Impact of Cohort-specific Online Discussion Experiences on Student Engagement and Learning

Final Presentation: 
Audience Level: 
All
Session Time Slot(s): 
Institutional Level: 
Higher Ed
Abstract: 

The purpose of this presentation is to describe the impact of a collaborative and multidisciplinary approach to improving student engagement and learning in asynchronous online discussion boards.  This interactive session will focus on specific methodology, design thinking, and evidence-based learning outcomes data, as well as the project’s replicability and scope.

Extended Abstract: 

A major focus of Ashford University’s strategic plan is to develop, implement, and assess innovative, multidisciplinary approaches to student learning and success.  In an effort to continuously improve the quality of our University’s course design and delivery, this team recently reviewed the literature (Berry, 2008; Du, Derrington, & Matthews, 2007; Levin, He, & Robins, 2006; Schellens & Valcke, 2006) on discussion forums, surveyed faculty and students, assisted in the development of a conceptual framework for online asynchronous discussions, and shared recommendations with University leadership that resulted in several specific strategies.  One such recommendation was to implement a cohort-based model of discussion forums in our high-enrollment general education courses.

At Ashford, discussion forums create a substantial venue for interaction, dialogue, and the collaborative construction of knowledge in our online courses.   Furthermore, it is commonly accepted among researchers and educators that asynchronous discussions can enhance online learning.  During the course of our review of the literature, as well as our thorough assessment of curriculum quality, faculty teaching, and student success data, we determined that the optimal course size for increased student engagement and learning is likely approximately 4 – 12 students per discussion.  While many of our upper-level undergraduate courses closely align with this recommended course size, the majority of our general education courses consist of approximately 25 – 35 students.

Fortunately, the fusion of learning technology, data analytics, and curriculum and innovation has fostered a culture of evidence-based decision-making at our University.  In order to address this challenge, a multidisciplinary team of academic leaders, faculty, instructional designers, assessment analysts, and learning technology specialists collaborated to develop a potential solution.  Specifically, we aimed to improve student engagement and learning in discussion boards by creating cohort-specific discussion experiences in our high-enrollment general education courses.  Additionally, in order to evaluate the success of this solution, we employed a variety of direct and indirect measures, including learning assessment through rubrics and end-of-course surveys for students and faculty.

To provide some additional context, our solution included the following breakdown in regard to number of students in each discussion:

  1. Courses with 12 or less students remained with one cohort per discussion.
  2. Courses with 13 – 25 students went to two cohorts per discussion.
  3. Courses with 26 or more students went to three cohorts per discussion.

In addition, we incorporated the following considerations:

  1. Students were assigned to a cohort group through a random, automated process.
  2. Discussion topics were divided so that students could only read and contribute to posts made by other members of the same cohort.
  3. However, the course could also include some open discussion topics that all students could read and contribute to as appropriate.

This project has been completed, and during the course of the session, the presenters will share detailed information about the research question, methodology, and results.  The presenters also hope for this session to be engaging and interactive, with a central focus on innovation and the project’s replicability and scope.

 

Position: 
6
Conference Session: 
Concurrent Session 4
Session Type: 
Discovery Session