Inter)nationally there is discussion about whether auditory processing disorders (APD) should be seen as a unique clinical diagnosis and what is the most appropriate diagnosis and referral of children in this target group. In this context, the Dutch Audiological Centres (AC) have different care pathways for children with so-called unexplained listening difficulties. The purpose of the current document is to provide professionals with tools to identify, diagnose and treat children with listening difficulties. The Dutch Position Statement Children with Listening Difficulties has been developed based on current scientific evidence of listening difficulties, and based on meetings held with professionals. Professionals in the Dutch Audiological Centres have reached a consensus with the following 9 statements: Definition: (1) The target group 'Children with listening difficulties' is not a unique and demonstrable clinical entity. (2) The problems of children with listening difficulties are multimodal. (3) The symptoms of children with listening difficulties may also occur in children with other developmental disorders such as AD(H)D, DLD, dyslexia and learning disorders. Detection and referral: (4) After detection of listening difficulties, children can be referred to a multidisciplinary centre. Diagnostics: (5) When diagnosing a child with listening difficulties, an audiologist, a speech language therapist and a behavioral scientist must be involved. (6) Listening difficulties are initially mapped using patient history (with client-centred focus) and, if available, a validated questionnaire. (7) In the case of children with listening difficulties, a speech-in-noise test is always carried out in addition to the pure tone and speech audiometry (8) The diagnostic procedure for listening difficulties starts from a broad perspective on development. Therapy: (9) For children with listening difficulties, intervention is focused on the client’s needs and focuses on action-oriented practice. This document informs professionals in the Netherlands, who are working with children who are referred because of listening difficulties in the absence of hearing loss, about the current evidence available and about the consensus in the Netherlands.
From the article: "The educational domain is momentarily witnessing the emergence of learning analytics – a form of data analytics within educational institutes. Implementation of learning analytics tools, however, is not a trivial process. This research-in-progress focuses on the experimental implementation of a learning analytics tool in the virtual learning environment and educational processes of a case organization – a major Dutch university of applied sciences. The experiment is performed in two phases: the first phase led to insights in the dynamics associated with implementing such tool in a practical setting. The second – yet to be conducted – phase will provide insights in the use of pedagogical interventions based on learning analytics. In the first phase, several technical issues emerged, as well as the need to include more data (sources) in order to get a more complete picture of actual learning behavior. Moreover, self-selection bias is identified as a potential threat to future learning analytics endeavors when data collection and analysis requires learners to opt in."
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low. As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers. To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is: How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems? HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases: I. Highway: non-professional drivers; II. Distribution Centre: professional drivers.
Traffic accidents are a severe public health problem worldwide, accounting for approximately 1.35 million deaths annually. Besides the loss of life, the social costs (accidents, congestion, and environmental damage) are significant. In the Netherlands, in 2018, these social costs were approximately € 28 billion, in which traffic accidents alone accounted for € 17 billion. Experts believe that Automated Driving Systems (ADS) can significantly reduce these traffic fatalities and injuries. For this reason, the European Union mandates several ADS in new vehicles from 2022 onwards. However, the utility of ADS still proves to present difficulties, and their acceptance among drivers is generally low.As of now, ADS only supports drivers within their pre-defined safety and comfort margins without considering individual drivers’ preferences, limiting ADS in behaving and interacting naturally with drivers and other road users. Thereby, drivers are susceptible to distraction (when out-of-the-loop), cannot monitor the traffic environment nor supervise the ADS adequately. These aspects induce the gap between drivers and ADS, raising doubts about ADS’ usefulness among drivers and, subsequently, affecting ADS acceptance and usage by drivers.To resolve this issue, the HUBRIS Phase-2 consortium of expert academic and industry partners aims at developing a self-learning high-level control system, namely, Human Counterpart, to bridge the gap between drivers and ADS. The central research question of this research is:How to develop and demonstrate a human counterpart system that can enable socially responsible human-like behaviour for automated driving systems?HUBRIS Phase-2 will result in the development of the human counterpart system to improve the trust and acceptance of drivers regarding ADS. In this RAAK-PRO project, the development of this system is validated in two use-cases:I. Highway: non-professional drivers;II. Distribution Centre: professional drivers.Collaborative partners:Bielefeld University of Applied Sciences, Bricklog B.V., Goudappel B.V., HaskoningDHV Nederland B.V., Rhine-Waal University of Applied Sciences, Rijkswaterstaat, Saxion, Sencure B.V., Siemens Industry Software Netherlands B.V., Smits Opleidingen B.V., Stichting Innovatiecentrum Verkeer en Logistiek, TNO Den Haag, TU Delft, University of Twente, V-Tron B.V., XL Businesspark Twente.
It is VHL’s mission to train high-quality, committed and innovative professionals who con-tribute to a more sustainable world , and who are able to organize and manage multi-stakeholder processes for sustainable change: graduates with transdisciplinary competences. Secondly, VHL aims to contribute to the SDG-agenda by linking its education and applied research to eight particular SDGs of which Resilient Communities is one. However, to operationalize SDGs in practice, and aligning targets and strategies of different stakeholders is difficult: ‘resilience’ and ‘sustainability’ refer to ‘wicked problems’ for which no definitive problem formulation, nor clear-cut solutions exist. Addressing wicked problems like ‘resilience’ and ‘sustainability’ requires transdisciplinary collaboration to manage and transform divergent values and conflicting interests, and to co-create sustainable innovations. This HBO postdoc views the 17 SDGs as a compass to align targets and strategies of citizens, government, civil society organizations, private sector and knowledge institutes who collaborate in Living Labs of VHL focusing on resilient communities/regions. Through spiraling action-reflection cycles, stakeholders will use the SDG compass to make success mechanisms, obstacles and trade-offs visible, assuming they stay engaged to overcome difficulties to improve interventions and innovations; this is expected to result in adapted sustainability practices and lessons learned on reaching community resilience. The postdoc’s aim is two-fold highlighting the link between research and education: (1) Design a methodology to integrate SDGs effectively in VHL’s applied research: using the SDGs as compass to improve performance and outcomes of transdisciplinary collaborations. (2) Develop a Roadmap for transdisciplinary education at course, curriculum, and institutional level with SDGs as compass. Future graduates require the competence to work together with others outside one own’s discipline, institute, culture or context. Living Labs offer a suitable learning environment to develop this competence
