Collaborative Interdisciplinary Computational Thinking

• In Event: 41.049 - Stories From the Field: Integrating Computational Thinking Across Curricular Domains

Abstract

Purpose: This study investigates the collaborative dimension of Computational Thinking (CT) at the undergraduate level. Rather than learning individually, collaboration can help reduce the anxiety level of students, improve understanding and thus create a positive atmosphere to learn CT. In order to understand collaborative process it is important to understand how students interact in social and physical environments and thereby participate in the collective process of problem solving and knowledge building in the context of learning CT.

This study investigates interaction between members in small interdisciplinary student groups/cohorts in an undergraduate CT class (majoring in Political Science, English, Sociology, and Animal Science, etc.). The underlying assumption being that through the formation of such a cohort model, students from different disciplines will bring diversities in the groups, socially interact with each other and in turn form situations where two or more people learn or attempt to learn CT together. This study attempts to provide naturalistic accounts of such social interactions between learners collaboratively learning CT in an undergraduate classroom setting.

Theoretical Framework: Chi (2009) in her DOLA framework proposes taxonomy to categorize different activities (active, constructive, interactive) demonstrated by students while learning. According to Chi, an interactive activity involves higher cognitive process than constructive, and constructive activity is cognitively higher than active activity. Based on Chi’s framework this study operationalizes what constitutes as an active, constructive or an interactive activity in terms of learning CT and applies these constructs to characterize the social interactions taking place between cohort members.

Methods: This study used ethnographically-informed qualitative data collection methods (observations and interviews).
Data Sources: Data collection was conducted in one academic term. Three student cohorts working during class time were video recorded during three class sessions. Each video is approximately 20 minutes in duration. Each cohort comprised 5/6 students. Students of each cohort were also individually interviewed.
Data Analysis: All transcribed recordings were coded by two independent coders. First, in each transcript, episodes of social interactions were identified. Second, each episode was then categorized based on the adapted framework.

Results: Undergraduate students coming from different disciplines struggle to learn computational concepts for the first time. These students can benefit from having a first line of support: a group of students in the class (cohort). The cohort is considered a safe place for students to share anxiety, express frustrations, ask questions, receive explanations and locate technical resources. However, not all cohorts function at the same level. In some cohorts interactions among students were more constructive in nature, whereas in others it was more active in nature. Having a student with prior programming experience and who is also willing to help others can play a crucial role in differentiating the type of interactions students have while learning.

Significance: This study provides a qualitative insight into how undergraduate students coming from different disciplines interact with each other while learning CT in a classroom setting. The findings of this study can be used to improve classroom scaffolding strategies.

Authors

• Bushra Chowdhury, Virginia Tech

• Aditya Johri, George Mason University