[ LAK11 ] Learning analytics introduction week

This post is my 'state of the course' after participating in week 1 of the LAK11 MOOC course on Learning Analytics. Topic was 'Introduction to Learning and Knowledge analytics'.

While my focus is primarily the corporate sector, most content, discussion and participants this week have been focusing on education. Some corporate topics have popped up in the forum, and I'm curious to see how the corporate 'sub-group' of this course shapes over the next weeks. I do find the MOOC principles and course format close to how I naturally learn. I always suck up information from all sides and slowly get to sense-making and simplifying to its core essence later on, so I'm not too concerned with the massive flow of information and interaction going on day by day.

What have I done this week?

• As this is my first MOOC, and I'm as eager to get the fundamentals of learning analytics as to experience this learning format, I got familiar with the MOOC concept and the tools used in the course. They are nicely introduced in the syllabus. For my own note-taking I'm using Evernote day to day, and reflect and sort out my notes in an almost proper text on this blog.
• Introduction in the Moodle forum. Writing your own introduction is not only good to connect up with other people, it makes you write out your own objectives. There is so much going on in a MOOC that without some focus you're easily overwhelmed. I will focus on corporate sector, and the things we can do with learning analytics, and not so much the tools for example. Then I browsed the intro forum for people I know and said hi. You can't be alone in a MOOC now can you?
• There were 3 papers for suggested literature, dealing mostly with key terms and methods of learning analytics in the educational space. It reaffirmed that I'm intellectually unable of reading academic papers. The 'who and who said what and what referencing who said what two years earlier' distracts me so much. But I tried and got some stuff out of it.
• The more complex and advanced the learning analytics (like pattern detection versus operational reports) the higher the satisfaction with the analytics system.
• The notion of 'action analytics'.
• Seems that a lot of focus went to getting operational reports and dashboards. In corporate LMS systems, this has always from the beginning been an instrumental part of the toolset and mandatory feature. But in open source LMS systems and educational ones, it might have been an second category priority that is now getting more attention.
• Also watched the video interviews on the site. That's a format that fits better with me.Here is some stuff I noted down:
• Erik Duval interview: he sees analytics as one way to help solve the abundance of information (all that learning material we created and left around in the LMS catalog or elsewhere on the intranet). Analytics can also help to automatically annotate or create meta data.
   Phil Long talks about 'the next ERP'.
• Also watched the replays of the sessions this week, and some articles that came via the daily mail, and some forum entries. As a natural side-story to the terminology and the state of the art of a few years back, people like Inge came up with privacy warnings, concerns about the quality of the data (garbage in is garbage out?), ethical questions because analytics do allow for evil, and the consequence of reducing the real world to an algorithm.

What sense did I make of it all?
The goal of the course is to come to some (personal) sense making of it all. So here is what I got so far. Disclaimer: it's an ongoing thing, I might have changed my mind by the time you read this.

• Corporate angle: the finality of education and corporate sectors is different, and that has its consequences for the analytics. The finality of education (from what I've read this week) is 'the grade', and thus learning and academic analytics will be about dashboards on how students are doing on their journey towards 'the grade' and how they match with the rest of the group, and signaling and remediation of students that might not get to 'the grade'.. It will be about the Key Performance Indicators the government cares about: dropouts and people failing. The finality of the corporate sector is not an educational grade. It is not even an accreditation. (That is an semi/false finality that got into corporations when they adopted the university model in their corporate 'universities'. Only within the operational aspects of corporate 'universities' can you apply much of the same that education analytics would.) The finality of corporations is valuable performance of their employees. Therefore, in my humble opinion, you cannot properly introduce learning analytics in a corporation without also getting your act together on performance analytics. They go hand in hand. (Who said that learning IS the work again?)
• Actionable :learning analytics in corporations should always be action-focused. We should always ask: what can we do with this intelligence? We should implement it for a reason. If we plan no follow up, then let's not worry about having the systems in place to get the intelligence.
• Another good usage of analytics I think is when it avoids bothering people to ask via endless surveys. If the data is there and you can (also legally and ethically) use it, it gives a better view on 'the truth' than asking people hastily at the end of a project or session.
• Don't do evil. I don't have a fully developed personal opinion on the privacy and ethical questions here. But as I've already written in my book, in my view it is the competent knowledge worker who owns the data on his own competence (including learning history and statistics), not the corporation. I do not buy into the idea that the one who captures and stores the data is the owner of it, although that is probably what the Terms of Conditions on facebook and google suggest... In fact, I'd like there to be standards on how learning analytics are stored so that they can be exported and transferred from one system to another, and go from corporate system to corporate system (or are stored in the cloud), as their owner (the individual) decides to...
• At some point I will have to brush up my Statistics 101 knowledge. I don't want to end up one of those people who can't read data. There's so much danger in that.
• And I would like to work around some 'use cases' on what we might leverage learning analytics for in corporations. I will make a few forum entries for that later on.