Farm visits: interdisciplinary outdoor learning for primary school pupils and Scotland’s Curriculum for Excellence

There is concern around children’s lack of knowledge and understanding of food sources and production, and more broadly around their apparent disconnection from nature. Spending time in the outdoors has been shown to yield a range of benefits, although the mechanisms underpinning these are not well understood. Studies have suggested, however, that there has been a decline in time spent outdoors by children. The introduction of the ‘Curriculum for Excellence’ guidelines in Scotland was heralded as an opportunity to address this decline. Although the guidelines advocate the use of outdoor environments, little research has been conducted, and little guidance is available, on how teachers can and do use outdoor learning in relation to the guidelines, particularly beyond ‘adventure’ activities. Farms are utilised as an educational resource around the world. This research explored the use of educational farm visits, as an example of outdoor learning, in the context of Curriculum for Excellence. A qualitatively driven, mixed methods study, comprising survey and case study methodologies, was undertaken. A questionnaire for teachers informed subsequent interviews with teachers and farmers, and ‘group discussions’ with primary school pupils. The study found that teachers can link farm visits and associated topics with the Curriculum for Excellence guidelines in a range of ways, covering all curriculum areas. There was a tendency however for farm visits to be associated with food and farming topics at Primary 2-3 (age 6-7), rather than used more widely. Issues to consider in the planning and conduct of farm visits were identified, and barriers and motivations for teachers, and for farmers volunteering to host visits, were explored. As well as practical examples of the use of farm visiting, this research offers a perspective on some of the theoretical literature which seeks to explain the benefits of spending time outdoors. Furthermore, five main recommendations for farm visiting in the context of Curriculum for Excellence are given. These relate to the type of visit appropriate to different age groups, opportunities for teachers to become more familiar with what farms visits can offer, and raising awareness of the organisations and networks which can support volunteer farmers to host visits.

Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs.