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In Germany, a lot of young children at risk of language difficulties still go undetected or are not assessed before preschool-age. For children where parents may suspect a disorder, this practice causes a lot of emotional distress alongside lost time for intervention. Thus, what contribution can parents and nursery staff make for the earlier detection of language difficulties? 34 children from four German kindergartens were tested with a standardized preschool screening for language problems by an SLT. Parents and nursery staff completed a questionnaire (FEE 3-4) that was designed to collect potential risk-factors and included the rating of children’s abilities across the main language domains. Outcomes from the FEE 3-4 were compared between parents and nursery staff as well as triangulated with results from the standardized screening. Agreement between parents and nursery staff re. individual children’s potential language difficulties was moderate (Kappa = 0.44, p = .050). Overall, nursery staff rated children’s language abilities more strictly and precisely than parents. Especially their rating of ‘word order’ (p = .022) and ‘verb endings’ contributed significantly to the identification of potential language difficulties similar to the standardized screening. The screening identified two children at risk without caregiver's concern, but not two others who were at risk of language disorder and for whom caregivers expressed concern. Caregiver’s awareness of early language difficulties appears to be rather intuitive. Young children at risk are most reliably detected if standardized instruments are used in combination with caregiver questionnaires. Ideally, this process includes data from parents and nursery staff to be interpreted by an experienced SLT, as the use of a standardized screening alone may lead to missed or mistaken identification where essential information about the child’s environment (e.g. risk factors) is not provided. If parents are concerned about children’s language, full assessment is clearly justified.
The increasing complexity of caseloads in SLT practice, e.g. due to higher comorbidity, lacking information or experience in the
treatment of complicated cases, calls for support from experienced as well as specialist practitioners from within the field - especially
for novice therapists. One way to tackle these challenges may be peer coaching and how it can be employed within the educational
and professional SLT setting.
Peer coaching was implemented across five semesters of a successive SLT study programme at a University of Applied Sciences in
Germany. The approach was embedded in a clinical reasoning seminar with 25 SLT students who each presented a challenging case
study from their current workload. All participants completed a short online survey to evaluate the feasibility of the team approach
within this setting as well as their personal benefit and development re. the discussed case studies.
Students felt encouraged by being able to share their experience and tackle actual challenges. They particularly valued receiving
answers from a broad range of other SLTs but also contributing to other students’ queries and providing practical solutions for
them. All participants felt that peer coaching was an appropriate approach for clinical reasoning to support their professional as well
as personal development. Other outcomes were a perceived increased ability to employ metacognitive reflection to be used with
their whole caseload but also a prospective need for further training. Some students suggested the employment of peer coaching
within their work setting.
In the educational as well as professional SLT setting, peer coaching can be successfully employed, triggering metacognitive
reflection re. practitioner’s thinking and acting, resulting in an increased awareness of needs and skills as part of the clinical
reasoning process.
he development of context-aware applications is a difficult and error-prone task. The dynamics of the environmental context combined with the complexity of the applications poses a vast number of possibilities for mistakes during the creation of new applications. Therefore it is important to test applications before they are deployed in a life system. For this reason, this paper proposes a testing tool, which will allow for automatic generation of various test cases from application description documents. Semantic annotations are used to create specific test data for context-aware applications. A test case reduction methodology based on test case diversity investigations ensures scalability of the proposed automated testing approach.
Smart city applications in the Big Data era require not only techniques dedicated to dynamicity handling, but also the ability to take into account contextual information, user preferences and requirements, and real-time events to provide optimal solutions and automatic configuration for the end user. In this paper, we present a specific functionality in the design and implementation of a declarative decision support component that exploits contextual information, user preferences and requirements to automatically provide optimal configurations of smart city applications. The key property of user-centricity of our approach is achieved by enabling users to declaratively specify constraints and preferences on the solutions provided by the smart city application through the Decision Support component, and automatically map these constraints and preferences to provide optimal responses targeting user needs. We showcase the effectiveness and flexibility of our solution in two real usecase scenarios: a multimodal travel planner and a mobile parking application. All the components and algorithms described in this paper have been defined and implemented as part of the Smart City Framework CityPulse.
Reliable information processing is an indispensable task in Smart City environments. Heterogeneous sensor infrastructures of individual information providers and data portal vendors tend to offer a hardly revisable information quality. This paper proposes a correlation model-based monitoring approach to evaluate the plausibility of smart city data sources. The model is based on spatial, temporal, and domain dependent correlations between individual data sources. A set of freely available datasets is used to evaluate the monitoring component and show the challenges of different spatial and temporal resolutions.
Interpolation of data in smart city architectures is an eminent task for the provision of reliable services. Furthermore, it is a key functionality for information validation between spatiotemporally related sensors. Nevertheless, many existing projects use a simplified geospatial model that does not take the infrastructure, which affects events and effects in the real world, into account. There are various available algorithms for interpolation and the calculation of routes on infrastructure based graphs and distances on geospatial data. This work proposes a combined approach by interconnecting detailed geospatial data whilst regarding the underlying infrastructure model.