Learning and qualitative data analysis with information technology: the role of exploration

Over the past 20 years there have been rapid developments in IT to create software that supports both learning and qualitative research. This thesis examines the design and use of that software, and argues that the exploratory approach in both learning and analysis produces superior outcomes. As such, the exploratory approach is seen as one that is particularly well supported by the software. A range of learning software and objects is discussed: Correlation Explorer, coMentor, learning websites, reusable learning objects, open educational resources, and videos. These are successive attempts by myself, and others, to develop software and other objects that support high quality learning. They do this in a variety of ways: by creating learning tools
that promote exploration, by encouraging online collaboration and sharing, and by providing materials that can be used in a range of learning contexts. Some of the problems of their use are discussed, such as mistaken conceptions, and finding and adapting learning objects. In a parallel fashion, this thesis argues that the development of software to assist qualitative data analysis has supported a range of analytic approaches. By their very nature these tend to be exploratory – the thesis argues that the core of qualitative analysis involves exploration of the data. The new analytic tools the software offers afford especially good support to exploratory analyses. These tools include text searching, code hierarchies, code queries, and the use of charts and diagrams.