Leading Active+Adaptive Learning Innovation in Math Pathways for Student Success, Engagement, and Equity

Concurrent Session 5
Blended Leadership Equity and Inclusion

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Brief Abstract

We successfully redesigned a college Math Pathway in Statistics targeted at social science students with the goal of making learning more relevant to students' lives and areas of study. We describe curricular and structural changes implemented with active learning and adaptive systems and share results on student impact.


Kiran Budhrani is the associate director for personalized and adaptive learning at the Center for Teaching and Learning at the University of North Carolina at Charlotte.
J. Garvey Pyke, Ed.D., is the Director of the the Center for Teaching at UNC Charlotte. His work involves fueling the enrollment growth at the university through online course development, creating high impact student success programs using personalized and adaptive learning, promoting faculty success and scholarly teaching through innovative faculty development programs, and overseeing the provision and support of enterprise academic technologies. Garvey is also an alumnus of OLC's IELOL program (2010) and has remained an active member of this professional community of practice and served as co-director of IELOL 2018 and as a faculty member of IELOL from 2019 - 2021. He has served on various conference committees for OLC Accelerate and has served on the Steering Committee for OLC Innovate.

Additional Authors

Dr. Colm Howlin is the Principal Researcher at Realizeit and leads the research and analytics team. He has been with the company since it was founded 8 years ago. He is responsible for the development of the Adaptive Learning Engine within Realizeit and the Learning and Academic Analytics derived from learner data. Colm has a background in Applied Mathematics earning his B.Sc. and Ph.D. in Applied Mathematics from the University of Limerick and was a Research Fellow at Loughborough University in the UK. Colm has over 10 years’ experience working on research, educational data, analytics and statistical analysis, including spending time as a Consultant Statistician before joining Realizeit.

Extended Abstract

Challenges of College Math
Successful completion of math gateway courses is essential to undergraduate students’ progress towards graduation. Common challenges in gateway courses include a lack of alignment to student interests and needs; the narrow focus on procedures and notations rather than practical application and examples; and the lack of personalization or remediation of lessons. An additional challenge is whether math courses in sequence are truly related to student success: for example, does a College Algebra prerequisite actually prepare students for statistics courses or does it merely lengthen time to degree?  All of these challenges have led to trends in high failure rates and equity gaps.

Transformation of Statistics as a Math Pathway for Social Science Students
In an effort to make math gateway courses more relevant and improve student success rates, the Math Pathways approach expands options for college students, enabling different paths through their math curriculum, depending on a students' course of study. We aimed to redesign a two-course math pathway sequence in Statistics targeted at social science students with the goal of making content more relevant to students’ lives and areas of study. We also removed the College Algebra requirement.  Therefore, several curricular and structural changes were implemented.

We outline our process in three phases:

Phase 1: CoDesign for strategic visioning among stakeholders
Large curriculum redesign projects involve a broad set of collaborators and stakeholders. A key approach explored was “co-design”, referring to the collective effort of designers and non-designers working together to address a specific design problem -- in this case, a curriculum redesign for a Statistics pathway for social science majors. A cornerstone of co-design partnerships is to involve diverse groups of stakeholders from different fields early as productive contributors to the course design process.

A 3-day in-person strategy and design workshop was held, bringing together 37 participants (internal and external stakeholders) comprising faculty members from Math and Social Science departments, instructional designers, librarians, academic administrators, adaptive learning vendor representatives, and design workshop facilitators to reimagine how to teach Statistics to non-math majors. The workshop resulted not only in a unified design strategy between the Math and Social Science departments on common outcomes, core concepts, assessments, but also ensured diverse ideas and perspectives were incorporated early into the project.

Phase 2: Development and implementation of an "Active+Adaptive" blended curriculum
A key curriculum reform required shifting lecture-based pedagogy into what we refer to as an "Active+Adaptive" blended curriculum model, which emphasizes active learning for deep classroom engagement with adaptive learning for mass personalization at scale. The design and development of the new Active+Adaptive Statistics courses required a huge collaborative effort among faculty teams, instructional design teams, and vendor teams that ranged over two years. In implementing the project, additional support was required from the other departments and the undergraduate education office such as preceptors, academic advisors, and the registrar.

Some of the strategies implemented include:
Embedding personalized support for course preparation and remediation through adaptive learning systems
Personalization of formative assessments by social science discipline
Replacing lectures with active learning application projects and examples contextualized to social science disciplines and real-world problems
Replacing exclusively procedural assessments with assessments focused on conceptual understanding
Remixing and enhancing content from open educational resources (OER) to reduce textbook costs
Providing learner supports through adaptive learning course orientation, classroom preceptors, and academic advisors
Providing faculty development programming on active learning pedagogy and adaptive learning systems

Phase 3: Evaluation results and impact on students
We compared preliminary outcomes among two cohorts of students enrolled in the redesigned Statistics I Active+Adaptive course and in the traditional Statistics I course in Fall 2020 and Spring 2021. Survey results show that students who engaged in the Active+Adaptive format had an increased growth mindset, sense of belonging, and motivation. Between Fall 2020 and Spring 2021, there was no statistical difference between the final grades in the traditional Statistics I course, requiring the College Algebra prerequisite vs. Active/Adaptive Stats not requiring the College Algebra prerequisite. This implies that students remained as successful in Statistics I without a full semester of College Algebra, indicating that it can be removed as a prerequisite barrier. We also found that in Fall 2020, the Active+Adaptive Statistics I course design decreased equity gaps between white students and minority students. Additionally, in Spring 2021 the equity gap was reversed with minority students performing better than white students.

Student feedback on the redesigned course was positive. Their comments highlight a regained interest in math and statistics:

  • "F​​or the first time in my life, I was excited about mathematical coursework."
  • "As a person who really struggles with math, I have had the best experience so far in this stats class."
  • "I loved this Stats class. I feel that the way this class was designed helped me learn a lot better."
  • "I am so glad I picked the right statistics class ... I am going [to] use what I am learning in this class with other classes I am taking this semester."

Session Goals
This presentation will:

  1. Identify curricular and structural reform strategies for redesigning Statistics for non-math majors
  2. Share strategies on designing, developing, and implementing the "Active+Adaptive" blended learning model
  3. Describe preliminary evaluation outcomes among two cohorts of students enrolled in the redesigned Statistics I Active+Adaptive course
  4. Brainstorm how similar strategies can be implemented at scale at other institutions