A review of problem- and team-based methods for teaching statistics in Higher Education

TL;DR

This paper reviews problem- and team-based collaborative learning methods for teaching statistics in higher education, highlighting their potential to enhance active learning and student responsibility.

Contribution

It provides a comprehensive review of problem- and team-based learning approaches and offers practical guidance for implementing these methods in university statistics courses.

Findings

Evidence suggests collaborative learning improves student engagement.

Active learning strategies align with educational recommendations.

Practical implementation tips facilitate adoption in higher education.

Abstract

The teaching of statistics in higher education in the UK is still largely lecture-based. This is despite recommendations such as those given by the American Statistical Association's GAISE report that more emphasis should be placed on active learning strategies where students take more responsibility for their own learning. One possible model is that of collaborative learning, where students learn in groups through carefully crafted `problems', which has long been suggested as a strategy for teaching statistics. In this article, we review two specific approaches that fall under the collaborative learning model: problem- and team-based learning. We consider the evidence for changing to this model of teaching in statistics, as well as give practical suggestions on how this could be implemented in typical statistics classes in Higher Education.