An Award-Winning Course Amps Up Active Learning for Student Success
Computer science education faces an inherent paradox: “You can’t learn to program without writing lots of little programs,” says Dr. Manuel Pérez Quiñones, professor and former associate dean in the College of Computing and Informatics at the University of North Carolina (UNC) at Charlotte.
Yet students entering UNC Charlotte’s computer science programs begin their journey from diverse starting points. Some have a fair amount of programming experience while many have none. A large number come from economically disadvantaged backgrounds; many are first-generation college students. Calibrating core courses to equally prepare them all and provide the practice they need is a continual challenge.
Amid these hurdles, the university saw an opportunity. “Our provost, Dr. Joan Lorden, really understands the value and impact of adaptive learning, and she actively promotes it for student success,” says Dr. Garvey Pyke, Director of the Center for Teaching and Learning.
The adaptive approach seemed an ideal match for what the College of Computing and Informatics (CCI) was looking to accomplish. Adaptive learning, which differentiates content and learning paths for students based on their individual knowledge levels and performance, complements the university’s focus on active learning, Pyke says. “Adaptive learning brings personalization to the learning and helps student preparation for class. If students are prepared better, they can be more active and engaged.”
Matching Student Needs with Faculty Interest
For Dr. Pyke and Dr. Pérez Quiñones, choosing where to launch this new approach was easy: Professor Celine Latulipe’s Data Structures and Algorithms course, a sophomore-level class that’s core to the computer science curriculum, averaging about 100 students per section.
Besides teaching and engaging in research in the field of human-computer interaction, Dr. Latulipe also conducts research in innovative curriculum and pedagogy. She already had flipped the Data Structures and Algorithms course and added an interactive digital textbook; she was ready to see if adaptive learning could further improve student outcomes.
“My goal with the platform is to help students take more ownership of their own learning,” says Dr. Latulipe, who earned a 2019 OLC Award for Innovation in Online Teaching for her work on the course. “It helps the students who need more practice or review of the fundamentals, while not forcing those who know exactly what they’re doing to do a whole bunch of extra work.”
A Framework for Success
In class, students engage in group activities including pair programming and team-based discussions and quizzes. Outside of class, they complete all preparatory coursework in Realizeit, an intelligent modern learning system that personalizes the learning experience for each student. Videos and the interactive text are part of this work, but a critical component is coding practice they get through CodeWorkout, a programming tool they access directly within Realizeit.
“Computing, like math, requires a lot of practice,” says Dr. Pérez Quiñones. “One challenge of that is you need feedback, people to answer questions and explain things, and that is very time intensive. With the enrollment we have now, it’s hard to provide that level of practice and feedback.”
That’s why the CodeWorkout integration with Realizeit is so important. Students can practice programming in the tool, then get immediate feedback and recommendations on next steps.
“The beauty is that on the spot you’ll get suggestions for what to do next. It’s not the TA sending you an email the morning after you do the work,” Dr. Pérez Quiñones says.
The impact was evident after the first semester. DFW (drop, fail, withdraw) rates fell to 9% from 13% the prior semester, and student survey feedback illustrated how the adaptive approach positively impacted study habits: 62% said the Realizeit quiz questions incentivized them to complete their coursework, and more than half said seeing their score in Realizeit motivated them to work hard on the prep work.
Encouraged by these outcomes, the university is using Realizeit in more courses. Dr. Latulipe now teaches at the University of Manitoba, but her award-winning design has been expanded to all five sections of the Data Structures and Algorithms course. Dr. Matt Davies’ Engineering Dynamics course using Realizeit also is showing strong results, and more courses are in the works for introductory math.
The value of these initiatives, Dr. Pyke says, extends beyond the classroom. “What’s great about these adaptive learning projects from a faculty perspective is they hit on all aspects of teaching, research and service,” he says. “This is definitely not a fad. To me it’s like a fulfillment of a promise. The idea of adaptive learning goes a long way back. We finally now have the computing power to do what others only dreamed of in the past.”