Background to the problem Dutch society demonstrates a development which is apparent in many societies in the 21st century; it is becoming ethnically heterogeneous. This means that children who are secondlanguage speakers of Dutch are learning English, a core curriculum subject, through the medium of the Dutch language. Research questions What are the consequences of this for the individual learner and the class situation?Is a bi-lingual background a help or a hindrance when acquiring further language competences. Does the home situation facilitate or impede the learner? Additionally, how should the TEFL professional respond to this situation in terms of methodology, use of the Dutch language, subject matter and assessment? Method of approach A group of ethnic minority students at Fontys University of Professional Education was interviewed. The interviews were subjected to qualitative analysis. To ensure triangulation lecturers involved in teaching English at F.U.P.E. were asked to fill in a questionnaire on their teaching approach to Dutch second language English learners. Thier response was quantitatively and qualitatively analysed. Findings and conclusions The students encountered surprisingly few problems. Their bi-lingualism and home situation were not a constraint in their English language development. TEFL professionals should bear the heterogeneous classroom in mind when developing courses and lesson material. The introduction to English at primary school level and the assessment of DL2 learners require further research.
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Within the Netherlands, Content and Language Integrated Learning (CLIL) in foreign language teaching can be considered a sibling of 'Language Oriented Content Teaching' (LOCT), a pedagogy in mainstream classes with second language learners of Dutch, where Dutch is used as language of instruction. This article characterizes two decades of research on LOCT through Dutch in multilingual schools and discusses its relevance for CLIL development.
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In this paper, we examine the process of designing robot-performed iconic hand gestures in the context of a long-term study into second language tutoring with children of approximately 5 years old. We explore four factors that may relate to their efficacy in supporting second language tutoring: the age of participating children; differences between gestures for various semantic categories, e.g. measurement words, such as small, versus counting words, such as five; the quality (comprehensibility) of the robot’s gestures; and spontaneous reenactment or imitation of the gestures. Age was found to relate to children’s learning outcomes, with older children benefiting more from the robot’s iconic gestures than younger children, particularly for measurement words. We found no conclusive evidence that the quality of the gestures or spontaneous reenactment of said gestures related to learning outcomes. We further propose several improvements to the process of designing and implementing a robot’s iconic gesture repertoire.
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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.
