Federal Aid to the States: Historical Cause of Government Growth and Bureaucracy

In recent years, members of Congress have inserted thousands of pork-barrel spending projects into bills to reward interests in their home states. But such parochial pork is only a small part of a broader problem of rising federal spending on traditionally state and local activities. Federal spending on aid to the states increased from $286 billion in fiscal 2000 to an estimated$449 billion in fiscal 2007 and is the third-largest item in the federal budget after Social Security and national defense. The number of different aid programs for the states soared from 463 in 1990, to 653 in 2000, to 814 by 2006. The theory behind aid to the states is that federal policymakers can design and operate programs in the national interest to efficiently solve local problems. In practice, most federal politicians are not inclined to pursue broad, national goals; they are consumed by the competitive scramble to secure subsidies for their states. At the same time, federal aid stimulates overspending by the states, requires large bureaucracies to administer, and comes with a web of complex regulations that limit state flexibility. At all levels of the aid system, the focus is on spending and regulations, not on delivering quality services. And by involving all levels of government in just about every policy area, the aid system creates a lack of accountability. When every government is responsible for an activity, no government is responsible, as was evident in the aftermath of Hurricane Katrina. The failings of federal aid have long been recognized, but reforms and cuts have not been pursued for years. Aid has spawned a web of interlocking interests that block reform, including elected officials at three levels of government, armies of government employees, and thousands of trade associations representing the recipients of aid. Yet the system desperately needs to be scaled back, not least because the rising costs of federal programs for the elderly are putting a squeeze on the federal budget. To help spur reform, this study examines the historical growth of the aid system and describes its failings. Congress should reconsider the need for aid and begin terminating activities that could be better performed by state and local governments and the private sector

Learning Outcomes at the Open University

This report describes the background, role, use of and support for learning outcomes at The Open University, UK (OU), which defines them as: A learning outcome is a statement of what a student is expected to know, understand and be able to do at the end of a module or qualification. All modules and qualifications have learning outcomes which explicitly describe the learning central to that study and to which the award of credit and qualifications is linked. Learning outcomes are intended to empower students by clearly setting out the expected achievements of study. Central to this definition is the need to offer opportunities for students to develop and demonstrate the knowledge, skills and other cognitive achievements they describe

Understanding student experience in the age of personalised study

Moves in higher education to provide personalised learning for students increase the importance of gaining and maintaining an understanding of the student experience. For some institutions, this increase in complexity may stretch current systems and data structures. The complexity is amplified where multiple start dates are offered to improve the personalisation of study. The Open University, OU, has over the years, continued to develop its Supported Open Learning, SOL, methods and as an institution is now prioritising Personalised Open Learning, POL. This increases the importance of accessible detailed pathway information. We describe the development of one possible approach intended to provide greater understanding of the student experience for staff interpreting progress data. Another outcome of personalisation is the fragmentation of student cohorts, as individuals each make their own study choices while progressing towards their study goal. A relatively straightforward programme of study can lead to 64 different study routes creating a further challenge for staff in understanding the differing student experiences. We show how this can be represented in a simple data structure that allows powerful queries. Our approach uses a multi-model database, with graphical capabilities. By creating this structure in the ArangoDB environment it was possible to readily test it with 150,000 records and query it using graphical queries in the native AQL language. The early response from faculty colleagues is very positive. They appreciate the graphical output and the ability to straightforwardly answer their questions on whether students experience greater success on one study route rather than another. We are therefore continuing to develop this model to support a qualification review for summer 2018. In our presentation we will describe the challenge and illustrate an approach we are taking: giving examples of the queries we are using and the kinds of data the system outputs

Embedding reflective thinking on approaches to learning - moving from pilot study to developing institutional good practice

Aim Asking undergraduates to reflect on their approaches to learning involves the individuals in effort and activity they may well consider to be an unwanted addition to their normal studies: unless the activity has clear links in their minds with their chosen subject. The same reaction is sometimes found in teaching academics, as supporting a reflective strand requires additional effort which they may be happy to give as long as it relates to their research, or is a one off pilot. There are therefore issues that very quickly arise when attempting, as this study does, to embed metacognitive thinking about approaches to learning and style into the everyday good practice of teaching staff in universities and in the routine thinking of students. Methodology This paper describes the progress of a study facilitating the embedding of one framework for developing an individual's approaches to learning into the everyday good practice of university teaching staff and the everyday learning experience of undergraduates. It is an interim report that sets out the five strands of this activity: introduction to the model for staff; first steps at putting into practice; collection of examples of activity; building a community of practice; assessing the effectives of interventions. It also describes some of the issues and challenges that arise when moving from pilot study to developing good practice within a large organisation. The basis of the initial stages is a consultative and collaborative approach to develop effective materials for informing colleagues and developing their understanding of the model whilst laying the foundations for a community of practice and empowering individuals to develop activities for embedding these ideas into their teaching and to share these along with commentary on the success of these interventions – not just within the group but also for their contemporaries to access. Finding It has previously been demonstrated that the Effective Lifelong Learning Inventory, ELLI, can be an effective tool for teachers and learners, providing a framework to think and talk with students about learning and about how to grow as learners within both their formal education and informal learning. However, this is a reasonably complex model with seven dimensions of learning, and it has been developed primarily for the school context. Whilst the model has been shown to be of value within universities, the higher education context offers several challenges to the approach and these are explored. The issues relating to colleagues being persuaded to embed these ideas into their everyday practice will be discussed along with the measures required for institutional support, including the demonstration of positive outcomes to interventions. Relevance This paper is relevant to anyone considering ways in which their understandings and experience of metacognitive thinking about approaches to learning and style may benefit the student body, the teaching of their colleagues and their institutio

10 Reasons to Oppose Virginia Sales Tax Increases

Northern and southeastern Virginians will vote in referenda this November to approve or reject increases in the retail sales tax to fund transportation projects. Northern Virginians will decide whether to increase the sales tax from 4.5 percent to 5.0 percent, an 11 percent increase. Virginians in the Hampton Roads area will decide whether to increase the sales tax from 4.5 percent to 5.5 percent, a 22 percent increase. Proponents of tax increases point to unmet transportation needs to support their cause. Yet state spending increased 13 percent in 1999, 7 percent in 2000, and 9 percent in 2001. If key transportation needs have not been met, the problem is not a lack of funds but legislators who have not properly prioritized the budget. If the sales tax referenda are passed, the state government will have a strong incentive to reduce what it would otherwise spend on transportation in northern Virginia and Hampton Roads. By some measures, northern Virginia already gets the short end of the stick with regard to the state budget. Tax increases are not just bad budget policy; they are also bad economic policy. Since higher taxes reduce economic growth, an added cost of higher sales taxes would be lower incomes for Virginians. During the 1990s Virginia taxes grew faster than incomes, and local property taxes have soared recently. Even modest restraint in nontransportation spending could save enough money to fund priority highway projects without tax increases. Further, the state could adopt a spending growth cap that channels excess future tax revenues to transportation needs and tax cuts

QUAL : A Provenance-Aware Quality Model

The research described here is supported by the award made by the RCUK Digital Economy program to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Assessing the Quality of Semantic Sensor Data

Acknowledgements The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Publisher PD