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Collaborative Programming in the Classroom: Approaches and Insights

Friday 11:40 AM–12:10 PM in Hall B

Part of the Education specialist track

Collaborative learning in the classroom has been shown to have a broad range of positive academic effects such as promotion of critical thinking, communication skills and improved learning outcomes. It also brings social and psychological benefits such as development of friendships, social support, reduced anxiety and increased appreciation for the course content.

Unlike a traditional classroom, programming education tends to offer few opportunities for group activities as students tend to be restricted to their personal laptops or to allocated workstations and are expected to learn in relative isolation. This is often due to environmental factors, such as it being hard to share a keyboard between more than two students at a time. Additionally, educators often design solo activities, because they are easy to implement and execute in the classroom. Since programming students aren’t trained to code with other people, they do not develop clear ideas on what good collaborations look like or how to collaborate with others successfully, which is an important skill in the workforce.

Pair programming is an oft cited solution, yet sharing workstations can be unsatisfying as one student is left in the backseat while the other drives. The situation is worsened for larger group sizes. Furthermore, there is no ability for a group of students to divide and conquer a problem as their attention and input must be the same as the driver.

This talk will introduce approaches to collaborative programming that extend beyond pair programming by allowing more than two students to collaborate in real-time on projects ranging from single Python scripts, to multi-file projects and Jupyter Notebooks. This has been accomplished across pairs, small groups, and even entire classrooms during both live classroom environments and for long-term group projects. We will also discuss the lessons we learnt along the way, as well as feedback from other educators and students.

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Stephen Tierney

Stephen is a Senior Lecturer at the University of Sydney in the fields of Statistics, Data Science and Machine Learning.

Alison Wong