In this paper, we present a method for generating faded worked examples as personalized exercises aimed at bridging the gap between knowledge of theoretical concepts and their application in the real world, which is particularly important in vocational education. Previous works suggest that faded worked examples are effective learning material that can also adapt to learners of different levels. Yet, there is no formulated method for automatically generating faded worked examples personalized to different learners in real-time. We develop a method for generating faded worked examples from scenarios, changing the faded positions and degree of fading based on the targeted skills and the learner’s proficiency level. We evaluate our method through a user study involving 13 computer science students from a German university, who practice specific computer networking skills. The results indicate significant improvement in the targeted skill over the untargeted one, highlighting the potenti al of our approach in vocational education settings. Our study is an early but promising step towards the future of personalized learning, paving the way for further research in adaptive and personalized vocational training.

Several works highlight how robots can navigate in a socially-aware manner by respecting and avoiding people’s personal spaces. But how should the robot act when there is no way around a group of persons? In this work, we explore this question by comparing three different ways to cross two conversing people in a hallway environment. In an online study with 135 participants, users rated the robot’s behavior on several items such as "social adequacy" or how "disturbing" it was. The three versions differ in the type of contact intention, i.e., no contact, nonverbal contact, and a combination of nonverbal and verbal contact. The results show that, on the one hand, users expect social behavior from the robot, so that they can anticipate its behavior, but on the other hand, they want it to be as little disruptive as possible.

Various works show that proxemics occupies an important role in human-robot interaction and that appropriate proxemic interaction depends on many characteristics of humans and robots. However, there is none that shows the relationship between an emotional state expressed by a user and a proxemic reaction of the robot to it, in a social interaction between these interactants. In the current experiment (N = 82), we investigate this using an online study in which we examine which proxemic response (i.e., approaching, not moving, moving away) to a person’s expressed emotional state (i.e., anger, fear, disgust, surprise, sadness, joy) is perceived as appropriate. The quantitative and qualitative data collected suggests that the robot’s approach was considered appropriate for the expressed fear, sadness, and joy, whereas moving away was perceived as inappropriate in most scenarios. Further exploratory findings underline the importance of appropriate nonverbal behavior on the perception of the robot.

Finger touch and mouse-based interaction are today's predominant modalities to interact with screen-based user interfaces. Related work suggests that new techniques interweaving pre-touch sensing and touch are useful future alternatives. In this paper, we introduce Fich, a novel pre-touch technique that augments conventional touch interfaces with tooltips and further "fingerover" effects, opening up the space in front of the screen for user interaction. To study Fich in-depth, we developed a Fich-enabled weather application and compared user experience and interface discovery ("serendipity") of Fich against the traditional input modalities Mouse and finger Touch in a user study with 42 subjects. We report on the results, implying Fich's user experience to be rated significantly higher in terms of hedonic quality and significantly lower in terms of pragmatic quality, as compared to traditional input modalities.

First impressions play an important part in social interactions, establishing the foundation of a person's opinion about their counterparts. Since interpersonal communication is essentially multimodal, people are judged during first encounters by both their verbal utterances and nonverbal behavior, such as how they utilize eye contact, body distance, and body orientation. In this paper, we argue that robots would provide better user experiences, including being perceived as more likable if they were able to make a good first impression when introduced to a new home. Moreover, we wanted to test if robots can improve their perceived impression by behaving in a proxemic-aware manner; i.e., by following established social norms, which prescribe, for example how far people should position themselves around other objects to improve the facilitation of social interactions. In order to test this hypothesis, we conducted a user study with 16 participants in a virtual reality setting, comparing the impression of two agents being introduced to their new homes by users. We found that the proxemic-aware agent was indeed perceived as significantly better considering multiple constructs, including perceived anthropomorphism and trustworthiness.

There seems to be a danger to carelessly replace routine tasks in homes through automation with IoT-technology. But since routines, such as watering houseplants also have positive influences on inhabitants' wellbeing, they should be transformed through carefully performed designs. To this end, an attempt to use technology for augmenting a set of houseplants' non-verbal communication capabilities is presented. First, we describe in detail how implicit interactions have been designed to support inhabitants in watering their plants through meaningful interactions. Then, we report on a field study with 24 participants, comparing two alternative design implementations based on contrasting embodied interaction technologies (i.e., augmented reality and embedded computing technology). The study results highlight shortcomings of today's smartphone mediated augmented reality compared to physical interface alternatives, considering measurements of perceived attractiveness and expected effects on determinants of wellbeing, and discusses potentials of combining both modalities for future solutions.

Digital drawing experiences are not only fused by the flexibility of digital materials but also influenced by the availability of interaction space. In this paper, we first present a prototype, which implements a method to turn the (mid-air) space above and aside a drawing screen in a desktop setting dynamically into sensory space for gestural and spatial input. Then we report on a user study exploring how participants experience digital drawing when the additional interaction space above and aside a screen is exploited for exemplary proxemic input techniques for zooming and panning a drawing. Our results show that the new multimodal input techniques are perceived as significantly more attractive than a baseline drawing condition which only utilizes touch based input. We conclude by discussing implications and limitations of our findings and input above and aside a drawing screen in general.

The number of commuters has been increasing for many years and the negative effects on wellbeing are therefore affecting more and more people. Following a user centered design process that focuses on known wellbeing determinants, such as relatedness and empathy, we developed the Honeypot socializing app. The app allows commuters to find other travelers to chat with and meet in person to enhance their wellbeing through fostering meaningful and contextual social interactions. First, we describe the development of the idea and the design of the app. Then, we report on a field study with 16 participants, which we carried out on trains. The study results show that the app helps to get in contact with fellow travelers and that it has the potential to promote the wellbeing of commuters in the long term.