You can download the final report (PDF) for the ON Course poject. Here are the conclusions and recommendations.

• Implementing a programme/course management system is a large, complex project.  Before embarking on it, an institution must ensure it has the time, people and funding available to make it a success.  Stakeholders at senior management team level must actively support and drive the project.
• The development of curricula within HEIs is a primary responsibility of academic staff. The implementation of a course management system should therefore include academics as key stakeholders.
• The time needed to populate a new, structured system from existing unstructured information should not be underestimated.  It is likely that it will have to be done manually, and will take considerable time and effort.
• Extensive and properly structured data about programmes can lead to more innovative uses of the information elsewhere.  Without a common, consistent structure and means of access, re-use (internal, public and third-party) is almost impossible.
• The provision of an XCRI-CAP feed was, correctly, a minimum requirement for the JISC programme of funding. However, institutions should recognise the value of their full programme data on a much broader level, understanding that it offers insights into one of its core activities (teaching and learning) and can inform other change management programmes across the campus.[1]
• Application development around the use of course data has so far tended to be marketing-focused. However through the use of visualisations and interactive tools, it could be put to much wider use. The HE community should consider how the use of course data might fundamentally change the design of curricula and provide the basis for discussion between staff and students about the re-production of academic life. Improving the level of oversight and insight into the core activity of teaching and learning can reveal and help question existing constraints in the curriculum design and QA processes, for example.[2]
• Course data, alongside other datasets (e.g. space utilisation, research activity, energy use, facilities, etc.) provides unprecedented insight into the nature of our organisations. Institutional managers should approach their organisations as objects of research and development. The knowledge and skills exist within HEIs, among students and staff, to better understand, critique and develop the form of the organisation.
• Data visualisation techniques provide a new and novel oversight of large and rich course datasets. However, due to the complexity of this data, the use of static visualisations quickly reaches their limit. Software applications are needed that help staff and students interact with the data and model new pathways to learning, reveal greater opportunities for collaborative and cross-disciplinary teaching, and critically evaluate the history and future of the institution.

As I haven’t posted on the blog for some time, I thought it would be useful to provide an update on progress over the past month or so, plus a preview of what I’m hoping to work on over the next few months.

Over the past few weeks I’ve written and submitted a paper to The International Conference on Information Visualization Theory and Applications. The contents of the paper follows the flow of most of my recent blog posts. Firstly, it covers the use of exploratory data visualizations in order to help make sense of large datasets. It then goes on to look at refining the data visualization process and considering the granularity of the data being presented. The final sections of the paper discuss the use of visual analytics – combining data visualization and statistical analysis, within the decision making process.

The next step for me is to look into designing and creating an application that will allow (for example) curriculum designers to view the complexity of the data available to them, as well as showing the potential impact of making alterations to seemingly small and insignificant areas of the curriculum.

As I’ve shown in earlier posts, there’s a lot of data to visualize – if you print some of the network graphs on A0 paper you can only just about make out which node represents which module, for example. As a result of this, I’d imagine that one of the main focuses of the next few weeks is finding visualization techniques and tools that will allow a lot of data to be shown to the user, but in such a way that it’s still a usable application.

As usual, I’ll be attempting to post regularly to document what I’ve been doing.