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Digital data are transforming higher education (HE) to be more student‐focused and metrics‐centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial drivers of current large‐scale technological efforts to collect and analyse student data in UK HE. The result is an expanding data infrastructure which includes large‐scale and longitudinal datasets, learning analytics services, student apps, data dashboards and digital learning platforms powered by artificial intelligence (AI). Education data scientists have built positive pedagogic cases for student data analysis, learning analytics and AI. The politicization and commercialization of the wider HE data infrastructure is translating them into performance metrics in an increasingly market‐driven sector, raising the need for policy frameworks for ethical, pedagogically valuable uses of student data in HE. Practitioner NotesWhat is already known about this topic Learning analytics, education data science and artificial intelligence are opening up new ways of collecting and analysing student data in higher education.UK government policies emphasize the use of student data for improvements to teaching and learning.What this paper adds A conceptual framework from "infrastructure studies" demonstrates how political objectives and commercial aims are fused to HE data systems, with data infrastructure becoming a key tool of government reform.A critical infrastructure analysis shows that student data processing technologies are being developed and deployed to measure university performance through student data.Implications for practice and/or policy Educators and managers in universities need to prepare robust institutional frameworks to govern their use of student data.Learning analytics practitioners, data scientists, learning scientists and social science researchers need to collaborate with the policy community and education technology developers on new policy frameworks to challenge narrow uses of student data as performance metrics. [ABSTRACT FROM AUTHOR]

Extensive knowledge of collocations is a key factor that distinguishes learners from fluent native speakers. Such knowledge is difficult to acquire simply because there is so much of it. This paper describes a system that exploits the facilities offered by digital libraries to provide a rich collocation-learning environment. The design is based on three processes that have been identified as leading to lexical acquisition: noticing, retrieval and generation. Collocations are automatically identified in input documents using natural language processing techniques and used to enhance the presentation of the documents and also as the basis of exercises, produced under teacher control, that amplify students' collocation knowledge. The system uses a corpus of 1.3 B short phrases drawn from the web, from which 29 M collocations have been automatically identified. It also connects to examples garnered from the live web and the British National Corpus. [ABSTRACT FROM AUTHOR]

The aim of this research is to explore the feasibility of knowledge-based expert systems (KBES) for the planning and scheduling of ready-mixed concrete supply. Ready-mixed concrete is a product as well as a service. This implies that the supply of ready-mixed concrete should satisfy the customers' needs in relation to both quality and supply. Due to the peculiar characteristics of the product which has a 'shelf life' of only a few hours and the dynamic nature of the operating environment, the planning and scheduling problems of ready-mixed concrete supply are usually unstructured and complicated. A prototype knowledge-based expert system for the planning and scheduling of ready-mixed concrete supply developed with a leading ready mixed concrete company in the UK will be presented. This paper begins with a brief summary of the ready-mixed concrete production and delivery process and the importance of its planning and scheduling decisions. This is followed by a discussion of the role, tasks performed, development and testing of the system developed. The paper shows the potential of KBES in planning and scheduling ready-mixed concrete supply. A full-scale version needs to be developed based upon validation and refinement of the current system. The indicators are that this will prove to be a valuable aid in decision-making. [ABSTRACT FROM AUTHOR]