First week of LAK12: trends and context of analytics in learning

After reading some of the great first blog posts from participants in the LAK12 course, I’ve decided that I’ll try to write one post a week in English. Although I do feel compelled to blog in English —I’ve never done it before— I just can’t help to feel strange by doing it.

I’m happy with the distributed course format and enjoyed the two live sessions. As so many people have blogs, I’ve just subscribed to their feed in my RSS reader, which is the most comfortable place for me to read posts.

The articles about educational datamining and academic analytics, although full of technical terminology I’m not familiar with, introduced me fairly well to the application of analytics to learning. However, I had to wait until Michael Chuis’ presentation of the McKinsey report to first hear about the use of analytics by the individual for learning to learn, which is my main interest. I’ve learned that learning to learn is also called metacognition, by the way.

Some of the stakeholders in learning analytics I’ve got to know this week are universities, IBM, McKinsey, the foundation of the Gates’, the White House and several companies specialized in analytics in controlled learning environments like LMSs. Michael Chui said that for every unit of benefit of data analytics for big organizational players, there’ll be 5 units of benefits of it for the end customer. I’ll be happy if this turns out to be so, specially in learning. However, for this to come true, an empowering talent development must also happen for the end user, in this case the learner who, as in so many other aspects of life, either programs or will be programmed.

If the need to focus on the learning analytics skills of the learner is one conclusion to which I got this week, the other is the need for transparency. As it seems that it is not so much the ownership of data that matters than who has capacity of putting the data to work, transparency is a key issue in every platform that wants users to give away data about themselves. Panagoitis Ipeirotis’ experiments in Amazon and Zappos showed that people welcome transparency. The approach that both parts, the provider and the user, learn and gain from the interaction and together create new knowledge and understanding, seems to be the right one. Last.fm, for example, understands this.

If the new era of data, of which we are only in the early adopter phase, embraced the principles —skilled end users and transparency— of the libre software and knowledge movement, great things might happen. It might bring a new era of metacognition where data might make it easier for individuals to assume that the main responsibility for our learning lies by ourselves. ¿Or am I just dreaming?