The field of dispute resolution has long been at the forefront of modernising legal education. Continuing this tradition, this chapter presents findings from an evaluation of an exercise introduced into the core law school curriculum at Monash University in Australia. In our compulsory litigation and dispute resolution units, we built an experiential exercise in which students resolved a dispute using both an online dispute resolution (ODR) platform and more traditional face-to-face mediation role-play. Students completed a short survey about their experience of the portal (n=64, response rate 30 per cent) and provided their reflective journals about the exercise for analysis (n=55). Drawing on the findings, we consider the benefits and limitations of this approach for facilitating students’ exposure to ODR. We explore themes including student understanding of ODR’s impacts on dispute processes and outcomes; appropriate conduct in dispute resolution settings; and the challenges of computer-mediated communication. We also identify means by which experiential activities can draw students’ attention to power disparities and access to justice challenges in ODR to develop their critical thinking about the rapid developments in this field.

By far the most lucrative venue for on-line advertising has been search, and much of the effectiveness of search advertising comes from the “adwords” model of matching search queries to advertisements. We shall therefore devote much of this chapter to algorithms for optimizing the way this assignment is done. The algorithms used are of an unusual type; they are greedy and they are “on-line” in a particular technical sense to be discussed. We shall therefore digress to discuss these two algorithmic issues – greediness and on-line algorithms – in general, before tackling the adwords problem. A second interesting on-line advertising problem involves selecting items to advertise at an on-line store. This problem involves “collaborative filtering,” where we try to find customers with similar behavior in order to suggest they buy things that similar customers have bought. This subject will be treated in Section 9.3.

qLegal is a leading commercial clinical legal education programme at Queen Mary University of London in which postgraduate law students provide supervised pro bono legal advice to technology start-ups and entrepreneurs. Within qLegal, clients often present with complex data protection issues, which relate to their business or the technology that they are developing. Law students who participate in qLegal have to learn and develop skills to respond to the needs of technology entrepreneurs. This requires students to demonstrate practical client management skills, as well as familiarity with an area of law (data protection) that is in a constant state of evolution as new technologies arise. This chapter explores the relationship between the law, law students’ skills development and technology entrepreneurs. In particular it focuses on the development of qLegal, its purpose and role as a clinical legal education programme. It explores the way in which qLegal offers an opportunity for knowledge transfer between entrepreneurs and aspiring lawyers. Further it examines how qLegal offers a means by which to test, expand and challenge students’ understanding of the application of law in unfamiliar fact scenarios, where limited, as well as newly emerging case law pushes students out of their comfort zone.

The CAVE experience is an immersive virtual reality (IVR) environment employing high-resolution, 3D video and audio technology. Using the CAVE, researchers at University College London designed an IVR scenario intended to echo the logical structure of a traditional ‘trolley scenario’ problem, and deployed this activity within an undergraduate Law and Ethics Course. In this chapter we explore how the use of virtual reality can offer students an unparalleled opportunity to reflect on the dissonance between the behaviour they adopt when faced with an ethical dilemma, and the theoretical stance they propose during class discussion. We explore how this personalisation gives rise to sustained student engagement borne out of a desire to understand the discrepancy between principle and practice. Our chapter considers the potential of IVR technology when teaching ethics to future and current professionals. We conclude by considering how such technology can offer more dynamic opportunities for student reflection and how IVR might be sensibly integrated into a broader legal ethics curriculum.

In this chapter we describe a discourse framework for understanding the historical development of modern reports into legal education in England and Wales by analysing the textual features of genre markers. We then apply this framework to a specific subset of topoi within such reports, namely the coverage given to digital technologies within legal education. We make three related claims. First, the discourse and rhetorics of reports on legal education has scarcely been analysed in the research literature, and we begin that process here. Second, the culture and context within which digital innovation is reported, analysed and recommended upon in regulatory reports is relatively shallow and ‘theory-lite’. We need to draw sophisticated insights into our understanding of digital in a variety of disciplines and discourses (e.g. media, education and discourse analysis generally), and apply those to legal education. Third, the genre-form of reports on innovation inhibit or constrain our ability to develop imaginative, theory-rich and persuasive accounts of digital cultures for legal education. Our case study has implications not just for law schools, but also and more significantly, for regulators and accreditors.

What will lead to meaningful change in legal education? And what should be the direction(s) of change? In the United States, as elsewhere, law schools are caught between critics who want them to be more responsive to the changing legal market and the needs of private employers, and critics who want them to do more to resist and shape the private market and promote the public good. These critiques are not wholly incompatible as a blueprint for curricular reform. Increasing students’ exposure to new skills and technologies, experiential training and projects, and collaboration with other professions, provides ‘opportunities for critical analysis and reflection’ as well as making students more employable.

Hitherto, hypothetical legal cases in legal education, otherwise known as ‘problem questions’, have been predominantly presented in written form. Lecturers provide students with a set of written facts and, through the exercise of skills such as research and argumentation, require students to advise a fictitious client. Whilst problem questions are easily accessible and provide useful training in issue identification and legal research, they can be enhanced through the use of novel methods. This chapter explores one such enhancement, brought about by rendering the very same facts within a computer game. It is argued that this environment is important practically and pedagogically as it imports an authenticity that adds to the careful analysis of facts, and expands the environment of traditional problem questions and opportunities for questioning and deduction. This chapter demonstrates the benefits of rendering traditional, written problem-based scenarios into computer game environments (including those using virtual reality) by drawing on work conducted at the University of Westminster.

An experience gap has opened up in the development of legal professionals. The workplace-based experiences (traineeships and articles of clerkship) that were once pivotal to the progress of law graduates from student to practitioner are either no longer available or are much diminished in scope and scale. Graduates are expected to have accessed such experiences through other avenues. This is a particular challenge for those law graduates who lack the family and social connections to help them start their engagement with the profession. In response to these changing circumstances Monash University has instituted a Clinical Guarantee assuring every law student a place in its Clinical Legal Education (CLE) programme if they so choose. In this chapter, we describe the Clinical Guarantee initiative, our progress to date, the way in which we have used technology to support provision and the challenges we have faced during implementation. We conclude by emphasising the value of CLE as preparation for modern legal practice, and outlining our intention to measure that value through future evaluation work. This chapter tells an Australian story, but one which resonates with experiences in other jurisdictions.

In this chapter, we shall study techniques for analyzing social networks. An important question is how to identify “communities,” that is, subsets of the nodes (people or other entities that form the network) with unusually strong connections. Some of the techniques used to identify communities are similar to the clustering algorithms we discussed in Chapter 7. However, communities almost never partition the set of nodes in a network. Rather, communities usually overlap. For example, you may belong to several communities of friends or classmates. The people from one community tend to know each other, but people from two different communities rarely know each other. You would not want to be assigned to only one of the communities, nor would it make sense to cluster all the people from all your communities into one cluster. Also in this chapter we explore efficient algorithms for discovering other properties of graphs. We look at “simrank,” a way to discover similarities among nodes of a graph. We then explore triangle counting as a way to measure the connectedness of a community. In addition, we give efficient algorithms for exact and approximate measurement of the neighborhood sizes of nodes in a graph, and we look at efficient algorithms for computing the transitive closure.

We turn in this chapter to the discovery of frequent itemsets. To begin, we introduce the “market-basket” model of data, which is essentially a many-many relationship between two kinds of elements, called “items” and “baskets,” but with some assumptions about the shape of the data. The frequent-itemsets problem is that of finding sets of items that appear in (are related to) many of the same baskets. The problem of finding frequent itemsets differs from the similarity search discussed in Chapter 3. Here we are interested in the absolute number of baskets that contain a particular set of items. In Chapter 3 we wanted items that have a large fraction of their baskets in common, even if the absolute number of baskets is small. The difference leads to a new class of algorithms for finding frequent itemsets. We begin with the A-Priori Algorithm, which works by eliminating most large sets as candidates by looking first at smaller sets and recognizing that a large set cannot be frequent unless all its subsets are. We then consider various improvements to the basic A-Priori idea, concentrating on very large data sets that stress the available main memory. Next, we consider approximate algorithms that work faster but are not guaranteed to find all frequent itemsets. Finally, we discuss briefly how to find frequent itemsets in a data stream.

We shall begin this chapter with a survey of the most important examples of recommendation sytems, for example, offering customers of an on-line retailer suggestions about what they might like to buy, based on their past history of purchases and/or product searches. Recommendation systems use a number of different technologies. We can classify these systems into two broad groups: 1) Content-based systems examine properties of the items recommended. For instance, if a Netflix user has watched many cowboy movies, then recommend a movie classified in the database as having the “cowboy” genre; 2) Collaborative filtering systems recommend items based on similarity measures between users and/or items. The items recommended to a user are those preferred by similar users. This sort of recommendation system can use the groundwork laid in Chapter 3 on similarity search and Chapter 7 on clustering. However, these technologies by themselves are not sufficient, and there are some new algorithms that have proven effective for recommendation systems.

Once characterised as a relatively stable profession, unfettered by the influence of modernity and strongly resistant to external forces, the legal services sector has in recent years exhibited marked change. Efforts to preserve profit margins increasingly eroded by the introduction of new fee models, the demand for increased billing transparency, rising client expectations, the adoption of technology and heightened market competition from high volume legal process outsourcers, have all contributed to the sector’s evolution. In what has been viewed as a clear shift towards corporatisation and commercialisation, the legal profession in a number of jurisdictions has moved away from the broader social mission on which it was founded and in which it existed as ‘a branch of the administration of justice and not a mere money-getting trade’. Free market ideologies have undermined ‘justice and rights in the discourse of law’, and in its place, the generation of profit has become the primary indicator of success.