A New Model To Help Learners Succeed In Math: Just-In-Time, Adaptive Remediation

Concurrent Session 5
Streamed Session

Watch This Session

Brief Abstract

Foundational math courses are an obstacle to student persistence and graduation. American Public University System is addressing this challenge at scale with Just-In-Time Adaptive Remediation, providing learners with personalized guidance and scaffolding to address their individual knowledge gaps. The program has positively impacted student success, with results to be shared at the presentation.

Presenters

Dr. Karen Srba is the Vice President of Academic & Instructional Technology for American Public University System (APUS). She reports to the President's Chief of Staff. Dr. Srba heads a team of eLearning professionals who design digital, interactive learning content and implements technological strategy for thousands of online courses. Dr.. Srba has over twenty-eight years of experience in systems integration, information security, project management and education. Prior to joining APUS she served as a subject matter expert in project management and was a STEM Innovation Lead and online instructor in Computer Science and Engineering. In addition, Dr. Srba served as a Government Practice Lead and contract manager for over 18 years as a contractor for the Federal Government. She earned her Doctorate Degree in Education Leadership and Management from the University of Pennsylvania and her Master’s Degree in Distance Education Leadership and E-Learning Technology from the University of Maryland University College and an undergraduate degree in Management and Technology from University of Maryland. She also earned several certificates in project management, computer science, engineering, psychology and education.

Extended Abstract

APUS’ Just-In-Time Adaptive Remediation framework uses the features and functionality of the adaptive learning engine from Realizeit to deliver students targeted guidance and scaffolding on concepts not yet learned through additional personalized content and practice exercises. The system uses machine learning to recognize the student’s strengths and weaknesses, serving material to the student and alerts to the faculty based on each student’s unique learning needs. The framework allows students to succeed where previously they have not.

The data to date, which will be shared in the presentation, illustrate that the Just-In-Time framework is making a positive impact on student success in Math 110, with students demonstrating consistent, steady growth in mastery and progress throughout the course.

Through attending this presentation, participants will be able to:

  1. Apply adaptive learning concepts to online learning
  2. Review a framework for just-in-time remediation for adult learners
  3. Understand how the faculty role is enhanced using the adaptive engine dashboard
  4. Be able to apply the concepts and framework to your remedial courses