How To Erase Higher Education’s “Learning Design Debt”

By Alison Pendergast and Carol Carter

Software engineers have a concept called “technical debt,” which is the accumulating downside impact of design decisions made when a product was first built. Technical debt weighs down the effectiveness of the product, and eventually the company has to erase it by rebuilding the underlying code. That’s difficult to do without disrupting the customer experience, so it’s tempting to make do with the old system as the technical debt keeps growing.

U.S. higher education has a “learning design debt,” and the accumulating downside impact is the degree attainment gap. Whether we are talking about first-generation, low-income, Pell grant, minority or working adult students, the limitations of yesterday’s learning design are weighing down the effectiveness of higher education for today’s students.

The bill for this learning design debt is coming due. According to 2015 research by Georgetown University’s Center on Education and the Workforce, the U.S. will be short five million degrees in 2020. To remain globally competitive, we must graduate more students with high-quality degrees that will be valued by today’s employer.

The need is not for more students. To meet our national higher education attainment goals, we have to find more effective ways to help the large numbers of students already enrolled (and those waiting at the door) to complete courses and degrees. Education remains the primary engine of economic mobility, but increasingly only for those who can complete a degree.

Universities have to design courses and degree programs for the growing population of non-traditional students including minority, low-income and first-generation students. While students from middle-class backgrounds have a 77 percent chance of graduating from college, students from families in the lowest income quintile have a 10 percent chance of earning a four-year degree, according to data from the Pell Institute.

In short, we have a degree attainment gap that is widening because what might have worked yesterday isn’t working for a large portion of today’s students.

Admissions Isn’t The Gatekeeper Anymore

Our colleges and universities are actually doing well on expanding access to higher education and attracting low-income, minority and first-generation students.

However, many of these students are failing to thrive–dropping out or stopping out. More than 31 million students have left college without graduating in the last 20 years according to a 2014 report from the National Student Clearinghouse Research Center. To put that into context, about one-fifth of the U.S. population over 25 has some college experience, but no degree.   The United States has been trying to close the degree attainment gap since 1972 when the Pell Grant program was launched. Since then, there have been several major initiatives aimed at achieving attainment goals.

Despite those efforts, we’re getting less effective at serving low-income students. In 1970, 72 percent of upper- and middle-class students were graduating from college, and 28 percent of the bottom two quartiles were earning degrees. In 2014, those rates were 77 percent and 23 percent. The degree attainment gap has grown.

However, one potential solution to the completion problem is emerging: Refactoring first-year, general education courses using learning design and technology to better serve the needs of students from traditionally underserved populations.

Gateway Courses Are The New Gatekeeper

One-third of all students in four-year colleges are clustered in just 25 of the school’s courses, according to the National Center for Academic Transformation (NCAT). These general education “gateway” courses are the sort of large-enrollment introductory courses that, in person, are typically held in auditoriums rather than classrooms–for example, Introduction to Psychology, College Math and Introduction to Biology.

These auditorium-format 101s are yesterday’s design solution to the problem of scale. And they are where most of the learning design debt in higher education is accumulating, effectively blocking the path to degree completion rather than enabling students to achieve their best possible outcomes.

For first-generation, low-income students who may be less prepared for college-level work due to having attended low-performing schools, gateway courses can be extremely challenging. Most students who fail out or stop out do so in their first two years of college when they are enrolled in introductory courses.

NCAT argues that these gateway courses have an enormous impact on student success and provide a leverage point for improvement. In 1999, its Program in Course Redesign (PCR) enlisted 30 institutions in a national competition to redesign one of their largest courses. In the end, 25 institutions saw significant increases in student learning, and 18 institutions saw increased retention.

The results aren’t unique to PCR. Georgia State University, after discovering that many students weren’t completing introductory math courses, launched a redesigned course in 2006. The pilot, in a hybrid format and using adaptive learning technology, more than halved the drop/fail/withdraw rate.

These initiatives took place more than 10 years ago. However, in the last decade, advances in educational technology and learning science have given us the ability to infuse evidence-based learning design into large-enrollment courses in a scalable way without dumbing-down the course or lessening the rigor of the curriculum. The goal is not to make the course outcomes easier, but instead, to use learning design and the resulting learning data from this new approach alongside traditional teaching and learning practices.

Combining Learning Science With Data

In 2009, John Hattie, a professor of education at the University of Auckland in New Zealand, published a meta-analysis of 800 pieces of research about factors that lead to student achievement. He ranked factors based on the size of their effect on student achievement. Near the top of the list was abundant practice, coupled with timely and targeted feedback. He also found that metacognition–students’ ability to reflect on their own learning–led to success as well.

Personalized learning technology is able to bake that formative practice and metacognition into an online course, giving students goal-directed practice with timely and targeted hints and feedback. However, what’s new is that this practice and feedback can be individualized to each student based on his or her own learning progress.

As students learn, the learning system is learning about them, generating data in real time and feeding that data to instructors. As students complete the activities and assessments, the system collects and analyzes the data, using it to make a predictive estimate of whether the student is making progress. Students with low learning estimates will adaptively receive more practice in the area that’s giving them trouble. Instructors can use the data to target instructional interventions such as reviewing concepts proving challenging for students, tutoring or coaching sessions.

By designing learning environments that enable the computer to function both as an “on demand” personal tutor for students, and a periscope for educators, surfacing issues earlier that may otherwise be out of sight, we can significantly enhance both the practice of teaching and the process of learning.

Designing Courses To Increase Success

Online and hybrid courses are attractive to students who need more flexibility while working toward a degree. Flexibility, combined with strategies that personalize the learning experience for each student based on his or her own progress opens a powerful new approach for educators looking for solutions to better serve students likely to fall into the degree attainment gap.

And personalized learning at scale is possible. New authoring tools simplify and accelerate collaborative learning design, enabling institutions to rapidly refactor “blocker” courses, all but wiping out learning design debt.

Personalized learning technology also aligns well with other academic support activities. Learning data generated from student interactions within the online learning environment can trigger individualized support and coaching services, helping more students stay on track to graduate.

At a time when a college degree is more necessary than ever, finding effective ways to leverage the affordances of technology and expand the practice of teaching is critical. Research from Carnegie Mellon and elsewhere shows that high-quality learning experiences that produce meaningful data about what students are learning and how students are progressing can enable more timely and actionable feedback to instructors, students and course designers at key points in the learning process critical to student success and achievement.

For more, see:

Alison Pendergast is Chief Marketing Officer of Acrobatiq, a Carnegie Mellon-backed education technology company. Follow them on Twitter: @acrobatiq

Carol Carter is founder of the LifeBound Coaching company, the GlobalMindEDmovement and the KEYS TO SUCCESS curriculum program used globally in college success courses. Follow her on Twitter: @CarolJCarter

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