Research has shown the potential of social robots to support learning in science, technology, and language. We contribute to this field by exploring how robots can support music learning. We report on a within-subjects experiment where 50 young learners practiced the piano in the presence of a robot assuming a non-evaluative and a self-assessment enhancing role implemented in a Wizard-of-Oz fashion. We examined whether the robot can make piano practice more fun, and whether initiating self-assessment to support self-regulated learning is a useful strategy for the robot. We collected quantitative self-report data to assess fun, learning, interest, engagement, and effort. We found a direct positive effect of fun on learning in the context of musical instrument practice. Path modeling showed a positive influence of having fun on learners' attitudes, interests, and learning outcomes in music education, particularly with the self- assessment robot role exhibiting superiority.

Smartwatches are emerging as an increasingly popular platform for longitudinal in situ data collection with methods often referred to as experience sampling and ecological momentary assessment. Their small size challenges designers of relevant applications to ensure usability and a positive user experience. This paper investigates the usability of different input techniques for responding to in situ surveys administered on smartwatches. In this paper, we classify different input techniques that can support this task. Then, we report on two user studies that compared different input techniques and their suitability at two levels of user activity: while sitting and while walking. A pilot study (N = 18) examined numeric input with three input techniques that utilize common features of smartwatches with a touchscreen: Multi-Step Tapping, Bezel Rotation, and Swiping. The main study (N = 80) examined numeric input and list selection including in the comparison two more techniques: Long-List Tapping and Virtual Buttons to scroll through options. Overall, we found that whether users are seated or walking did not affect the speed or accuracy of input. Bezel rotation was the slowest input technique but also the most accurate. Swiping resulted in most errors. Long-List Tapping yielded the shortest reaction times. Future research should examine different form factors for the smartwatch and diverse usage contexts.

Within the Child-Computer Interaction community fun and learning have been a prominent, recurring theme. However, despite the general interest, fun is often handled as a commonsense notion, without a commonly accepted definition, underlying theoretical framework and ways of measurements. Without these, both researching, designing for, implementing, and evaluating fun elements in learning activities are challenging, and can result in contradicting findings. This workshop aims to address these issues by bringing together participants from different disciplines with various backgrounds who has an interest and experience in the topic of fun in learning.

The role of fun in learning, and specifically in learning to code, is critical but not yet fully understood. Fun is typically measured by post session questionnaires, which are coarse-grained, evaluating activities that sometimes last an hour, a day or longer. Here we examine how fun impacts learning during a coding activity, combining continuous physiological response data from wristbands and facial expressions from facial camera videos, along with self-reported measures (i.e. knowledge test and reported fun). Data were collected from primary school students (N = 53) in a single-occasion, two-hours long coding workshop, with the BBC micro:bits. We found that a) sadness, anger and stress are negatively, and arousal is positively related to students’ relative learning gain (RLG), b) experienced fun is positively related to students' RLG and c) RLG and fun are related to certain physiological markers derived from the physiological response data.