Apple's iPad has attracted a lot of attention since its release in 2010 and one area in which it has been adopted is the education sector. The iPad's large multi-touch screen, sleek profile and the ability to easily download and purchase a huge variety of educational applications make it attractive to educators. This paper presents a case study of the iPad's adoption in a primary school, one of the first in the world to adopt it. From interviews with teachers and IT staff, we conclude that the iPad's main strengths are the way in which it provides quick and easy access to information for students and the support it provides for collaboration. However, staff need to carefully manage both the teaching and the administrative environment in which the iPad is used, and we provide some lessons learned that can help other schools considering adopting the iPad in the classroom.

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iPad in Education: A Case Study of iPad Adoption and Use in a Primary School

This paper on artificial intelligence in education (AIEd) has two aims. The first: to explain to a non-specialist, interested, reader what AIEd is: its goals, how it is built, and how it works. The second: to set out the argument for what AIEd can offer teaching and learning, both now and in the future, with an eye towards improving learning and life outcomes for all. Computer systems that are artificially intelligent interact with the world using capabilities (such as speech recognition) and intelligent behaviours (such as using available information to take the most sensible actions toward a stated goal) that we would think of as essentially human. At the heart of artificial intelligence in education is the scientific goal to make knowledge, which is often left implicit, computationally precise and explicit. In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also designed to be a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us more fine-grained understandings of how learning actually happens. Although some might find the concept of AIEd alienating, the algorithms and models that underpin ed tech powered by AIEd form the basis of an essentially human endeavor. Using AIEd, teachers will be able to offer learners educational experiences that are more personalised, flexible, inclusive and engaging. Crucially, we do not see a future in which AIEd replaces teachers. What we do see is a future in which the extraordinary expertise of teachers is better leveraged and augmented through the thoughtful deployment of well designed AIEd. We have available, right now, AIEd tools that could support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles. In the near future, we expect that teaching and learning will increasingly be supported by the thoughtful application of AIEd tools. For example, by lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies - in and beyond school - and new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time. If we are ultimately successful, we predict that AIEd will help us address some of the most intractable problems in education, including achievement gaps and teacher retention. AIEd will also help us respond to the most significant social challenge that AI has already brought - the steady replacement of jobs and occupations with clever algorithms and robots. It is our view that this provides a new innovation imperative in education, which can be expressed simply: as humans live and work alongside increasingly smart machines, our education systems will need to achieve at levels that none have managed to date. True progress will require the development of an AIEd infrastructure. This will not, however, be a single monolithic AIEd system. Instead, it will resemble the marketplace that has developed for smartphone apps: hundreds and then thousands of individual AIEd components, developed in collaboration with educators, conformed to uniform international data standards, and shared with researchers and developers worldwide. These standards will also enable system-level data collation and analysis that will help us to learn much more about learning itself – and how to improve it. Moving forward, we will need to pay close attention to three powerful forces as we map the future of artificial intelligence in education, namely pedagogy, technology, and system change. Paying attention to the pedagogy will mean that the design of new edtech should always start with what we know about learning. It also means that the system for funding this work must be simultaneously opened up and refocused, moving away from isolated pockets of R&D and toward collaborative enterprises that prioritise areas known to make a real difference to teaching and learning. Paying attention to the technology will mean creating smarter demand for commercial grade AIEd products that work. It also means the development of a robust, component-based AIEd infrastructure, similar to the smartphone app marketplace, where researchers and developers can access standardised components that have been developed in collaboration with educators. Paying attention to system change will mean involving teachers, students, and parents in co-designing new tools, so that AIEd will appropriately address the inherent “messiness” of real classroom, university, and workplace learning environments. It also means the development of data standards that promote the safe and ethical use of data. Said succinctly, we need intelligent technologies that embody what we know about great teaching and learning, embodied in enticing consumer grade products, which are then used effectively in real-life settings that combine the best of human and machine. We do not underestimate the new-thinking, inevitable wrong-turns, and effort required to realise these recommendations. However, if we are to properly unleash the intelligence of AIEd, we must do things differently - via new collaborations, sensible funding, and (always) a keen eye on the pedagogy. The potential prize is too great to act otherwise.

Intelligence Unleashed: An argument for AI in Education

Multi-user interfaces are said to provide “natural” interaction in supporting collaboration, compared to individual and noncolocated technologies. We identify three mechanisms accounting for the success of such interfaces: high awareness of others' actions and intentions, high control over the interface, and high availability of background information. We challenge the idea that interaction over such interfaces is necessarily “natural” and argue that everyday interaction involves constraints on awareness, control, and availability. These constraints help people interact more smoothly. We draw from social developmental psychology to characterize the design of multi-user interfaces in terms of how constraints on these mechanisms can be best used to promote collaboration. We use this framework of mechanisms and constraints to explain the successes and failures of existing designs, then apply it to three case studies of design, and finally derive from them a set of questions to consider when designing and analysing multi-user interfaces for collaboration.

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Mechanisms for collaboration: A design and evaluation framework for multi-user interfaces

To be successful university learners, students need to develop skills in self-directed learning. This encompasses a range of cognitive and meta-cognitive skills including generating one's own learning goals, planning how to tackle a problem, evaluating whether learning goals have been met, and re-planning based on this evaluation. The educational affordances of blogs offer opportunities for students to become self-directed learners in a supportive social environment. Based on qualitative analysis of design diaries written by 113 computer science students about a creative project, this paper presents a framework of the ways in which blogging activities can assist groups of students and their teachers in the development of a range of cognitive, social and self-directed learning skills. Although the students in this study used the commenting feature of blogs effectively for the purpose of praising and encouraging their peers, and giving hints and tips for solving problems, they did not coach each other on higher order skills. The paper discusses how this could be achieved in order to extend the educational value of blogging within a university learning community.

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The educational affordances of blogs for self-directed learning

Our present understanding of young children's touch-screen performance is still limited, as only few studies have considered analyzing children's touch interaction patterns so far. In this work, we address children aged between 3 and 6 years old during their preoperational stage according to Piaget's cognitive developmental theory, and we report their touch-screen performance with standard tap and drag and drop interactions on smart phones and tablets. We show significant improvements in children's touch performance as they grow from 3 to 6 years, and point to performance differences between children and adults. We correlate children's touch performance expressed with task completion times and target acquisition accuracy with sensorimotor evaluations that characterize children's finger dexterity and graphomotor and visuospatial processing abilities, and report significant correlations. Our observations are drawn from the largest children touch dataset available in the literature, consisting in data collected from 89 children and an additional 30 young adults to serve as comparison. We use our findings to recommend guidelines for designing touch-screen interfaces for children by adopting the new perspective of sensorimotor abilities. We release our large dataset into the interested community for further studies on children's touch input behavior. It is our hope that our findings on the little-studied age group of 3- to 6-year-olds together with the companion dataset will contribute toward a better understanding of children's touch interaction behavior and toward improved touch interface designs for small-age children. HighlightsWe investigate small-age children's touch performance on smart phones and tablets.Children's touch performance improves significantly from 3 to 6 years.We discuss findings in terms of Piaget's preoperational developmental stage.We recommend design guidelines by considering children's sensorimotor skills.Dataset released for the largest children touch-screen study up to date (89 children).

Touch interaction for children aged 3 to 6 years

This paper presents a classroom study that investigated the potential of using touch tabletop technology to support children's collaborative learning interactions. Children aged 7-10 worked in groups of three on a collaborative planning task in which they designed a seating plan for their classroom. In the single-touch condition, the tabletop surface allowed only one child to interact with the digital content at a time. In the multiple-touch condition, the children could interact with the digital content simultaneously. Results showed that touch condition did not affect the frequency or equity of interactions, but did influence the nature of children's discussion. In the multiple-touch condition, children talked more about the task; in the single-touch condition, they talked more about turn taking. We also report age and gender differences.

Around the table: are multiple-touch surfaces better than single-touch for children's collaborative interactions?

The potential of tabletops to enable groups of people to simultaneously touch and manipulate a shared tabletop interface provides new possibilities for supporting collaborative learning. However, findings from the few studies carried out to date have tended to show small or insignificant effects compared with other technologies. We present the Collaborative Learning Mechanisms framework used to examine the coupling of verbal interactions and physical actions in collaboration around the tabletop and reveal subtle mechanisms at play. Analysis in this way revealed that what might be considered undesirable or harmful interactions and intrusions in general collaborative settings, might be beneficial for collaborative learning. We discuss the implications of these findings for how tabletops may be used to support children's collaboration, and the value of considering verbal and physical aspects of interaction together in this way.

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Actions speak loudly with words: unpacking collaboration around the table

In order to build learning systems that care we need to increase our understanding of the affective and motivational dimensions of learning. This will allow us to develop a model of our learners which extends beyond their behaviours to the feelings and motivations which underlie those behaviours. In this paper we focus on children's (10 yrs) help-seeking behaviour when using an interactive learning environment (ILE) called Ecolab II. We relate help-seeking strategies to lachievement goal orientation (AGO) by constructing detailed child profiles and directly observing behaviour.

Victoria Bonnett

Papers

Around the table: are multiple-touch surfaces better than single-touch for children's collaborative interactions?

Actions speak loudly with words: unpacking collaboration around the table

Scaffolding effective help-seeking behaviour in mastery and performance oriented learners

Modelling Achievement Goal Orientation in the Ecolab: A Simulated Science Environment for Primary-aged Children