In my previous post I looked at how an institution might use their own xcri-cap feed to help advertise relevant courses to existing students. The benefits of using a standardised approach in that case is that the data structures in the specification has already been thought out and tested, so development time can be reduced through the re-use of common code and tools.
However the true benefit of an interoperability standard is in the ability to re-use the data itself.
The Skills Dashboard demonstrator uses the aggregated feeds from #coursedata projects by interrogating the Jisc #coursedata aggregator API. The aggregated feeds provide a snapshot of the state of play across regions and sectors, and can be analysed to determine the level of course availability, in an area, a level or a region.
Whereas the Plymouth Moodle block allowed the student to see possible progression routes, the skills dashboard allows institutional course planners to look at the market saturation for particular courses, and compare that data with their own web-site analytics to determine peaks in demand. On a simple level this allows a web team to undertake informed Search engine optimisation around search terms, but at a deeper level it can provide a powerful tool to review an institutional offering and identify gaps and opportunities.
With a Dashboard using aggregated data, the more data the better the information upon which decisions can be made will become.
In this case the dashboard uses the aggregated data from our testing tool – the #coursedata aggregator. The aggregator allows users to submit and manage their feeds http://coursedata.k-int.com/FeedManager/ which it pops into a SOLR index. This can be queried using Elastic Search syntax, either through a simple discovery interface, http://coursedata.k-int.com/discover/ or through the API as in this case.
So a query like
http://coursedata.k-int.com:9200/courses/course/_search
will return ALL the data in the Aggregator- the headline at the minute reads
{"took":17,"timed_out":false,"_shards":{"total":5,"successful":5,"failed":0},"hits":{"total":8642,"max_score":1.0,"hits": ...
so 8642 courses at the minute.
By building on this additional parameters can be added like:
http://coursedata.k-int.com:9200/courses/course/_search?q=fine%20art
which returns
{"took":214,"timed_out":false,"_shards":{"total":5,"successful":5,"failed":0},"hits":{"total":400,"max_score":1.2260234,"hits": ...
So 400 fine art courses listed at the minute.
By refining the search further, and linking it to other data sets a rich picture can be built.
More details on the Aggregator can be found in the GitHub wiki … why not see what you can build?
https://github.com/k-int/XCRI-Aggregator/wiki
