A Scalable Methodology to Guide Student Teams Executing Computing Projects

Jeffrey Saltz

doi:10.1145/3145477

2018-07-11

PRE

Jeffrey Saltz *

Robert Heckman

DOI：10.1145/3145477

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摘要

En 中文

This article reports on a sequential mixed-methods research study, which compared different approaches on how to guide students through a semester-long data science project. Four different methodologies, ranging from a traditional just assign some intermediate milestones to other more Agile methodologies, were first compared via a controlled experiment. The results of this initial experiment showed that the project methodology used made a significant difference in student outcomes. Surprisingly, the Agile Kanban approach was found to be much more effective than the Agile Scrum methodology. Based on these initial results, in the second semester, we focused on use of the Kanban methodology. The findings in the second, more qualitative phase, confirmed the methodology's usefulness and scalability. A key issue when using the scrum methodology was that the students had a very difficult time estimating what could be completed in each of their two-week sprints. The Kanban board, which visually shows and limits work-in-progress, was found to be an effective way for students to communicate with each other as well as with their instructor. In addition, Agile Kanban also streamlined the work required for instructors to efficiently provide guidance to student teams and to understand each team's status. In summary, a scalable Kanban methodology, which can be applied to a wide variety of student computing projects, was found to an effective methodology to guide and manage student projects, improving student outcomes and minimizing instructor workload.

Keyword:

Project-based learning

scalable methodologies

project methodologies