Learning is the fundamental process that teachers are seeking to influence in the classroom. But what is learning? And how can we, as teachers, help our students to learn effectively? Learning has been a topic of investigation for centuries. Educators have sought to understand learning through philosophy, observation, experiments and even neuroscience. Learning is a complex human behaviour. As a teacher, you need to understand how your students learn. That means becoming aware of some of the theories people have put forward about how learning works, developing an appreciation for how you can know whether or not learning has occurred in your classroom, and understanding what you can do to maximise the learning possibilities for all of your students. What you will learn in this chapter might surprise you – for example, some of the intuitive ideas we have about learning seem not to be how learning really occurs. This is an important insight for you as a teacher. The students in your classes will often have intuitive ideas about their learning that may not be accurate, particularly when it comes to the use of technology in education. These ideas may come from their home, informal education settings they interact with, from popular media representations, or from elsewhere. It is important to be aware of how learning really works.

In a democracy the people are said to lead. And yet within Australia’s liberal democracy, the people elect individuals to represent them. This sets up a unique role for political leadership. Debates about leadership churn are especially relevant in Australia given the number of political leaders that were replaced in the decade following the 2007 federal election.

Understanding the friction between leadership and liberal democracy provides us with a deeper grasp of our institutional setup. In considering political leadership in Australia, this chapter begins by considering the tension that exists between leaders in liberal democracies and the democratic institutions they work within. It then outlines some of the theories about leaders and leadership. It goes on to investigate and discuss Australian political leadership by considering the different types of political leadership in Australia. Following this, debates about recent leadership failure and supposed ‘poor leaders’ in Australia will take place. This chapter also deals with questions related to structure and agency as well as the political leadership gender gap in Australia.

The hope is that legal rules relating to AI technologies can frame their progress and limit the risks of abuse. This hope is tentative as technology seriously challenges the theory and practice of the law across legal traditions. The use of interdisciplinary and comparative methodologies makes clear that AI is currently impacting our understanding of the law. AI can be understood as a regulatory technology and confirms that AI can produce normative effects some of which may be contrary to public laws and regulations.

This chapter examines how the personality of political leaders shapes their ability to campaign effectively, win votes and achieve success once in political office. We begin by examining how personality has been conceptualised in politics and why it is considered important. In our discussion of personality, we include psychological traits, such as the ‘big five’ (e.g., Extroversion, Conscientiousness), and Machiavellianism or charisma, but also individual characteristics including motivation, intelligence and traits ascribed to individuals based on their biological features, such as height. Next, we review existing research to examine the relevance of personality for leadership emergence (i.e. how personality contributes to individuals becoming political leaders), and leadership effectiveness (i.e. how personality may contribute to successful performance in political office). Finally, we examine the methods that researchers have used to examine personality and political leadership and consider future directions for research. The use of relevant measures of personality is considered as well as enhanced approaches to their analysis.

As synthetic voices enter the mass market, there is an increasing need for voice personalisation, that is, a voice for the text-to-speech system that not only conveys information but also exudes a persona much like the human voice. We begin this chapter with a historical overview of the field starting with model-based approaches, to concatenative systems and finally to contemporary implementations of parametric synthesis. We then examine how the confluence of increased computational speed at reduced costs, the availability of large data sets, and advances in machine learning and artificial intelligence enable a whole new approach to speech synthesis, including the ability to create high-quality personalised voices. We then examine the role of crowdsourcing in developing a scalable method for voice customisation and adaptation. We discuss the benefits and challenges of acquiring recordings of novice voice talent of all ages from around the world and using these recordings for voice building. We conclude by discussing the impact of personalised voices and implications for future work.

The Marriage Act 1994 introduced the possibility of marrying on ‘approved premises’. Within a decade of it coming into force, such weddings accounted for half of all civil weddings, and by 2017 almost 70 per cent of all weddings were taking place in such venues. There was a corresponding decline in weddings in Anglican churches and register offices. The fact that couples were increasingly choosing a civil wedding did not, however, indicate that they were rejecting religion: the period also saw the rise of ‘celebratory ceremonies’ held separately from the legal wedding where there were particular obstacles to couples marrying in accordance with their beliefs or wishes. Some couples, meanwhile, married according to religious rites alone, sometimes unaware that such ceremonies had no standing in law. The courts struggled with the question of how such ceremonies should be classified, while official concerns focused in particular on Muslim marriage practices. Finally, in 2020, the restrictions imposed by COVID-19 exposed many of the problems with the current law and brought out the distinction between the celebratory and legal aspects of getting married.

Cities around the world try to set standards for their digital agendas, and constructing smart city ‘roadmaps’. Taking stock of emerging approaches that seek to apply or supplement existing rules on privacy and data protection and to sustain public confidence and support in the face of innovation and change, challenges are explored. Drawing on a growing critical literature, in law, planning, and other fields, that seeks to identify the nature and implications of these developments beyond the promotional language of ‘smart’, the work of Sidewalk’s masterplan for a site in Toronto, and the Los Angeles Department of Transportation’s new and influential approach to mobility data (data associated with ridesharing, ‘micromobility’ such as e-scooters, and in time autonomous vehicles), is assessed. Two facets of urban technology that relate to (and ultimately enable) the delivery of personalised services by public authorities and others are then considered: ratings and reputation (highlighting Chinese cities deploying aspects of the emerging 'social credit’ systems) and facial recognition (noting that the ability to recognise individuals in this way, without a conventional and more deliberate identification (e.g. supplying a name, entering a password, or older biometric systems such as fingerprint scanning), is a key part of many proposed personalised services).

The legal approach to regulating data-driven personalisation has relied heavily on extending and reusing legal categories and concepts – in particular, the idea of privacy of personal information and the legitimating role of consent in permitting the use of personal information – that were originally devised to deal with a very different problem. This chapter argues that this approach is fundamentally flawed for two reasons. Firstly, data-driven personalisation – unlike the traditional core of privacy – is deeply enmeshed in contractual relationships, and both the gathering and the use of data are mediated by contractual terms. As this chapter shows, the result is that ‘privacy’ and ‘consent’ do not provide an adequate evaluative framework to model or mitigate the deleterious impact of data-driven personalisation on individuals. Secondly, consent derives its normative force from the presumption that it is necessarily autonomy-enhancing. As this chapter shows, however, data-driven personalisation has a strong derelationalising effect which erodes rather than enhances the data subject’s autonomy, calling into question the assumptions underpinning privacy-based approaches. The chapter concludes by arguing that dealing with these problems requires adopting a new, more substantive approach, which works to explicitly restrict the processes, structures, and purposes through which and for which personalisation is used.

In this chapter, I discuss the role of personalisation in a wider narrative of the development of democratic societies, in terms of digital modernity, driven by a vision of data-driven innovation over networked structures facilitating socio-environmental control. This chapter deals with narratives of how modernity plays out and is implemented by institutions and technologies, which are inevitably partial, and selective in what they foreground and ignore. It begins with a discussion of digital modernity, showing how data-driven personalisation is central to it, and how privacy not only loses its traditional role as a support for individuality, but becomes a blocker for the technologies that will realise the digitally modern vision. The chapter develops the concept of the subjunctive world, in which individuals’ choices are replaced by what they would have chosen if only they had sufficient data and insight. Furthermore, the notions of what is harmful for the individual, and the remedies that can be applied to these, become detached from the individual’s lived experience, and reconnected, in the policy space, to the behaviour and evolution of models of the individual and his or her environment.

Over recent years, economists, lawyers and regulators have become increasingly interested in the role played by ‘network effects’ in the digital economy: namely, the phenomenon whereby a platform becomes increasingly valuable to its users, the more users it succeeds in recruiting. Whether user-generated content on Youtube and Facebook, proprietorial messaging services such as Whatsapp, or two-sided markets such as Uber and Airbnb, it is now widely recognised that many of today’s most successful technology businesses enjoy a dominance based upon achieving a critical mass of users, which makes it near-impossible for less well-used platforms to compete. What is less widely recognised is that data-driven personalisation operates in a comparable (albeit not identical) manner: as the volume of users increases, personalisation becomes ever more sophisticated, generating a ‘second-order’ network effect that can also have significant implications for the viability of competition. This paper unpacks the distinction between first-order and second-order network effects, showing how both can create significant barriers to competition. It analyses what second-order network effects imply for how governments can and should regulate data-driven personalisation, and how states might help their citizens to regain control over the value that they create.

This is the introductory chapter to the edited collection on 'Data-Driven Personalisation in Markets, Politics and Law' (Cambridge University Press, 2021) that explores the emergent pervasive phenomenon of algorithmic prediction of human preferences, responses and likely behaviours in numerous social domains – ranging from personalised advertising and political microtargeting to precision medicine, personalised pricing and predictive policing and sentencing. This chapter reflects on such human-focused use of predictive technology, first, by situating it within a general framework of profiling and defends data-driven individual and group profiling against some critiques of stereotyping, on the basis that our cognition of the external environment is necessarily reliant on relevant abstractions or non-universal generalisations. The second set of reflections centres around the philosophical tradition of empiricism as a basis of knowledge or truth production, and uses this tradition to critique data-driven profiling and personalisation practices in its numerous manifestations.