Analysis of Collaborative Learning in a Computational Thinking Class
Bushra Chowdhury, Austin Cory Bart, Dennis Kafura · 2018
Collaborative learning can help reduce the anxiety level of learners, improve understanding and thus create a positive atmosphere for learning. This study analyzes students' collaborative learning experiences within small interdisciplinary "cohorts" while learning computational thinking in a university-level class. The cohort allows students from different disciplines to contribute diverse perspectives, socially interact with each other and in turn create situations where two or more students learn together. This study uses both qualitative and quantitative means to explore students' collaborative learning experiences. Ethnographically-informed qualitative data using Stahl's collaborative framework is analyzed. The analysis revealed that most students found the cohort model to be valuable in learning computational thinking by allowing them to ask about and explain problems, especially with students from different disciplines who perceive and explain a problem differently. Quantitative data from a multi-term survey complements and confirms the findings from the qualitative data. Our study helps to inform those teaching foundational computing concepts to a diverse audience of learners.