This is about figuring out how to map a process of implementing an anlytics programme within an institution.
Called the ROMA model (Rapid Outcome Mapping Approach) with respect to Learning analytics, there are seven steps identified:
- Define a clear set of overarching policy objectives
- Map the context
- Identify the key stakeholders
- Identify learning analytics purposes
- Develop a strategy
- Analyze capacity; develop human resources
- Develop a monitoring and learning system (evaluation)
In the previous post Numbers are not enough to change, I argued that learning analytics is a ‘hard sell’ mainly because I have not yet found any convincing evidence that it can be used in a way to support and enhance learning beyond what a conscientious tutor might already do with a simple tick box spreadsheet, if the learning design is relatively serial programmed (step A proceeds before step B etc). So with respect to points 1-3, it’s possible to answer these questions on faith that somehow learning analytics will provide overall good; but on points 4-7 I am not sure how that can be legitimately applied given that there’s no strong empirical evidence to suggest that it works.
In the post Beyond Prototypes, the issue remains the same that strong empirical evidence for the benefits for student learning as a result of learning analytics, seems scant, or absent. However, the ROMA model (as in any iterative plan-implement-lessons learned cycle), benefits from learning from any starting point and hence could be the very thing that allows the implementation to address the concerns felt by the identified communities within the institution (student, educators, technical and the overall ecology of practices).
Reference:
Ferguson, R. et al. (2014) Setting Learning Analytics in Context: Overcoming the Barriers to Large-Scale Adoption. Journal of Learning Analytics, 1, 120-144.