Newly Added Resources
National Broadband Plan (Chapter 11 Education)
Federal Communication Commission
Sharing meaningful information about students is difficult, and Chapter 11 of this online version of the federal publication offers recommendations for change. Consult this section: 11.2 Unlocking the Power of Data and Improving Transparency.
Data and Accountability
National Governors Association, NGA Center for Best Practices
Governors are the only state leaders who oversee the entire pipeline from birth through postsecondary education and into the workforce. As such, governors and their staff play a critical role as performance managers, ensuring that education institutions are meeting state goals and producing positive outcomes for all students. Governors use data to monitor progress and identify areas for needed policy action and design accountability systems to ensure that outcomes are met across the education pipeline. See Related Content and Resources.
11 Ways to make Data Analytics work for K-12
Irving Hamer, Education Week, October 14, 2014
The drive to close achievement gaps and eliminate chronic low performance has become a quest for the K-12 Holy Grail. We know what we are looking for and why, and see clues to success everywhere.
In public education, the promise of data-informed decisions that drive instruction, improve student and school performance, and close achievement gaps appears limitless. But schools, districts, and most K-12 leadership teams are not close to realizing the kinds of data-driven benefits that already exist in fields like financial services, medicine, and science. There are numerous reasons for this…. Read more….
Big Data and Analytics in K-12 Education; The Time is Right
Center for Digital Education, 2013
Technology for integrating systems, data and analytical tools makes it easier to support data-driven improvements in teaching and learning. The article looks at the current state, the future, and designing a plan.
Overcoming the Data Deluge in Higher Education
Center for Digital Education, November 5, 2014
Find out how the right technology foundation can help universities use data to make more informed, strategic decisions and boost performance.
Analytics in Higher Education, 2015
A Collaboration Between EDUCAUSE and Gartner
ECAR RESEARCH HUB
Analytics is one of higher education’s top IT-related issues. Institutions need solid methods for campus BI/data reporting and analytics to support campus priorities and decision-making. We’ve reached an inflection point where the maturation of analytics tools and the amount of data available have reached critical mass to engage in data informed solutions. Analytics can provide insight in areas such as reducing students’ time to degree, improving student learning outcomes, targeted recruitment, business process optimization, alumni relationship management, and increasing research productivity. In 2015 EDUCAUSE will update and extend the 2012 ECAR analytics study to understand the nature, magnitude, and future directions of analytics in higher education and provide guidance to institutions enhancing or developing analytics programs. Read more…..Current Landscape Report, Institutional Analytics Report and Learning Analytics Report are forthcoming.
NMC Horizon Report: 2015 Higher Education Edition
Top 10 IT Issues 2015 (A number of these relate to data.)
The annual EDUCAUSE Top 10 research — including the IT issues and strategic technologies
reports— is used by higher education leaders and decision makers to anticipate and articulate challenges and inform their actions and decisions to address them. The list of top IT issues is developed by a panel of experts
, comprised of IT and non-IT leaders, CIOs, and faculty members, and then voted on by the EDUCAUSE community. The top 10 strategic technologies were selected from the analysis of a vetted set of 107 technologies presented to EDUCAUSE members in a survey in summer 2014.
Talking about the Facts of Education Data with Policymakers
Data Quality Campaign (DQC)
DQC prepared this document to help its state and national partners respond accurately to policymakers who have questions about education data.
Closing the Gap; Turning Data into Action
AASA and COSN
Closing the Gap gives educators the resources they need to turn data into action to strengthen instructional practices. Consult About
•Reports based on broad input from the K-12 educational community including up-to-date information on student information systems (SIS) and learning management (LMS) software solutions.
•Best practices for implementing SIS/LMS software systems.
•Professional development resources designed to help district and school leaders facilitate their training of other district and school leaders.
Data Analytics Bibliography
Texas Higher Education Data
This site is Texas’ primary source for statistics on higher education. It also includes numerous reports that link K-12 and workforce data with students in higher education. The site contains reports, statistics, queries, interactive tools, and downloadable data, along with links on enrollment and success, course and facilities inventories, interactive institutional locator maps, and degrees offered. Many data tables are available by race/ethnicity and gender. Higher education data on the site are submitted and certified by higher education institutions. The most extensive data concerns public institutions. However, independent and for-profit institutions also report some data to the Texas Higher Education Coordinating Board. Data sharing agreements with the Texas Education Agency and the Texas Workforce Commission allows for matching and track of students’ progress.
The site includes sections for policymakers, parents/students/K-12 educators, media, institutions/researchers, and career/workforce educators. Tools on these sites include the Texas Closing the Gaps Dashboard
as well as data on high school to college (including seventh-grade cohort data, the Tracking Postsecondary Outcomes Dashboard and dual credit enrollment).
“DQC’s Six Federal Policy Principles”
Kristin Yochum, DQC, June 26, 2013
In July 2013, DQC began implementing new federal policy efforts around the federal implications of education data, working with all branches of the federal government and other federally facing national partners to strengthen the effective use of education data for student achievement at the federal level.
DQC has accomplished this by advancing the following six principles in all their work:
1. Reduce Burden on States while Ensuring that Essential Data are Collected and Reported
2. Promote Transparency and Data Accessibility
3. Break Down Silos
4. Build Capacity of Stakeholders to Use Data
5. Ensure Privacy, Security, and Confidentiality of Data
6. Serve as a Catalyst for Building, Maintaining, and Innovating Data Infrastructure
Data Analytics and Policy
Using Data to Guide State Education Policy and Practice
National Governors Association Center for Best Practices, February 15, 2012
This issue brief outlines how governors can promote the greater use of data in their states by:
- collecting more actionable data designed to meet identified stakeholder questions, such as information on
students’ mastery of standards, the effects of academic interventions on student performance and a clearer link
between school and district expenditures and student performance
- linking multiple data systems through the adoption and use of common, open data standards, and
- providing new tools for aggregating and analyzing data that ease educators’ ability to offer individualized instruction
and support policy- makers’ ability to monitor performance.
This SREB report updates the region’s goals for today’s education realities. It frames six goals with outcome measures and the policies to achieve them. One essential policy that states need to improve performance: States should develop and maintain education data systems that link data on students, teachers and schools from state education and related agencies and then ensure education leaders use the data to inform policy decisions.
The Data Quality Campaign (DQC) is a nonprofit, nonpartisan, national advocacy organization based in Washington, D.C. Launched in 2005 by 10 founding partners, DQC now leads a partnership of nearly 100 organizations committed to realizing the vision of an education system in which all stakeholders — from parents to policy-makers — are empowered with high-quality data from the early childhood, K-12, postsecondary and workforce systems to make decisions that ensure every student graduates from high school prepared for success in college and the workplace. To achieve this vision, DQC supports state policy-makers and other key leaders to promote the effective use of data to improve student achievement.DQC Resources
: DQC offers an online guide to many resources, including publications, videos and website features. Also consider Data Quality Campaign (DQC) Resources
- What, specifically, is the role of big data in education?
- How can big data enrich the student experience?
- Is it possible to use big data to increase retention?
- To what extent can big data contribute to successful outcomes?
More specifically, this article says we must ask what it means to “know” with predictive analytics. Furthermore, once an administration “knows” something about student performance, what ethical obligations follow?
Creating a culture of evidence on campus requires clear policies and processes with respect to the use of data. A key component of a solid analytics program is a system that is built on trust and normalized processes, transcending individual personalities or arbitrary decision-making. This article asserts that a holistic approach to data management starts with effective governance, rational policies and reliable procedures.
The immediate major challenge for the nation and every state is to ensure their populations have the levels of education necessary to meet the job requirements of the next 15 years. Reaching this goal, or even getting close, will not happen with promises and policies alone. It will take a concerted, unified, statewide effort at every level to get the job done. And many different areas in higher education, K-12, and state policy require our attention. Ten key recommendations that SREB developed with governors, legislators, state K-12 and higher education chiefs and national policy experts are described in this report. Essential data are integral to the success of this effort.
Effective Use of Data Analytics to Inform Action — K-20
This guide provides stakeholders with practical information about the knowledge, skills and abilities needed to more effectively access, interpret and use education data to inform action. It includes an overview of the evolving nature of data use, basic data use concepts, and a list of skills necessary for effectively using data.
This IES practice guide offers five recommendations to help educators effectively use data to monitor students’ academic progress and evaluate instructional practices. The guide recommends that schools set a clear vision for school-wide data use, develop a data-driven culture and make data part of an ongoing cycle of instructional improvement. It also recommends teaching students how to use their own data to set learning goals.
The job of a teacher is to be faithful to authentic student learning. Currently, the profession is fixated on results from one test, from one day, given near the end of the school year. That data can be useful; however, teachers spend the entire year collecting all sorts of immediate and valuable information about students that informs and influences how they teach, as well as where and what they review, re-adjust and re-teach.
Engaging students in using data to address scientific questions has long been an integral aspect of science education. Today’s information technology provides many new mechanisms for collecting, manipulating and aggregating data. In addition, large online data repositories provide the opportunity for totally new kinds of student experiences. This site provides information and discussion for educators and resource developers interested in effective teaching methods and pedagogical approaches for using data in the classroom.
This source addresses what teaching with data means, why teach with data, and how to teach with data. Many examples are offered.
How can teachers capitalize on data about student learning that are generated in their classrooms every day? How can this information best be collected and used to increase student learning? Research by Dylan William and his colleagues have shown important increases in student learning when teachers:
- Clearly define the purposes of each lesson that they teach;
- Use lessons to collect evidence on how students learn; and
- Use collected evidence and promptly re-direct students as needed.
Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings that they learn in.
The author examines the potential for improved research, evaluation and accountability through data mining, data analytics and Web dashboards. So-called “big data” make it possible to mine learning information for insights regarding student performance and learning approaches.
Bringing teachers into the big data discussion is crucial because they are the ones, along with parents and students, who will benefit from advances in research and analysis. Projects that let teachers know which pedagogic techniques are most effective or how students vary in their style of learning enable instructors to do a better job. Tailoring education to the individual student is one of the greatest benefits of technology, and big data help teachers personalize learning.
There is a big unknown for high schools: How do students fare after they graduate? This paper calls on states to take four definitive steps to unlock the power of high school postsecondary performance data:
- Action #1: Improve the ability to measure students’ postsecondary success.
- Action #2: Make the postsecondary success data available statewide.
- Action #3: Provide technical assistance to help districts translate data and reports into action.
- Action #4: Reward districts and schools that improve students’ enrollment and postsecondary performance.
Colorado has developed a Web-based portal that enables teachers to track individual students’ test scores over time and gauge their progress.
Data Analytics in Higher Education
Many IT and institutional research professionals believe that their institutions are behind in their endeavors to employ analytics. One purpose of this ECAR 2012 study on analytics was to gauge the current state of analytics in higher education — to provide a barometer by which higher education professionals and leaders can assess their own current state. Another purpose was to outline the barriers and challenges to analytics use and provide suggestions for overcoming them.
This report provides information about how leading institutions in higher education and vendors are building capacity in analytics to improve student success.
Learning analytics promises to harness the power of advances in data mining, interpretation and modeling to improve understandings of teaching and learning and to tailor education to individual students more effectively. Still in its early stages, learning analytics responds to calls for accountability on campuses and aims to leverage the vast amount of data produced by students in academic activities. The authors predict that data analytics will enter the main stream within two to three years. That means it is almost upon us!
This issue is dedicated to the topic of analytics. Three articles are noted below.
The article discusses the promise of learning analytics in higher education. Learning analytics are said to provide educators the tools, technologies and platform for meaningful learning experiences that can engage, inspire and prepare current and future students for success. Several technological developments that served as catalysts for the move toward the growth of analytics in business, industry and education are noted including data warehouses and the cloud. The three current areas of innovation for business intelligence technology are also cited.
This is the first in a three-article EDUCAUSE Review series exploring analytics.
Creating a culture of evidence on campus requires clear policies and processes with respect to the use of data. A key component of a solid analytics program is a system that is built on trust and normalized processes, transcending individual personalities or arbitrary decision-making. A holistic approach to data management starts with effective governance, rational policies, and reliable procedures.
See Issue #10: Using Analytics to Support Critical Institutional Outcomes.
It is the successful intersection of information technology and information ownership that becomes the important factor in whether campus data analytics efforts yield usable results. Too often, that intersection does not take place. EDUCAUSE offers eight strategic questions for using analytics to support critical institutional outcomes.
Relevant News Articles
Doug Guthrie, U. S. News and World Report, August 15, 2013
Big data, not MOOCs, will give institutions the predictive tools they need to improve outcomes for individual students. … Beyond online learning, administrators understand that big data can be used in admissions, budgeting and student services to ensure transparency, better distribution of resources and identification of at-risk students.