Applying Learning Analytics in Designing for Professional Skills and Attributes in Tertiary Education

1. Introduction

Professional skills and attributes are an important part of what universities promise their students they will have attained upon graduation. Graduate profiles include lists of such skills and attributes, for example communication skills, resilience, and ability to work in groups. Yet, in learning environments with a strong focus on disciplinary content, the development of professional skills and attributes is often unclear. Even if they are listed in the learning outcomes of individual courses, they are not necessarily assessed, and they are even less likely to be assessed at different levels in an overall degree. Part of the reason for this may lie in the challenge of assessing skills and attributes for which the development relies on incremental changes over time and the honing of skills through repetition, such as group work skills. This is quite different from assessing the achievement of self-contained bits of disciplinary knowledge, for example in the form of an exam. Similarly, when it comes to providing feedback, it is more challenging to provide feedback on a particular skill or attribute that can always be improved, than on a skill or a bit of knowledge that has demonstrably been achieved.

In this chapter we explore the use of learning analytics (LA) to track skills acquisition in tertiary education environments, with a specific focus on group work skills. In other words, what kind of data can we collect to provide meaningful feedback to individual students on the development of skills such as the ability to work in teams or communication skills. We discuss the importance of design for learning with regards to developing professional attributes and skills for collaborative group work projects, before considering the role of learning analytics in shaping educational experiences that relate to such skills and attributes. Finally, we outline a design process used to develop and implement an iterative learning analytics evaluation approach to the design for learning in collaborative team projects. In this context, the promise of learning analytics lies in its iterative potential, which means it can be used for both summative and formative assessment, but is especially useful in formative assessment for learning, rather than summative assessment of learning. The formative part is particularly important in the context of developing professional attributes and skills, as there is a formative and iterative element to the development of such skills. Using learning analytics data on a continuous basis, and visualising such data as part of the process, potentially allows us to track the development of professional attributes and skills as part of the learning process, meaning we can also adjust our learning design in a just-in-time manner.

The purpose of this chapter is to offer general concepts, principles, and methods for planning and implementing evaluation processes for collaborative team projects, with a specific focus on utilising the power of LA to enhance team and individual performance and learning outcomes at varying levels. We conclude by outlining the feasibility of the approaches we are proposing, as well as the resources needed, and the risks involved.