Learning analytics is clearly a term which is used to describe a particular phenomena which can happen on a wide range of scales.
Personally I am interested in how my individual socio-demographic circumstances can predict my success (or otherwise) in any aspect of learning and how interventions can be targeted to assist me. I am not entirely selfish - I am also interested in how this can be applied to other people!
Professionally part of my role is to tutor aspiring medics and dentists for the UCAT exam (formerly UKCAT) which is an aptitude test which must be taken as part of an application to most medical / dental schools. Upon registration the UCAT consortium ask each applicant to complete a series of socio-demographic questions - racial group, gender, level of education reached by parents (the vast majority of applicants are 17 years old) and the job title of their parents (presumably used, alongside education, as a proxy for social class). They already have the applicants post code and know if the applicant lives in a household with low enough income to qualify for a bursary. This information is not supplied to universities but is reportedly used to improve the UCAT for future cohorts. Pilot questions are trialed with a view to ensuring they are as equitable as possible. I imagine that groups of statisticians gather around data sets and assess why various questions performed better with people who had parents with higher educational qualifications, or who shared a similar ethnic group. (Incidentally - I teacher friend of mine got very frustrated one year when the disadvantaged children who attended her inner city primary school were faced with SATS questions about going to the theatre, and using a honey dipper - two activities which were entirely unknown to most of the children she taught. A huge failing in learning analytics or a cynical attempt to maintain existing privilege? Who knows!)
The articles we read in this activity looked at early learning analytics on a national scale. I also find this very interesting. The variation between educational styles and ethos between different countries is driven by many factors including culture, politics, and resources and comparisons can be meaningful and meaningless depending on how well the complexities of the issues are understood.
The first paper compares the USA to other countries - noting not only a lack of progress when compared to nations of similar levels of development and wealth - but also an up and coming threat from developing nations with very large populations.
The second paper introduces the idea of using analytics to shape future US educational policy at the national level with a coherent and unified plan. Affordability and value are key values but a need to match education to likely economic requirements is also significant.
The basic premise of these discussions assumes that there is a direct correlation between graduate level education and national prosperity. There is, at least in these papers in this time, little discussion about what is being studied and to what end - merely that high levels of education are necessary for continued improvement.
As is pointed out - both papers were written before 'learning analytics' was a phrase and though they clearly are referring to a similar process the absence of 'big data' on quite the scale as it is available today means that it's not exactly the same thing being discussed. However - the idea of analytics is clearly here with a vision to use data to improve polcy.