Something to talk about: Content and Language Integrated Learning in Modern Languages in British Higher Education. A case study of German at Aston University

This thesis investigates Content and Language Integrated Learning (CLIL) in German undergraduate programmes in the UK. At its core is a study of how one German department integrates the teaching of language and content in its undergraduate programmes and how instructors and students experience this approach. This micro-context is embedded in the wider macro-context of UK Higher Education and subject to outside forces - be they political, economic, socio-cultural - whose effects will manifest in more or less obvious ways. Data was collected via an online survey of Heads of German at British universities to determine the status quo of CLIL in UK Higher Education and to investigate how certain institutional parameters determine the introduction of CLIL in Higher Education. This project employs a mixed-method case study approach and is based on student questionnaires and semi-structured interview with German teaching staff. The study brings to light a number of significant aspects. For example, contrary to popular belief, content provision in the L2 is rather common at British universities, which is currently not reflected in the research. Student data indicates that German students perceive clear advantages in the university’s approach to CLIL. They consider German-taught content classes challenging yet beneficial for their language development. Staff interviews have yielded intriguing information about perceived advantages and disadvantages of CLIL, about its implications for classroom practice, and about instructors’ attitude towards teacher training, which echo findings from similar investigations in European contexts. Finally, the results of the macro-analysis and the case study are compared and contrasted with findings from European research on ICLHE/CLIL to determine differences and similarities with the British context, a set of recommendations is made regarding CLIL practice at the case study institution, and some implications these indings may have for the future of CLIL in British higher education are discussed.

Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations

In this paper, we focus on the design of bivariate EDAs for discrete optimization problems and propose a new approach named HSMIEC. While the current EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, we employ the Selfish gene theory (SG) in this approach, as well as a Mutual Information and Entropy based Cluster (MIEC) model is also set to optimize the probability distribution of the virtual population. This model uses a hybrid sampling method by considering both the clustering accuracy and clustering diversity and an incremental learning and resample scheme is also set to optimize the parameters of the correlations of the variables. Compared with several benchmark problems, our experimental results demonstrate that HSMIEC often performs better than some other EDAs, such as BMDA, COMIT, MIMIC and ECGA. © 2009 Elsevier B.V. All rights reserved.