Prior learning assessment could be higher education's next big disruptive force, and ACE and CAEL are poised to catch that potential gold rush. But many remain skeptical about academic credit for work experience.
When Rowland Hussey Macy opened his namesake store in 1858, understanding consumer behavior was largely a matter of guessing. Retailers had little data to assess what customers wanted or how variables like store hours, assortment or pricing might impact sales. Decision making was slow: managers relied on manual sales tallies, compiled weekly or annually. Dozens of stores failed, including several of Macy’s original stores.
Predictive analytics, in the early days of retail, were rudimentary. Forward-thinking retailers combined transactional data with other types of information -- the weather, for example -- to understand the drivers of consumer behavior. In the 1970s, everything changed. Digital cash registers took hold, allowing companies to capture data and spot trends more quickly. They began A/B testing, piloting ideas in a test vs. control model, at the store level to understand the impact of strategy in near real time.
In the early days of AOL, where I worked in the 1990s and early 2000s, we were quick to recognize the risk to brick-and-mortar stores, as online retailers gathered unprecedented data on consumer behavior. Companies like Amazon could track a customer’s movements on their site using click-stream data to understand which products a customer was considering, or how long they spent comparing products before purchasing. Their brick-and-mortar counterparts, meanwhile, were stuck in the 1800s.
Unexpected innovations, however, have a funny way of leveling the playing field. Today, broadband ubiquity and the proliferation of mobile devices are enabling brick-and-mortar stores to track cell phone signals or use video surveillance to understand the way consumers navigate a store, or how much time they spend in a particular aisle. Sophisticated multichannel retailers now merge online behavior with in-person information to piece together a more holistic picture of their consumers, generating powerful data that drive changes in layout, staffing, assortment and pricing. A recent study found that 36 percent of in-store retail purchases -- worth a whopping $1.1 trillion -- are now influenced by the use of digital devices. Retailers who leverage online research to drive brick-and-mortar sales are gaining a competitive advantage.
The use of big data and predictive analytics in higher education is nascent. So-called disrupters often claim that the lecture hasn’t changed in 150 years, and that only online learning can drive transformative, game-changing outcomes for students. Of course, these claims ring hollow among today’s tech-savvy professors.
Since my transition into higher education, I have been struck by the parallel journey retailers and educators face. Both have been proclaimed obsolete at various points, but the reality is that the lecture, like the retail experience, has and will continue to evolve to meet the new demands of 21st-century users.
Like brick-and-mortar stores, lectures were once a black box -- but smart faculty members are beginning to harness the presence of mobile devices to capture unprecedented levels of data in traditional classrooms. And smart institutions are combining real-time engagement data with historic information to spot challenges early and change the academic trajectory for students.
Historical sources of student data (FAFSA, GPA, SAT, etc.) have predictive validity, but they are a bit like the year-over-year data retailers used: limited in depth and timeliness. The heart of a higher education institution is its professors -- and its classes. In addition to professors being experts in their fields, providing unique learning opportunities to their students, studies have shown that when professors have positive relationships with students, it leads to greater student success.
Some of the most interesting early data are coming from the big, first-year lecture courses. While most students experience these as a rite of passage, they also hold great potential as models of how behavioral data can improve engagement and completion rates for students. Faculty are no longer powerless in the face of larger classes and limited insight into their students' learning behavior. They can track how well students are engaging in traditional lecture classes and intervene with students who aren’t engaged in the behaviors (note taking, asking questions and attendance) that correlate with success.
Historically, professors have relied on piecemeal solutions to gather insights on student behavior. So-called student-response systems and learning management software, like digital cash registers in the ’70s, provide useful data -- but they don’t provide the sort of real-time analytics that can inform an instructor’s practice or to identify students in need of additional support and coaching.
A more recent brand of solutions -- in full disclosure, including ours at Echo360 -- are designed to work in conjunction with great teaching, while providing instructors with the tools to track and measure student engagement: Are students taking notes? Are they asking questions? These tools give administrators and instructors insight into how students are interacting and participating both in class, as well as with content or readings before and after class. No more waiting for summative tests to demonstrate that a student misunderstood a concept weeks or months earlier.
The analogy between retail and education has its limitations. The mission and objectives in education are more nuanced, and frankly, more important. However, education, like every sector, has what we call a moment of truth.
For retailers, that moment of truth is centered around the purchase decision. Sophisticated marketers and retailers have used behavioral data to become incredibly skilled at understanding and shaping that purchase decision to achieve extraordinary results.
It’s time to use those learnings for a higher calling. The explosion of digital devices in the classroom allows us to understand the learning process wherever it is happening on campus, and to support education’s vital moment of truth -- a transaction of knowledge between professors and students.
Frederick Singer is CEO and founder of Echo360, which provides active learning and lecture capture services to more than 650 higher ed clients in 30 countries.
This revised framework marks a significant step in the conversation about measuring students’ preparedness for the workforce and for life success based on how much they've learned rather than how much time they’ve spent in the classroom. It also provides a rare opportunity for faculty members at colleges and universities to take the lead in driving long-overdue change in how we define student success.
The need for such change has never been stronger. As the economy evolves and the cost of college rises, the value of a college degree is under constant scrutiny. No longer can we rely on piled-up credit hours to prove whether students are prepared for careers after graduation. We need a more robust -- and relevant -- way of showing that our work in the classroom yields results.
Stakeholders ranging from university donors to policy makers have pushed for redefining readiness, and colleges and universities have responded to their calls for action. But too often the changes have been driven by the need to placate those demanding reform and produce quick results. That means faculty input has been neglected.
If we’re to set up assessment reform for long-term success, we need to empower faculty members to be the true orchestrators.
The D.Q.P. provides an opportunity to do that, jelling conversations that have been going on among faculty and advisers for years. Lumina Foundation developed the tool in consultation with faculty and other experts from across the globe and released a beta version to be piloted by colleges and universities in 2011. The latest version reflects feedback from the field, based on their experience with the beta version -- and captures the iterative, developmental processes of education understood by people who work with students daily.
Many of the professionals teaching in today’s college classrooms understand the need for change. They’re used to adapting to ever-changing technologies, as well as evolving knowledge. And they want to measure students’ preparedness in a way that gives them the professional freedom to own the changes and do what they know, as committed professionals, works best for students.
As a tool, the D.Q.P. encourages this kind of faculty-driven change. Rather than a set of mandates, the D.Q.P. is a framework that invites them to be change agents. It allows faculty to assess students in ways that are truly beneficial to student growth. Faculty members don't care about teaching to the assessment; they want to use what they glean from assessments to help improve student learning.
We’ve experienced the value of using the D.Q.P. in this fashion at Utah State University. In 2011, when the document was still in its beta version, we adopted it as a guide to help us rethink general education and its connection to our degrees and the majors within them.
We began the process by convening disciplinary groups of faculty to engage them in a discussion about a fundamental question: “What do you think your students need to know, understand and be able to do?” This led to conversations about how students learn and what intellectual skills they need to develop.
We began reverse engineering the curriculum, which forced us to look at how general education and the majors work together to produce proficient graduates. This process also forced us to ask where degrees started, as well as ended, and taught us how important advisers, librarians and other colleagues are to strong degrees.
The proficiencies and competencies outlined in the D.Q.P. provided us with a common institutional language to use in navigating these questions. The D.Q.P.’s guideposts also helped us to avoid reducing our definition of learning to course content and enabled us to stay focused on the broader framework of student proficiencies at various degree milestones.
Ultimately the D.Q.P. helped us understand the end product of college degrees, regardless of major: citizens who are capable of thinking critically, communicating clearly, deploying specialized knowledge and practicing the difficult soft skills needed for a 21st-century workplace.
While establishing these criteria in general education, we are teaching our students to see their degrees holistically. In our first-year program, called Connections, we engage students in becoming "intentional learners" who understand that a degree is more than a major. This program also gives students a conceptual grasp of how to use their educations to become well prepared for their professional, personal and civic lives. They can explain their proficiencies within and beyond their disciplines and understand they have soft skills that are at a premium.
While by no means a perfect model, what we’ve done at Utah State showcases the power of engaging faculty and staff as leaders to rethink how a quality degree is defined, assessed and explained. Such engagement couldn’t be more critical.
After all, if we are to change the culture of higher learning, we can't do it without the buy-in from those who perform it. Teachers and advisers want their students to succeed, and the D.Q.P. opens a refreshing conversation about success that focuses on the skills and knowledge students truly need.
The D.Q.P. helps give higher education practitioners an opportunity to do things differently. Let’s not waste it.
Norm Jones is a professor of history and chairman of general education at Utah State University. Harrison Kleiner is a lecturer of philosophy at Utah State.