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.

Assessing learning in Higher Education

Assessing Learning in Higher Education addresses what is probably the most time-consuming part of the work of staff in higher education, and something to the complexity of which many of the recent developments in higher education have added. Getting assessment ‘right’– that is, designing and implementing appropriate models and methods, can determine the future lives and careers of students. But, as Professor Phil Race comments in his excellent and thought-provoking foreword, students entering higher education often have little idea about how exactly assessment will work, and often find that the process is very different from anything they have previously encountered. Assessing Learning in Higher Education contains innovative approaches to assessment drawn from many different cultures and disciplines. The chapter authors argue the need for changing assessment and feedback processes so that they embrace online collaboration and discussion between students as well as between ‘students’ and ‘faculty’. The chapters demonstrate that at some points there is a need to be able to measure individual achievement, and to do this in ways that are valid, transparent, authentic – and above all fair. Assessment and feedback processes need to ensure that students are well prepared for this individual assessment, but also to take account of collaboration and interaction. The respective chapters of Assessing Learning in Higher Education all of which are complete in themselves, but with very useful links to ideas in other chapters, provide numerous illustrations of how this can be achieved.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.