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For all the talk of big data being the next big thing in learning technology, few people mention that in workplace learning there just aren’t any examples of big data to speak of. The data collected just isn’t at the same scale. However, big data has led to an explosion in data analysis tools and techniques that learning technologists can use in their work. Throughout 2014 I’ve been dipping into data science MOOCs, learning the basics of R programming, and thinking about how to apply this within learning and development. These are some of my initial thoughts and notes.
Can understanding big data techniques help us to improve learning outcomes and performance?
Big Data as a term started appearing following the success of online services such as Facebook, Google Search and Twitter which gather data on hundreds of millions of people. Data including their likes and dislikes, online behaviours, website usage patterns, shopping patterns; it all has value and can be sold to the highest bidder. Now that users can also register for other online services using their Facebook, Twitter or Google logins, they literally leave a trail of ‘digital exhaust’ behind them. This data is all collected and analysed on the assumption that it is valuable to someone, somewhere, or at least may be one day. The data gathered by just one service like Facebook amounts to over 500 terabytes per day! This is the scale that big data operates at, and the harvesting of personal data is BIG business. Jaron Lanier is not wrong in suggesting that next time you post a status update, they really should be paying YOU!
Edtech and learning technology entrepreneurs clearly want a slice of this action, hence the buzz. However, even the largest organisations only have relatively small amounts of learning related data. Even an organisation with half a million employees will only have learning related data measured in little old Gigabytes. That’s not big data at all.
However, if there is one big takeaway from the big data world then it is the renewed focus on data analysis and data driven insights. Take a look at any MOOC catalogue to see the popularity of data science courses.
Your first steps in building a data science skillset
We are fortunate to have a wealth of data science tools at our fingertips: statistical programming languages like R and data analysis tools like Hadoop, Pig and Tableau. Data analysis can ultimately be performed on any data set, large or small, so many of these tools and techniques can be applied in the context of learning and development. Many of the available tools are open source and free to download, with good tutorials and strong communities. Couple that with a data science MOOC and you’ll be off to a flying start.
Handily, data science already intersects with learning and development in disciplines such as Educational Data Mining and Learning Analytics. EDM started life back in about 2000 when web based training took off in academia, hence is more research driven, while Learning Analytics is a more recent discipline and driven more by learning practitioners and vendors. There is over a decade of research and books on EDM and Learning Analytics, yet this extensive body of knowledge has largely been ignored by the corporate L&D world until relatively recently. If a MOOC is where you dip your toe in to data science, then this body of knowledge is where you jump in, immerse yourself, and learn! And use the free tools to try out ideas, they are there to help you learn, and their communities will support you.
It’s worth remembering that data science is not something that a learning technologist can just pick up easily; you can’t just download a copy of Tableau and expect to gain amazing insights into learner performance. Learning technologists need to understand the basic principals of data science but many organisations may be better off employing an experienced data scientist to really do an analytics programme justice. Or if you have a business intelligence team, tap into them.
Good data science always starts with a question. Why are our learners failing particular courses? How can we identify learners who need support? Who is at risk of failing? Where is engagement dropping off? What are the characteristics of our most successful learners? Which learning paths lead to the best outcomes? How do successful learners impact organisational performance?
These are types of questions that learning analytics and data science will answer. Even if you aren’t the person to do the analysis, an understanding of the principals of data science should be on every learning technologist’s CV.