Network Analysis Uncovers Five Distinct Learning Experiences in a Graduate Instructional Design Course

• In Event: AERA Roundtable Session Friday 9:45 am - Gold 4
  In Roundtable Session: Exploring innovative pathways for learning: Reflection, network modeling, iterations, and theoretical analysis

Abstract

A primarily qualitative study employing quantitative network analysis analyzes student reflections from a graduate course on assessment and evaluation in instructional design and development. Thirteen reflection papers were qualitatively coded using MAXQDA, focusing on what worked, what students struggled with, and how they connected their experiences to learning theories. A network analysis of code co-occurrences revealed five emergent clusters representing different “learning ecologies”: management and collaboration, engagement and identity, personal growth and transformation, cognitive and metacognitive processing, and practical and relational coursework. Each cluster revealed distinct challenges and learning pathways that informed design recommendations aimed at improving future course iterations. These findings show the power of leveraging qualitative data through network analysis to generate deeper, student-informed insights for learning design.

Authors

• Dori Do, University of Alabama - Birmingham

• Jonan Phillip Donaldson, University of Alabama - Birmingham