2020, Inglese, Terry, Korkut, Safak, Dornberger, Rolf

This chapter presents the instructional design of a language-training model for professional and vocational purposes on behalf of the Swiss railway industry, specifically designed for German-speaking train drivers and train operators, who work for the Schweizerische Südostbahn (SOB). In fact, around 50 train drivers and train operators need to learn Italian and be able to communicate clearly and confidently in this language by 2021. Thanks to the opening of the Gotthard Base Tunnel in 2016, some Swiss railway companies are expanding their business portfolios also in the Italian speaking region of Switzerland. Augmented Reality (AR), specifically the Blippar app, is used here as an additional and motivating guide to learning technical terms and nouns, verbs, and dialogue structures, in short: essential railway communication features between train drivers and train operators. The final goal of the chapter is to describe how railway professional trainees, learning a new language, are actively designing their own language learning contents, using AR.

2020, Menon, Dilip, Korkut, Safak, Inglese, Terry, Dornberger, Rolf, Dornberger, Rolf

Today, all smartphones contain a variety of embedded sensors capable of monitoring and measuring relevant physical qualities and quantities, such as light or noise intensity, rotation and acceleration, magnetic field, humidity, etc. Combining data from these different sensors and deriving new practical information is the way to enhance the capabilities of such sensors, known as sensor fusion or multimodal sensing. However, the authors hypothesize that the sensing technology that is embedded in smartphones may also support daily life task management. Because one of the biggest challenges in mobile sensing on smartphones is the lack of appropriate unified data analysis models and common software toolkits, the authors have developed a prototype for a mobile sensing architecture, called Sensing Things Done (STD). With this prototype, by applying multimodal sensing and gathering sensor data from performing a specific set of tasks, the authors were able to conduct a feasibility study to investigate the hypothesis set above. Having examined to what extent the task-related activities could be detected automatically by using sensors of a standard smartphone, the authors of this chapter describe the conducted study and provide derived recommendations

2018, Dornberger, Rolf, Inglese, Terry, Korkut, Safak, Zhong, Jia, Dornberger, Rolf