Mark Somerville, Vice President, Global Product Management, Business Intelligence, SunGard

SunGard has made a significant commitment to analytics in higher education. This is the dawning of a whole new era of BI in higher education. This is an exciting and robust field, analytics in general, and even more so in higher education, which has some ground to make up in comparison with other industries.

From an industry perspective, it is interesting to see the wave of acquisitions and consolidations of BI tool companies and the elevation of BI as a strategic concern.

From SunGard’s perspective, we have been making a commitment to BI for some time, starting with a foundational layer of the operational data store (ODS) and data warehouse (DW) since institutions need to establish a foundation for extracting and managing data resources. We need consistency in data governance and management. SunGard has taken this a step further by constructing a “performance management suite,” bundled around admission performance. They are developing other bundled performance management suites around advancement and retention. These are addressing clearly articulated needs from our clients.

What does the market hold? From a sophistication perspective, institutions are evolving from focusing on “what has happened?” Institutional leaders want to know “What is happening?” in real time and “What is going to happen?” so they can understand and affect the future, moving toward optimization. SunGard is heavily into performance improvement and extending its stack so it can bring all these elements together.

Most institutions are not making a one-vendor approach when it comes to BI and analytics. Sungard believes in this approach. Given the emerging future of BI, it will be hard for one vendor to dominate. Technology is only 20% of the analytics equation – it also takes organizational, governance, strategy, leadership, and cultural considerations to make analytics work in higher education. Institutions need to leverage the assets that exist and the foundation that is in place. Institutions are asking themselves: “How do we use and get the most value from what we have?”

Commercial enterprises and applications do a much better job of analytics data mining and scraping the environment for data-based insights, many of which are not expected. We hope to bring such exploratory, expeditionary analytics to higher education.

We see these trends at EDUCAUSE. The focus on affordability is clear. The trend of “zero cost licenses” is clearly big news. In multi-vendor environments, there is massive commodification and it is not about the tools. We will see more price competition and the stitching together of different applications. We will also see more acquisitions.

Things are becoming more sophisticated. The price points are coming down. It’s all about unlocking data and using information in decision making. There has never been a better time for analytics in higher education and the world in general.

Tom Wagner, Vice President Product Manangement, Retention, Student Success, Sungard

Tom Wagner has product responsibility for Course Signals, which Sungard is offering in partnership with Purdue University. It focuses on course-level success providing a very early warning in Week 2, 3, 4 which predict students most likely to fail. It does this through engagement with LMS, gradebook, and student system data and provides communication tools to reach out with faculty-composed emails specific to the course. The system also provides a signal posted to home page. Students are offered a variety of meeting options – faculty office hours or meet at the center. Course Signals scrapes and analyzes data from grade book, activity log files, focusing on effort, grade book, academic prep and grades from other courses, and demographics. All of this enables a combination of at-risk characteristics and behavior, compared with those of successful students in the past.

In version 1, SunGard provides integration to Banner and PeopleSoft and WebCT/Vista. In November it will integrate with the Blackboard academic suite and with other LMSs next year.

Future plans include sharing of the algorithms across institutions, working with development partners to provide improved algorithms and improve the tools that are made available to advisers. Academic advisors want the most up-to-date information on all advisees.

Future plans include improving the student view, working on a mobile strategy and expanding the ERP and LMS platforms with which Course signals can integrate.

Tim Gilbert, Senior VP and Chief Marketing Officer, Campus Management

Campus Management is an ERP provider, 20 years old, serving a range of institutions, including for-profit and not-for profit.

Here at EDUCAUSE there is a great deal of interest in ERP systems and predictive analytics. End users want access to data anytime, anywhere. The data primarily rests in the ERP, with analytics on institutional effectiveness drawing on ERP and LMS. The holy grail is real-time and data you can trust.

Many institutions are not able to provide a single version of the truth and data that can be trusted. So there are two abiding problems: 1) do we have access to the data that we need, and 2) can we trust that date to provide a “single version of the truth.” To solve this problem, Campus Management has partnered with eThority to offer a zero cost license executive dashboard system. Colleges, universities and online education providers running on Campus Management’s CampusVue Student can expand upon the new CampusVue Performance Analytics system, powered by eThority, to introduce an enterprise-wide executive information system that will incorporate data from most any third-party software into one unified reporting solution.

The companies have integrated eThority Enterprise with Campus Management’s CampusVue Student system to deliver to desktops easy to use, graphically rich reporting. The solution is a data warehouse with pre-built service modules, called ETL (Extraction, Transformation and Load), that extract data from CampusVue Student, enabling real-time strategic analysis based on a 360° view of an institution’s various campuses, colleges and programs.

The solution can be expanded to introduce other powerful reporting capabilities, enabling institutions to perform complex, real-time analysis based on data from multiple software applications, including those outside the CampusVue Ecosystem, such as Constituent Relationship Management (CRM), Learning Management Systems (LMS), or Financial and Human Resources applications. Executives will be able to query the system with “what-if” scenarios or to create unique reports in real time rather than rely upon static, built-in reports.

“The integration of eThority with our CampusVue Student system takes reporting to a more effortless level with the power and agility that postsecondary education has sought for years,” says Timothy B. Loomer, President and Chief Executive Officer of Campus Management. “Our clients can query the data in their administrative systems at will, and then expand this capability into an executive information system across all their software very rapidly and affordably.”

John Fontaine, Senior Director, Platform Evangelism, Blackboard

Blackboard has been developing the capacity to create apps through its learning platforms. It is pursuing new functionality around the concept of “Openness,” emphasizing how they can make it easy to get information into and out of Blackboard. How do you create actionable information from information that is in Blackboard – that is the challenge.

To support this Open Database, three technologies are being leveraged: 1) everything in Blackboard is in a relational database and Blackboard provides complete documentation on the schema and how to get info out – the schema documentation is maintained with the software; 2) What tools should I use to run reports? Blackboard is using the Eclipse Business Intelligence and Reporting Tools (BIRT) to create and run reports on Eclipse against Blackboard; and 3) building blocks, apps, and reports can be dropped into apps and uploaded into the system. Reports can be run and viewed in excel, PDF, this is built into the system.

Blackboard has launched a Community of Practice on reporting and analytics. It identified some common reporting needs and things the Blackboard user community wanted to understand. Blackboard gave the community a grant to develop canned reports, and make them available through a Listserv at the University of Illinois Chicago. This is called Project Astro. Users can join the Listserv and get access to reports, share their reports through this process and build the Astro tool.

In the future Blackboard will continue to develop its outcomes project. It will look to improve data analysis activities and with the Blackboard community and partners to build value for the users. They are also partnering with SunGard on Campus Signals. In the past the LMS has been a difficult application from which to access and extract data. John Campbell of Purdue found 44 significant identifiers that affected success and 20 were from the LMS. This will leverage the Open database capability.

Phil Ice, Director of Course Design, Research and Development, American Public University System

The American Public University System is a highly regarded practitioner in the field of embedded predictive analytics. APUS consists of the American Public University and the American Military University and is one of the charter members of the Transparency by Design initiative. APUS has been recognized for its online learning environment through winning the 2009 Sloan Consortium’s Ralph E. Gomery Award for Quality Online Education and twice receiving Sloan’s Effective Practice Award.

Phil Ice believes that typical practice in higher education is to use “end point data” to look retrospectively at what has happened. By then it is too late to help the students who have failed. APUS is committed to a more aggressive approach. They look at “real time, actionable intelligence.” They utilize embedded calls that propagate data back for analysis that can be efficacious in real time.

In the APUS business culture, performance is taken seriously every day – perpetually. They have the capacity to look at data any time. As it is, their current practice is to take a comprehensive look every week at all 77,000 currently enrolled students, who are ranked in order based on their likelihood of not retaining. APUS has the capacity to drill down to examine and intervene with individuals using a variety of scenarios and tailored interventions.

They examine every aspect of the student to determine their broad profile characteristics and their precise transactional information revealing student interaction with content, other students, and faculty. All this is done in quantifiable ways so APUS can understand what is causing the problems and the precise nature of the problem that is being caused.

These practices enable the achievement of substantial consistency across the enterprise and the practice of continuous quality improvement. What makes their approach even more exciting is that their approach to statistical analysis doesn’t just use garden-variety statistical regression, but dynamic models that actually learn from themselves.

APUS is exploring whether this sort of practice is a line of business that could be offered to other learning enterprise.

John Lewis, Vice President of Higher Education Services and Chief Software Architect, Unicon

Unicon is a consulting and services firm that works with institutions to leverage technology, including open source applications and technologies, to solve problems at low costs.

Affordable analytics is on everybody’s mind. From Unicom’s perspective, recruitment and retention is a key area of focus for analytics and action. Losing students in the first year creates a huge burden for institutions. Anything they can do to help retain students will be worth the investment. Institutions are coming to realize this.

There has been a quantum leap in the amount of data available for analysis and management. It used to be that having access to a terabyte of data was a big deal. Today I have access to that much storage on my desktop as a backup drive.

How do we make sense of this information – that is the question. We need to manage this data and get into the analysis of LMS data where we have insights on student engagement and assignment. Institutions are looking for help in these areas.

Analytics is in demand by decision makers and in the higher education news, on the exhibit floor and in
presentations at EDUCAUSE 2010 and in numerous blogs, books, and was also
a key issue at the recent National Productivity Conference.

This White Paper has been built around a series of interviews with industry and vendor leaders, seasoned
practitioners, and thought leaders. These interviews started at EDUCAUSE 2010 and have continued
afterward, tapping resources from Inside Higher Education and The Chronicle of Higher Education to
draw a comprehensive view of what’s new in analytics practice.

Over the next months, this analysis and descriptions of best practices and vendor products and services
will be refined and extended further. Additional vendors and practitioners will be interviewed. Comments
and reactions from vendors and practitioners will be sought. Our team will convene conversations with vendors, practitioners and
thought leaders about how to advance the perspectives, tools, practices, and policies needed to deepen
and accelerate the effective use of analytics in higher education.

We are not assessing what the average institution is doing to deploy and leverage analytics, although we are highlighting affordable analytics and best practices in a wide range of different types and sizes of institutions. Surveying the
population to determine typical practice has proven minimally helpful in identifying potential breakthrough
practices.

What’s New in Academic Analytics?

Instead, we have emphasized the best practices, new products and services, and fresh and affordable
approaches that are defining the leading edge of analytics in higher education. We have also included
insights on best practices from other sectors. These have the potential to be widely deployed and taken
up by other institutions. These are developments that will make a difference in advancing the practice of
“Action Analytics.”

What are Action Analytics?

Academic Analytics are business intelligence, reports and analytics that provoke appropriate action in pursuit
of optimizing performance – of students, faculty, staff, and organizations. Action Analytics provide
actionable intelligence in the context of the imperative to improve performance and deliver value. To
fulfill this promise, Academic Analyticss require substantial enhancement in organizational capacity plus
changes in culture and behavior at the faculty, institutional, state, and national levels. Action analytics is
much more about change management than tools, techniques, and data sets. It is about establishing a
serious performance improvement and optimization culture.

In addition to gathering insights on successful analytics practice in higher education,
we are showcasing the following insights from a global survey on the dramatic rise in analytics.
Recommendations from the Report, “Analytics: The New Path to Value.” The MIT Sloan
Management Review and the IBM Institute for Business Value have collaborated on a global executive
study and research project focusing on the use of analytics.

The tag line for their report – “How the Smartest Organizations Are Embedding Analytics to Transform
Insights into Action” – could have been written by the Public Forum on Action Analytics. However, their
findings suggest that other industries are several years ahead of higher education in deploying advanced
analytics for strategic purposes. We can learn from them, adapting their insights to our distinctive
settings, yet understanding how we must change the higher education culture in order to systemically
enhance performance and productivity.

A Five-Point Methodology for Advancing Analytics. The Report highlighted the importance of
building vision, capacity, and management practices for analytics. The authors suggested the following
five-point methodology for successfully implementing analytics-driven management and rapidly creating
value. With minor modifications, it fits the needs of analytics in higher education.

• Focus on the biggest opportunities first.
• Attack a big important problem that can demonstrate value and catalyze/mobilize the organization to action.
• For higher education, student access, affordability, and success is our “killer app” that can be
  used to demonstrate value and mobilize the energies of leadership, faculty, staff, and policy
  makers.
• Start with questions, not data. Understand the problem – and the insights needed to solve it –
  before working on the data that will yield the insights.

For years higher education institutions have been working on improving student access,
affordability, and success, in response the strong accountability pressures. Many of the data
needs for that purpose are well defined. However, today’s fresh insight is that this must be achieved in the uncomfortable context of a “New Normal” that requires reimagined performance, productivity, and financial sustainability.

1. Analytics practices must be embedded in processes and brought to life in a way that they are understood, embraced and acted upon by non-experts. In higher education, this translates into “analytics for the masses,” which involves the clear communication of insights on “what works” to improve student success and institutional
   performance. These insights, analytics applications, and intervention mechanisms must be
   embedded over time in academic and administrative processes. This creates a new imperative
   and capacity to act at the front-line level.
2. Keep existing capabilities while adding new ones. Most analytics started with localized pilot
   capabilities. As centralized, enterprise-wide analytics capabilities and oversight grew, enterprises
   have found it is shrewd to keep distributed, localized capabilities in place. In higher education, many of the initial uses of advanced analytics began as pilot programs, enrollment management operations, or demonstration projects of predictive analytics. They are
   spreading as predictive analytics have been embedded in processes and applied at the enterprise level.
3. Build the analytics foundation according to an information agenda. While opportunistic
   applications of analytics can rapidly create value, the true power of analytics will be realized
   through enterprise-wide applications. This requires enterprise-wide plans for data, information,
   and analytics applications and how they align with institutional strategies. In higher education, some market-driven (for-profit) institutions have developed such enterprise wide analytics applications and plans, but most institutions are behind. All the more reason to focus attention on Action Analytics as an enterprise-level issue for strategic visioning, planning, and capacity development.

   This Report is a useful guide and leading indicator of where higher education could be in a few years.
   Later on, we adapt this five-point methodology to the challenge of building organization capacity for
   Action Analytics in higher education.

   Narratives have been organized into eight critical elements of Academic Analytics:

   1. Begin with a Clear Focus on What It Will Take to Improve Student Access,
      Affordability, and Success – Then Raise the Stakes Even Higher.
      Improving student access, affordability and success is the “killer app” of analytics in higher education.
      Thoughtful investments in student success typically yield substantial and rapid returns on investment.
      Institutions across the country are demonstrating this daily and have the ROI to prove it.

      Accountability for Access, Affordability and Success. Improving student access, affordability and
      success has been the initial focal point of many national, state, and institutional initiatives. These have
      been funded by institutions, governments, and foundations/associations such as the White House, the Bill
      & Melinda Gates Foundation, The Lumina Foundation, The Education Trust, and The National
      Association of System Heads. Accountability analytics (student outcomes, success rates, employment
      rates) have been a key focus of these initiatives from the start. They are also used extensively by state
      higher education agencies providing incentives through “performance funding” and other inducements.
      Embedded, Formative Analytics. To truly optimize student success, Action Analytics reaches beyond
      “summative,” accountability statistics about student access, affordability, and success. We need realtime,
      “formative” analytics that are embedded in institutional processes – administrative and academic –
      and widely used by front-line success makers. This is critical element of institutional tools, application,
      and capacity.

      Improving Performance, Productivity and Financial Sustainability in the New Normal. Such
      analytics also enable institutions to address faculty performance (impact of the actions of individual
      faculty on student success), the effectiveness of institutional practices and processes that produce
      student outcomes, and academic and administrative productivity. Moreover, a new context for student
      success initiatives has emerged: rediscovering financial sustainability in the “New Normal.” Many of the
      state and national initiatives on student success are expanding their scope to include faculty and process
      performance and academic and administrative productivity. Leading institutions are also taking this
      broader and deeper view.

      Action Analytics can be a way to improve results on all these dimensions at once.
      EDUCAUSE 2010 Showcased the Next Generation Learning Initiative. Through the efforts of the Bill
      & Melinda Gates Foundation this initiative’s focus includes learning analytics and the funding of individual
      institutions that have the potential to develop breakthrough practices in improving student success and
      institutional performance. Improving and scaling data analytics is just one priority; others include
      advancing blended learning programs, novel forms of interactive learning (such as gaming), and
      promoting high-quality open courses online.

      To be ultimately successful, however, the Next Gen Learning Initiative will need to extend successful
      pilots to department, college and enterprise scale. Moreover, individual institutions and systems of
      institutions will need to leverage these successes to build organizational cultures and behaviors that
      support performance and productivity improvement. These sorts of capacity building efforts are a primary
      focus of Action Analytics and this White Paper.
      What’s New in Analytics?

      2. Open Up Access to Data to All and Broaden the Scope of Data Used in
      Analytics.
      This requires breaking down existing data silos, achieving better data governance and management, and
      developing the capacity to extract and use data from a broader range of data sources.
      Data, Data Everywhere. Most institutions are awash in data, but the data are often isolated in separate
      data silos, sometimes trapped by proprietary restrictions or simply a lack of adequate data mapping.
      Moreover, mainstream enterprise resource planning systems (ERP) have proven disappointing from an
      analytics perspective. Their primary orientation has proven to be operational and transactional; they have
      been more oriented to back office operations than student success. Similarly, first-generation learning
      management systems (LMS) were architected to focus on course content (often imprisoned within the
      structure of individual courses) rather than context, community and student success.
      Opening Minds and Architectures. The new generations of enterprise systems and analytics
      applications are striving to overcome these limitations by creating new architectures and approaches that
      leverage open architecture and open source. Institutions are re-establishing better data governance and
      management practices, while vendors are enhancing data mapping and building new analytics
      tools/applications. Together, these efforts will help liberate and leverage data resources and achieve
      Action Analytics. Leading institutions are enhancing their capacities in these areas.
      The full spectrum of data sources needed for Action Analytics include:
      • administrative enterprise resource planning (ERP) systems (Student, Finance, Financial Aid,
      Human Resources, Advancement; and Grants Management, CRM and Procurement);
      • learning management systems (LMS) and other academic systems (library, assessment tools,
      content management, mobile and social solutions, as well as academic support services);
      • third-party administrative systems (parking, residence halls, food services, auxiliary enterprises);
      • alignment systems (aligning strategies, actions and measures) and internal evaluations/surveys,
      and external data sources(comparative data on other institutions, NSSE and CCSSE).
      Progressively, institutions are extracting and making sense of broader collections of these data in data
      warehouse-based applications. Many of the new analytic applications advance data governance and
      management practices, as well and encourage institutions to invest more energy in these activities.
      At EDUCAUSE 2010, The Future of LMS Was on Everyone’s Mind. There was considerable
      emphasis on and interest in the “opening up of LMS” to analytics. Virtually all of the LMS providers
      emphasized providing easier access to their data resources and gains in the use of analytics (Blackboard,
      Desire2Learn, eCollege/Pearson, Sakai/Kuali).
      Especially provocative were the new embedded reporting and analytics offerings that were showcased in
      joule, a Moodle-based learning management platform offered by Moodlerooms. Features embedded in
      joule are user-friendly reporting and analytics tools that are a part of the enterprise wrapper around opensource
      Moodle. These analytics draw from LMS, assessment, and ERP data sources, allowing the
      institution to explore, analyze, and report on a variety of student success, faculty performance, and
      academic productivity indicators. These analytics will be available to authorized administrators, faculty,
      staff, and the student, in real-time.
      Post-EDUCAUSE, The Conversation Continued. The focus on learning management systems received
      a boost from the Inside Higher Ed column on “The For-Profit LMS Market,” which contrasted the
      comparative benefits of Blackboard and eCollege. Much of eCollege’s early growth was due to its
      What’s New in Analytics?
      White Paper 6 December 5, 2010
      deployment by for-profit learning enterprises, where its “top-down” approach aligned with the operational
      needs of the for-profit (market-driven) providers.
      3. Create the Next Generation of Analytics Applications: Enterprise-wide,
      Affordable and User Friendly.
      This requires moving beyond business intelligence tools/analytics tools to robust analytics applications.
      These must include affordable analytics for every type and size of institution. Rather than analytics for
      power users, we need analytics for the masses, empowering authorized faculty, administrators and staff
      to become active analytics participants. Moreover, students need analytics on their performance in
      comparison with students who have been successful.
      Report Writers and Business Intelligence Tools. Just a few years ago, analytics in higher education
      meant purchasing report writers and/or business intelligence (BI) tools. These tools were expensive and
      required expert power users who would develop reports that were then “pushed” to passive end users.
      Acquisition and Consolidation. In the last three years, the major BI companies were acquired by
      technology or ERP companies and their tools integrated into the ERP stack (IBM acquired Cognos then
      SPSS, Oracle acquired Hyperion, SAP acquired Business Objects, other ERP providers made BI tools
      available to their customers, such as Datatel offering Business Objects). Over time, these vendors have
      increased the sophistication of the analytical applications deploying the BI tools.
      User-Friendly Advanced Analytics Applications. Today, BI tools for power users are being
      supplemented and/or succeeded by advanced analytics applications that are more user friendly. Many of
      these applications incorporate Microsoft products (Office and SQLServer) which have advanced analytics
      capabilities embedded in them and are sufficiently inexpensive at scale to the extent that they have
      greatly reduced the price of analytics applications. These applications are user friendly; they enable the
      personalization and contextualization of data that can be “pulled” by active end users. These robust
      analytics applications combine a broader range of components than earlier BI tools:
      • extract transfer and load (ETL),
      • data warehouse (DW),
      • Online Analytic processing (OLAP),
      • Business intelligence (BI) elements,
      • Predictive modeling and data mining,
      • Accessed by powerful visualization and presentation tools.
      The components in this array are illustrated by “Open-Architecture Enabled Action Analytics.” The total
      cost of ownership of these analytics applications are coming down in total cost of ownership and
      becoming embedded in institutional enterprise infrastructures.
      At EDUCAUSE 2010, The Attention on Analytics Applications Was Spread Among a Variety of
      Vendors. These included: companies specializing in analytics, LMS vendors, and ERP providers. All
      had an analytics story to tell. Among analytics companies, iStrategy Solutions has been an early pioneer
      in providing a pre-packaged analytics application that can be rapidly implemented in ERP environments
      where the data mapping to ERP data sources pre-exists. iStrategy reported at EDUCAUSE 2010 that it
      had completed its data mapping to Peoplesoft, SunGard, and Datatel and was turning its attention to data
      mappings to major LMS vendors. iStrategy is one of the options offered by Datatel to its customers as an
      integrated analytics solution.
      Another analytics company, eThority, announced big news at EDUCAUSE 2010. eThority has taken its
      solutions (analytics, dashboarding and reporting for higher education) and made them available under the
      What’s New in Analytics?
      White Paper 7 December 5, 2010
      sobriquet “DataTalent” through a so-called Zero Cost License. They took this approach after a six-month
      analysis of the marketplace and collaboration with industry analysts to find an “over-the-top” strategy.
      They concluded that in the consumer data market, users were deploying Google, Wikipedia, and others to
      achieve open and free access, so eThority took its platform and made it available at zero cost. This is a
      500-user license of eThority’s basic, fully functional analytics offering, not a stripped down version; there
      are premium services available as well. eThority is also providing a user-friendly predictive analytics
      capability as part of its product offering (as opposed to a power-user version available through standard
      BI tools).
      Mainline ERP Providers. At EDUCAUSE 2010, all of the ERP vendors showcased their deployment of
      BI tools and analytics applications embedded in the ERP stack (SunGard, Oracle, Datatel, Campus
      Management, Jenzabar, SAP). In virtually all cases, analytics occupied a more prominent position than
      last year. Earlier this fall, SAP sponsored a symposium on business intelligence at Northern Kentucky
      University. SunGard attracted considerable attention with its acquisition of Purdue University’s Campus
      Signals application and its performance management suites in admissions, and soon-to-follow
      advancement and retention. Campus Management announced it was now offering the eThority platform
      to its customers to address analytics needs. Datatel deploys Business Objects on the operational side
      and iStrategy on the integrated analytics application side.
      ERP for Lifelong Learning. Another interesting development on the ERP front is the emergence of
      vendors like Destiny Solutions that offer an ERP-like solution for continuing education and lifelong
      learning offerings. Stimulated by the pressing demand for universities to discover new sources of
      revenues, post recession, such solutions provide a means for maintaining a lifelong relationship with
      learners, embedding analytics and customer relationship management capabilities to track and enhance
      learners’ success.
      Five Insights from the Vendors. From conversations with the ERP, LMS, and Analytics Application
      vendors at EDUCAIUSE 2010, five characteristics emerged as the bottom line on the new generation of
      analytics applications:
      • First, greater affordability and substantial pressure for continuing cost reductions was a
      pervasive theme. Institutions are demanding this and the vendors are responding. Vendors
      expressed the desire to provide analytics solutions for any type of institution, and touted
      examples of community colleges, small professional schools, and mid-sized universities that had
      deployed affordable analytics applications. The financial crisis will accelerate the affordability
      imperative.
      • Second, the need is widely recognized for analytics that are designed and delivered for the
      masses and are user friendly and widely available. While some power-user-based reports will
      continue to be “pushed” out to users, over time analytics increasingly will be “pulled” by ever more
      sophisticated end users using applications crafted for the masses.
      • Third, multi-vendor analytics environments on many campuses will continue to be the
      norm. Many leading-edge institutions are hedging their bets against a single vendor solution.
      Indeed, no single vendor solution exists for the multitude of analytics needs and opportunities
      necessary to achieve the ultimate solution – Action Analytics.
      • Fourth, the conversation about new analytics capabilities is closely linked to the
      emergence of the enterprise technology that will succeed LMS 1.0. On the exhibit floor and
      in the hallways at EDUCAUSE 2010, a favorite topic of conversation centered on “What is your
      next LMS decision going to be?” Institutional leaders are exploring many options, including no
      formal LMS at all. These conversations inevitably included enhancing the analytics that existing
      What’s New in Analytics?
      White Paper 8 December 5, 2010
      LMSs have been unable to provide or support adequately.
      • Fifth, there is greater sophistication in talking about the future uses of affordable analytics
      among vendors and campus executives: presidents, provosts, CFOs, CIOs, and campus
      planners. Over the past several years, the ERP, LMS, and Analytics vendors have been
      educating the marketplace – and one another – on how to move beyond the limitations of the
      existing ERP and LMS stacks. What new analytics needs will be required to deal with emerging
      institutional needs. Likewise, campus leaders have been facing greater pressure to provide
      accountability statistics and to improve performance, which requires embedded, formative
      analytics.
      Enterprise-wide, Embedded Analytics Will Be Required. Part of the greater sophistication as it relates
      to analytics is the realization that enterprise-wide, embedded analytics capabilities need to be
      strengthened even if some immediate needs must continue to be addressed by specialized, targeted
      analytics applications (for retention, for example). Over time, the continuing development of enterprise
      analytics capabilities remains mission critical.
      Prospects for the Third National Symposium. Our Third National Symposium on Action Analytics will
      bring vendors, practitioners and thought leaders together to discuss the challenges of developing and
      enhancing the enterprise capacity (technologies, processes, practices, policies, and skills) necessary to
      support Action Analytics and accelerating their penetration into the higher education marketplace. The
      collaboration between and among the higher education technology industry, institutional practitioners and
      thought leaders is essential to the success of Action Analytics.
      4. Deploy New Modes for Users to Engage and Visualize Data and New Means of
      Alerting, Intervening, and “Nudging” Learners Toward Success.
      Reporting and analytics practices have evolved from standard reports and power-user-provided queries
      and drill downs. The new gold standard is real-time, dynamically generated views that are available not
      just to institutional leaders, but to authorized faculty, advisers, counselors, residence hall and student life
      advisors, and other staff who are front-lend users who are expected to intervene to enhance student
      success.
      A Variety of Visualization Modes Are Utilized. These views are expressed in a variety of forms:
      portals, dashboards, executive information systems, data books, and special alerts, with drill downs to
      cohorts and individuals. Visualization techniques are improving. Dashboards are evolving from
      traditional speedometer and traffic light icons to more sophisticated physical representations, made
      available to decision makers and front line action takers. Many institutions are developing multi-faceted
      advisement and engagement capabilities that enable them to engage learners – and faculty and staff –
      with important alerts or actions through a variety of means.
      At EDUCAUSE 2010, Visualization Was an Important Feature. Presentation and dashboarding were
      key elements of the representations of all the ERP, LMS, and Analytics vendors. In addition, firms like
      iDashboard announced its new X Platform, which extends iDashboard’s dashboard software into a
      comprehensive business intelligence platform which includes alerts, analytics and reports. With these
      features, the user can be notified immediately if a threshold is exceeded on a particular measure,
      performing what-if analysis in real-time and drill down from a dashboard to a report.
      Institutional Examples of Best Practice. A number of institution examples illustrate the importance of
      engaging campus executives, faculty, advisors, other staff, and learners with actionable intelligence.
      What’s New in Analytics?
      White Paper 9 December 5, 2010
      University of Maryland Baltimore County has achieved a great deal of attention for its use of
      iStrategy’s dynamic views of real-time data to create an executive information view for President
      Freeman Hrabowski and his executive team, as well as front-line data users.
      University of Central Florida is generally regarded as one of the top institutional practitioners of
      online, blended and e-learning. In addition, it utilizes extensive data mining and analytics to advance
      the success of its students and to compare and contrast student assessment of faculty performance
      in online, blended and e-learning environments.
      Purdue University’s Signals Program has received a great deal of favorable publicity and is being
      offered commercially by SunGard. It focuses on course-level success, providing a very early warning
      in Weeks 2, 3, 4, which predict students most likely to fail. It does this through engagement with
      LMS, grade book, and student system data and provides communication tools to reach out with
      faculty-composed emails specific to the course. The system also provides a signal posted to a
      student’s home page. Students are offered a variety of meeting options – faculty office hours or meet
      at the center. Course Signals scrapes and analyzes data from grade book, activity log files, focusing
      on effort, academic prep and grades from other courses, and demographics. All of this enables a
      combination of at-risk characteristics and behavior, compared with those of successful students in the
      past.
      South Orange County Community College District has developed (in-house) Sherpa, a
      recommendation engine that will guide students to make informed decisions regarding courses,
      services and information. Sherpa is an enterprise architecture with embedded analytics. Sherpa was
      conceived and shaped by the realization that today’s students are accustomed to receiving
      recommendations in things they are considering doing or buying – movies, books, restaurants, music,
      and directions. So why not build recommendations, “nudges”, and lifelines into the online academic
      experience? And why not use computational power to provide learners with options and alternatives
      that students like them have used to be successful? Sherpa has been constructed as part of the
      Student Services side of the college, not the academic. This has made it easier to develop the
      architecture and frameworks, which can be used in administrative and academic support functions,
      then expanded to include purely academic functions. To start off, Sherpa was applied to the “lowhanging”
      fruit of student course selection, aiding students in registering for courses and providing
      intelligence tools to immediately identify and select alternatives if their desired course selections are
      not available.
      Ball State University worked with the assessment firm EBI to turn the University’s years-long
      experience with its Making Achievement Possible (MAP) program into a technology-based platform
      that could become a marketable software product. MAP-Works is an online system that acts as an
      early-warning indicator of student success and retention. Most institutions are concerned with firstyear
      retention, as well as helping students connect to their major in the sophomore year. MAP-Works
      helps many colleges and universities gather data about their first- and second-year students,
      providing appropriate feedback to students, faculty, and staff members so as to enhance student
      success and/or intervene with those students deemed at risk. Survey reports within the first-year
      experience, sophomore transition, and transfer sections mentioned above are produced by using
      MAP-Works. The MAP-Works application has also been installed at a number of other institutions.
      Signals, Sherpa and MAP-Works also feature the use of embedded predictive analytics, to varying
      degrees. These predictive analytics can be useful in identifying when students are deviating from
      pathways to success, based on past experience. These are important elements of the next stage of
      Action Analytics – using embedded analytics to drive optimization of future outcomes.
      What’s New in Analytics?
      White Paper 10 December 5, 2010
      5. Use Embedded, Predictive Analytics to Optimize Performance.
      In the recent past, much of institutional analytics was historic. Backward facing analyses, using
      longitudinal data to understand student performance and identify “at-risk students” (after the fact) and
      develop policies and practices to deal with future “at risk.” Today, the focus is extending to include
      analysis of real-time activities and levels of engagement of students, enabling identification of “at-risk
      behavior” using predictive analytics that examine past performance of student cohorts.
      These predictive analytics are being embedded in institutional processes. Leading-edge institutional
      users of analytics are using embedded analytics to optimize the performance of individual processes.
      Ultimately, they will address optimizing institutional performance.
      Competing on Analytics. Thomas Davenport and Jeanne Harris have developed a widely used
      typology in their book Competing on Analytics, which illustrates the cumulative use of reporting and
      analytics to achieve optimization of enterprise performance. As institutions move beyond “accountability”
      analytics to “formative” analytics, they are finding they must embed analytics in academic and
      administrative processes, as demonstrated in the following institutional examples.
      Rio Salado College is one of the campuses of the Maricopa Community College District. Rio Salado,
      Capella University, Ball State University, and Purdue University were recently featured as exemplary
      practitioners in the AASCU/SCUP-sponsored Webinar on Predictive Analytics: Building a Crystal Ball
      on Student Success. Rio Salado has progressively developed a strong action analytics capability,
      based on their “homegrown legacy” systems. They have assembled the capacity to measure both
      actual academic performance and the levels of engaged behavior of individual students, and then
      match that against past patterns of success in a manner that has strong predictive power. Data
      mining and predictive analytics meet current student activity. So rather than dealing predictively with
      “at-risk students” based on demographic and personal characteristics and past behaviors, they deal
      with “at-risk behaviors” based on current, real-time activities. The facts on the ground (or online, if
      you will), yield 70% predictive accuracy. Leveraging this capability has become a fundamental part of
      Rio Salado’s academic management practice. This capability is not an add-on to existing systems; it
      is fundamentally embedded. It yields systematic interventions to improve success, log-in behavior,
      and site engagement. This is a new set of unobtrusive metrics for the logged-in student.
      Capella University has embedded these predictive applications and practices into their enterprise
      platforms based on the same analytics tools used by the credit card companies to determine when
      patterns of purchasing behavior change, suggesting that a card may have been stolen and is in the
      hands of someone other than its owner. Within days of a course starting, they can determine which
      students are in jeopardy, what their likely grades will be based on continuation of that behavior, and
      the likelihood of their registering for the next semester. Like many of the online universities in the
      market-driven (for-profit) sector, Capella has embedded predictive analytics as a fundamental
      component of its academic processes. A more detailed case study describes Capella’s strategic use
      of analytics.
      American Public University System is a highly regarded practitioner in the field of embedded
      predictive analytics. In the APUS business culture, performance is taken seriously every day –
      perpetually. They have the capacity to look at data any time. As it is, their current practice is to take
      a comprehensive look every week at all 77,000 currently enrolled students, who are ranked in order
      based on their likelihood of not being retained. APUS has the capacity to drill down to examine and
      intervene with individuals, using a variety of scenarios and tailored interventions. They examine every
      aspect of the student to determine their broad profile characteristics and their precise transactional
      information revealing student interaction with content, other students, and faculty. All this is done in
      quantifiable ways so APUS can understand the precise nature of the problem and its cause(s).
      What’s New in Analytics?
      White Paper 11 December 5, 2010
      These practices enable achieving substantial consistency across the enterprise and enhance the
      practice of continuous quality improvement. What makes their approach even more exciting is that
      their approach to statistical analysis doesn’t just use garden-variety statistical regression, but dynamic
      models that actually learn from themselves.
      At EDUCAUSE 2010, New Learning Environments Were a Hot Topic. One of the most interesting
      sessions involved Adam Honea and Angie McQuaig discussing the University of Phoenix’s plans for its
      new technology environment, which they describe as a “cloud-based EaaS (education as a service)
      platform, a learning platform that learns.” The University of Phoenix is investing heavily in this so- called
      “Learning Genome Project,” which is deploying Facebook-like capabilities in a product that aims to
      reimagine the LMS. Rather than rely on surveys to determine how students think they want to learn, the
      new platform – as conceived – would infer from student behavior how they actually learn best and
      respond accordingly. The inference is that online, personalized education, supported by embedded
      analytics, will mark the standard curriculum for extinction. The power of this vision will need to wait for
      confirmation until the new system is developed and operational.
      The Market-Driven Institutions Share Their Tradecraft. A key factor in the advance of Action Analytics
      has been the new found willingness of the market-driven institutions to share information on their
      analytics practices. For years, most of the for-profits have been secretive about their analytics tradecraft,
      and rightly so since it was regarded as a source of competitive advantage. However, many of the forprofits
      are participating in the Transparency by Design initiative and institutions like Capella University
      and American Public University System have been recognized with awards for their approaches to
      student success. Capella University is one of the participating partners in the Action Analytics initiative
      and is working with the Minnesota State Colleges and Universities, University of Minnesota, and the
      College of Saint Scholastica in a project called Minnesota Action Analytics. This project aims to illustrate
      the dividends from multi-institutional, cross-sector collaborations on analytics.
      The Need for Research on “What Works in Embedded Analytics,” Married with Intervention and
      Communications Systems. In an insightful post to the Public Forum, John Hammang of AASCU
      suggested that our conversations need a probing discussion and research on what kind of analytics seem
      to be working to help achieve access and success outcomes. We need even more openness to sharing
      analytics tradecraft. We are increasingly learning that currently observed student behaviors (sign-ons,
      downloads, task completions, etc.) are much more valuable predictive elements of successful
      outcomes than demographics or even past performance. University of Maryland Baltimore County,
      University of Central Florida, and others are building sizeable data-based outcome analyses that are
      beginning to identify correlates of success. Also, we need to marry such research with communication
      systems. The Sherpa approach has great potential to facilitate the messaging but at this stage in its
      development it lacks the analytical sophistication of what to communicate about. That said, the Sherpa
      arsenal of messaging tools and presentation capabilities is first rate and easily customizable by users.
      There is tremendous potential for rapid progress and continuous improvement if we can craft research on
      what works, share and compare results and the tradecraft for application, and marry research and
      tradecraft with the systems for engagement and communication that can be embedded in institutional life.
      Embedded Analytics in the Sloan/IBM Research. As already mentions, the Sloan/IBM research
      reiterated the fundamental importance of embedded analytics and the needs to clearly articulate and
      share the insights on the value of analytics-guided interventions through effective
      communication/engagement systems.
      What’s New in Analytics?
      White Paper 12 December 5, 2010
      6. Create a Culture of Performance Measurement, Improvement, and
      Optimization.
      Throughout the conversations with vendors, practitioners and thought leaders, a consistent theme
      emerged: developing analytics is as much about changing culture and behaviors as it is about data,
      information, and analytics. To successfully confront the challenges facing higher education in the coming
      decade, institutions must evolve and embrace a culture of performance measurement, improvement, and
      optimization.
      Cultures of Performance Measurement and Improvement. It is not surprising that the leading-edge,
      market-driven institutions are winning awards for their best practices in analytics. Many practitioners in
      traditional institutions have been highly impressed with the sophistication and institutional commitment to
      analytics practices and the manner in which they are aligned with enterprise strategy.
      These institutions have also achieved highly performance-oriented cultures. Their cultures are
      characterized by top-down, business-and-results-oriented practices. Traditional institutions have a
      different culture. While there is much to be admired and learned from these thought leaders in their use
      of analytics, traditional colleges and universities will have to chart their own paths to address the
      challenges of the times.
      Evolving Cultures in Traditional Institutions. Most not-for profit institutions are moving from a culture
      of reporting to a culture of evidence. In many cases, institutions are on the way to a culture of
      performance. Even the most advanced have room for improvement since most are still based on a
      decentralized, individualized, faculty-centric culture that makes teaching and learner performance
      improvements challenging.
      While almost all not-for-profit universities have retrenched in various ways over the past several years
      (market-driven institutions and community colleges have grown, but many community colleges have done
      so in the face of declining resources), few traditional universities have fundamentally enhanced
      productivity and performance. The challenges of the next 10 years will likely require most institutions to
      do so.
      Insights on Analytics from other Sectors. As already mentioned, the MIT Sloan Management Review
      has launched the New Intelligent Enterprise Initiative to explore the future of analytics. Erik Brynjolfsson,
      director of the MIT Center for Digital Business, has made the following observation:
      “What we’re going to see in the coming decade are enterprises whose whole culture is based on
      continuous improvement and experimentation – not just of specific processes, but of the entire
      way the company runs. I think this revolution can be fairly compared to the scientific revolution
      that happened centuries ago. Great revolutions in science have almost always been preceded by
      great revolutions in measurement.”
      If Brynjolfsson is correct the future belongs to analytics-savvy organizations that can create new levels of
      performance and value. Higher education enterprises that are not able to achieve to this level of
      performance will lose ground to those that do meet the rising expectations of consumers who will
      encounter the new standards of performance in their dealing with enterprises from other sectors and in
      analytics-savvy organizations in higher education. They will also face eroding support from state and
      national policy makers pushing for more adaptive, higher performance approaches. New providers that
      provide lower-cost, good-value learning options will likely thrive.
      What’s New in Analytics?
      White Paper 13 December 5, 2010
      Turning Analytics into an Enterprise Planning and Development Activity. Across the broad expanse
      of American higher education, analytics are being supported by a broad and effective coalition of
      practitioners, policy makers, foundation funders, and advocates. Their efforts are accelerating the uptake
      of existing pilots and practices, introducing new approaches, and focusing on scaling analytics practices.
      But the greatest challenges lie ahead, in building the capacity of individual institutions and networks or
      systems of institutions by:
      • pairing accountability-based summative analytics with embedded formative analytics that are part
      of the institutional fabric and aligned with institutional strategies, initiatives, and processes;
      • making analytics an integral ingredient in enterprise planning and capacity development, with the
      clearly articulated intent of enhancing performance and productivity to achieve financial
      sustainability; and
      • progressively creating a culture of performance measurement and enhancement.
      As part of the Pubic Forum on Action Analytics we will be showcasing the efforts of institutions and
      systems/networks of institutions that are achieving these best practices. We will also be developing a
      body of knowledge of planning and organizational capacity development insights that can be turned into
      developmental experiences for institutional and system leaderships interesting in building their capacity to
      follow these paths.
      Adapting The Sloan/IBM Five-Step Methodology. A starting point in this development is to
      suggest how the Sloan/IBM Five-Point Methodology can be adapted to building enterprise capacity in
      higher education. This methodology recommends a constant combination of action and thought. For an
      institution looking to make analytics strategic and build its organizational capacity to enhance productivity
      and performance, we suggest the following actions. These are ways of accelerating the development of
      analytics and aligning theme with enterprise-wide strategy:
      • Focus on the biggest opportunities first – improving student access, affordability and
      success. Start with your institution’s existing initiatives, projects, and pilots in this area, plus a
      synthesis of the insights from the many other projects and pilots currently underway across
      American higher education. Understand your existing enterprise capacity in data, information,
      and analytics. Act, study, learn, think, and challenge, concurrently.
      • Start with questions, including what will enhanced student success need to look like in the
      context of the “New Normal” that requires reimagined performance, productivity, and
      financial sustainability for institutions. The notion that higher education needs to be
      “reimagined” and that analytics will play an important part in achieving high planes of
      performance and productivity is inescapable. Moving existing initiatives to a higher plane of
      accomplishment should recognize that the performance bar will be higher – and will be raised
      continuously over time. Existing initiatives should be adjusted to this emerging reality.
      • Expand “analytics for the masses” and embed predictive analytics into academic and
      administrative process. This point is consistent with our key findings about the leading edge of
      analytics practice in higher education: open, affordable, user-friendly analytics, accessed through
      new communication and engagement vehicles, and embedded in academic and administrative
      processes.
      Such dynamic, end-user-focused applications and capabilities communicate the utility of analytics
      and the value derived from using them in real-time to enhance performance. Progressively, these
      capabilities need to be embedded in institutional processes. As staff, faculty, and students
      What’s New in Analytics?
      White Paper 14 December 5, 2010
      dynamically utilize analytics to intervene and shape student success and increase institutional
      performance, they will model the new behavior that is part of a culture of performance
      improvement.
      • Keep existing capabilities while adding new ones and building an enterprise analytics
      capability. In higher education, many of the initial uses of advanced analytics began as pilot
      programs, enrollment management operations, or demonstration projects of predictive analytics.
      As an enterprise perspective develops and as new, dynamic analytics are acquired/developed
      and embedded in processes, many institutions are evolving existing applications. Many
      institutions are taking multi-vendor approaches to meeting their analytics needs.
      • Build an enterprise-wide information agenda and use that to shape the analytics
      foundation. In higher education, some market-driven (for-profit) institutions have developed
      such enterprise-wide analytics applications and plans, but most institutions have not. This is all
      the more reason to focus attention on Action Analytics as an enterprise-level issue for strategic
      visioning, planning, and capacity development. The analytics capacity should be aligned with
      institutional strategy, plans for capacity building, and the enterprise-wide agenda for data,
      information and analytics.
      How does one change the organizational culture of a college or university to focus on measuring and
      enhancing performance?
      Experience in higher education and other industries has demonstrated that the best way to achieve
      cultural change is to select an activity or process that everyone agrees is important, then deploy new
      analytics capabilities (like the embedded analytics descried above) to improve outcomes, demonstrate
      value, and change the behavior of faculty and staff – modeling the new behavior in a performancefocused
      environment. Evaluate and articulate the outcomes and why they are important. Then expand
      deployment to other processes and practices. This approach is embedded in the five-step methodology
      described above.
      7. Expand Institutional Analytics to Include K-20 and Workforce Analytics.
      One of the important elements of Action Analytics is that the focus has expanded beyond traditional
      higher education in two important directions: 1) the scope of comparative analytics has expanded to
      consider the full pre-K-20 continuum and 2) analytics that illustrate the linkage between learning and work
      and employability are increasingly sought after.
      K-20 Analytics. Virtually every state is pursuing a serious K-16 or K-20 initiative to improve the linkages
      between K-12 and postsecondary education. These state-wide programs are pursuing the intent of
      improving student readiness for college, reducing remediation, and “tuning” to improve comparability of
      credits. Many community colleges and universities have charting pathways/bridging programs to get
      students on a college path while in high school and guarantee three-year baccalaureate degrees for
      students who get on the right pathway and follow the roadmap. The University of Massachusetts just
      announced three-year degree tracks, building on advanced placement programs in high school.
      To support these initiatives, states are developing model K-12 and postsecondary data systems and
      developing longitudinal data systems to enable K-20 tracking. The NCHEMS/SHEEO document “The
      Ideal State Postsecondary Data System: 15 Essential Characteristics and Required Functionality, by
      Peter Ewell and Hans L’Orange, argues that certain characteristics and the resulting functionality are
      essential in an effective longitudinal data system. Like the “Ten Essential Elements” of state K-12
      longitudinal data systems proposed by the Data Quality Campaign (DQC), these characteristics are
      What’s New in Analytics?
      White Paper 15 December 5, 2010
      intended to promote educational progress and alignment among and between each state’s K-12 and
      postsecondary data resources.
      Over time, these data systems will link K-20 and perhaps extend to the workforce, as well.
      The Linkage Between Learning and Employability. The workforce linkage is spawning efforts at
      institutions, employers, and commercial and employment agencies, often working in concert. Individual
      institutions are positioned to use data mining to understand the success paths followed by their students.
      Many institutions are increasing their efforts to collaborate directly with employers to align offerings with
      employment competence needs. State and federal workforce agencies are redoubling their efforts to
      provide useful data that map competency and skills needs to career opportunities. And large employment
      services, temp agencies, and workforce agencies are mining their vast data resources to illuminate
      employability trends, patterns, and opportunities, often using real-time data. We interviewed
      Monster.com to determine what the market leader is doing.
      Monster Government Solutions is focusing on providing data and analytic services to public sector
      and education clients that enable employers, government, education and job seekers to align their
      efforts and make sense of the learning/employment connection. Their theme is “Creating the High
      Performance Workforce” which is reflected in a series of White Papers and other resources. Their
      strategic intent is to create “real-time labor intelligence” that is actionable.
      Monster is in a unique position to be a partner in learning/workforce solutions. It recently acquired
      hotjobs.com, so it now has access to a trove of searchable data resources and analytics capabilities:
      • Supply Side – over 110 million US resumes, 980K new resumes added monthly, more than
      11 million unique job seeker visitors each month, and Monster manages nearly 24 million Job
      Seeker Accounts nationwide;
      • Demand Side – One of the world’s largest jobs data sources, on average over 200,00
      current jobs listings, the ability to access additional posting from hundreds of job boards, plus
      government, non-profit, and industry association sites; and
      • Research and Analytics Teams – Dedicated analyst and research team, plus partnerships
      that extend research capacity into employer surveys, curriculum development, and economic
      forecasting that can be woven into customized solutions.
      Monster Government Solutions’ current analytics-based services include a subscription service,
      custom reports, research briefs and research on demand (aggregated- and detail-level supply and
      demand data that can supplement the analytics efforts of local, regional and state governments,
      employers, and institutions to make sense of market conditions and align curricula with strategic skills
      requirements). Current opportunities focus is on regional analytics with strong economic
      development applications.
      Future possibilities – data mining on student success factors, filling knowledge gaps, and
      employability. Monster.com and other commercial and government employment agencies are
      positioned to be participants in more ambitious analytics-based efforts to make sense of the learning and
      work environment using longitudinal and real-time data, and making predictions. For example, future
      possibilities could include combinations of the following:
      • Data mining to analyze the pathways, competences, and habits of students who achieve
      successful job placement and career success (through longitudinal analysis);
      What’s New in Analytics?
      White Paper 16 December 5, 2010
      • Collaborations with institutions, employers, and workforce agencies to create more powerful
      analyses of successful pathways and working the results into institutional and employer programs
      and practices;
      • Identifying knowledge and competence gaps that impede employment for particular jobs and
      collaborating with institutions to develop corrective, knowledge gap filler programs that can be
      used by graduates to rapidly fill gaps and prepare them for employment, independent of the
      degree program; and
      • Providing guidance and ongoing data support to institutions and other learning enterprises that
      aspire to become “success makers” in coaching and positioning their students for ongoing
      success.
      These possibilities could be accelerated with the wider use of learners/worker portfolios, made portable
      and detached from particular institutions so learners could demonstrate the patterns of their competences
      and accomplishments lifelong.
      At EDUCAUSE 2010, Workforce Analytics Were on the Vendor’s Radar Screen. Many of the
      technology vendors were keen to extend analytics into K-12 and the workforce. This suggested that
      higher education could be poised on the cusp of a dramatic increase in analytic applications that leverage
      enterprise analytics and data mining of institutional data; span K-20 and workforce boundaries; and
      involve collaborations with multiple parties including employers, commercial and government employment
      agencies, and other sources of data.
      8. Utilize Action Analytics to Support Reimagining Higher Education, Post
      Recession.
      In addressing point #6, we have already discussed the importance of a “reimagining” mindset in the
      deployment of analytics in higher education. The imperative to raise performance through Action
      Analytics has been given a boost by the Great Recession.
      This Time Around It Is Different. For the past three years, institutions have been “staunching the flow”
      of the resource drain caused by the financial crisis. Given the weakness of the recovery, the expiration of
      stimulus funding, and the political victories scored by Republicans in the mid-term elections, the situation
      is likely to be even worse over the next several years. In past recessions, institutions have responded to
      temporary financial shortfalls by making temporary adjustments, counting on conditions to improve quickly
      after the recession. This time around, there will be no post recession rebound in state and federal
      resources for higher education, and family finances will be even more stretched as many institutions
      continue to raise tuitions to fill budget gaps. The current financial model and levels of performance are
      unsustainable and unacceptable.
      Acting Strategically to Enhance Performance and Productivity, Achieving Financial Sustainability.
      Rather than “muddling through,” institutions should act strategically, setting their sights on a new plane of
      financial sustainability by 2020, achieved by improving performance, productivity and value. This will
      require what the Lumina Foundation calls “Navigating the New Normal,” in which institutions will need to
      meet higher expectations with fewer resources and new approaches. Most institutions have not achieved
      the summative and formative analytics capacity, nor have they universally adopted a performance culture
      – all of which they will need in order to achieve financial sustainability in the new normal.
      In our paper Linking Analytics to Lifting out of Recession, Linda Baer and Donald Norris recommended
      that institutions can reach a new plane of financial sustainability by leveraging their use of technologyenabled
      process reinvention and analytics, and focusing on performance, productivity, and value. Using
      What’s New in Analytics?
      White Paper 17 December 5, 2010
      these approaches, institutions can discover, demonstrate, and deploy operational efficiencies; innovations
      that improve performance and reduce costs, reimagined processes and practices, and fresh sources of
      revenue – all at once. This will require Transforming Online Learning and Competence Building, which
      includes a five-stage model describing the evolution of institutions in turning online learning into an
      powerful instrument for improving performance, productivity, value, and employability. Substantial
      summative and formative analytics capabilities, embedded in the practices of every institution, will be
      critical in achieving these goals and in the process achieving financial sustainability.
      In the final analysis, analytics in higher education began with a laser focus on improving student access,
      affordability, and success, the so-called “killer app” in higher education. In the face of changing times and
      a New Normal, however, leaders are needed to adapt this focus to embrace substantial improvements in
      performance and productivity to achieve financial sustainability.
      The Five Stages in Analytics
      Based on the Insights and conversations that have generated “What’s New in Analytics in Higher
      Education?” we are drafting a companion piece called “The Five Stages in Analytics.” It will be posted on
      the Public Forum on Action Analytics soon.
      Engaging in Conversation About “What’s New in Analytics?”
      This paper is a work in progress. It will be progressively refined and revised, based on input from
      vendors, practitioners, and thought leaders.
      We are inviting vendors, practitioners and thought leaders to comment on this White Paper, post their
      own views of what’s new, and share white papers and case studies. We will use this ongoing
      conversation to prepare for the Third National Symposium on Action Analytics, which will bring vendors,
      practitioners, and thought leaders together to address how they can collaborate in advancing analytics
      capacity in higher education and develop the performance optimization culture on campuses that will be
      critical to this effort.

]]> http://www.gilfusacademicanalytics.com/what%e2%80%99s-new-in-analytics-in-higher-education-insights-on-the-leading-edge-from-interviews-with-vendors-practitioners-and-thought-leaders/feed 0 http://www.gilfusacademicanalytics.com/workforce-development-analytics-an-interview-with-lee-ramsayer http://www.gilfusacademicanalytics.com/workforce-development-analytics-an-interview-with-lee-ramsayer#comments Sat, 16 Oct 2010 13:16:42 +0000 admin http://www.gilfusacademicanalytics.com/?p=392 To complete the interview series begun at EDUCAUSE, we are progressively conducting a series of conversations with vendors and practitioners to describe the leading edge of analytics products and practice that affect higher education.  This will result in a white paper later this fall.

One of the important elements of Action Analytics is that the focus has expanded beyond traditional higher education in two important directions: 1) the scope of comparative analytics has expanded to consider the full pre-K-20 continuum and 2) analytics that illustrate the linkage between learning and work and employability are increasingly sought after.

The workforce linkage is spawning efforts at institutional, employer, and employment agencies, often working in concert.  Individual institutions are positioned to use data mining to understand the success paths followed by their students.  Many institutions are increasing their efforts to collaborate directly with employers to align offerings with employment competence needs.  State and federal workforce agencies are redoubling their efforts to provide useful data.  And large employment services, temp agencies, and workforce agencies are mining their vast data resources to illuminate employability trends, patterns, and opportunities, often using real-time data.

To understand where learning to use workforce analytics might be heading, we approached the largest employment enterprise in the United States, Monster.com.  Other examples will also be collected through interviews and posted over time to complete he snapshot.

Lee Ramsayer, Vice President, Sales, Monster Government Solutions

Lee Ramsayer is a seasoned higher education technology professional who was Vice President of Sales and Marketing for the Higher Education Vertical at Oracle Higher Education and later worked for Intelliworks, the CRM company.  Lee is now leading the effort at Monster.com to leverage its trove of data on jobs, job seekers, and the workforce through a growing arsenal of analytics services and products.

Monster Government Solutions is focusing on providing data and analytic services to public sector and education clients that enable employers, government, education and job seekers to align their efforts and make sense of the learning/employment connection.  Their theme is “Creating the High Performance Workforce,” which is reflected in a series of White Papers and other resources.  Their strategic intent is to create “real-time labor intelligence” that is actionable.

Monster is in a unique position to be a partner in learning/workforce solutions.  It recently acquired hotjobs.com, so it now has access to a trove of searchable data resources and analytics capabilities:

• Supply Side – tens of millions of US resumes and thousands of new resumes added daily; more than 11 million unique job seeker visitors each month; and Monster manages nearly 24 million Job Seeker Accounts nationwide;
• Demand Side – One of the world’s largest jobs data sources; on average over 200,000 current jobs listings; ability to access additional posting from hundreds of job boards, plus government, non-profit, and industry association sites; and
• Research and Analytics Teams – Dedicated analyst and research team, plus partnerships that extend research capacity into employer surveys, curriculum development, and economic forecasting that can be woven into customized solutions.

Monster Government Solutions’ current analytics-based services include a subscription service, custom reports, research briefs and research on demand (aggregated- and detail-level supply and demand data that can supplement the analytics efforts of local, regional and state governments, employers, and institutions to make sense of market conditions and align curricula with strategic skills requirements).  Current opportunities focus is on regional analytics with strong economic development applications.

Monster offers other analytics-supported services that complement and extend these capabilities:

• Making It Count Program – high-energy presentations to 9^th, 11^th and 12^th graders to help students recognize the value of an education and assist in sharing their career choices in the context of exiting talent gaps in the region’;
• Monster FastWeb Scholarship Engine and PinAid.org – Internet’s largest scholarship site profiling 1.3 million scholarships worth over $3 billion;
• Monster Career Planning Tools, Career Fairs, and Leadership Programs – services to job seekers and students.

Future Possibilities – Data Mining on Student Success Factors, Filling Knowledge Gaps, and Employability. In addition to these current uses of its data and analytics resources, Monster is positioned to be a participant in more ambitious analytics-based efforts to make sense of the learning and work environment.

For example, future possibilities could include any of the following:

• Data mining to identify the pathways, competences, and habits of students who achieve successful job placement and career success (through longitudinal analysis);
• Collaborations with institutions, employers, and workforce agencies to create more powerful analyses of successful pathways and working the results into institutional and employer programs and practices;
• Identifying knowledge and competence gaps that impede employment for particular jobs and collaborating with institutions to develop corrective, knowledge gap filler programs that can be used by graduates to rapidly fill gaps and prepare them for employment, independent of the degree program; and
• Providing guidance and ongoing data support to institutions and other learning enterprises that aspire to become “success makers” in coaching and positioning their students for ongoing success.

These examples are consistent with the sense that emerged from conversations with other practitioners and vendors:  higher education is poised on the cusp of a dramatic increase in analytic applications that:

• Involve enterprise analytics capabilities and data mining of existing data resources;
• Span traditional institutional, organizational and sector boundaries; and
• Involve partnerships, collaborations, and business relationships with multiple parties.

We will include other workforce-oriented interviews as part of this ongoing conversation on “What’s New in Analytics?”

]]> http://www.gilfusacademicanalytics.com/workforce-development-analytics-an-interview-with-lee-ramsayer/feed 0 http://www.gilfusacademicanalytics.com/what%e2%80%99s-new-in-academic-analytics-at-educause-%e2%80%93-the-second-day-of-educause-conversations-on-october-13 http://www.gilfusacademicanalytics.com/what%e2%80%99s-new-in-academic-analytics-at-educause-%e2%80%93-the-second-day-of-educause-conversations-on-october-13#comments Wed, 13 Oct 2010 12:50:23 +0000 admin http://www.gilfusacademicanalytics.com/?p=379 Wednesday’s program at EDUCAUSE was initiated by Gary Hamel’s rousing address on “Reinventing Management for a Networked World.” Hamel, well known management guru and professor at the London Business School, laid out a set of principles for Management 2.0 and discussed a set of seven steps enterprises could take to be as nimble as the times demand. Clearly, higher education has to be much more adaptable in a profoundly networked world and Hamel’s message dovetailed well with one of the emerging themes we are seeing in analytics: The need for Reimagining Higher Education, post recession, to adjust to changing times and establish financial sustainability.

Other presentations related to the need for reinvention and the key supportive role of analytics in that task. Ira Fuchs, the new EDUCAUSE Project Manager for the Next Generation Learning Challenges Initiative, and Linda Baer, Senior Program Officer with the Bill & Melinda Gates Foundation, chaired a session to discuss Next Generation Learning Challenges. Later, Linda Baer and Michael Offerman conducted a session on “Action Analytics: Setting the National Agenda,” in which they discussed the Gates-funded efforts to raise the visibility and develop organizational capacity for institutions for finding new and collaborative ways to advance the deployment of embedded analytics that could dramatically improve the performance of institutions across the nation.

Our interviews today encompassed a cross-section of vendors and practitioners who shared with us their insights on developing analytics tools and practices, and the role of analytics in improving performance and reimagining higher education. By the time we have finished these interviews, both at the EDUCAUSE Conference, live, and in telephone interview follow-ups, we expect to cover the following vendor categories: 1) All of the major ERP and LMS providers, which include analytics components, including providers of “everyperson” tools like Microsoft; 2) major consulting/analytics firms such as IBM, 3) specialized analytics providers such as iStrategy, eThority, and Nuventive; and 4) and new analytics players identified at EDUCAUSE who have a special perspective, practice, service, or product that may be of particular interest.

Kevin Meldorf, Product Manager, Business Intelligence, Datatel

Datatel is one of the major ERP providers and is committed to providing its clients with powerful integrated solutions conceived in partnership with other vendors. Datetel has won awards for its past partnership efforts, such as its portal built on Microsoft Sharepoint ®.

Datatel’s vision, according to Kevin Meldorf, is building solutions not tools based on the Enterprise Education Platform strategy developed by the Gilfus Education Group.. It is integrating higher education intelligence into the toolset. Datatel deploys Business Objects on the operational side and iStrategy on the integrated analytics application side. Many of Datatel’s ERP and analytics customers are community colleges and small to mid-sized colleges and universities, both public and private. Therefore, affordability is a key concern. Datatel has been pleased that it can provide a holistic solution like iStrategy that fits the budget of such customers.

So Datatel’s strategy is to loosen up the ERP stack and to forge partnerships with various providers to create integrated, affordable enterprise capabilities. It is looking to provide a 360 degree view of the student. Datatel is looking to better integrate with LMS platforms so data can be extracted and brought into the analytics warehouses, and its first foray in that area is with Moodlerooms. As for the future, predictive analytics is their next thing. Being able to predict the future and manipulate data like in the film “Minority Report,” monitoring and acting on student progress – that is the vision. Datatel is currently working on fresh offerings in this area.

What does Kevin Meldorf worry about? Culture. There is a sea change going on in education. Like the movie “Waiting for Superman” we need a cultural change in the willingness to change culture in order to ask the tough questions necessary to really improve performance. Embedded predictive analytics will be critical to this community effort.

William Graves, Senior Vice President, Sungard

Bill Graves is recognized as a thought leader in analytics and performance improvement who has participated in both of our National Symposia on Action Analytics. Bill’s blog and writings on academic performance are worth following.

Bill referenced Sungard’s interest in embedded analytics, as reflected in their offering Course Signals, an early intervention system that warns students who are at risk of underperforming in a course and facilitates faculty intervention and support. This is now available to higher education institutions through a joint effort by Sungard Higher Education and Purdue University It draws data from ERP and LMS in providing a real-time read on the student’s behavior. Tomorrow we will feature an interview with Thomas Wagner of Sungard to talk in greater depth about these offerings.

Bill Graves suggested that analytics has several facets: 1) formative assessment and analytics for continuous improvement; 2) summative analysis that captures information after the course/semester is over. This is not just reporting. We need to know what is happening over time to use it longitudinally. We need a better student record that draws from many sources. Primitive is our capacity to mine and analyze in an expeditionary way.

There are different levels of usage. First, the individual dealing with personal productivity. Then, information productivity where the individual responsible is held accountable compared to norms. This is a culture of evidence.

Mark Olson, Associate Partner, Global Business Services, IBM

Mark Olson is a seasoned veteran of the higher education industry, who previously held positions at the University of Southern California, Columbia University, Sallie Mae, and NACUBO. He is also a co-author of several books, including The Business Value Web. He mentioned a number of large projects IBM consulting teams have undertaken, utilizing IBM’s consulting acumen and leveraging its suite of predictive analytics, performance tools and services to serve K-12 and higher education. These projects have helped clients to embed performance metrics in their processes, achieving the combination of analytics, architecture, and business processes that IBM features in its commercial and education analytics applications.

Higher education CIOS are well aware that IBM has invested heavily in analytics as demonstrated by their acquisition of Cognos and SPSS. The IBM SPSS Statistics 19 offering is their predictive analytics product. They support this with the White Paper, Seven Reasons You Need Predictive Analytics Today.

The conversation with Mark actually focused on several key philosophical and practical issues facing higher education. IBM’s Smart Planet mantra of instrumentation, integration, and intelligence, when applied to the challenge of reimagining higher education, leads one to ask the question, “Analytics and predictive modeling, to what end?” What will happen when no child left behind goes to college? It’s not enough to achieve financial sustainability if you can’t really clarify what the core mission of the university will be. A key challenge is to figure out just what performance is.

Brian Parish, President, IData

IData does several things: 1) tech consulting services, 2) Institutional Research and reporting services, and 3) product offerings. They provide business process tools for data definition and management. IData is one of the constellation of new companies that are services companies helping clients to deal with analytics issues. They help clients to understand knowledge challenges and to know their data. We know the data and related business processes in and out.

How do you get it right? Set the right goals and follow through. IData is like a data Sherpa for its clients. It helps understand linkages with ERP, particular tools, and the organizational mindset. Another issue is trust. You could do everything right on a project and still be wrong is you don’t co-create a sense of trust around the authenticity and rightness of the data.

IData’s products and services are very affordable. Great services as a value price – great at the price of good. The data cookbook product is an especially good value.

Michael Offerman, President, Capella University

Capella has embedded these predictive applications and practices into their enterprise platforms based on the same analytics tools used by the credit card companies to determine when patterns of card-use behavior suggest that a card may have been stolen and is in the hands of someone other than its owner. Within days of a course starting, they can determine which students are in jeopardy, what their likely grades will be based on continuation of that behavior, and the likelihood of their registering for the next semester. Like many of the online universities in the market-driven (for-profit) sector, Capella has embedded predictive analytics in its fundamental academic processes.

Vernon Smith, Vice President for Academic Affairs, Rio Salado College

Rio Salado College is one of the constellation of institutions under Maricopa Community College. They were recently featured in the AASCU/SCUP-sponsored Webinar on Predictive Analytics: Building a Crystal Ball on Student Success as an exemplary practitioner.

Rio Salado has progressively developed a strong action analytics capability, based in their “homegrown legacy” systems. They have assembled the capacity to measure both actual academic performance and the levels of engaged behavior of individual students and match that against past patterns of success in a manner that has strong predictive power. Data mining and predictive analytics meet current student activity. So rather than dealing predictively with “at-risk students” based on demographic and personal characteristics and past behaviors, they deal with “at-risk behaviors” based on current, real-time activities. The facts on the ground (or online) if you will, yielding 70% predictive accuracy. Leveraging this capability has become a fundamental part of Rio Salado’s academic management practice.

This capability is not an add-on to existing systems; it is fundamentally embedded. It yields systematic interventions to improve success, log-in behavior, and site engagement. This is a new set of unobtrusive metrics for the logged-in student.

Future…build capacity…how about employability…

Vijay Kumar, Senior Associate Dean and Director, Office of Educational Innovation and Technology, MIT

Vijay Kumar is a driving force behind the Open Educational Resources (OER) movement and an author of Opening Up Education: The Collective Advancement of Education through Open Technology, Open Content, and Open Knowledge. At MIT he shepherds the Open Courseware (OCW) initiative and their newest project, which is styled “Greenfields,” and will combine OER resources with “Open Educational Practices.” OEP will provide the “know how” on how to deploy OER in a way that is localized and effective for different types of learners. OER/OEP are the necessary combination to accelerate the penetration of open resources into widespread application. This is a highly significant initiative.

Where do the analytics come into this equation? They are fundamental to effective deployment of OER/OEP, tailored to different settings. Consider this example, Professor David Pritchard at MIT has developed a vast body of knowledge on successfully teaching physics to students of different ability levels and in various settings and how to truly measure mastery of content, effective engagement, and demonstration of the habits necessary to be successful in learning physics. OEP provides the assessments necessary to measure attainment and success and identifies the behaviors that are necessary for success. Embedding these measures and metrics into enterprise systems supporting teaching and learning, including the providing of interventions when learners deviate from patterns of successful behavior, will enable not only the deployment of OER/OEP, but the enhancement of student success. Institutions need to acquire the capacity to embed this sort of “know-how” in their enterprise systems if they are to realize the benefits of OER/OEP.

]]> http://www.gilfusacademicanalytics.com/what%e2%80%99s-new-in-academic-analytics-at-educause-%e2%80%93-the-second-day-of-educause-conversations-on-october-13/feed 0 http://www.gilfusacademicanalytics.com/what%e2%80%99s-new-in-academic-analytics-at-educause-%e2%80%93-the-first-day-of-educause-conversations-on-october-12 http://www.gilfusacademicanalytics.com/what%e2%80%99s-new-in-academic-analytics-at-educause-%e2%80%93-the-first-day-of-educause-conversations-on-october-12#comments Tue, 12 Oct 2010 13:57:03 +0000 admin http://www.gilfusacademicanalytics.com/?p=246 Tuesday was kickoff day at EDUCAUSE, and we used the occasion to talk with a number of analytics leaders who have perspectives on new developments today and in the future. Their full interviews will be posted and linked to our site, as will white papers, case studies, and applications briefs. Keep following the posts over the next week as the full body of knowledge supporting these conversations is built.

Rob Curtin, Chief Applications Officer, US Education, Microsoft

Microsoft has played a pivotal role in the ferment in the world of analytics in higher education. Several years ago, Microsoft coined the term, “BI for the Masses” and began emphasizing analytics applications that leveraged its basic tools. Many vendors embedded Microsoft tools in their applications; because of lower costs for multiple users, this tended to drive down the cost of analytics in higher education, a trend that is continuing today.

Rob Curtin emphasizes this strategy and its implications. Microsoft has embedded key analytics elements in its two premier offerings, SQL Server and Office, providing the capacity for this tandem to serve the analytics needs of customers who have already purchased these basic products. This also embeds analytics capability in what Curtin refers to as the “Everyday DNA” of faculty, staff, administrators who use these products and the institution itself. This is how users get accustomed to using analytics in their daily work and decision making.

Curtin sees his pattern continuing as learning management systems evolve over the next few years from LMS 1.0 to 3.0. Again, many of the basic academic functions that are part of LMS are also include in the basic Microsoft tandem of SQL/Office, and can be available to the user along with the embedded analytics capabilities in them. Over time, Curtin sees the academic user as becoming a savvier, accomplished user. Deploying a sports metaphor, he sees academic users as moving from surveying box scores at the end of the game or season averages to real time “pitch counts” and focusing on analytics dealing with action as it is unfolding.

Mark Maxx, CEO, iStrategy Solutions

iStrategy’s primary offering is a pre-built analytic application that combines ETL, Data Warehouse, OLAP, and Business Intelligence in an optimized, pre-built combination. iStrategy offers Student, Financials, Human Resources, Advancement, and NCATE Reporting as its modules. It provides out-of-the-box integration to PeopleSoft, Datatel and SunGard. The application provides self-service access to institutional information and dynamic changes in variables and dynamic drill downs to cohorts and individual data plus executive dashboard with key performance indicators.

iStrategy’s major advantages lie particularly with institutions that utilize one of the three major ERP vendors and can utilize the data mapping already in place. For such clients, iStrategy can stand up its installation in 1-2 days, including the Data Warehouse build. This is followed by a period of scrubbing and reining the pre-built data definitions and features, to reflect each institution’s differences.

Mark Maxx says that iStrategy’s biggest accomplishment this year has been the completion of the data mapping of all of its modules to the three major ERP vendors. This has taken five years. The major challenge now is learning management systems, so that clients can extract LMS data into the data warehouse to be analyzed in combination with ERP data. iStrategy is moving forward to add LMS mapping to its portfolio. Many of its current customers are anticipating this offering in their own efforts to extract academic data to the warehouse, using knowledge transferred by IStrategy.

According to Maxx, affordability is a key differentiator for iStrategy. They are attractive to small institutions like the Maryland Institute College of Art (MICA), mid-sized institutions like University of Maryland Baltimore County, and large R1s like the University of Michigan, SUNY Stoneybrook, and the University System of Georgia. Most of these institutions use iStrategy’s student module, but many use particular combinations of modules. As is the case with many vendors, the larger institutions are taking multi-vendor strategies when it comes to analytics, utilizing iSS in conjunction with other vendor tools and applications.

Michael Psenka, President, eThority

eThority provides analytics solutions for a range of industries: higher education, health care, human resources, manufacturing and retail. They have been known for providing a “single-point of truth” for institutions striving to make sense of competing data sources. Among their first successes were university medical centers attempting to manage and make sense of grants and contracts and project management in the complicated work of funded projects. Last year at EDUCAUSE, eThority featured its efforts to create easily available predictive analytics that could be launched and customized by end users without specialized training or insight that characterize most existing analytics products.

eThority’s big news at EDUCAUSE was that it has taken its Codie-award winning solutions (analytics, dashboarding and reporting for Higher Education) and made them available under the sobriquet “DataTalent” through a so-called Zero Cost License. They took this approach after a six-month analysis of the marketplace and collaboration with industry analysts to find an “over-the-top” strategy. They concluded that in the consumer data market, users were deploying Google, Wikipedia, and others to achieve open and free access, so eThority took its platform and made it available at zero cost. This is a 500 user license of eThority’s basic analytics offering, not a stripped down version; although there are premium services available as well.

By eThority’s reckoning 60% of their Zero Cost License customers will never pay a penny, utilizing the capability as it stands to fulfill their needs. On the other hand, 40% will utilize a variety of means to extend into pay-for-use: premium product offerings, extensibility, predictive analytics and more.

eThority also buys into the notion of embedded analytics as key to successful emergence of higher education as a sophisticated analytics market. They see predictive analytics needing to be embedded in processes and becoming a key tool in daily operational decisions. Learning data will be set up as new information becomes available. This is the higher education equivalent of health care’s “sentinel events,” which alert medical personnel that intervention is necessary.

eThority has always specialized in visualization. They are excited about a new offering called the “Variance Map,” developed by Dr. Jim Young, their statistical and predictive analytics guru. The variance maps enables users to understand the population dynamics of groups and compare individual performance with the population to spot variance and act on it.

eThority has a range of clients from small institutions to large research universities such as the University of Michigan. eThority’s platform and ZCL can be used to augment existing environments and support data collaborations with other data sets and platforms.


Lou Pugliese, President, Moodlerooms

On its Website, Moodlerooms portrays itself as “Where Dell Cloud Services, Managed Open-Source and Software-as-a-Service Converge” Moodlerooms is the largest managed service provider for Moodle. It uses a wrap-around product called Joule to provide an enterprise solution offering for Moodle, including plug-in academics such as folio, analytics, content management, mobile and others, plus administrative applications that enable the platform to interface with administrative applications and services.

At EDUCAUSE, Moodlerooms has a large booth presence and is featuring Joule 2.0 a product that follows on top of Moodle 2.0 which has new features such as flex flash, gradebook and assessment, and social networking. Joule 2.0 features a social network environment called Social Mix, Content Management, Data Repository capabilities, and a set of analytics that enable faculty to deal with performance. These tools are useful to individual faculty who are looking at the performance of students in individual courses (not just achievement, but level of engagement, participation in social network environments and other embedded measurements) and administrators. They are also adaptable to the needs of Provosts who can import historical data into the analytics engine and look at performance over time.

The Moodlerooms analytics include a basic analytics offering embedded in Moodle, plus a more advanced analytics capability, featuring strong visualization capabilities developed in partnership with eThority. Both of these are free.

This is part of Moodlerooms strategy to accelerate the uptake of Open Source applications and to serve the 36 million educators using Moodle worldwide. In Lou Pugliese’s view, the offering of assorted, high-value and free services associated with enterprise versions of open source applications is the next step in serving as an accelerant to its spread.

What does the future hold? In Pugliese’s view, the capacity of LMS systems must be expanded, collecting the evidence in an institution about student progress and success, including financial aid, a gradebook that sits in the student ERP, predictive analytics, and others. The future of embedded analytics in academic and administrative processes is very bright. Access to the Moodlerooms White Paper will be made available,

Ellen Wagner, WCET

.

Ellen is Executive Director of WCET. Ellen is working with WICHE to do a business reengineering of WCET. This involves a deep-dive business analysis and look at what we have do to get smarter about what is necessary for success.

WCET is also involved in the Transparency by Design project which is highlighting the performance of institutions that are willing to expose their performance outcomes to the public.

Embedding analytics are key to these efforts. In higher education at large, the sensibilities around large-scale data collection are evolving. Faculty and administrators are becoming aware that these data exist and are coming to the realization that “perhaps we can use these in education, like businesses are doing.” We are bringing to the education sector the insights from commercial applications. We want to use this information to make better decisions. Rather than conducting hypothesis-driven models of research, we are looking at pattern recognition. This involves recognizing patterns in academic and administrative processes so you can respond autonomically.

Affordability is key. University and colleges have no lack of data; in fact they are literally awash in it and much of it is hiding in plain sight. We need new frameworks for using information. Not that much of a stretch – we need to reframe the opportunity. Sage Road is a consulting firm/project firm that is primarily interested in using traditional statistical methodologies, but using them in different ways. Sage Roads Solutions offers a combination of three elements: 1) program evaluations, 2) data analysis and evaluation, 3) organizational development, and 4) technology implementation. The program evaluations’ focus is less on “have I got solution for you” than finding out just what the “value propositions are in analytics – we know there is value there, we just have to find what it is.”

Technorati Tags: Academic Analytics, Education Analytics, eThority Analytics, Higher Education Analytics, iStrategy Solutions, Microsoft Analytics, WCET Analytics

]]> http://www.gilfusacademicanalytics.com/what%e2%80%99s-new-in-academic-analytics-at-educause-%e2%80%93-the-first-day-of-educause-conversations-on-october-12/feed 0 http://www.gilfusacademicanalytics.com/action-analytics-innovators-launch-collaborative-community-to-forward-student-access-affordability-and-success http://www.gilfusacademicanalytics.com/action-analytics-innovators-launch-collaborative-community-to-forward-student-access-affordability-and-success#comments Fri, 01 Oct 2010 19:19:06 +0000 admin http://www.gilfusacademicanalytics.com/?p=191

The Public Forum on Action Analytics and the Action Analytics Community of Practice are now available to higher education institutions and technology vendors worldwide via edu1world.org. Funded by a grant from the Bill and Melinda Gates Foundation to the Minnesota State Colleges and Universities to support the Action Analytics Education Partnership (AAEP), these online communities are designed to propel collaboration and sharing regarding best practices in the application of Action Analytics in higher education. AAEP is leading the way in leveraging technology to reinvent academic and administrative processes that improve productivity, reduce costs, and foster innovations that improve student success.


Irvington, VA (PRWEB) May 19, 2010 — The Action Analytics Education Partnership has officially launched 2 new online communities – The Public Forum on Action Analytics and the Action Analytics Community of Practice – funded by a generous grant from the Bill and Melinda Gates Foundation to the Minnesota State Colleges and Universities system in partnership with Capella University, these online communities are designed to propel collaboration and sharing of best practices in the application of Action Analytics among thought leaders and distinguished practitioners in higher education nationwide.

Action Analytics is a multi-dimensional effort aimed at fundamental and strategic education reform. Action Analytics strategically uses data, statistics, predictive modeling, and visualization to promote student success, achieve institutional efficiency, and demonstrate transparency. These critical efforts are designed to impact every level of education in the context of massive economic, social, and technological change. The ultimate goal is to use the pursuit of student access, affordability and success to achieve financial sustainability and discover a viable, replicable model by 2020.

The private Community of Practice is being launched with key participants from the second National Symposium on Action Analytics, held May 5-7 in St. Paul, MN. Other designated thought leaders and distinguished practitioners will also be invited to participate. This core group will generate new ideas and new syntheses of insights on an ongoing basis and will then share their findings with the larger Public Forum on Action Analytics, which is open to all edu1world members. Participants in the Public Forum will in turn contribute their opinions and expertise. Through this process, a rich collection of expert and practitioner opinion and best practices will be compiled and made available to all.

Dr. Donald M. Norris, President of Strategic Initiatives Inc., serves as Moderator for the new online communities.

The effort is being spearheaded by the Action Analytics Education Partnership (AAEP), which includes the Minnesota State Colleges and University System (MNSCU), Capella University, the Shank Institute of Innovative Learning, and Strategic Initiatives, Inc.

Dr. Linda L. Baer, Senior Vice Chancellor of Academic and Student Affairs for MNSCU, is a driving force within the Action Analytics movement. According to Dr. Baer, “MNSCU has been very active with our own Action Analytics initiatives with a state-level focus on performance improvement and accountability across all 32 of our institutions. We have successfully used Action Analytics to improve the performance of underserved students and to revitalize our STEM programs. We will be contributing these experiences and many, many others to the online Community of Practice.”

Capella University is an international leader in the use of advanced analytics techniques. Dr. Michael J. Offerman is the newly appointed Interim President at Capella. He formerly served as University President from 2001 through 2007. During that time, Capella grew from 2,000 to 22,000 students and developed its award-winning learning outcomes assessment model. Says Dr. Offerman, “We have been using predictive modeling and analytics to deal not just with recruitment and strategic enrollment management but to ascertain students’ likely success, to determine when their behaviors and level of engagement suggest they may be at risk of failing, and to effectively intervene.”

At the end of May, AAEP will conduct a webinar summarizing the results from this week’s National Action Analytics Symposium.

About Action Analytics Education Partnership
AAEP includes the Minnesota State Colleges and University System (MNSCU), the nation’s fifth largest system of 2-year colleges and state universities; Capella University, a 17-year old proprietary university; Shank Institute of Innovative Learning, a non-profit think tank focusing on future technology and learning organizations; and Strategic Initiatives, Inc., a for profit developmental consulting firm. The partnership’s mission is to capture, share, and implement best practices in analytics and predictive modeling and to encourage the breadth and depth of use of action analytics across higher education institutions and other enterprises as a tool to ultimately improve institutional performance and student success and to light the way to establishing financial sustainability in higher education.



]]> http://www.gilfusacademicanalytics.com/action-analytics-innovators-launch-collaborative-community-to-forward-student-access-affordability-and-success/feed 0 http://www.gilfusacademicanalytics.com/the-world-of-educational-dashboards http://www.gilfusacademicanalytics.com/the-world-of-educational-dashboards#comments Fri, 01 Oct 2010 19:18:29 +0000 admin http://www.gilfusacademicanalytics.com/?p=189

The World of Educational Dashboards

More sophisticated educational dashboards may contain complimentary operational management and governance functionality as well, aiding in compliance with the No Child Left Behind Act as well as other federal, local, and state mandates—from facilities management to attendance tracking. As education becomes more progressive, the lines between student management and school management are becoming blurred, creating enormous new opportunities for vendors of data integration and dashboard products.

Fundamental features of a student assessment dashboard will include:

• Real time management of student reference information and test content (questions and answers)
• Marks management and comparative assessment: Tracking of student progress against state standards
• Accessibility for students, parents, and educators
• Security management and protection of student data
• Role-based functionality
• Complex trending and reporting in order to better understand and remediate student deficiencies.

Additional functionality and features may include such things as:

• Lesson and activity management and testing content creation
• Class management and scheduling
• Management of extra curricula activities
• Student disciplinary and registration management
• Collaborative stewardship (teacher-family) of student development
• Multi-lingual empowerment

Beyond student assessment, an ambitious educational dashboard will tackle areas of:

• Financial management
• Human resources and administration management
• Compliance

One of the biggest challenges to successfully implementing an educational dashboard will be attaining operational consistency. Often there will be threats to data integrity and business continuity due to the inevitable reliance on an antiquated technical infrastructure at institutions of learning: No matter how data is pushed or pulled, no matter how sophisticated or ambitious the dashboard architecture may be, information will originate from school servers in some shape or form. Great attention must be paid to the data lifecycle (and data assets) from the beginning of the definition phase because, in an educational environment, responsibility and accountability for data assets are frequently ill-defined if at all. (Happily, although it is unlikely to trickle down to K-12 education any time soon, many institutions of higher learning—such as University of California Berkeley—are starting to put formal data stewardship programs in place.)

Dashboard data may be coming from different schools, different districts, even different states, provinces, and time-zones; it then will have to be merged with state standards, learning content, student information systems, and other varied data stores. Modularity will be critical. Sets of business functions that address different business segments—i.e. student assessment, school budgeting, human resources, food services, inventory, etc.—should exist in a somewhat self-contained framework. It is one thing to track and report on a student’s progress and effectively manage their achievement, but it’s a whole other ballgame to balance school enrollments and procure textbooks. An open and service-oriented dashboard platform will be vital. Modularized functionality that can be quickly wrapped up and exposed to data consumers will assure that the dashboard will be able to hold its business relevance for a long time and that common manual workarounds (such as the infamous islands of Microsoft Excel data) do not crop up in upstream or downstream business processes. Pre-agreed upon XML standards that model student assessment data will also help ensure not only better data integrity and controlled data redundancy but more robust operational continuity as well. Different standardized models of data exchange will probably be needed for each different functional area of a dashboard that goes beyond student assessment. Both data and application architectures will quickly get complicated, especially when ad-hoc reporting and OLAP type analysis are required.

Quality assurance practices for educational dashboards are famous for being cumbersome and risk prone. Consider these substantial impediments:

• A large number of geographic locations are capturing, creating, and sending data to the dashboard.
• Each geographic location may have its own unique set of infrastructure constraints.
• Assuring that each geographic location has performed adequate unit testing is very difficult.
• It is impossible to perform a thorough end-to-end QA test—both manual and automated—of all pieces because many components exist outside any domain of control.

In fact, some problems with data integration and backward compatibility will not get exposed until QA starts. Project managers must be prepared for this ahead of time. When one has to deal with educational infrastructure, there should be no assumptions—from browser compatibility to server uptime to proper data recovery practices. In fact, before approving and funding an educational dashboard project, a small discovery project should be commissioned. Such an undertaking will take stock of the current state of infrastructure—both hardware and software—and make sure that risks and potential caveats due to “old technology” or lack of best practices are well understood. Something as silly and simple as a failure to backwardly support legacy malformed HTML can throw a wrench in your dashboard plans.

Although there will be unique challenges to the creation, implementation, and maintenance of educational dashboards, essential best practices of Business Intelligence will apply. At the highest level, the benefits of an educational dashboard look very much like those of a typical BI dashboard: users apply the best possible business processes against understandable and custom tailored classifications of reputable data in order to perform improved analysis and reporting, and acquire decision making in real time.

Achieving the consistent ability to do basic student performance analysis is daunting—project risk is high by nature; time to market is long due to the reliance on third party providers of learning content, lagging school technical infrastructures persist, as well as ever-mutating state educational standards. Thus, adding more comprehensive functional components to an assessment dashboard should be undertaken with extreme caution. Get the basics right first! Most school systems and states commence their educational dashboard projects by tackling the student assessment piece first, subsequently fulfilling broader business and technology requirements—in finance, human resources, and state compliance—effectively merging governed student bodies with well governed school systems.



]]> http://www.gilfusacademicanalytics.com/the-world-of-educational-dashboards/feed 0 http://www.gilfusacademicanalytics.com/gilfus-analytics-for-pk-20 http://www.gilfusacademicanalytics.com/gilfus-analytics-for-pk-20#comments Fri, 01 Oct 2010 19:10:13 +0000 admin http://www.gilfusacademicanalytics.com/?p=180 Academic Analytics is the term for business intelligence used in an academic setting. There is an increasing distinction made between academic analytics and traditional BI because of the unique type of information that university administrators require for decision making. – Wikipedia

Why Academic Analytics

By leveraging technology, skills, and process improvements to bring data and analysis to bear on both strategic and operational decision-making, an academic analytics approach can empower decision-makers throughout the organization, make clear areas where resource allocations are at odds with priorities, and provide mechanisms to compare performance against professional standards or peer benchmarks.

In addition to alignment with strategic priorities, a successful academic analytics implementation can facilitate the integration of relevant and strategic information. Academic analytics can be seen as a key enabler for moving the an institution  forward, as it directly impacts the decision making process at the highest levels of the University.

Academic Analytic Tools and Systems

To obtain transparency and critical insights Academic analytics relies on the extraction of data from one or more systems, typically a course management system/learning management system and a student information system as these two system provide the most critical insight into organizational, faculty  and student performance. Some of the systems in today’s marketplace for Higher Education include Oracle, Datatel, Sungard, Jenzabar, Campus Management, Blackboard, Moodle, Sakai, Desire2Learn and eCollege. The central connection of data from these systems into a single logical entity provides the most granular insight possible. This data may be assembled live or stored into a data warehouse for ongoing use.

By creating a centralized source strong correlations can be made from individual user demographics, acceptance and enrollment, academic performance, and financial data and  tied to real time activities, participation and content interactions



Once appropriate connectors are deployed into each system centralized data models can create an efficient means for assembling and constructing meaningful correlations that can be utilized as a baseline for executive and stakeholder insights. Role based dashboards and visualizations can then provide individuals with the intelligence they require to make strategic and effective decisions.

Challenges with Academic Analytics

1. Deploying Academic Analytics is highly technical and requires participation from central IT organizations, putting more pressure on critical institutional resources.
2. Most academic institutions do not have the capabilities within their IT organizations to support the ongoing needs for critical academic analytics.  A true initiative requires ongoing analysis and collaboration between technical staff and institutional researchers.
3. It is only the top tier of institutions that have dedicated institutional research shops solely focused on institutional effective and overall transparency. Even then most advanced Institutional Research organizations focus on historical performance and not real time performance.
4. Most of today’s vendors only provide tools that speak to the technical need of obtaining the data and turning into information. Leaving most of the assemblance of the data and visualizations up to institutional staff. This once again leaves academic institutions aspiring to achieve insight but spending most of their every day activities in configuration and manipulation.
5. The design and deployment of a highly capable academic Analytics Architecture is incredibly costly and demands expertise in a variety of systems with individuals focused on connectors to the main enterprise systems, the data stores and data models, visualizations, custom reporting and access control.

Academic Analytics Architecture

By deploying a centralized reference architecture in the cloud and consolidating expertise around the various systems, data models and visualizations the Gilfus Education Group aspires to provide academic analytics capabilities to a diverse set of academic institutions.



Academic Analytics and Digital Dashboards

Factors such as student recruitment and admission, teaching load, graduation rates, staff turnover, generated funds, and proposal-to-award ratios all affect a university’s performance. However, few higher education institutions are able to capture and report their many data points on all levels. A digital dashboard is a management tool for setting and measuring expectations at every organizational level, with easy-to-understand charts and reports of the status of progress throughout the year.

Such tools help users

• Set performance goals and track performance indicators
• Spot trends easily
• Establish measures and criteria for monitoring progress
• Identify, track, trend, and correct problems
• Design and print charts and reports
• Evaluate and understand the organization’s health
• Identify operational efficiencies
• Meet regulatory requirements

How do you deliver dashboards that end users will adopt and use?

That was the gist of the insightful and humorous presentation titled “Dashboards to Die For” delivered by John Rome, at the Association for Institutional Research Conference (http://www.airweb.org) this year in Chicago. You’d be hard-pressed to find a bigger advocate of dashboards in higher education than Arizona State University’s John Rome.

Like many academic institutions ASU already had a load of data around campus. It simply needed a way to make it valuable to various constituents. ASU needed a flexible development tool and design principles to put the icing on the cake. John chose a dashboard tool which enabled his team to create education dashboards quickly by pulling academic analytics data from any system, including the data warehouse. John then consulted a few dashboard and design experts, including Edward Tufte, Stephen Few, and Wayne Eckerson.

Higher Ed Analytics and Dashboards in Action

Here are a few of John’s recommendations:

• Embed education dashboards as a link within the institutional portal so users have one click access to the information
• The best screen size for the dashboard window is 1024 x 768
• Place the most important elements for viewing in the upper left hand corner and the least important in the lower right corner to mimic the way the eye scans a page.
• Avoid decorative education dashboard elements, like dials, gauges, and images and cluttering the screen with images and color.
• Design your executive education dashboard pages to mimic popular Web layouts.
• Create a summary screen of the most important information.
• Place elements on the screen so users don’t have to scroll.
• Add a “design tip” logo that users can click to get techniques about using the displayed elements and offer an online help center that provides a directory of topics for using the dashboards.
• Avoid pie charts, 3-D elements, and gradient shading.
• Accommodate color blindness.
• Accent key variables with subtle hues or patterns.

Assembling Academic Analytics

Developing a deploying an academic analytics initiative requires expertise across a number of critical areas.

1. Define the systems that maintain the most critical data for reporting
2. Develop capabilities to capture data from the various systems required
3. Implement a centralized data store to cleanse data and develop data models and business logic
4. Determine end user stake holders and applicable information for each stakeholder and define roles
5. Create meaningful executive dashboards and visualizations for effective decision making.
6. Maintain and support the series of systems and processes for ongoing evaluation.

Resources for Academic Analytics

7 Things You Should Know About Analytics”, EDUCAUSE 7 Things You Should Know series. April 2010.

Arnold, Kimberly E. “Signals: Applying Academic Analytics”, EDUCAUSE Quarterly, Volume 33, Number 1, 2010.

Campbell, John P. “Academic Analytics: A New Tool for a New Era” ELI Web Seminar, October 8, 2007. Audio and slides from the presentation.The session will be based on an article [PDF 601 KB] published in the July/August 2007 EDUCAUSE Review by John Campbell, Peter DeBlois, and Diana Oblinger.

Dawson, Shane, Liz Heathcote, and Gary Poole.  “ Harnessing ICT potential: The adoption and analysis of ICT systems for enhancing the student learning experience” International Journal of Educational Management Volume 24, Number 2, 2010, pages 116-128.

Goldstein, Philip J. with Richard N. Katz, “Academic Analytics: The Uses of Management Information and Technology in Higher Education”, ECAR Research Study Volume 8, 2005.  The following chapters specifically discuss using learning analytics to increase student retention and monitor student academic success.

• Chapter 7, Advanced Applications of Academic Analytics (Page 76, Student Services)
• Chapter 9, Academic Analytics in the Future of Higher Education

Kunnen, Eric J. and John Fritz, “Using Analytics to Intervene with Underperforming College Students”. EDUCAUSE Learning Initiative, Annual Conference, January 20, 2010.  This is a video recording of a presentation.

Norris, Donald, Linda Baer, Joan Leonard, Louis Pugliese, and Paul Lefrere “Action Analytics: Measuring and Improving Performance That Matters in Higher Education”, EDUCAUSE Review, Volume 43, Number 1, January/February 2008.

Norris, Donald, Linda Baer, Joan Leonard, Louis Pugliese, and Paul Lefrere “Framing Action Analytics and Putting Them to Work”, EDUCAUSE Review, Volume 43, Number 1, January/February 2008.

Oblinger, Diana G. and John P. Campbell, “Academic Analytics” EDUCAUSE White Paper, October 2007.

]]> http://www.gilfusacademicanalytics.com/gilfus-analytics-for-pk-20/feed 0 http://www.gilfusacademicanalytics.com/higher-education-analytics-and-dashboards http://www.gilfusacademicanalytics.com/higher-education-analytics-and-dashboards#comments Fri, 18 Jun 2010 02:49:32 +0000 admin http://www.gilfusacademicanalytics.com/?p=93 How do you deliver dashboards that end users will adopt and use?
Posted by admin on June 11, 2010

That was the gist of the insightful and humorous presentation titled “Dashboards to Die For” delivered by John Rome, at the Association for Institutional Research Conference (http://www.airweb.org) this year in Chicago.

You’d be hard-pressed to find a bigger advocate of dashboards in higher education than Arizona State University’s John Rome.

Like many academic institutions ASU already had a load of data around campus. It simply needed a way to make it valuable to various constituents. ASU needed a flexible development tool and design principles to put the icing on the cake. John chose a dashboard tool which enabled his team to create education dashboards quickly by pulling academic analytics data from any system, including the data warehouse. John then consulted a few dashboard and design experts, including Edward Tufte, Stephen Few, and Wayne Eckerson.

Higher Ed Analytics and Dashboards in Action

Four years later, ASU has deployed dozens of dashboards in multiple departments and now has its own tips and techniques for delivering user-friendly and powerful dashboards. Here are a few of John’s recommendations:

• Embed education dashboards as a link within the institutional portal so users have one click access to the information
• The best screen size for the dashboard window is 1024 x 768
• Place the most important elements for viewing in the upper left hand corner and the least important in the lower right corner to mimic the way the eye scans a page.
• Avoid decorative education dashboard elements, like dials, gauges, and images and cluttering the screen with images and color.
• Design your executive education dashboard pages to mimic popular Web layouts.
• Create a summary screen of the most important information. (See figure 2.)

Figure 2.


• Place elements on the screen so users don’t have to scroll.
• Add a “design tip” logo that users can click to get techniques about using the displayed elements and offer an online help center that provides a directory of topics for using the dashboards.
• Avoid pie charts, 3-D elements, and gradient shading.
• Accommodate color blindness.
• Accent key variables with subtle hues or patterns. See figure 3.

Figure 3.


• My favorite: “Power comes with interaction/drills.”
• Make education dashboards actionable. Here ASU has bidirectionally linked its dashboards to various operational systems, such as its PeopleSoft HR system, so authorized users can update the status of employees, classes, students, professors, etc.
• Use non-serif fonts and 12 point text for the main body and smaller fonts for supplementary text.
• Provide metadata about metrics, including SQL used. Offering screen cams and FAQs also helps.
• Use ranking to help users assess key elements within the data and to promote friendly competition among peer groups.
• Use charts rather than tables.
• Abbreviate numbers appropriately, so that $13,002,000 is $13M
• Perform usability studies to understand how users interact with the dashboard.
• Customize the dashboards by role.

Of all the design tips, the most powerful are to make the dashboards drillable, actionable, and instantly informative. ASU has put a lot of time and thought into the design and delivery of its dashboards for higher education analytics and the effort has paid off. But don’t just take my word for it; check out ASU’s public dashboard Web site where you can get more information about the dashboards used at the University: http://dashboard.asu.edu.

]]> http://www.gilfusacademicanalytics.com/higher-education-analytics-and-dashboards/feed 0