need to increase student fees to compensate for the loss of state funding. This may well result in different student demographics (for example, a decline in leisure learners, but an increase in full-time students who find the OU a cheaper option than campus students), although it is too early in the process to assess these impacts.
MOOCs therefore enter the market at a time of great uncertainty, when panarchic effects are high for the OU (and all UK universities). This may account for the more cautious response from UK universities (Fazackerley 2012) compared with that in North America.
This analysis can be summarised in a subjective scoring, allocating a score of 1 (weak resilience) to 10 (strong resilience) for each of the four factors. A score of 20 or lower would indicate an overall susceptibility to this particular digital factor, but it will also highlight individual areas of weakness. For the Open University, such a scoring is set out in Table 1.
| Resilience factor | Score | Comments |
| Latitude | 8 | Based on ability and history of adapting to technological change |
| Resistance | 8 | Large institution with established systems and high reputation risk, solution plays to strengths |
| Precariousness | 7 | Not immediate, but comes in time of change and has direct relevance to OU model |
| Panarchy | 6 | UK subject to considerable upheaval in higher education sector |
| Total | 29 | An area of concern, but resources and practices allow adaptation. Dealing with large-scale systems and the impact of UK sector changes are priorities for reinforcing resilience |
Table 1: Resilience factors for MOOCs for the UK Open University.
