Full text met HU account Although people all over the world learn sign languages as a second language (SL2), there is scant literature on sign language acquisition processes to guide professionals in the field. This study focuses on one of the modality-specific phenomena that SL2 learners with a spoken language background encounter that do not exist in their native language (L1): the use of space for grammatical reasons. We analyzed the sign language production data of two learners of Sign Language of the Netherlands (NGT) who we followed for four years. Data comprise interviews that were coded for use of space. Use of space was operationalized by measuring the number of occasions of pointing signs, agreement verbs, classifier verbs, and spatially modified signs from the nominal domain. In addition, we identified examples of typical L2 signing (e.g. errors of overgeneralization, omissions, et cetera). Data show that learners initially produce modified signs that have a gestural counterpart. It might be that they "borrow" signs from the gestural domain, or they produce these highly iconic structures because their gestural inventory has helped them to acquire these structures. Furthermore, the data show that particularly classifier verbs and agreement verbs within a constructed action sequence pose challenges for the learners, and we observed some general error patterns that have been found in L1-learners, such as stacking and reversing the movement path of agreement verbs
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Sociocultural and dialogic theories of education have identified the need to integrate both pedagogical content and language knowledge into teachers’ professional development to promote effective interaction with students about subject content. In this intervention study, a meta-perspective on language was developed to understand how experienced teacher educators (N = 29) conceptualize ongoing language development in professional learning and teaching (referred to as language-developing learning in this study) as part of their pedagogical content knowledge. The data were analysed using content analysis. Language-developing learning was mainly conceived as teacher-oriented professional development. In this process, the language aspect was regarded not only as a tool that applies regulatory and explanatory language but also as a target that connects academic knowledge and interpersonally oriented language. The results increase our awareness of teacher educators’ practical knowledge of academic and interpersonal language in specific disciplinary contexts of teacher professional development in higher education.
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.
