A call to action for student success analytics.

Baer, Linda L. ; Norris, Donald M.

INTRODUCTION

LEVERAGING ANALYTICS TO OPTIMIZE STUDENT SUCCESS is an institutional strategy whose time has come. It has the potential to dramatically improve retention and graduation rates, reduce the total cost of completion of certificates and degrees, enhance the full-spectrum development of learners, enable greater career connections, and serve as a differentiator for institutions that acquire this organizational capacity.

EXAMPLES OF STUDENT SUCCESS ANALYTICS FROM SUCCESSFUL INSTITUTIONS

The use of analytics in higher education and the interventions they drive has been advancing perceptibly among leading institutions, with tangible results. Our research on leading analytics institutions (Norris and Baer 2013) determined that many were deploying analytics along seven distinctive dimensions that span the student life cycle and the student experience. The dimensions at the top of this typology are the most highly developed and mature. Those at the bottom are less well developed and emergent. They are also sources of substantial opportunity for future improvement. Leveraging the actions and interventions along these dimensions has the potential to substantially reduce and/or mitigate risk and increase student success, as demonstrated in figure 1.

MANAGING THE STUDENT PIPELINE

Institutions have been using descriptive/diagnostic analytics for years to shape their entering classes, refine policies, and identify at-risk students for mentoring and special interventions. Institutions are using these strategic enrollment management techniques to improve their yield/conversion rates, enhance current enrollments, and improve retention. In recent years, predictive analytics have been added to these practices. The University of Texas at Austin (University Innovation Alliance n.d.(c)) is recognized as a best-practice leader for its use of predictive analytics to identify at-risk students and craft mentoring and support experiences for them. Georgia State University (University Innovation Alliance n.d.(b)) is often cited as a leader in overall analytics-driven retention improvement practices that continue throughout the student life cycle.

ELIMINATING BARRIERS, OBSTACLES, AND RISKY STRUCTURES/PRACTICES/PROCESSES

Institutions have used descriptive and diagnostic analytics to create best practices that reduce risk in structures, processes, and policies. Favorite best practices have included reinventing the first-year experience; eliminating barriers, bottlenecks, and inconsistencies of approach; providing better mentoring and advising; and advancing peer-to-peer learning and tutoring (supplemental instruction). In addition, a focus on guided course and service pathways, instead of the traditional cafeteria-based choices that often overwhelm students, improves student decision making (see Bailey, Jaggars, and Jenkins 2015). A number of organizations have been actively promoting such best practices tailored to the institutional context.

DYNAMIC INTERVENTIONS IN REAL TIME

For some time, for-profit institutions like the American Public University System, Capella University, and the University of Phoenix have used embedded practice techniques--from organizations such as the Education Trust, Achieving the Dream, and the Bill & Melinda Gates Foundation--and these best practices are now being deployed in many nonprofit institutions' predictive analytics to identify at-risk behavior in online students. Purdue University uses its Course Signals product with embedded predictive modeling to give students a red, yellow, or green light warning of their progress, starting with students in large freshman courses. Rio Salado College has developed similar alert/intervention capabilities.

Some software providers have used prepackaged analytics applications to enable institutions to provide dynamic, real-time assessment, alert, and intervention capabilities to frontline faculty and staff. An important contribution of such user-friendly applications is that they enable "analytics for the masses," liberating the energies of analytics staff to undertake higher-level predictive and prescriptive analytics. Such applications have formed the foundation for institutions like the University of Maryland, Baltimore County to become recognized leaders in building cultures of evidence and student success improvement (Hrabowski, Seuss, and Fritz 2011).

INTEGRATED PLANNING AND ADVISING/ALERT SYSTEMS (IPAS)

Over the past decade, many leading institutions have developed sophisticated student advising and pathway planning systems. Systems at Sinclair Community College, Valencia College, Arizona State University, Austin Peay State University, Georgia State University, and many other institutions have demonstrated that impressive improvements are possible when students are advised into guided, planned pathways and provided with a combination of education planning, counseling and coaching, risk targeting and intervention, and transfer and articulation guidance.

Arizona State University's eAdvisor is a case in point. Before eAdvisor, 33 percent of first-year students were in "exploratory" majors; that figure is now 8 percent. Students also use eAdvisor to map their degree plan and track progress toward completion. Implementing eAdvisor has generated $7.3M in advising cost savings per year and $6.5M in instructional cost savings per year. Student success has also improved since deploying eAdvisor; for example, the four-year graduation rate has improved by more than 9 percent (University Innovation Alliance n.d.(a)).

These advising systems have expanded to include dynamic, analytics-driven interventions and additional functions. The Bill & Melinda Gates Foundation has supported the development and definition of these so-called integrated planning and advising/alert systems (IPAS), which have emerged as "an institutional capability to create shared ownership for educational progress by providing students, faculty, and staff with holistic information and services that contribute to the completion of a degree or other credential" (Brown, Dehoney, and Millichap 2015, "Analytics, Advising, and Learning Assessment," [paragraph] 4).

Civitas Learning's Degree Map application provides advisors and students with the ability to see student progress in a major, what courses remain, and what happens when a student considers a change of major, including time and cost. The Degree Map implementation at Austin Community College has shown that "when students have more clarity and control of their degree path, they're significantly more likely to make progress on their learning path and, best of all, cross the finish line and complete a certificate or degree. These are the kinds of outcomes all of our student success innovations are shooting for!"(Civitas Learning 2015, sidebar).

The EDUCAUSE Center for Analysis and Research (ECAR) has benchmarked the characteristics of these emerging IPAS services (Yanosky 2014). These systems offer a range of services that seek to realize a comprehensive vision of a technology-enabled and integrated digital environment that provides students, advisors, and faculty with the following capabilities:

* Education planning (identifying the degree and the best path to its achievement)

* Progress tracking (asking whether the learner is on course toward degree completion)

* Advising and counseling (offering services such as mentoring and tutoring)

* Early-alert systems (initiating proactive intervention with at-risk students)

Over time, these systems promise to expand their functionality and become the primary multifaceted vehicle for institutions managing the overall context of student success. For example, through Civitas Learning predictive models, campuses can identify which interventions are working, for whom, and by what mode they should be delivered. In addition, these tools provide insight into which inspirations can be provided to take a student to the next level.

NEXT-GENERATION PERSONALIZED AND COMPETENCE-BASED LEARNING

Personalized, adaptive learning and competence-based learning promise to usher in a new era of pervasive learning analytics defined by Brown, Dehoney and Millichap (2015, 'Analytics, Advising, and Learning Assessment," [paragraph] 3) as "the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs." These embedded learning analytics, which automatically and continuously collect data on learner progress and attainment, require far more robust analytical and management tools. The Bill & Melinda Gates Foundation and other groups have been actively supporting the development of next-gen learning through pilot projects a1 a range of institutions and support of a variety of cloud-based vendor offerings in order to help build the industry.

Moreover, the coming emergence of in-the-cloud next-generation digital learning environments (NGDLE) will enable the seamless combination of learning and developmental experiences from a wide range of sources: institution-centric courses, prior learning, free-range learning experiences, and other co-curricular and work activities. NGDLE will need to incorporate three types of learning data: dispositional (e.g., incoming GPA, biographic and demographic data); course activity and engagement (e.g., keystrokes, selections, time on task); and learner artifacts (e.g., essays, blog posts, media products) (Brown, Dehoney, and Millichap 2015).

Taken together, these developments will expand the diversity of learning and developmental experiences to be measured as well as the dimensions of student success and how they can be measured. Over the next few years, institutional leaders should carefully track the development of NGDLE and the new solutions available.

BIG DATA AND DATA SCIENCE

For several years, a relatively small number of institutions have been using data mining and Big Data applications to illuminate issues relating to retention and student success. The addition of data science experts enables institutions to profoundly understand student success for individuals and student clusters to a degree never before possible. Such institutions have been able to move beyond implementing generalized best practices to creating personalized learning experiences and interventions that optimize learning for individual students.

Data science can create predictive models that lead to prescriptive interventions. Civitas Learning is pioneering the deployment of such data science-based techniques. The results are being embedded in many of the dimensions of the student success typology. Over the next few years, these applications will support dramatic growth in "student success science," which brings together data science and student success efforts to increase information, knowledge, and action related to improving student persistence and completion.

EXPANDING SUCCESS TO INCLUDE EMPLOYABILITY AND CAREER SUCCESS

Many institutions are expanding the definition of student success to include employability and career success. The Lone Star College System introduced an integrated Education and Career Positioning System (ECPS) that lets students, faculty, advisors, and parents simulate, navigate, validate, and plan a student's education-to-career options to determine the best individual journey for that student (Lone Star College 2016). The ECPS takes into account students' individual interests, values, skills, and academic records and distributes personalized analytics directly to them for planning purposes. Colorado State University's comprehensive, holistic approach to student success considers all elements of the student experience (curricular, co-curricular, and work) (Lamborn and Thayer 2014). Over time, it is clear that these systems will expand to manage and account for the full range of learner developmental experiences, including entrepreneurship and innovation activities, design competitions, co-op experiences, and a myriad of others.

The leading analytics institutions we studied were leveraging analytics in five to seven of these categories, but even these leaders are far from tapping the full potential of optimizing student success.

WHAT'S NEXT IN STUDENT SUCCESS OPTIMIZATION?

We are poised on the cusp of substantial changes in the technologies, tools, and practices of student success analytics. For example, consider the following forces:

INCREASING APPLICABILITY OF ACCOUNTABILITY ANALYTICS AND PERFORMANCE FUNDING

Accountability statistics and performance funding will grow in importance among policy makers and funders at local, state, and federal levels. Regional accrediting agencies are focused on improving student persistence and success.

CONTINUING GROWTH IN STUDENT SUCCESS BEST PRACTICES

More and more proven best practices like those promoted by Complete College America, Completion by Design, and the John N. Gardner Institute for Excellence in Undergraduate Education will be made available to and deployed by institutions to improve their performance and accountability statistics.

DEVELOPMENT OF DIY ANALYTICS AND VISUALIZATION TOOLS

Vendors will provide voice activated, user-friendly analytics tools enabling "predictive analytics for the masses" and enhanced visualization. These tools will improve the capacity of institutions to enhance their own analytics offerings.

GROWTH IN PERSONALIZED AND COMPETENCE-BASED LEARNING

The continuing development and market penetration of competence-based and personalized learning with embedded analytics in individual courses and MOOCs will enrich the complexity of student success analytics.

NEXT-GENERATION DIGITAL LEARNING ENVIRONMENTS

The emergence of cloud-based NGDLE will support "connected learning" and increase the complexity of managing student success. NGDLE will focus on increasing data integration, customer-friendly access, and data use. It will also result in improved cost and functionality, which will enable lower costs for administrative systems and increasing investment in academic systems (Brown, Dehoney, and Millichap 2015).

EMERGING COMPETENCE MARKETPLACES

As competence-based learning penetrates the learning marketplace, competence marketplaces will emerge that make the competence requirements of jobs public and influence the preparation of learners and the behavior of employers. As described in "Data, Technology and the Great Unbundling in Higher Education" (Craig and Williams 2015), Linkedln is in the process of executing its strategic intent of positioning itself to serve as a competence marketplace for the three billion plus members of the global workforce. It is developing software tools and interfaces to parse and match competencies from job descriptions and resumes as well as interface tools to bring these offerings to its audience-including many college and university students. Colleges and universities will be part of this learning, competence, and employment ecosystem.

As they evolve, competence marketplaces will influence the choices, pathways, and knowledge gap decisions of many learners; the behavioral patterns and choices of future learners are likely to be very different from today.

CALL TO ACTION FOR LEADERSHIP IN THE AGE OF ANALYTICS

The rapidly changing landscape of higher education requires leadership that understands change, culture, and transformation. Leaders need more understanding of the role that data and analytics play in supporting targeted change, including improving student progress and success. Leaders need to courageously articulate and follow through on bold courses of action to elevate student success optimization to an institutional imperative (Baer and Norris 2015).

ARTICULATE A BOLD VISION FOR STUDENT SUCCESS AS "MISSION CRITICAL"

If you don't know where you're going, any road will get you there. In the article "No More Excuses," Michael Crow (2012) reflects on setting a bold vision for the future of Arizona State University built on his goal of transforming ASU into what he calls a "New American University"--an institution combining the highest levels of academic excellence, inclusiveness to a broad demographic, and maximum societal impact. His vision includes increasing graduate numbers, graduation rates, and freshman retention rates while also expanding ethnic and economic diversity. Within 10 years, ASU had met and exceeded its goals with significant increases in enrollment and student persistence.

Many presidents--at research universities, comprehensive universities, community colleges, for-profits, and liberal arts colleges--also have improved student success and reduced achievement gaps through bold vision backed up by people, resources, and programming proven to improve student persistence.

As Baer and Norris (2015, p. 19) note, "Optimizing student success should be Institutional Strategy #1. Effective change management should be deployed to execute this strategy and build the organizational capacity required for its success."

RAISE THE "STUDENT SUCCESS ANALYTICS IQ" OF LEADERS AND EVERYONE IN THE INSTITUTION

Leaders need to become highly familiar with analytics concepts and capabilities. They need to embrace student success science, which represents some of the best thinking on student success in decades. This field is bringing together the best in student success theory and practice with deep data and predictive modeling to maximize options and opportunities for decision makers. We find ourselves at a teachable moment where "optimizing student success" may become a reality with everyone from executive leadership to frontline faculty and staff playing roles in delivering on that promise.

NURTURE PARTNERSHIPS, COLLABORATIONS, SHARING, AND SOLUTION PROVIDERS IN YOUR ANALYTICS STRATEGY

The advances in student success analytics are being enabled by the pervasive sharing of talent, best practices, and know-how. They are also encouraging new solution providers and familiar vendors to deliver new applications and support services. Leading institutions are sharing their accomplishments with peers and forming consortia to share best practices and create federated data systems that can reveal insights that were previously unobtainable (Kolko 2015). Sharing and collaboration will be critical in achieving analytics goals at an affordable cost.

"CONNECT THE DOTS" ACROSS THE INSTITUTION AND BUILD ORGANIZATIONAL ANALYTICS CAPACITY

In most institutions, data and student success analytics capabilities and responsibilities are fragmented. This can be corrected by developing a formal institutional analytics strategy to "connect the dots" across all dimensions of student success optimization, resulting in a clear plan and road map. This plan should articulate how we know "what works" in improving student persistence and success, thereby allowing leaders to focus on the combination of best practices and personalized interventions that provide the maximum return on investment. The planning process begins with a cross-institutional review of where the institution is in terms of data use, analytics reporting, predictive modeling, and prescriptive follow-through. The nature of partnerships, collaborations, and solution provider relationships should also be assessed. This requires careful examination of human resources, skills and competencies, fiscal investment, and infrastructure capacities. These assessments build the foundation for how the institution will move forward over a three-to-five-year period of investment in strengthening its capacities for the improved use of data and analytics.

Connecting the dots is important because it allows leaders to leverage what is going on now to move to where the work on student persistence and completion must go for future student success. These connections provide the organizational glue to make optimizing student success a coherent, pervasive institutional strategy (Norris n.d.).

EMPOWER CROSS-CAMPUS TEAMS THAT ACT TO IMPROVE STUDENT SUCCESS

Cross-campus teams are important to optimizing student success. These teams can also reach other institutions, K-12 schools, and employers. Such collaboration begins by working with high schools to improve student course-taking behaviors that lead to college readiness. It then moves to building dual enrollment programs to encourage students to begin taking courses for college credit early and often and advances to transfer and articulation agreements across colleges and universities--agreements that really work for students! It also includes deep conversations with businesses, industries, and stakeholders who benefit from strong college and university graduates. Leaders must lead the conversations and expect that cross-team and cross-institutional decisions will be made.

BUILD SUPPORT SYSTEMS FOR STUDENT SUCCESS USING STUDENT SUCCESS SCIENCE

Identify best-in-class student support services and evaluate where the campus is in providing them. Student support services staff need to be trained in institutionally grounded best practices. They should also be given the authority and responsibility to develop new services and evaluate the outcomes of those services in terms of the benefits to the student and the institution. Further, they should be provided with the tools and technology needed to maximize services to students on-site, online, and in mobile formats. They need to understand and be able to use data sources to improve decision making. Student success science enables campuses to merge the power of best theory and practice in student persistence and success with the best data and predictive models, resulting in the best support services delivered in a timely manner.

BUILD A CULTURE OF STUDENT SUCCESS IMPROVEMENT

Peter Drucker famously observed that "Culture eats strategy for breakfast, technology for lunch" (Aulet 2014, p. 11). Without proper attention to cultural change, student success analytics strategies will flounder. Great leaders know how to build a culture of urgency throughout the institution. Accreditation can be leveraged to create a centerpiece for many sustained activities on campus through linkages among planning, culture, and sustainable change. Leaders should have a change management plan in place that includes a comprehensive communication plan to allow the campus and stakeholders to clearly understand the nature and impact of the change, have a say, participate in articulating the change, and own the results.

DEPLOY DESIGN THINKING TO REINVENT STUDENT SUCCESS PRACTICES

Achieving a culture of student success improvement can be accelerated by deploying design thinking principles and practices. Design thinking uses prototyping, research, and learner-centric planning. It focuses on simplifying and humanizing institutional processes and practices on the way to optimizing student success. Design thinking must be a core competence of an organization whose culture is truly focused on student success (Kolko 2015).

As noted in the Harvard Business Review (2015, p. 55), "Once confined to product development, design thinking has become central to strategy, innovation, and organizational culture."

DEMONSTRATE THE ROI FROM ANALYTICS AND NEXT-GENERATION TECHNOLOGIES

Institutional leaders face the challenge of investing in student success analytics in a time of tight resources and substantial institutional investments in transactional ERP (enterprise resource planning) systems and LMS (learning management systems) that have failed to provide data and analytics breakthroughs. Student success analytics can be shown to have a highly favorable ROI when they yield improvements in student success. Through collaboration, partnering, and sharing, institutions can drive down the cost of new analytics capabilities. Moreover, in investigating NGDLE, institutions should look to reducing the costs of transactional administrative systems and reinvesting those savings in analytics and new learning technologies.

LEAD AND THRIVE IN THE AGE OF ANALYTICS

The award winners of the Aspen Prize for Community College Excellence were innovative colleges with courageous leaders (Wyner 2014). These leaders created urgency around student success by taking ownership of failure; shifting focus to what matters most; planning for big change; allocating resources for student success, understanding that college is not a destination but part of the learner's larger road map; and choosing and developing leaders.

Such leadership requires aggressively building team competencies. It may require creating new positions such as chief analytics strategy officer (CASO). Leading means supporting data system development, maximizing team capacities to deliver insights from the data, communicating activities and outcomes on a regular basis, and making analytics a part of building and sustaining a strong future for students and the institution.

Thriving in our disruptive times requires a combination of leadership, strategy, innovation, and performance excellence. Embracing student success analytics provides an opportunity for institutional leaders to achieve this combination.

REFERENCES

Aulet, B. 2014. Culture Eats Strategy for Breakfast. TechCrunch, April 12. Retrieved June 17, 2016, from the World Wide Web: http://techcrunch.com/2014/04/12/culture-eats-strategy-for-breakfast/.

Baer, L. L., and D. M. Norris. 2015. What Every Leader Needs to Know About Student Success Analytics. White paper developed for Civitas Learning. Retrieved June 17, 2016, from the World Wide Web: https://cdn2.hubspot.net/hubfs/488776/Summit/Partner_Summit_2016/White_Paper_What_Leaders_Need_to_Know_About_Analytics.pdf.

Bailey, T. R., S. S. Jaggars, and D. Jenkins. 2015. Redesigning America's Community Colleges: A Clearer Path to Student Success. Cambridge, MA: Harvard University Press.

Brown, M., J. Dehoney, and N. Millichap. 2015. What's Next for the LMS? EDUCAUSE Review 50 (4). Retrieved June 17, 2016, from the World Wide Web: http://er.educause.edu/ero/article/whats-next-lms.

Civitas Learning. 2015. Degree Map[TM] Posts Positive Persistence Gains at Austin Community College. Civitas Learning Space, June 2. Retrieved July 7, 2016, from the World Wide Web: www.civitaslearningspace.com/degree-map-posts-positive-persistence-gains-at-austin-community-college/.

Complete College America. 2014. The Game Changers. Retrieved June 17, 2016, from the World Wide Web: http://completecollege.org/the-game-changers/.

Craig, R., and A. Williams. 2015. Data, Technology and the Great Unbundling of Higher Education. EDUCAUSE Review 50 (5). Retrieved June 17, 2016, from the World Wide Web: http://er.educause.edu/articles/2015/8/data-technology-and-the-great-unbundling-of-higher-education.

Crow, M. M. 2012. "No More Excuses": Michael M. Crow on Analytics. EDUCAUSE Review 47 (4). Retrieved June 17, 2016, from the World Wide Web: http://er.educause.edu/articles/20l2/7/no-more-excuses-michael-m-crow-on-analytics.

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Kolko, J. 2015. Design Thinking Comes of Age. Harvard Business Review, September. Retrieved June 17, 2016, from the World Wide Web: http://hbr.org/2015/09/design-thinking-comes-of-age.

Lamborn, A., and P. Thayer. 2014. Student Success Initiatives: What's Been Achieved; What's Next. Presented at the Colorado State University Professional Development Program, January 14. Retrieved June 17, 2016, from the World Wide Web: http://highered.colorado.gov/ColoradoCompletes/CSU_SSI_NowAndFuture.pdf.

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Norris, D. M. n.d. Connecting the Dots to Optimize Student Success. Retrieved June 17, 2016, from the World Wide Web: http://drive.google.com/a/strategicinitiatives.com/file/d/oBoCqeHHcp-YzWlZWRUFZbUZsSkk/view.

Norris, D. M., and L. L. Baer. 2013. Building Organizational Capacity for Analytics. N.p.: EDUCAUSE. Retrieved June 17, 2016, from the World Wide Web: https://library.educause.edU/~/media/files/Hbrary/20l3/2/pub90l2-pdf.pdf.

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Wyner, J. 2014. What Excellent Community Colleges Do: Preparing All Students for Success. Cambridge, MA: Harvard Education Press.

Yanosky, R. 2014. Integrated Planning and Advising Services: A Benchmarking Study. Louisville, CO: EDUCAUSE Center for Analysis and Research. Retrieved June 17, 2016, from the World Wide Web: http://net.educause.edu/ir/library/pdf/ERS1312.pdf. management in higher education. Contact: linda.baer@civitaslearning.com; 651.271.7519.

DR. DONALD NORRIS is president/founder of Strategic Initiatives, a strategic change management firm that specializes in leading and navigating change, crafting and executing strategy, leveraging innovation, and attaining performance excellence. He is a seasoned administrator, researcher, thought leader, and expert practitioner. He has been active in the Society for College and University Planning (SCUP) for over 30 years and was awarded the Distinguished Service award in 1984 and the Founders Award in 2014 for lifetime contributions to the field of planning in higher education. He has co-authored a series of books and monographs for SCUP that have dramatically influenced the field of strategic planning over the past 30 years: A Guide for New Planners (1984), Transforming Higher Education: A Vision for Learning in the 21st Century (1995), Unleashing the Power of Perpetual Learning (1997), Transforming e-Knowledge: A Revolution in Knowledge Sharing (2001), and A Guide to Planning f or Change (2008). His most recent writings include Transforming in an Age of Disruptive Change (SCUP, 2013), Excellence on the Edge: Resilience and Performance in Disruptive Times (Strategic Initiatives, 2013), and A Toolkit for Building Organizational Capacity for Analytics (Strategic Initiatives, 2012). His ongoing focus is on providing guidance in strategy, execution, and capacity building in leveraging analytics to optimize student success. Contact: dmn@strategicinitiatives.com; 703.447.7563.

by Linda L. Baer and Donald M. Norris

AUTHOR BIOGRAPHIES

DR. LINDA BAER is a senior fellow with Civitas Learning. She has served over 30 years in numerous executive-level positions in higher education, including senior program officer, postsecondary success for the Bill & Melinda Gates Foundation, senior vice chancellor for academic and student affairs in the Minnesota State College and University System, senior vice president and interim president at Bemidji State University, and interim vice president for academic affairs at Minnesota State University, Mankato. Her ongoing focus is to inspire leaders to innovate, integrate, and implement solutions to improve student success and transform institutions for the future. She presents nationally on academic innovations, educational transformation, the development of alliances and partnerships, the campus of the future, shared leadership, and building organizational capacity in analytics. Recent publications have been on smart change, shared leadership, successful partnerships, innovations/transformation in higher education, and analytics as a tool to improve student success. She and Dr. Norris have collaborated for over a decade on issues relating to analytics, optimizing student success, and change management in higher education. Contact: linda.baer@civitaslearning.com; 651.271.7519.

DR. DONALD NORRIS is president/founder of Strategic Initiatives, a strategic change management firm that specializes in leading and navigating change, crafting and executing strategy, leveraging innovation, and attaining performance excellence. He is a seasoned administrator, researcher, thought leader, and expert practitioner. He has been active in the Society for College and University Planning (SCUP) for over 30 years and was awarded the Distinguished Service award in 1984 and the Founders Award in 2014 for lifetime contributions to the field of planning in higher education. He has co-authored a series of books and monographs for SCUP that have dramatically influenced the field of strategic planning over the past 30 years: A Guide for New Planners (1984), Transforming Higher Education: A Vision for Learning in the 21st Century (1995), Unleashing the Power of Perpetual Learning (1997), Transforming e-Knowledge: A Revolution in Knowledge Sharing (2001), and A Guide to Planning for Change (2008). His most recent writings include Transforming in an Age of Disruptive Change (SCUP, 2013), Excellence on the Edge: Resilience and Performance in Disruptive Times (Strategic Initiatives, 2013), and A Toolkit for Building Organizational Capacity for Analytics (Strategic Initiatives, 2012). His ongoing focus is on providing guidance in strategy, execution, and capacity building in leveraging analytics to optimize student success. Contact: dmn@strategicinitiatives.com; 703.447.7563. Figure 1 Analytics-Based Interventions to Manage/Mitigate Risk and Optimize Student Success Dimensions Interventions 1. Manage the Student * Manage the student pipeline, select students Pipeline carefully, refine policies/practices * Accelerate enrollment of eligible students * Provide targeted mentoring and support for at-risk students 2. Eliminate * Reinvent first-year experience Bottlenecks * Eliminate course bottlenecks and Barriers * Improve performance in weed-out courses * Improve consistency in grading and faculty practices * Implement best practices such as Complete College America's (2014) suggested list: (1) performance funding, (2) co-requisite remediation, (3) full time = 15, (4) structured schedules, and (5) guided pathways. * Develop guided pathways as opposed to the cafeteria model of course choice for students 3. Dynamic * Dynamic, real-time interventions by faculty, Interventions advisors, and mentors to * Automatic assessment of risks and generation Address Risky of alerts enables continuous monitoring and Behavior intervention 4. Leverage * Individual pathway planning Individual * Intervene when learners deviate from success Planning paths illuminated through predictive analytics and Advising/ * Monitor progress/risky behavior and intervene Alert in full spectrum of curricular and co-curricular Systems (IPAS) areas * Combine best practice and data science-driven interventions 5. Next-Gen * Embedded analytics enable autonomic, real-time Learning intervention in the individual courses/learning experiences * Increases dramatically the number, focus, and effectiveness of interventions; accelerates student learning 6. Big * Student success science provides profound Data/Data insights in all aspects of the interventions Science that optimize student success support at the individual, cluster, and cohort levels * Dynamic, real-time interventions based on predictive analytics * Cross-institutional data mining generates insights for interventions 7. Academic and * Mentoring and advising provide success-making Employability interventions, guided by pathways to success Success research Dimensions Risks Mitigated and/or Managed 1. Manage the Student * Inefficient yields/conversion of enrollments at all Pipeline stages * At-risk students in the admissions pipeline and after enrollment through to completion 2. Eliminate * Risky structures, policies, and practices in core Bottlenecks offerings and Barriers * Inconsistent grading and faculty practices that impede student success * Risky approaches to remediation, degree planning, and execution of plan * Provides clearer choices for students, which can keep students on a focused course of academic action 3. Dynamic * Risky behavior identified based on predictive Interventions analytics in real-time to * Multiple factors are included in determination Address Risky of risk, and these assessments can personalize Behavior interventions 4. Leverage * Steer learners toward proven success paths quickly Individual * Reduce risky choices Planning * Reduce deviation from pathways and Advising/ * Add career and employment considerations early in Alert student's career Systems (IPAS) 5. Next-Gen * Enable personalization of learning style and Learning outcomes, thus building commitment * Focus on tangible outcomes * Utilize embedded analytics in courses to mitigate risky learning behavior * Consider the entire "connected learning" record for each learner 6. Big * Reduces the risk of "best practice"-based Data/Data interventions that are not tailored to the Science individual * Big Data and Data Science-based interventions are used throughout the student life cycle 7. Academic and * Reduce risk of mismatch between learning and Employability employability Success * Reduce risk of learner lack of engagement and disillusionment