Student challenges, strategies, and learning within the Data Mine Learning Community

This full research paper looks at data science, which is an increasingly important topic of interest across all academic disciplines and industry sectors. As such, the need for curricula and learning designs for teaching data science has never been greater. One increasingly useful institutional initiative for building critical skills is living learning communities. This study reports on a data science learning community, called The Data Mine. As part of this learning community, students worked on similar tasks, took classes together, and participated in industry related assignments. This exploratory study aims to understand how students navigated learning challenges, what self-regulation strategies were adopted, and what students ultimately learned through participating in the Data Mine Learning Community. Students were grouped into achievement categories using a developed rubric on course assignments and their responses to open ended questions were evaluated for patterns among those in their achievement group. The results indicate that the highest performing students avoided coding errors and were highly focused on learning gains.

I. Introduction

Computational and data science are critical pieces to all levels of education in the 21^st century across academic disciplines [1], [2]. The challenge for data science education is that it is inherently interdisciplinary, requiring skills from statistics, computer science, and domain knowledge to which it is applied [3]. One potential solution that allows for both disciplinary content and data science skills to be taught is the use of living learning communities.