Initiatives: Adaptive Learning

Adaptive learning is an educational method which uses computers as interactive teaching devices to orchestrate the allocation of human and mediated resources according to the unique needs of each learner. Computers adapt the presentation of educational material according to students' learning needs, as indicated by their responses to questions, tasks and experiences. The technology encompasses aspects derived from various fields of study including computer science, education, psychology, and brain science.

The Kirwan Center is currently working on two adaptive learning projects:

  • The ALT-Placement Project is piloting the efficacy and feasibility of replacing a high-stakes mathematics placement exam with a process that empowers students to assess and improve their mathematics knowledge and skills using adaptive learning tools. The project’s hypothesis is that these adaptive tools will deliver just-in-time remediation while also providing diagnostics that will be a more reliable measure of students’ knowledge, thus enabling more accurate mathematics course placements that will increase persistence and lower costs. Seven Maryland community colleges and four USM institutions are participating in the pilot, which runs through February 2019.
  • The Adaptive Learning in Statistics (ALiS) project is focused on developing, piloting, and scaling a credit-bearing, introductory course in college-level statistics that is built on a sophisticated adaptive learning platform. The goal of the project is to determine whether a flexible learning approach, utilizing standardized learning outcomes, can unify content and improve learning outcomes in gateway mathematics courses without increasing costs, as well as facilitate the transfer of credit between institutions. Five Maryland community colleges and four USM institutions participated in the pilot in 2017-2018, and five institutions are extending the pilot into 2018-2019.

Our Work in Adaptive Learning

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November 5, 2017

ALT-Placement Project: Investigating Adaptive Learning Tools for Mathematics Remediation and Placement

Among the many obstacles college students face in their pursuit of higher education is the discovery that they are insufficiently prepared for a college-level curriculum and must enroll in remedial courses to make up deficits in their knowledge and skills. This detour from college-level courses is expensive in both time and money, and it often means the end of the college road for these students, particularly for those deficient in mathematics. According to a U.S. Department of Education study (NCES, 2013), only 27 percent of all students enrolled in developmental math will complete their degrees.

November 15, 2017

The Kirwan Center is seeking institutional partners from across Maryland to participate in a Kresge Foundation funded project starting in Spring 2018 that will pilot the efficacy and feasibility of replacing the high-stakes mathematics placement exam process currently in use with a process that empowers students to assess and remediate their mathematics knowledge using adaptive learning tools instead. Our hypothesis is that these adaptive tools will deliver just-in-time skills remediation while also providing better diagnostics that will be a more reliable measure of students’ knowledge, thus enabling more accurate mathematics course placements that will increase persistence and lower costs.

May 31, 2017

Completion of college-level requirements in the mathematical sciences is one of the most common and vexing impediments to college completion rates, especially for students from lower-income families and underrepresented minorities, where high-school preparation in mathematics is often insufficient. For this reason, important efforts to improve student outcomes in higher education have focused on the application of learning science and use of digital learning platforms to improve the delivery of mathematical sciences content, and on the identification of common learning outcomes and broadly accepted standards in foundational courses—especially introductory statistics.