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Descriptors:
Decision Making; Schools of Education; Data; Teacher Education Curriculum; Teacher Education Programs; Information Literacy; Information Skills; Skill Analysis; Skill Development; Management Information Systems; Data Analysis; Data Collection; Data Interpretation; Educational Needs; Curriculum Implementation; Knowledge Base for Teaching; Policy Analysis; Systems Approach; Change Strategies; Educational Change

Abstract:
Data-driven decision making has become increasingly important in education. Policymakers require educators to use data to inform practice. Although the policy emphasis is growing, what has not increased is attention to building human capacity around data use. Educators need to gain data literacy skills to inform practice. Although some professional development opportunities exist for current educators, fewer formal courses and opportunities for data literacy development in schools of education have been developed and implemented. This article explores issues around the growing need for data-driven decision making in programs in schools of education. The issues are complex and the actors needed to bring about change are multiple. A systems perspective to explore course and programmatic implementation is presented. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Leadership; Data Collection; Evaluation Utilization; Information Utilization; Program Effectiveness; Classroom Techniques; Educational Practices; Use Studies; Praxis; Theory Practice Relationship; Barriers; Educational Policy; Educational Improvement; Improvement Programs; Program Attitudes; Change Strategies; Management Information Systems; Interviews; School Visitation; Mail Surveys; Focus Groups; Semi Structured Interviews; School Districts

Abstract:
In the present study, an examination is conducted in three school districts of how data are used to improve classroom practice. In doing so, we explore the effects that attitudes toward data, principal leadership, and computer data systems have on how data are used to affect classroom practice. Findings indicate that educators are ambivalent about data: they see how data could support classroom practice, but their data use operates in the presence of numerous barriers. Many of these barriers are due to principal leadership and computer data systems; these barriers often have negative effects on attitudes toward data and disrupt the progression from using data to inform classroom practice. It is hypothesized that many of these barriers can be removed through effective district policies to improve structures and supports for using data. (Contains 1 figure, 8 tables and 6 footnotes.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Academic Achievement; Politics of Education; Educational Indicators; Management Information Systems; Evaluation Utilization; Information Utilization; Data; School Districts; Data Collection; Evaluation Methods; Case Studies; Sampling; Performance Factors; Educational Assessment

Abstract:
Data have long been considered a key factor in organizational decision-making (Simon, 1955; Lindblom & Cohen, 1979). Data offer perspective, guidance, and insights that inform policy and practice (Newell & Simon, 1972; Kennedy, 1984). Recently, education policymakers have invested in the use of data for organizational improvement in states and districts with such initiatives as Race to The Top (United States Department of Education, 2010) and the development of statewide longitudinal data systems (Institute for Education Sciences, 2010). These and other initiatives focus attention on how data can be used to foster learning and improvement. In other fields, including economics and business, much work has been done to identify leading indicators that predict organizational outcomes. In this paper, we conceptualize how leading indicators might be used in education, using examples from a small sample of school districts with reputations as strong users of data. We define leading indicators as systematically collected data on an activity or condition that is related to a subsequent and valued outcome, as well as the processes surrounding the investigation of those data and the associated responses. Identifying leading indicators often prompts improvements in a district's system of supports. To develop this concept, we describe four examples of how districts identified and used key indicators to anticipate learning problems and improve student outcomes. We also describe the infrastructure and other supports that districts need to sustain this ambitious form of data use. We conclude by discussing how leading indicators can bring about more intelligent use of data in education. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Simulation; Information Technology; Educational Assessment; Management Information Systems; Educational Games; Computer Software; Business Education; Marketing; Automation; Time Management; Factor Analysis; Reliability; Professional Training; Training Objectives; Outcomes of Education; Correlation

Abstract:
Enterprise Resource Planning (ERP) systems have had a significant impact on business organizations. These large systems offer opportunities for companies regarding the integration and functionality of information technology systems; in effect, companies can realize a competitive advantage that is necessary in today's global companies. However, effective training for the incorporation and use of these large-scale systems is difficult and challenging; improved strategies for effective training include the use of business simulations. The question of the effectiveness of training remains--"How do we measure learning?". In a recent "Simulation & Gaming" article "Business Simulations and Cognitive Learning", Anderson and Lawton (2009) focus on research associated with the assessment of cognitive learning in business simulations. They indicate that little progress has occurred in objectively assessing cognitive learning in simulations and call for research that might help determine whether simulations accomplish what they purport to achieve in terms of participant learning. In this research note, objective measures of learning are presented. The results of objective measures of learning are compared with those of self-assessed perceptions of learning in the context of an ERP business simulation game. Based on the comparisons of learning measures, self-assessed measure results were not different from those of objective measures; moreover, learning did occur. (Contains 4 tables and 1 note.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Academic Achievement; Higher Education; Foreign Countries; Achievement Rating; Academic Records; Student Records; Statistical Data; Program Administration; Management Information Systems; Program Evaluation; Program Descriptions; Governance; Technical Support

Abstract:
This report seeks official endorsement from Universities UK and GuildHE for the Higher Education Achievement Report (HEAR) to be adopted sector-wide for students entering higher education in academic year 2012-13. Following extensive trialling and detailed consideration, the report clearly outlines the HEAR and the additional information it provides as something that is valued by institutions, students and employers. Containing a rich range of information about the student's performance and achievements, the HEAR records their time in higher education much more broadly and fully than the traditional academic transcript, and at the same time captures relevant information of interest to employers. In time, it is hoped that the wider information contained in the HEAR will eclipse the degree classification and, where appropriate, serve as a replacement for it. Appended are: (1) Acronyms; and (2) Glossary. (Contains 26 footnotes.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
School Personnel; Educational Change; Public Education; Change Strategies; Leadership Effectiveness; Leadership Qualities; Performance Technology; Personnel Management; Principals; Strategic Planning; Data Analysis; Data Collection; Data Interpretation; Management Information Systems; Guidelines

Abstract:
With the interest and assistance of more than a dozen key state education leaders and staff from across the country, the Center on Reinventing Public Education (CRPE) is investigating what it would take for states to attract and support talented school leaders. This report explains CRPE's efforts to create a coherent framework that any state could use to draft a strategy for developing and supporting a principal corps for the coming decades. It explains why principals are key to so many of today's education efforts and why now is the time to take action. The report then presents a focused data guide and a framework for thinking about policies that can help states get started. State Principal Data Guide: Data Elements is appended. (Contains 22 footnotes.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Early Childhood Education; Systems Development; Management Information Systems; Data; Data Collection; Database Management Systems; State Standards; Guidelines; Primary Sources; Program Development; Information Management

Abstract:
Early education leaders--inside and outside of government--are looking for new ways to improve quality, accountability, and efficiency across many different programs serving young children and their families, and they see investment in data systems as a pivotal part of that effort. However, it can be challenging to develop and implement effective data systems that successfully build on existing platforms and serve multiple purposes. If done well, a data system can provide critical information to support policy decisions, steer continuous quality improvement, create cost savings, and improve customer service. If done poorly, a data system can create new administrative burdens, incur unexpected costs, and tarnish an agency's reputation. To ensure your data project provides the greatest benefits to all involved, download this guide to learn about key considerations across the "Assess, Plan, Do, Evaluate" cycle of data systems development. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Leadership Responsibility; Leadership; Educational Change; School Restructuring; School Culture; Management Information Systems; Educational Improvement; Improvement Programs; Evaluation Utilization; Information Utilization; Research Utilization; Professional Development; Educational Responsibility; Participative Decision Making; Administrative Principles; Information Sources; Access to Information; Data; Change Strategies; Performance Factors; Organizational Effectiveness

Abstract:
Although data driven-decision making has been the mantra of school reform for the last 10 years, school leaders benefit from frequent discussions in how to engage teachers in the process. As a result, the purpose of this paper is to apply Reeves' (2004) framework concerning Antecedents of Excellence in creating a school culture that routinely uses data to inform instruction. The authors argue principals must focus on three antecedents as precursors to effective data use: leadership responsibilities, professional development responsibilities, and school culture responsibilities. Additionally, the authors highlight shared leadership as being instrumental when creating a data-driven culture. Applications for practice are included. (Contains 1 figure and 4 footnotes.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Risk Management; School Safety; Management Information Systems; Professional Development; School Community Programs; Police School Relationship; Improvement Programs

Abstract:
Providing a comprehensive approach for achieving the goal of a safe environment where the priority can be teaching and learning is vital to every district's success. To attain this goal, school districts must educate students, staff, and the community and provide tools that help anticipate potential problems and prevent them from escalating into crises. From the time students leave their homes and step onto the school bus or walk through the front door, administrators and staff are responsible for ensuring their health and safety. This prompted the Cleveland Independent School District (ISD) in Cleveland, Texas, to work within the district and with outside groups to ensure that they are providing a safe environment while meeting the compliance requirements of the law. Beginning this school year, the district is implementing a new safety program that includes an automated risk management system. From training staff and students to tracking and documenting report actions, having the data provided by an automated system ensures that the district will have the knowledge to act appropriately if and when it is faced with an issue that compromises the safety of its students and staff. Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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Descriptors:
Teacher Effectiveness; Evidence; Expertise; Educational Change; Teacher Evaluation; Evaluation Methods; Methods Research; Robustness (Statistics); Management Information Systems; Performance Factors; Statistical Bias; Accountability; Merit Rating; Achievement Rating; Evaluation Problems; Measurement Techniques; Measurement Objectives

Abstract:
Using computers to evaluate teachers based on student test scores is more difficult than it seems. Value-added modeling is a genuinely serious attempt to grapple with the difficulties. Value-added modeling carries the promise of measuring teacher quality automatically and objectively, and improving school systems at minimal cost. The essence of value-added models lies in the precise way they calculate expected scores for the students of each teacher. The mathematical ideas on which they are based are complicated and appear inaccessible to anyone with less training than upper-division university statisticians. A fairly small community of scholars, made up of both advocates and skeptics, has been responsible for developing the calculations (McCaffrey et al. 2003; National Research Council 2010). All of these experts agree that the results should be used with caution. However, because numbers in official printouts are so specific and appear so authoritative, it will prove problematic in practice to prevent them from dominating decisions about promotion and dismissal. Some concerns that have been raised previously about value-added modeling include the possible influence of missing information such as student mobility, large variations in results from year to year, the need for many years of data to obtain reliable results, and the absence of suitable pretests in some subject areas. In this article, the author describes a very particular worry he has had for some time, but for which he only recently was able to obtain any evidence. Deem this an invitation to grapple with the sorts of decisions that lurk behind the mathematics. (Contains 1 figure.) Note:The following two links are not-applicable for text-based browsers or screen-reading software. Show Hide Full Abstract

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