JISC

This study was carried out by Delyth Chambers Consulting Ltd, APS, and the Supporting Professionalism in Admissions Programme. The main aims of the study were to:

• Consider the Schwartz principles and identify those that might benefit from monitoring in a systematic manner supported by technology: provide high level technical models of how this could be done [NB excluding the topic of feedback to unsuccessful applicants which is the focus of Study 1].
• Gather information on ways in which those involved in all sides of the process perceive fairness in admissions to be demonstrated, and evaluate whether any of these perceptions could be informed by data reported electronically.
• Identify and model the data, mechanisms or metrics which can help individual HEIs monitor the fairness of their admissions processes and decisions.

The study found that although fairness in admissions is an aim of all higher education institutions (HEIs), it is a complex concept which does not lend itself easily to technological mechanisms. Users of the admissions process - applicants and their schools and advisors - generally consider that the process is fair and efficient, and have few suggestions to offer to improve it.

There is however a mismatch between what HEIs consider to be fair, and what students and schools consider to be fair, especially around the use of contextual data provided during the applications process.

HEIs monitor admissions activity in an attempt to measure fairness, efficiency and effectiveness and examples of good practice in these areas were identified. There are few tools available to help them do this, but the analysis and modelling work carried out by the study suggests four broad areas, where further work would assist HEIs in the design and delivery of future systems that would address fairness issues:

1. Provision of statistical tools for use in the review of performance data, particularly at the time of end of cycle reviews.
2. Measurement of the effectiveness of widening participation activities in relation to outcomes
3. Technology-supported decision-making.
4. Policy and process changes as a result of monitoring

For more information, see the final report, available from the foot of the page.