption level and the savings rate is consistent with the life-cycle hypothesis.     

A regression model of the heteroscedastic error variance

We introduce a regression model of the heteroscedastic error variance. A repetitive use of the least squares method is shown to provide the best linear unbiased estimator of the parameter vector of the model.