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Using Multivariate Statistics

Introducing sociocultural theory 1. Sociocultural contributions to understanding the foreign and second language classroom 2. Rethinking interaction in SLA: Developmentally appropriate assistance in the zone of proximal development and the acquisition of L2 grammar 3. Subjects speak out: How learners position themselves in a psycholinguistic task 4. The output hypothesis and beyond: Mediating acquisition through collaborative dialogue 5. Playfulness as mediation in communicative language teaching in a Vietnamese classroom 6. Social discursive constructions of self in L2 learning 7. Second language learning as participation and the (re)construction of selves 8. Side affects: The strategic development of professional satisfaction 9. The appropriation of gestures of the abstract by L2 learners 10. Second language acquisition theory and the truth(s) about relativity 11. From input to affordance: Social-interactive learning from an ecological perspective Bibliography Index

Sociocultural Theory and Second Language Learning

The culture of education

What Video Games Have to Teach Us about Learning and Literacy

Although augmented reality (AR) has gained much research attention in recent years, the term AR was given different meanings by varying researchers. In this article, we first provide an overview of definitions, taxonomies, and technologies of AR. We argue that viewing AR as a concept rather than a type of technology would be more productive for educators, researchers, and designers. Then we identify certain features and affordances of AR systems and applications. Yet, these compelling features may not be unique to AR applications and can be found in other technological systems or learning environments (e.g., ubiquitous and mobile learning environments). The instructional approach adopted by an AR system and the alignment among technology design, instructional approach, and learning experiences may be more important. Thus, we classify three categories of instructional approaches that emphasize the ''roles,'' ''tasks,'' and ''locations,'' and discuss what and how different categories of AR approaches may help students learn. While AR offers new learning opportunities, it also creates new challenges for educators. We outline technological, pedagogical, learning issues related to the implementation of AR in education. For example, students in AR environments may be cognitively overloaded by the large amount of information they encounter, the multiple technological devices they are required to use, and the complex tasks they have to complete. This article provides possible solutions for some of the challenges and suggests topics and issues for future research.

Current status, opportunities and challenges of augmented reality in education

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

The use of augmented reality (AR) in formal education could prove a key component in future learning environments that are richly populated with a blend of hardware and software applications. However, relatively little is known about the potential of this technology to support teaching and learning with groups of young children in the classroom. Analysis of teacher–child dialogue in a comparative study between use of an AR virtual mirror interface and more traditional science teaching methods for 10-year-old children, revealed that the children using AR were less engaged than those using traditional resources. We suggest four design requirements that need to be considered if AR is to be successfully adopted into classroom practice. These requirements are: flexible content that teachers can adapt to the needs of their children, guided exploration so learning opportunities can be maximised, in a limited time, and attention to the needs of institutional and curricular requirements.

/pdf/making-it-real-exploring-the-potential-of-augmented-reality-5f1kqqcz8e.pdf

“Making it real”: exploring the potential of augmented reality for teaching primary school science

What do we mean by the word ‘context’ in education and how does our context influence the way that we learn?
What role can technology play in enhancing learning and what is the future of technology within learning?
Re-Designing Learning Contexts seeks to re-dress the lack of attention that has traditionally been paid to a learner’s wider context and proposes a model to help educators and technologists develop more productive learning contexts. It defines context as the interactions between the learner and a set of inter-related resource elements that are not tied to a physical or virtual location. Context is something that belongs to an individual and that is created through their interactions in the world. 
Based on original, empirical research, the book considers the intersection between learning, context and technology, and explores:

the meaning of the concept of context and it’s relationship to learning
the ways in which different types of technology can scaffold learning in context
the Learner-Centric ‘Ecology of Resources’ model of context as a framework for designing technology-rich learning environments
the importance of matching available resources to each learner’s particular needs 
the ways in which the learner’s environment and the technologies available might change over the coming years
the potential impact of recent technological developments within computer science and artificial intelligence.
This interdisciplinary study draws on a range of disciplines, including geography, anthropology, psychology, education and computing, to investigate the dynamics and potential of teacher-learner interaction within a learning continuum, and across a variety of locations. It will be of interest to those teaching, researching and thinking about the use of technology in learning and pedagogy, as well as those involved in developing technology for education and those who use it in their own teaching.
For practical examples of the way the Ecology of Resources framework has been used visit: http://eorframework.pbworks.com.

Re-Designing Learning Contexts: Technology-Rich, Learner-Centred Ecologies

Boundaries between formal and informal learning settings are shaped by influences beyond learners' control. This can lead to the proscription of some familiar technologies that learners may like to use from some learning settings. This contested demarcation is not well documented. In this paper, we introduce the term ‘digital dissonance’ to describe this tension with respect to learners' appropriation of Web 2.0 technologies in formal contexts. We present the results of a study that explores learners' in- and out-of-school use of Web 2.0 and related technologies. The study comprises two data sources: a questionnaire and a mapping activity. The contexts within which learners felt their technologies were appropriate or able to be used are also explored. Results of the study show that a sense of ‘digital dissonance’ occurs around learners' experience of Web 2.0 activity in and out of school. Many learners routinely cross institutionally demarcated boundaries, but the implications of this activity are not well understood by institutions or indeed by learners themselves. More needs to be understood about the transferability of Web 2.0 skill sets and ways in which these can be used to support formal learning.

/pdf/beyond-web-2-0-mapping-the-technology-landscapes-of-young-3yvhc3ngsc.pdf

Beyond Web 2.0: mapping the technology landscapes of young learners

Interdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for use in education and training. In this paper, we use three case studies to illustrate how learning sciences research can inform the judicious analysis, of rich, varied and multimodal data, so that it can be used to help us scaffold students and support teachers. Based on this increased understanding of how best to inform the analysis of data through the application of learning sciences research, we are better placed to design AI algorithms that can analyse rich educational data at speed. Such AI algorithms and technology can then help us to leverage faster, more nuanced and individualised scaffolding for learners. However, most commercial AI developers know little about learning sciences research, indeed they often know little about learning or teaching. We therefore argue that in order to ensure that AI technologies for use in education and training embody such judicious analysis and learn in a learning sciences informed manner, we must develop inter‐stakeholder partnerships between AI developers, educators and researchers. Here, we exemplify our approach to such partnerships through the EDUCATE Educational Technology (EdTech) programme. Practitioner NotesWhat is already known about this topic? The progress of AI Technology and learning analytics lags behind the adoption of these approaches and technologies in other fields such as medicine or finance.Data are central to the empirical work conducted in the learning sciences and to the development of machine learning Artificial Intelligence (AI).Education is full of doubts about the value that any technology can bring to the teaching and learning process.What this paper adds? We argue that the learning sciences have an important role to play in the design of educational AI, through their provision of theories that can be operationalised and advanced.Through case studies, we illustrate that the analysis of data appropriately informed by interdisciplinary learning sciences research can be used to power AI educational technology.We provide a framework for inter‐stakeholder, interdisciplinary partnerships that can help educators better understand AI, and AI developers better understand education.Implications for practice and/or policy? AI is here to stay and that it will have an increasing impact on the design of technology for use in education and training.Data, which is the power behind machine learning AI, can enable analysis that can vastly increase our understanding of when and how the teaching and learning process is progressing positively.Inter‐stakeholder, interdisciplinary partnerships must be used to make sure that AI provides some of the educational benefits its application in other areas promise us. [ABSTRACT FROM AUTHOR]

/pdf/designing-educational-technologies-in-the-age-of-ai-a-dg9ex9nrgl.pdf

Designing educational technologies in the age of AI: A learning sciences‐driven approach

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