We begin our testing in small pilot programs, then at scale with thousands of students.
This helps us determine how to improve the learning experience, and how to teach students the knowledge and skills they need. It also ensures that the investment our students and corporate and institutional customers make is well spent.
We achieve this continued excellence by doing applied research in four critical areas:
Learning Science
We use specific methodologies to study and understand the different ways people learn, and use the information we learn to better each of our programs, products and courses.
Instructional Design
Over the years, we have studied hundreds of learning models to determine which ones best allow our students to learn effectively across individual programs and courses.
Data Science
Our analysts are constantly reviewing massive data sets to extract insights used in the design of our products.
Psychometics
We research individual learning behaviors to enhance our programs and courses for all students.
Kaplan has been, and continues to be, recognized for its investment in testing and research, and its systematic learning engineering approach.
Our work is pioneering and is used as a model in the education sector—in fact, learning scientists and instructional designers from other leading traditional institutions partner with Kaplan to test their own learning science innovations at scale.
Ithaka S + R
Learning Engineering at Kaplan University (October 2016)
There is a growing gap between the findings of the basic science of learning and the practices and conditions of learning at institutions of higher education. To bridge that gap, Kaplan established systems to test and apply the general principles in context. Facilitated by a centralized curriculum development process, a competency-based approach to assessment, and a disciplined “Research Pipeline,” Kaplan has fostered a learning climate that is increasingly shaped by evidence rather than inertia or intuition. Its efforts provide an example from which others in the higher education community—not-for-profit, public, and for-profit—can learn a great deal.
U.S. Department of Education
Ed Tech Developer’s Guide: A primer for Developers, Startups and Entrepreneurs (April 2015)
The Office of Educational Technology at the U.S. Department of Education spotlighted our “Kaplan Way” checklist Kaplan’s learning engineering approach as a “best practice” for edtech developers to assess whether an edtech product or service aligns with learning science principles, and; thus increase the odds it will produce good outcomes for students.
Learning Leadership
Kaplan employs teams of professionals worldwide that include Instructional Designers, Learning Scientists and Engineers, Data Scientists, and Education Technologists. Each plays a critical role in our learning engineering work.
Overseeing the work of these teams is our Kaplan Learning Innovation team, which provides training, formulates strategy and best practices, and gives access to new learning research across all Kaplan academic and product teams. KLI also conducts bi-annual, business-wide learning reviews to measure and align our learning engineering efforts, something that is unique in the education industry.
New Book: How Learning Data Can Improve Student Results
David Niemi, Kaplan’s vp of measurement and evaluation and part of its Learning Innovation team, edited Learning Analytics in Education, a collection of essays from leading experts in learning analytics, who explain their methods, describe examples, and point out new underpinnings for this new and growing field.
Kaplan Learning Innovation
Advisory Board
The Kaplan Learning Innovation team is guided by an advisory board of distinguished learning scientists, who connect us to cutting-edge learning science developments and consult with Kaplan businesses on the design of research pilots.
Richard Clark
Emeritus Professor of Educational Psychology and Technology, University of Southern California
Research interests: Strategies for human performance improvement including organizational gap analysis, the development of advanced expertise using cognitive task analysis and fully guided teaching; performance motivation; and current applications of instructional technology for instruction and assessment
Richard Mayer
Distinguished Professor of Psychology, University of California-Santa Barbara
Research interests: How people learn (i.e. the science of learning), how to help people learn (i.e. the science of instruction), and the intersection of cognition, instruction, and technology
Janis Cannon-Bowers
Senior Research Scientist and Associate Professor, University of Central Florida
Research interests: Instructional design and training
Valerie Shute
Mack & Effie Campbell Tyner Professor of Education, Florida State University
Research interests: The design, development, and evaluation of advanced systems to support competencies
Jeroen Van Merrienboer
Professor of Learning and Instruction and Research Director, School of Health Professions Education (SHE), Maastricht University
Read Jeroen Van Merrienboer’s Bio
Research interests: Design of learning tasks, including “germane” cognitive load theory and how computers can improve design processes
John Sweller
Professor Emeritus, University of New South Wales
Research interests: Designing instruction to facilitate learning and problem solving
