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In this chapter we look at three ‘grammars’ of educational technology, or ways of using and thinking about education technology, that are informed by the advent of the internet: online mediated communities of practice, educational online multi-user role-play games, and connectivism. Each of these grammars has different strengths and weaknesses. Our critique leads us towards a new way of understanding the role of educational technology which we call a ‘dialogic’ grammar or a dialogic foundation for the design of educational technology.
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This chapter explores the concept of dialogic space in education and its relationship with technology. We begin by tracing the history of communications technology and its intersection with education, leading up to the emergence of AI chatbots as dialogue partners, arguing that technology has an important role in fostering dialogic spaces. We explore the modern concept of dialogic space, which is rooted in educational research and emphasises the importance of creating a space for open and...
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This chapter explores the concept of ‘dialogic time’ and its potential to offer insights into the design and use of technology in education. Education, we argue, is centrally concerned with drawing students into participation in the longer-term dialogues of culture such as science and the arts. We suggest that the design of educational technology and associated pedagogy can bring different temporal contexts into a dialogic relation. Considering dialogic time as well as dialogic space can...
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Designing educational technology implies ways of thinking about the role of technology in the educational process. This chapter looks at some of the most influential ways in which the role of digital technology in education has been imagined: technology as a ‘teaching machine’, as a ‘tool for thinking’ and as a ‘learning environment’.
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This chapter explores the relationship between technology and human meaning making. We begin by challenging the common assumption that technology is a threat to human meaning and move on to explore why collaborations with technology can lead to a greater sense of meaning for learners. We argue that education for meaning requires the design of educational technology that helps learners to more deeply participate in systems that unite the biological, social and technological.
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This chapter summarises our investigation into the theory of education technology and outlines a dialogic theory which we hope will prove useful as a foundation for educational technology design. To better understand education and how to improve it, we need to take technology seriously and listen to its ‘voice’. Our proposed dialogic theory focuses on how to design educational technology to support the opening, widening and deepening of dialogic spaces. It is about designing pedagogy with...
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Artificial intelligence (AI) plays an increasingly prevalent role in the global education system. However, there is a lack of transparency about how AI technologies work alongside certain ethical guidelines. Achieving the global benefits of AI-driven learning and education requires international dialogue on the commonplaces of ethical considerations. This paper, taking China and Finland as two contextual cases, analyzes how AI-related policies at the national level have focused on...
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The twenty-first century has brought with it a growing variety of authentic and engaging learning environments. While significant portions of human learning still take place in traditional classrooms, researchers and educators have innovated several learning experiences that are embodied, project-based, inquiry-driven, collaborative, and open-ended. Furthermore, there has been greater acknowledgement of the varying timescales and contexts where meaningful learning takes place, as well as...
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Accurate prediction of indoor temperature is critical for climate change adaptation and occupant health. The aim of this study is to investigate an improved deep ensemble machine learning framework (DEML), by adjusting the model architecture with several machine learning (ML) and deep learning (DL) approaches to forecast the sensor-based indoor temperature in the Australian urban environment. We collected ambient station-based temperatures, satellite-based outdoor climate characteristics,...
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The design of an automatic solver for mathematical word problems (MWPs) dates back to the early 1960s and regained booming attention in recent years, owing to revolutionary advances in deep learning. Its objective is to parse the human-readable word problems into machine-understandable logical expressions. The problem is challenging due to the existence of a substantial semantic gap. To a certain extent, MWPs have been recognized as good test beds to evaluate the intelligence level of agents...
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