The aim of this dissertation is to examine how adult learners with a spoken language background who are acquiring a signed language, learn how to use the space in front of the body to express grammatical and topographical relations. Moreover, it aims at investigating the effectiveness of different types of instruction, in particular instruction that focuses the learner's attention on the agreement verb paradigm. To that end, existing data from a learner corpus (Boers-Visker, Hammer, Deijn, Kielstra & Van den Bogaerde, 2016) were analyzed, and two novel experimental studies were designed and carried out. These studies are described in detail in Chapters 3–6. Each chapter has been submitted to a scientific journal, and accordingly, can be read independently.1 Yet, the order of the chapters follows the chronological order in which the studies were carried out, and the reader will notice that each study served as a basis to inform the next study. As such, some overlap in the sections describing the theoretical background of each study was unavoidable.
MULTIFILE
In response to globalisation and internationalisation of both higher education and the job market, The Hague University of Applied Sciences (THUAS) has seen a considerable increase in English-medium courses, i.e. non-language subjects taught through English. Internationally, the rise of English-medium instruction (EMI) has led to research on, and discussion about the possible side-effects of a change in instructional language. More specifically, whether using a foreign language as the medium of instruction has a negative impact on teaching and learning. This paper reports the findings of a pilot research project into the implications of English-medium instruction (EMI) as perceived by students and teachers of the bachelor program Commercial Economics at the Faculty of Business, Finance and Administration at THUAS. Research methods used to collect data include face-to-face interviews with both students and lecturers involved in EMI subject courses, a student questionnaire, and lesson observations. Despite regular exposure to English and an adequate self-perceived English proficiency, results show that a considerable number of students, as well as teaching staff are experiencing difficulties with English-medium instruction and that for many EMI is not as efficient in transferring academic content as instruction in the mother tongue.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
