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Globally observed trends in aging indicate that older adults constitute a growing share of the population and an increasing demographic in the modern technologies marketplace. Therefore, it has become important to address the issue of participation of older adults in the process of developing solutions suitable for their group. In this study, we approached this topic by organizing a hackathon involving teams of young programmers and older adults participants. Below we describe a case study of that hackathon, in which our objective was to motivate older adults to participate in software engineering processes. Based on our results from an array of qualitative methods, we propose a set of good practices that may lead to improved older adult participation in similar events and an improved process of developing apps that target older adults.

This paper presents a new method of engaging older participants in the process of application and IT solutions development for older adults for emerging IT and tech startups. A new method called SPIRAL (Support for Participant Involvement in Rapid and Agile software development Labs) is proposed which adds both sustainability and flexibility to the development process with older adults. This method is based on the participatory approach and user empowerment of older adults with the aid of a bootstrapped Living Lab concept and it goes beyond well established user-centered and empathic design. SPIRAL provides strategies for direct involvement of older participants in the software development processes from the very early stage to support the agile approach with rapid prototyping, in particular in new and emerging startup environments with limited capabilities, including time, team and resources.

The article focuses on predicting trustworthiness from textual content of webpages. The recent work Olteanu et al. proposes a number of features (linguistic and social) to apply machine learning methods to recognize trust levels. We demonstrate that this approach can be substantially improved in two ways: by applying machine learning methods to vectors computed using psychosocial and psycholinguistic features and in a high-dimensional bag-of-words paradigm of word occurrences. Following [13], we test the methods in two classification settings, as a 2-class and 3-class scenario, and in a regression setting. In the 3-class scenario, the features compiled by [13] achieve weighted precision of 0.63, while the methods proposed in our paper raise it to 0.66 and 0.70. We also examine coefficients of the models in order to discover words associated with low and high trust.

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Abstract. This paper presents a cooperative location-based game for the elderly with the use of tablets equipped with mobile application. The game was designed to tackle at once several crucial topics related to the issue of aging, namely the social inclusion, education in the field of modern technology, motivation for learning as well as physical activity. Mixed-aged teams consisting of two players: a junior and a senior took part in the game. The preliminary results suggest that the game can successfully address a number of issues including improving the elderly technical skills, increasing the elderly physical activity as well as positive intergenerational interaction. The paper describes the game setup in details and presents some initial data gathered during the gameplay.

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Including older adults as full stakeholders in digital society.