Why Personalized Adaptive Learning?

Research has demonstrated the potential of adaptive learning to reduce dropout rates, improve learning outcomes, and expedite students’ learning journal to achieve their educational goals faster (Imhof, Bergamin, & McGarrity, 2020), especially when it is combined with a complete course redesign (Brown, et al., 2020). At the University of Central Florida, personalized adaptive learning (PAL) courses have demonstrated improved student success, retention, and satisfaction. In our course redesign initiative, most redesigned PAL courses showed increased student success compared to the same class without PAL (Pegasus Innovation Lab, 2022). The positive impact includes:

  • Improved student success by providing just-in-time personalized scaffolding on challenging concepts and problems, remediation, and acceleration that drives degree completion. 
  • Improved course retention in high-enrollment, general education gateway college courses that typically have higher withdrawal rates and lower success rates across all disciplines and demographic groups.
  • Improved learning equality by providing personalized adaptive resources to all students across course modalities and diverse demographic segments.
  • Improved student satisfaction by providing personalized learning interventions based on student-specific analytics in large-enrollment classes.

The complete summary report of the course redesign projects can be viewed at the Pegasus Innovation Lab website. At UCF, we offer instructors the opportunity to redesign their courses using various adaptive platforms, such as Realizeit and Acrobatiq. 

Why Realizeit?

Realizeit’s self-learning engine continuously adapts to each learner’s changing ability and manages its own accuracy and performance. The platform constantly measures each learner’s knowledge and ability so that it can map, shape, and drive a personalized learning experience. Realizeit is a “content agnostic” adaptive platform and does not contain content, unless a publisher etext is imported (“ingested”) into the system. Faculty and IDs can create original content and courses in Realizeit if desired. Realizeit can:

  • Recommend unique learning pathways based on students’ prior knowledge and current performance;
  • Recommend content based on student performance through major and minor reviews;
  • Variablize algorithmic problem sets to ensure students get sufficient opportunity to develop master on a topic before proceeding to subsequent lessons;
  • Allow students to have increased agency in learning.

Starting in the summer of 2024, accessing the Realizeit platform costs approximately $31.50 via Shopify and $40 via the UCF bookstore.  There may be an additional cost if the course uses publisher content or e-text. Read more about Realizeit at:

Why Acrobatiq?

Acrobatiq by VitalSource is a learning platform that delivers active, personalized learning through embedded adaptive practice. Acrobatiq  provides impactful online learning experiences from most content sources, including etexts in the VitalSource bookshelf catalog, open educational resources (OER), or instructor-created content. Acrobatiq can:

  • Convert static content to interactive courses in just hours;
  • Build online learning experiences from scratch and enhance OER;
  • Turn students from passive readers to active learners;
  • Improve learning, knowledge retention, and persistence;
  • identify at-risk learners, spot trends, and continuously refine courses and student outcomes with Acrobatiq’s powerful data analytics.

The cost for the Acrobatiq platform is $12. If using publisher content from VitalSource, there is an additional eTextbook fee. Read more information about Acrobatiq at:

What other options?

UCF instructors can access various adaptive courseware options through various commercial vendors, including ALEKS, Knewton, and LearnSmart. Our PAL instructional designers continuously evaluate new adaptive platforms in the market. Interested faculty members can work with PAL instructional designers to evaluate new and existing adaptive courseware and effective adaptive features for teaching.


Brown, M., McCormick, K., Reeves, J., Brooks, C., & Grajek, S. (2020). 2020 EDUCAUSE Horizon Report: Teaching and Learning Edition. EDUCAUSE.

Imhof, C., Bergamin, P., & McGarrity, S. (2020). Implementation of Adaptive Learning Systems: Current State and Potential. In P. Isaias, D. G. Sampson, & D. Ifenthaler (Eds.), Online Teaching and Learning in Higher Education. Springer Nature Switzerland.

Pegasus Innovation Lab (2022). UCF Digital Learning Course Redesign Initiative Final Data Report.