Anonymizing student team data of online collaborative learning in Slack

Research data on the activities of student teams in online learning environments are relevant for evaluating instructional methods, strategies, tools, and materials. For research data sharing and publication purposes, these personal data must be anonymized or pseudonymized as recommended by data protection and privacy policies. This paper addresses issues related to anonymizing and pseudonymizing student data on the Slack teamwork platform, one often employed in educational and business settings. Issues are discussed from two perspectives: data extraction and data transformation. Difficulties and challenges concerning data extraction and transformation are described. The complexities of these two processes are considered, and a starting point for developing more efficient methods is put forward.