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Is it possible to educate a fire officer to deal intelligently with the command and control of a major fire event he will never have experienced? The authors of this paper believe there is, and present here just one solution to this training challenge. It involves the development of an intelligent simulation based upon computer managed interactive media. The expertise and content underpinning this educational development was provided by the West Midlands Fire Service. Their brief for this training programme was unambiguous and to the point: (1) Do not present the trainee with a model answer, because there are no generic fires. Each incident is novel, complex, and often "wicked" in that it changes obstructively as it progresses. Thus firefighting demands that Commanders impose their individual intelligence on each problem to solve it; (2) A suitable Educational Simulator should stand alone; operate in real time; emulate as nearly as possible the "feel" of the fireground; present realistic fire progress; incorporate the vast majority of those resources normally present at a real incident; bombard the trainee with information from those sources; provide as few system-prompts as possible; (3) There should also be an interrogable visual debrief which can be used after the exercise to give the trainees a firm understanding of the effects of their actions. This allows them to draw their own conclusions of their command effectiveness. Additionally, such a record of command and control will be an ideal initiator of tutorial discussion; (4) The simulation should be realisable on a hardware/software platform of 10 000 British Pounds; and (5) The overriding importance is that the simulation should "emulate as nearly as possible the feelings and stresses of the command role". [This paper was first published in "Interactive Learning International," Vol. 8, no. 2, 1992, 109-126, by John Wiley & Sons Ltd. (EJ446122).]

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]