L2 pronunciation training should unequivocally be linked to complex daily life experiences (Derwing 2017). Each client comes from a different background, participates in a different environmental context and engages in different activities within those contexts (Threats 2008). This is a particularly challenging aspect in the L2 practice (Derwing 2017). The International Classification of Functioning, Disability and Health, also known as the ICF-Model (WHO 2001, 2013), offers a conceptual framework that acknowledges the intricate dimensions of human functioning and incorporates personal and contextual factors that can influence participation in daily live (Heerkens and de Beer 2007; Ma, Threats, and Worrall 2008). This paper provides an exploration of the application of this model to pronunciation and intelligibility difficulties in L2 learning. We apply the model to a specific L2 learner, Mahmout and demonstrate how its use allows for consideration of factors much broader than the phonological or phonetic challenges Mahmout faces. Mahmout must be able to generalize that what he has learned into functional communicative competences to improve his participation. The ICF-model (WHO 2001, 2013) is used globally in a broad array of healthcare professions, including Speech and Language Therapists (SLT’s). Yet, it is not a customary tool, nor probably an obvious one, used by L2-professionals (Blake and McLeod 2019). Of course, our goal is not to classify pronunciation problems of L2 learners as disabilities. The model proves a useful tool to view the individual L2 learner as a whole, and part of a larger system. It may allow L2 professionals to tailor their intervention to the individual’s needs and situation and will consequently be able to establish priorities in instruction to enable appropriate goal setting for each individual (Blake and McLeod 2019). It allows identification of influencing barriers or facilitating factors within the stagnation or improvement of pronunciation (Blake and McLeod 2019; Howe 2008).
Little is known about the effects of different instructional approaches on learner affect in oral interaction in the foreign language classroom. In a randomized experiment with Dutch pre-vocational learners (N = 147), we evaluated the effects of 3 newly developed instructional programs for English as a foreign language (EFL). These programs differed in instructional focus (form-focused vs. interaction strategies- oriented) and type of task (pre-scripted language tasks vs. information gap tasks). Multilevel analyses revealed that learners’ enjoyment of EFL oral interaction was not affected by instruction, that willingness to communicate (WTC) decreased over time, and that self-confidence was positively affected by combining information gap tasks with interactional strategies instruction. In addition, regression analyses revealed that development in learners’ WTC and enjoyment did not have predictive value for achievement in EFL oral interaction, but that development in self-confidence did explain achievement in EFL oral interaction in trained interactional contexts.
Background: Persons with profound intellectual and multiple disabilities (PIMD) are vulnerable when it comes to experiencing pain. Reliable assessment of pain-related behaviour in these persons is difficult. Aim To determine how pain items can be reliably scored in adults with PIMD.Methods: We developed an instruction protocol for the assessment of pain-related behaviour in four phases. We used videos of 57 adults with PIMD during potentially painful situations. The items were assessed for inter-rater reliability (Cohen's kappa or percentage of agreement).Results: The developed instruction protocol appeared to be adequate. Twelve items had satisfactory inter-rater reliability (n = 9: .30–1.00; n = 3: 85%–100%).Discussion: Calibrating and adjustments to the instructions and item set appeared to be crucial to reliably score 12 items in adults with PIMD. Further research should focus on creating an assessment instrument based on these reliably scored items.
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.
