Moving the Needle on Student Success
Adaptive Learning Technology Harnesses Power of Instruction
Gallup studies have revealed declining confidence among business leaders in today’s college graduates. Subject mastery, critical reasoning, and math and language skills, many believe, are in short supply among those moving from college to employment. At the same time, community college graduation rates nationally have remained low.
All this is true, despite a decade of very aggressive improvements in course delivery, assessment, and student support. Flipped and blended classes, studentintuitive learning management systems, e-Portfolios that enable students to chronicle and celebrate their academic and related successes, interventions based on predictive as well as real-time analytics, and a myriad of other innovations have become part of community college life.
These enhancements have enriched the learning experience for many community college students, but they have proven resistant to scale and sustainability. Most important, they have not significantly advanced institutional measures of competency and completion.
I recall partnering with VW a few years ago to recruit its initial American workforce. Many thousands applied. Those with certain qualifications were extensively tested; some were interviewed. When it came to critical reasoning, few college graduates demonstrated skills much above those with only high school degrees — despite virtually every college course syllabus assuring them their study would enhance these very skills. Disappointing? No, embarrassing!
I began to ask, what will move the competency and completion needles up? The answer was in front of me. The tried and true power of instruction adapted to the individual learner, now powered by technology. Combine inference engines that dissect learning behavior with the recognition that some concepts are antecedent building blocks to others, and a giant step forward will be taken. Of course, you can get similar results through an army of tutors and mentors, but, more reliably and economically, the path upward is through adaptive learning technology arriving in our marketplace.
Here’s how it works. Antecedent concepts become building blocks — like rungs on a ladder — to grasping key learning objects. Secure students on each algorithm-identified rung and they will ascend to master top-rung concepts. Each student then progresses along a personalized pathway. A key result: subject mastery, a major step toward satisfying employer expectations.
“Recommendations” served up by these inference engines to struggling students are based on psychometrics, personal learning styles, and real-time identification of learning obstacles. They optimally fit each learner because the adaptive learning engine, after ingesting the learning journeys of millions of students, identifies those who have encountered the same challenge facing the current adaptive learner and serve up perfect-fit antecedent concepts at the precise right time to lead the learner to concept mastery.
the overall result? As the student grasps concept after concept he/she
gains academic self-confidence. Statements like “I’m just not good with
math” go away, driving up institutional engagement and probabilities of
“achieving the dream.” After all, “nothing succeeds like success!” This
is where completion and competence should be rooted – in the very act of
James L.nCatanzaro retired in 2015 from Chattanooga State Community College after 24 years as its president. He served more than 37 years as a community college president in four states. He is currently senior education advisor to Knewton Learning and director of the Higher Education Research and Development Institute, South.
is a continuation of a series authored by principals involved in the
Roueche Graduate Center, National American University, and other
national experts identified by the center. John E. Roueche and
Margaretta B. Mathis serve as editors of the monthly column, a
partnership between the Roueche Graduate Center and Community College
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