Final post

Description of the project deliverables and links to each item.
This section contains a summary of everything we have produced during the project that may be of interest to others.

Overview of STAR-Trak:NG: this is a short pdf’d PowerPoint highlighting our hypothesis, potential activity streams and potential user groups for the application. It can be accessed by clicking here: STAR-Trak NG Overview (opens in new window)

Good Practice Guide: this was originally planned to cover how STAR-Trak:NG can be embedded within the home institution, including: lessons learned, portfolio of stakeholder use cases relating to student support business processes; online presentation. Due to the greater emphasis during the project on software development this deliverable is currently limited to a) the section later in this post on how to manage change, and b) a draft proposal for operationalisation within Leeds Metropolitan which can be accessed by clicking here: STAR-Trak NG Draft Operationalisation Proposal (opens in new window). This deliverable will be taken forwards with the internal project.

STAR-Trak:NG application: since commencement of the project further discussions have taken place with regards to the most appropriate way to ensure the sustainability of the STAR-Trak application. Pending final decisions on this the codebase and ETL routines have not yet been released. However a diagram of the STAR-Trak high-level architecture can be accessed by clicking here: STAR-Trak NG Architecture (opens in new window)

Installation and configuration manual: This will be uploaded once the software development has been completed and installation and configuration fully tested.

Student activity dataset: the project plan envisaged that we would be able to create an activity dataset, based on the premise that the project would be in pilot during the life of the project. However due to the greater emphasis during the project on software development the pilot has not commenced and hence it is not possible to produce a dataset. However we have produced a data warehouse schema and this can be accessed by clicking here: STAR-Trak NG Data Warehouse Schema (opens in new window)

Workplan: The project workplan can be accessed here:STAR-Trak NG Workplan (opens in new window)”.

Risk Register: A risk register based on the CRAMM methodology can be accessed here: STAR-Trak NG Risk Register (opens in new window).

Next Steps: STAR-Trak has generated tremendous interest from academics and corporate planners within Leeds Metropolitan, and from JISC and HEFCE. Within the constraints of budgets every effort has been made to make it usable, through configuration parameters and by abstracting the application layer from the sorurce data systems, by other institutions. We recommend the following as next steps:
A. Investigate the potential benefits and viability of a developing a national activity data schema and datasets
B. Support the development of a sector-wide community of institutions who have this type of application: to share ideas and best practice on technology and use
C. Support analysis into the potential for sector-wide use of STAR-Trak, and evaluate options for sustainability of the solution
D. Fund the next stage of development of STAR-Trak to make it more simple for other sector organisations to implement. This would include i) technical development of the interfaces, development of the administration functionality to improve the user interface, and

What other institutions can do to benefit from our work: The approach below is based on Kotter’s 8-step change model (
1. Review own retention strategy (whether explicit or implicit) and create a sense of urgency around the need to provide solutions that will assist in its success
2. Get the great and the good on board – particularly those who are leaders in the business and in IT
3. Develop a vision of an enhanced relationship between staff and students based on STAR-Trak or similar; and create a high-level plan that encompasses cultural as well as systems and process change
4. Spend time talking about the vision and refining it based on the feedback that you receive. We found informal workshops to be good for this stage, in addition to water-cooler conversations
5. Identify the organisational barriers to change (technological barriers are trivial in comparison) and remove or neutralise them
6. Implement the solution in phases: simply letting students see the student record data we hold about them may be a substantial improvement and count as a short-term win, as may using the data warehouse and improving understanding of BI
7. Pilot long and deep, and learn from the feedback. Our current view is that at least a full year is required to really learn about what works and what doesn’t
8. Make use of the underlying data warehouse to produce business intelligence for senior management; periodically re-vitalise the service by engaging with internal users and the wider community

Most significant lessons: This project has provided a wonderful opportunity to engage with colleagues across Leeds Metropolitan and produce a truly innovative solution for our University, that we hope to implement.
A. Ensure you are student-facing. The initial driver for STAR-Trak was corporate: retaining students. From an individual student’s perspective that looks a little different: a student doesn’t want to be judged in terms of the risk of them dropping out, may be alarmed by a red “traffic light” against their name, and may be sensitive about who can see what data about them.
B. Understand what data is critical to understanding retention. For us it is a subset of the student record data (such as demographics, entry qualifications, whether they came through clearing) and attendance data. We suspect that this data will give us around 90% of the information that we need. The other activity data is almost the icing on the cake – but clearly we need to evaluate this over time.
C. Develop a canonical data model for the domains you are interested in. It surprised us that even within the same department colleagues had different interpretations of data ! We have not formally developed such a model, but have laid the foundations for its development (which is the subject of a different programme) through workshops.

Addressing the big issues: STAR-Trak has faced differnt issues from those faced by most other projects in the programme. For us the issues have been:
A. Use of data: we have already discussed this in a previous post (opens in new window)
B. Algorithms: STAR-Trak contains a number of configurable parameters that feed into a system-generated “score” of a student’s risk of dropping out – or level of engagement depending on whose perspective you are taking. The detail of this will be provided in the installation and configuration manual as mentioned earlier in this post.


About robmoores

Practice & Innovation Manager at a leading UK retailer, with an interest in service orientation, whether applied to IT or business.
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