Re‐engineering the Horizontally Challenged Pastoral System

The writers, as Section Manager and head of an inner‐city school under Special Measures, describe the school's approach to the re‐engineering of its pastoral system from a horizontal, year‐based system, to a semi‐vertical 'family' system. The article explores the reasons for the change, the planning process and the outcomes and suggests ways of implementing successful change over a short time‐frame.     

Exploring the Future: a missing dimension in environmental education

There is considerable current interest in the need for a more ecologically sustainable future. If environmental educators wish to make a significant contribution to this debate, two prior and interrelated areas of concern need addressing. The first is recognition that teaching about global issues can sometimes be counterproductive and lead to feelings of despair. The second, explored in more detail here, is the need to know how young people conceptualise the future in the first place. A recent study of pupils’ views of the future is described which provides important information about their attitudes to the local and global environment.     

The enterprise knowledge dictionary

One of the difficulties in knowledge management is that knowledge sources are widely distributed and exist in many forms. Earl's taxonomy (2001) of knowledge management systems, particularly, the Technocratic school – Systems, Cartographic, and Engineering, is described to provide a context for discussing the different types of knowledge management systems. To support these different types of knowledge sources, we describe an Enterprise Knowledge Dictionary (EKD) and the supporting n-tier architecture that allows access too many types of knowledge though a common interface. The EKD provides a knowledge portal that allows the knowledge seeker to locate and access the various types of knowledge for a domain of interest from a single source. In addition to facilitating knowledge reuse, the EKD is used for evaluation and planning of knowledge management activities within the firm. An n-tier architecture that both optimizes knowledge management implementations and minimizes the load on transaction processing systems is outlined.     

Genetic research and testing in sport and exercise science: A review of the issues

Abstract

This review is based on the BASES position stand on “Genetic Research and Testing in Sport and Exercise Science”. Our aims are first to introduce the reader to research in sport and exercise genetics and then to highlight ethical problems arising from such research and its applications. Sport and exercise genetics research in the form of transgenic animal and human association studies has contributed significantly to our understanding of exercise physiology and there is potential for major new discoveries. Researchers starting out in this field will have to ensure an appropriate study design to avoid, for example, statistically underpowered studies. Ethical concerns arise more from the applications of genetic research than from the research itself, which is assessed by ethical committees. Possible applications of genetic research are genetic performance tests or genetic tests to screen, for example, for increased risk of sudden death during sport. The concerns are that genetic performance testing could be performed on embryos and could be used to select embryos for transplantation or abortion. Screening for risk of sudden death may reduce deaths during sporting events but those that receive a positive diagnosis may suffer severe psychological consequences. Equally, it will be almost impossible to keep a positive diagnosis confidential if the individual tested is an elite athlete.     

Personal Best (PB) goal-setting enhances arithmetical problem-solving

Personal Best (PB) goals are defined as specific, challenging, and competitively self-referenced goals involving a level of performance or effort that meets or exceeds an individual’s previous best. Much of the available research underpinning arguments for PB goal-setting is self-report-based; thus, the causal effect of PB goals on learning outcomes remains in question. The present experiment examined the impact of PB goal-setting (against a no-goal condition) on 68 Year 5 and 6 schoolchildren’s problem-solving during an arithmetic fluency-building activity, SuperSpeed Math. Equivalence of the two conditions was established across a range of prior ability and self-report motivational variables, including prior mathematical ability; Personal Best, Mastery, and Performance goal orientations at the individual and classroom level; mathematics self-concept; and valuing of and interest in mathematics. Controlling for initial problem-solving performance, students who set PB goals in subsequent rounds showed a small but reliable advantage over students in the control condition. These results suggest PB goals may provide a way for students to experience both challenge and success in a range of classroom activities. Suggestions for future research based on these initial findings are made.     

Using causal models to bridge the divide between big data and educational theory

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.