Aim: There is often a gap between the ideal of involving older persons iteratively throughout the design process of digital technology, and actual practice. Until now, the lens of ageism has not been applied to address this gap. The goals of this study were: to voice the perspectives and experiences of older persons who participated in co-designing regarding the design process; their perceived role in co-designing and intergenerational interaction with the designers; and apparent manifestations of ageism that potentially influence the design of digital technology. Methods: Twenty-one older persons participated in three focus groups. Five themes were identified using thematic analysis which combined a critical ageism ‘lens’ deductive approach and an inductive approach. Results: Ageism was experienced by participants in their daily lives and interactions with the designers during the design process. Negative images of ageing were pointed out as a potential influencing factor on design decisions. Nevertheless, positive experiences of inclusive design pointed out the importance of “partnership” in the design process. Participants defined the “ultimate partnership” in co-designing as processes in which they were involved from the beginning, iteratively, in a participatory approach. Such processes were perceived as leading to successful design outcomes, which they would like to use, and reduced intergenerational tension. Conclusions: This study highlights the potential role of ageism as a detrimental factor in how digital technologies are designed. Viewing older persons as partners in co-designing and aspiring to more inclusive design processes may promote designing technologies that are needed, wanted and used.
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In this study, we compared the impact of audio-, video-, and text-chat interaction on target language use during online learner-learner interaction and on learner affect amongst adolescent learners of German as a foreign language. Repeated measures and ANOVA analyses revealed a high percentage of target language output in all conditions for all four tasks, especially in text- chat. Audio-chatters produced the most output and used the most meaning negotiation, compensation strategies, self-repair and other-repair strategies. Learners in all conditions gained in enjoyment, willingness to communicate and self-efficacy. Anxiety reduced for text-chatters. Task effects partly determined the quantity of L2 output, while condition effects determined meaning-oriented and form-focused processing.
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Developing and realizing an innovative concept for the Active Aging campus in two years, where students, teachers, companies, residents of surrounding Campus neighborhoods will be invited to do exercise, sports, play, meet and participate. This includes, on the one hand, providing input with regard to a mobility-friendly design from an infrastructural perspective and, on the other hand, organizing activities that contribute to Healthy Aeging of the Zernike site and the city of Groningen. It is not only about having an Active Aging campus with an iconic image, but also about the process. In the process of realization, students, teachers, researchers, companies and residents from surrounding districts will be explicitly involved. This includes hardware (physical environment / infrastructure), software (social environment) and orgware (interaction between the two).
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
Nederland kent ongeveer 220.000 bedrijfsongevallen per jaar (met 60 mensen die overlijden). Vandaar dat elke werkgever verplicht is om bedrijfshulpverlening (BHV) te organiseren, waaronder BHV-trainingen. Desondanks brengt slechts een-derde van alle bedrijven de arbeidsrisico’s in kaart via een Risico-Inventarisatie & Evaluatie (RI&E) en blijft het aandeel werknemers met een arbeidsongeval hoog. Daarom wordt er continu geïnnoveerd om BHV-trainingen te optimaliseren, o.a. door middel van Virtual Reality (VR). VR is niet nieuw, maar is wel doorontwikkeld en betaalbaarder geworden. VR biedt de mogelijkheid om veilige realistische BHV-noodsimulaties te ontwikkelen waarbij de cursist het gevoel heeft daar echt te zijn. Ondanks de toename in VR-BHV-trainingen, is er weinig onderzoek gedaan naar het effect van VR in BHV-trainingen en zijn resultaten tegenstrijdig. Daarnaast zijn er nieuwe technologische ontwikkelingen die het mogelijk maken om kijkgedrag te meten in VR m.b.v. Eye-Tracking. Tijdens een BHV-training kan met Eye-Tracking gemeten worden hoe een instructie wordt opgevolgd, of cursisten worden afgeleid en belangrijke elementen (gevaar en oplossingen) waarnemen tijdens de simulatie. Echter, een BHV-training met VR en Eye-Tracking (interacties) bestaat niet. In dit project wordt een prototype ontwikkeld waarin Eye-Tracking wordt verwerkt in een 2021 ontwikkelde VR-BHV-training, waarin noodsituaties zoals een kantoorbrand worden gesimuleerd (de BHVR-toepassing). Door middel van een experiment zal het prototype getest worden om zo voor een deel de vraag te beantwoorden in hoeverre en op welke manier Eye-Tracking in VR een meerwaarde biedt voor (RI&E) BHV-trainingen. Dit project sluit daarmee aan op het missie-gedreven innovatiebeleid ‘De Veiligheidsprofessional’ en helpt het MKB dat vaak middelen en kennis ontbreekt voor onderzoek naar effectiviteit rondom innovatieve-technologieën in educatie/training. Het project levert onder meer een prototype op, een productie-rapport en onderzoeks-artikel, en staat open voor nieuwe deelnemers bij het schrijven van een grotere aanvraag rondom de toepassing en effect van VR en Eye-Tracking in BHV-trainingen.
