by Sara Briggs, opencolleges.edu.au
“The problem with learning data, historically, is that we’ve always gone for the low-hanging fruit,” says Elliott Masie for the American Society for Training and Development. “Learning professionals have collected inexpensive, easily acquired data from people while they are in our domain, usually the classroom or program. In a big learning data world, we will need to rethink our data sources.”
Since big learning data is just evolving, it is difficult to be prescriptive about such issues. Part of the innovation process is an active and open dialogue, along with collaboration on these risks. However, to add to this discussion, here are a few approaches that you might consider to better align big learning data with these concerns.
Learners have the right to know how learning data will be used, shared, stored, or leveraged. We should develop a clearly stated system so that there are no surprises.
Who gets to see the aggregated data of 1,000 learners? Who gets to see a single learner’s data? Levels of privacy, as well as designated access to them, should be carefully considered.
3. Value to the learner
Big learning data can provide great value back to the learner. What have other learners who have taken the same program found most difficult? What are the types of questions that learners most often get wrong? What remedial actions have been most successful for other learners who failed that question or program?
4. Depth of measurement
We have looked at whether learners passed an exam, but more valuable data might include the answer, as well as characteristics of how learners answer the question. For example, how long it took them to answer and whether their mouse hovered over a wrong answer for a while.
Some data that we will use in big learning data will be more expensive to get than what we have traditionally used. But what we easily collect tends to be superficial or inaccurate. Collecting data through interviews with managers of learners, says Masie, costs more but yields much more data.
8. Many factors influence learning
We need to have an anthropological view of the learning process to understand that there are many factors that may influence learning. We need to realize that learning may influence or may support or destroy the impact of learning, thus broadening our view of potentially relevant data.
9. Presenting data
We need to adopt a strategic approach to presenting data. How do we display data so that it brings meaning to people? If you are given this data, what do you do with it strategically and how do you handle it?
This refers to the extent to which individuals making decisions are ready to operate with a massively enhanced set of data.
Institutions will need to upgrade, alter, or change learning systems to prepare for big data use.
We need to understand where, how, and in what way it’s appropriate to share and use that data, simply because it can yield such powerful results.
Technology is revolutionizing the way learning and development practitioners do their work. Leveraging big data is the next logical step in this evolution. We now have access to volumes of data, but we must understand what it can tell us, what is does tell us, and as importantly what it can’t capture.
13 Challenges For Big Data In Education; excerpted from opencolleges.edu.au; image attribution flickr user anthonypbruce