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I just wrapped up week 1 of the Learning Analytics and Knowledge MOOC. The focus of week 1 were the key questions “Why Learning Analytics?” and “Why Now?” My experience with this MOOC illustrates the key drivers at a micro level. A MOOC, or Massive Online Open Course, is designed to have no true center for all the activity. We use twitter, a wiki, distributed contributions across participant blogs (using many different blogging platforms in different languages), and a diigo group. Even the core resources posted to the course wiki are many and varied in format: Week 1 activities included many readings in PDF and web page formats, a few hours of video, and two live webcasts. It’s a microcosm of the web; a small sample of how the web works (and this isn’t even counting physical assets that provide data exhaust). How does a MOOC participant manage to transform all this data to gain insights for his/her specific context? Some structure was provided in a wiki, and daily email update alerts with summaries are sent to participants. But even with this support, it was noted on the first webcast that the way to approach the dailies was to skim and pursue items of interest. Thus, it’s essentially manual labor (wish I had some data mining and analytics skills to help me explore and discover the items most relevant to my focus- corporate learning application as opposed to academic). The challenge was truly illustrated because of business priorities and some family illness; I didn’t get to the content until the weekend.  The data was piled pretty high- some great contributions from my peers in the course (shameless plug for participants to read this thoughtful piece from Wolfgang Greller’s Weblog). I was already behind and had no system to analyze and prioritize. It was difficult to anything more than baseline consumption of required data, and a few additional morsels. Of course, with the amount of effort to just consume the data in that timeframe, time for reflection was inadequate. Even on this relatively small scale, the information was quite a bit to manage. Despite daily emails with updates to provide some structure and only being a week in, the information comes fast, grows quickly, and is loosely structured. At it’s most simple level, this is why analytics is needed now: information is growing at a pace we cannot effectively make meaning from-let alone decisions-without analytics methods and tools.[blockquote type=”blockquote_line” align=”left”]At it’s most simple level, this is why analytics is needed now: information is growing at a pace we cannot effectively make meaning from-let alone decisions-without analytics methods and tools.[/blockquote] The course itself had amazing examples of the demands on institutions and the strategic benefits being realized by organizations applying analytics effectively; it is clear that if data is harnessed and analyzed effectively, it generally supports better decision making. And in the current climate of accountability for results, we have to use the arrows in our quiver to deliver. The one thing that I think is very different in a corporate setting versus an academic setting is this: Despite other metrics like completion and retention, it is clear in an academic setting that learning is at the core of the institution and the ecosystem is focused on supporting learning. Corporate settings often don’t have this focus. Learning is on part of a larger business ecosystem. Learning is often treated as a very separate entity from the other parts of the business. Using learning analytics to forward business goals as well as using external data from the business as part of the learning analytics process may present more cultural challenges for the corporate context.