There are a few ways in which we might understand this first theme of ‘learning’ with data. First and foremost, we can understand this block as concerned with established ideas about student ‘learning’, and how they are increasingly shaped by data-driven technologies and practices. Key questions here might be: how is student learning enhanced by data, or how are students disadvantaged by them? Further, we might also understand this theme in terms of the ‘learning’ needed – by students, teachers, and others – to understand how data-driven technologies function, or how to interpret the results. We might think of terms like ‘data literacy’ here, and ask important questions about how we can foster understanding of the often-complex techniques and implications associated with data collection and processing. In addition, we might also understand this theme of ‘learning’ in terms of the computer systems which are deployed to make sense of, and train themselves using, data. The techniques of ‘machine learning’, which underpin most of what we generally refer to as ‘artificial intelligence’ in current times, make explicit use of the term to describe how data is utilised, and such ‘learning’ is being increasingly employed in educational settings.
Thinking through these notions of ‘learning’ (as well as others we might surface through discussion), this block will encourage you to develop a critical understanding of data as it relates to the figure of the ‘learner’. You will approach this by reading a set of core and secondary literature that will elaborate on key aspects of this theme, and develop your understanding through: group discussion in our ‘Tweetorial’ (in week 3); undertaking the ‘track some personal data‘ task (in week 4); and summarising this section of the course in your end of-block reflections (in week 5). You will also explore, and in some ways practically apply, these themes of learning through the weekly data visualisation task.