Funding Student Learning: How to Align Education Resources With Student Learning Goals

Identifies factors preventing the education finance system from supporting high-level student learning. Recommends transparent, flexible, and strategic funding mechanisms and practices, including student-based funding and school-linked accounts

Smart School Budgeting: Resources for Districts

In an era of aggressive public education reform, school districts face increasing pressure to produce higher levels of student performance with increasingly limited resources. The economic downturn has forced many districts to tighten their belts, and careful thought must be given to how each and every dollar is spent. Optimally, district leaders should work with stakeholders in their communities to set goals, analyze current spending, provide transparency in their budgeting, and consider cost-saving and reallocation strategies. The Rennie Center has created a toolkit, Smart School Budgeting: Resources for Districts, aiming to assist district leaders in decision-making about school budgeting. Smart School Budgeting is intended to push school leaders to take a more deliberative approach to school budgeting. The resources presented in the toolkit act as a starting point for districts examining their own budgeting processes. The document is designed as a user-friendly summary of existing literature and tools on school finance, budgeting, and resource allocation that directs district leaders and school business officials to practical and useful information to shape resource decisions. Each section includes an overview of a critical topic in school budgeting, summaries of useful documents and resources, relevant case studies (if available), and a resource list with hyperlinked documents for easy access. The toolkit is organized around the following topics: introduction and context for school budget analysis; setting goals; types of budgets; strategies for analyzing spending; tools for budget analysis; and cost-saving strategies.This toolkit was released at a public event on October 3, 2012

Differentiating KMS Strategy from Business Strategy, KM Strategy and IS/IT Strategy

The era of the new millennium has witnessed a wide range of the revolutionized technology that affects our lives and the way an organization is conducted. The contemporary business sectors start to recognize the potential use of knowledge management in the new organizational processes. As a result, increasing numbers of organizations pay attention to the creative value of leveraging knowledge as one of their potential assets. Therefore, organizations start to focus on knowledge as one of the important elements in competitive advantage that needs to be utilized efficiently and effectively. They have shown a great attention of knowledge management in their business strategy incorporated with technology. The role of technological tools and applications is essential in supporting and enhancing knowledge management strategy. There has been a transition from traditional information system to new a concept of knowledge management system employed by organization to sustain competitive advantage in dynamic and unstable environment. Further, to shift the paradigm of knowledge management systems concept from business sectors, this study focused on the KMS applications and tools particularly in Institutions of Higher Education (IHE) environment. The purpose of this study is to (a) identify the relationship of business strategy, knowledge management (KM) Strategy, knowledge management systems (KMS) strategy, information system (IS) strategy and information technology (IT) strategy, particularly in the context of IHE, (b) describe those strategies and their relationship based on the context of IHE. This will provide guidance and effective methods for formulating the KMS strategy with the aim to align it with business strategies and ensuring success of its implementation

Performance Management and Employee Outcomes: What Performance Management Processes Drive Improvement of Employee Performance?

[Excerpt] Performance management (PM) systems can be a key driver of employee performance when designed strategically to go beyond operational or legal requirements. Organizations aspire for performance management processes to help employees develop, improve employee-manager communications, align individual and organizational goals, and help employees and teams reach their highest potential (Pulakos). These four items all drive employee performance and, ultimately, business performance. To align PM to organizational aspirations, companies are changing their PM processes in new ways (see Figure 1). Sometimes they do so with limited data on results, like when dropping performance ratings. Changes, even in uncharted territory, do generally improve individual performance. Of companies that participated in Deloitte’s 2017 Human Capital Survey, 90% that have redesigned performance management see direct improvements in engagement, 96% say the processes are simpler, and 83% say they see the quality of conversations between employees and managers increases (Schwartz et al.). This is because organizations are strategically implementing effective PM versus doing the bare minimum. To highlight improvements made to PM systems, we will point out changes and results in three key areas: employee evaluation, goal setting, and feedback

Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future

The editorial process at our leading information systems journals has been pivotal in shaping and growing our field. But this process has grown long in the tooth and is increasingly frustrating and challenging its various stakeholders: editors, reviewers, and authors. The sudden and explosive spread of AI tools, including advances in language models, make them a tempting fit in our efforts to ease and advance the editorial process. But we must carefully consider how the goals and methods of AI tools fit with the core purpose of the editorial process. We present a thought experiment exploring the implications of two distinct futures for the information systems powering today’s journal editorial process: an AI-augmented and an AI-driven one. The AI-augmented scenario envisions systems providing algorithmic predictions and recommendations to enhance human decision-making, offering enhanced efficiency while maintaining human judgment and accountability. However, it also requires debate over algorithm transparency, appropriate machine learning methods, and data privacy and security. The AI-driven scenario, meanwhile, imagines a fully autonomous and iterative AI. While potentially even more efficient, this future risks failing to align with academic values and norms, perpetuating data biases, and neglecting the important social bonds and community practices embedded in and strengthened by the human-led editorial process. We consider and contrast the two scenarios in terms of their usefulness and dangers to authors, reviewers, editors, and publishers. We conclude by cautioning against the lure of an AI-driven, metric-focused approach, advocating instead for a future where AI serves as a tool to augment human capacity and strengthen the quality of academic discourse. But more broadly, this thought experiment allows us to distill what the editorial process is about: the building of a premier research community instead of chasing metrics and efficiency. It is up to us to guard these values

Opportunity-Rich Schools and Sustainable Communities: Seven Steps to Align High-Quality Education With Innovations in City and Metropolitan Planning and Development

Details challenges and steps for linking quality education and community and economic vitality, including establishing a shared vision and metrics, aligning investments for prosperity, and expanding access via transportation. Lists promising practices

State Strategies to Improve Quality and Efficiency: Making the Most of Opportunities in National Health Reform

Examines ten states' initiatives to address key components of quality and efficiency improvement, including data collection, aggregation, and standardization; public reporting; payment reform; consumer engagement; and provider engagement

U.S. SDG Data Revolution Roadmap

One year after adopting the SDGs, in an addendum to its Open Government National Action Plan, the U.S. Government committed to develop an SDG Data Revolution Roadmap that "charts the future course of efforts to fill data gaps and build capacity to use data for decision-making and innovation to advance sustainable development." The U.S. Government's SDG Data Revolution Roadmap will outline the government's commitments-to-action from 2017-2018. With a deadline of June 2017, it will be developed by the U.S. Government "through an open and inclusive process that engages the full range of citizen, non-governmental, and private sector stakeholders."This report represents the beginning of that engagement process. On December 14, 2016, the Center for Open Data Enterprise and the Global Partnership for Sustainable Development Data convened a Roundtable to develop recommended priorities for the U.S. Government's SDG Data Revolution Roadmap The Roundtable brought together more than 40 stakeholders from government, civil society, and the private sector with expertise in achieving and promoting sustainable development

Out of Many, One: Toward Rigorous Common Core Standards From the Ground Up

Analyzes high school standards for English in twelve states and math in sixteen states designed for college- and career-readiness. Examines their alignment with the American Diploma Project's benchmarks for common core standards. Discusses implications