This document discusses the need for performance management systems rather than just data-driven decision making. It argues that decision-makers need a knowledge base that integrates different categories of data, interprets the data using benchmarks, and includes process data to help identify better practices. An effective knowledge base would provide tools to analyze process data in order to improve performance, not just audit it. It provides examples of the types of data that could be included in a knowledge base to help decision-makers enhance student outcomes and return on resources.
Lecture presented by Vivian Praxedes D. Sy at PAARL's Summer Conference on the theme "Library Analytics: Data-driven Library Management", held at Pearl Hotel, Manila on 20-22 April 2016
Lecture presented by Fernan R. Dizon at PAARL's Summer Conference on the theme "Library Analytics: Data-driven Library Management", held at Pearl Hotel, Manila on 20-22 April 2016
The needs of researchers in key disciplines are changing rapidly and this has important implications for the library’s role in enhancing research productivity and impact.
Librarians can build a roadmap for supporting 21st Century research needs that draws on both published research sources and institution-specific user research. Several key trends from recent studies and ideas for institution-specific user research tools are highlighted within.
Lecture presented by Vivian Praxedes D. Sy at PAARL's Summer Conference on the theme "Library Analytics: Data-driven Library Management", held at Pearl Hotel, Manila on 20-22 April 2016
Lecture presented by Fernan R. Dizon at PAARL's Summer Conference on the theme "Library Analytics: Data-driven Library Management", held at Pearl Hotel, Manila on 20-22 April 2016
The needs of researchers in key disciplines are changing rapidly and this has important implications for the library’s role in enhancing research productivity and impact.
Librarians can build a roadmap for supporting 21st Century research needs that draws on both published research sources and institution-specific user research. Several key trends from recent studies and ideas for institution-specific user research tools are highlighted within.
Communications and context: strategies for onboarding new e-resources librari...NASIG
Presented by Bonnie Thornton.
This presentation details onboarding strategies institutions can utilize to help acclimate new e-resources librarians with an emphasis on strategies for effectively establishing and perpetuating communications with stakeholders.
Jill Lindsey, OERC Director of Operations and Research, gave a keynote presentation at the Ohio Confederation of Teacher Education Organizations (OCTEO) Fall 2015 conference. The conference was designed to provide professional development opportunities for Teacher Educators.
Communications and context: strategies for onboarding new e-resources librari...NASIG
Presented by Bonnie Thornton.
This presentation details onboarding strategies institutions can utilize to help acclimate new e-resources librarians with an emphasis on strategies for effectively establishing and perpetuating communications with stakeholders.
Jill Lindsey, OERC Director of Operations and Research, gave a keynote presentation at the Ohio Confederation of Teacher Education Organizations (OCTEO) Fall 2015 conference. The conference was designed to provide professional development opportunities for Teacher Educators.
Ellen Wagner, Executive Director, WCET.
Putting Data to Work
This session explores changing data sensibilities at US post-secondary institutions with particular attention paid to how predictive analytics are changing expectations for institutional accountability and student success. Results from the Predictive Analytics Reporting Framework show that predictive modeling can identify students at risk and that linking behavioral predictions of risk with interventions to mitigate those risks at the point of need is a powerful strategy for increasing rates of student retention, academic progress and completion.
presentation at the 15th annual SLN SOLsummit February 27, 2014
http://slnsolsummit2014.edublogs.org/
This presentation was given by Iain Bradley of the Data Modernisation Unit, Department for Education of the United Kingdom at the GCES Conference on Education Governance: The role of Data in Tallinn on 12 February during the afternoon session workshop on Developing data systems.
The design of data systems within education can be challenging due to a lack of easily accessible information and a large variety of stakeholders with differing needs. Architecting Academic Intelligence is the process of centralizing and making accessible the student administrative information to the every member of the administration, faculty and staff of the City Colleges of Chicago so as to more efficiently promote student success.
This presentation was provided by Steve Hiller of The University of Washington Libraries, during the NISO Forum "Performance Measures and Assessment" held on June 1, 2009
Libraries routinely gather and report data about their budgets, collections, staff, services, and so forth. But libraries need to do a better job of using these data to help them improve their existing services and communicate value to their stakeholders.
Summarising User Research - D2I presentation CSCDUG 130723.pptxRocioMendez59
13 July, 2023 - CSCDUG Online Event
Presenting the Sector-led Standard Safeguarding Dataset
Colleagues from Data to Insight, the LA-led service for children’s safeguarding data professionals, are delivering a DfE-funded project in partnership with LAs to define a new “standard safeguarding dataset” which all LAs will be able to produce from their safeguarding information systems.
At this session, they shared what they’ve learned so far from user research with LA colleagues and discussed their early thinking about what a better standard dataset might look like. Participants shared their own thoughts about how to improve these systems and processes.
Presenters
Alistair Herbert
Alistair is the lead officer for Data to Insight, the LA-led service for children’s safeguarding data professionals. With a career focused on local authority children’s services data work, he knows about safeguarding data, information systems, and cross-organisation collaboration.
John Foster
John is a Data Manager for Data to Insight. He has supported a range of children’s services data work, most recently at Shropshire Council. He led Data to Insight’s project to introduce the first national benchmarking dataset for Early Help, and is the user research lead for Data to Insight’s Standard Safeguarding Dataset project.
Rob Harrison and Joe Cornford-Hutchings
Rob and Joe are new Data Managers joining Data to Insight from the private and public sector respectively. They bring between them a wealth of experience and technical expertise, and will be working together to support design and implementation of the new Standard Safeguarding Dataset through 2023-24.
Metrics is a hot topic within all fundraising fields. Measurement models have been established for monitoring the work of frontline fundraisers in order to assess the variety of activities performed as well as the schedule, pace, and outcomes of those activities. With this information in hand, choices can be made about which fundraising activities are most effective in achieving the desired donor behavior, most obviously giving.
TNB Roundtable slide deck by Mary-Kim Arnold of Rhode Island FoundationD.E. Finn
From the January 19, 2016 TNB Roundtable session.
Featured guest: Mary-Kim Arnold, director of evaluation and learning at the Rhode Island Foundation.
Topic: "Insights from a Community Foundation's Evaluation Director"
Measuring What Matters: Noncognitive Skills - GRIT
0602DATASUMMITTSTEWART(1).PDF
1. SchoolMatters.com
A Service of Standard & Poor’s
School Evaluation Services (SES)
Michael Stewart
Director of Research & Analytics
February 3, 2006
2. School improvement efforts have focused on data-driven decision-
making, and the essential need for better databases.
But policy-makers don’t just need data-driven decision-making
systems; they need performance management systems.
What’s the difference between data bases and knowledge bases?
Most databases include one or more broad categories of data:
• Input data – such as financial and human resources
• Contextual data – such such as demographics
• Outcome data – such such as student achievement
These are often stored in fragmented - not integrated - systems.
Decision-Makers Need a Knowledge Base,
Not Just a Better Data Base
3. A Knowledge Base:
• Integrates different categories of data into a unified system
• Interprets the data using performance benchmarks
• Includes, but goes beyond, input, output, and demographic
data, by addressing the need for process data
• Uses a data structure designed to address policy questions
Decision-makers can’t manage their way to better performance if
they know their inputs and outcomes without knowing where to
find better processes.
By providing tools to “mine” process data, a knowledge base can
be used to improve – not merely audit – performance.
This isn’t a call to reinvent the wheel, but to integrate the
components already available in order to build a knowledge base.
4. Student Performance
• State test scores, participation rates, and
trends disaggregated by student group
• College prep test scores & participation
(e.g., ACT, SAT, PSAT, AP)
• Post-secondary patterns (as available)
• Graduation rates
• Dropout rates
• Attendance rates
Return on
Resources
Relationship between student achievement
and spending in demographic context
Spending and Revenue
• Spending per student, by function, and
by program
• How spending increases are
allocated over time
• Local, state and federal revenue
• Operating margins
• Compensation
• Fund balance
• Taxes
• Debt
School
Environment
• Student characteristics
(economically disadvantaged
backgrounds, disabilities, limited
English proficiency)
• Class size and school size
• Teacher characteristics
• School safety
• Facilities, technology, infrastructure
Community
Demographics
• Urban, suburban, rural locale
• Adult education levels
• Household income levels
• Single parent households
• Property values
• Labor force, (un)employment
• Other demographics
5. Performance Benchmarks
States compared to other states.
Districts compared to other districts in same state.
Schools compared other schools in same state.
Successful organizations continually search for new
ideas by comparing themselves to other organizations to
learn from their best practices.
This is known as benchmarking. It’s a data-driven
process by which learning and innovation can trigger
fundamental breakthroughs in thinking and practice
7. Standard & Poor's Observations Report is a written analysis on this district's
Return on Resources.™ The report analyzes the district's academic and financial
performance in demographic context. In addition to state comparisons, the report
also compares the district with a composite of demographically similar peer
districts.
Ann Arbor Public Schools
8. Standard & Poor's Observations Report is a written analysis on this district's
Return on Resources.™ The report analyzes the district's academic and financial
performance in demographic context. In addition to state comparisons, the report
also compares the district with a composite of demographically similar peer
districts.
Ann Arbor Public Schools
All Students
Economically
Disadvantaged
Students
9. Reading Proficiency Grade 4 Economically Disadvantaged
Outperformed for 2 Years
Outperformed for 1 Year
Outperformed for 3 Years
10.
11. School improvement
teams can use the
workbook as a guide for
collaborating with “better
performers” to identify
effective processes and
adapt best practices.
Standard & Poor’s also
offers professional
development programs
that provide training to
school improvement
teams in benchmarking
methods.