Personal Blogs

from the innovations listed and discussed in the 2016 report on 'Innovating Pedagogy' by OU, three may definitely be associated with analytics. Here is my list and reasons:

1. Epistemic cognition: this innovation may not seem to be one that would benefit from data but in fact, it is very much dependent on data (or, evidence). an analogy here can be the concept of evidence based medicine which had been gaining more and more traction in practices and training of medical personnel. common tools (although still imperfect) are the systematic reviews of literature and meta analyses which aim to compile a very wide range of data from very wide ranges of sources, languages, and evidence levels and present compact conclusions based on these data sets. thereby, the learners (readers) are granted with the ability to get acquainted with the entire range of available data, its quality and when possible, conclusions or recommendations based on it. this methodology needs to be applied to all categories of knowledge, especially to social sciences in which robust data sets are scarce, with the potential exception of sociology, working on large surveys (even in sociology though, random sampling is the enemy of big data analysis). so, in essence, although i don't propose that we can measure epistemic cognition through analytics, i propose that we need analytics for epistemic cognition.
2. Immersive learning (IM): as defined in the article, IM pretty much refers to applications of virtual reality (VR) into, especially, vocational training. vocational training may probably be the hardest field in education in regards to learning analytics. expanding the dental school example in the article, we may use data based on the exit examinations of dental schools (comparing these students in IM programs with others) but as for training 'better' dentists, we shall eventually need to define parameters in these professionals practices (such as patient satisfaction rates, complication rates, infection rates etc.) (which is already implemented by the NHS in UK and several insurance companies in US) and use analytics to see whether these parameters get better with IM. on the other facet of IM, big data on real life procedures is essential for developing reliable VR models. this is where analytics (not necessarily learning analytics) is definitely needed.
3. Citizen science: can be a very fertile source of data. the collaborative efforts that may be defined as citizen science are, by definition, on the virtual space and each and every one are potentially traceable. the discussion here might be on the methodology to be used to promote participation and to measure its impact. i can visualize an Amazon (or Netflix) type of promotion in which social network users demonstrating any slightest interest in sciences (STEM mostly, but could be any) are softly introduced and encouraged to participate in citizen science by targeted ads and suggestions. metrics in K12 may be based on standardized examinations (such as PISA), but may be virtually impossible in adults. i couldn't even think of a way to develop metrics for adults involved in citizen science.