Big Learning Data Working Retreat

Yesterday, I had the privilege of attending the Masie Learning Consortium Big Learning Data Working Retreat in Chicago. The retreat included learning leaders and individuals interested in learning analytical as we investigated the implications of Big Data (>50K data points) focused on learning. Rather than a lecture, the working session focused on participation and contribution from the members present as we discussed the who and what of big learning data. 

During the session, several learning data topics stood out to me. While not wholly formed truths about big learning data, these nuggets help to paint a picture of where learning data is headed as big data emerges in the domain:

Who are data scientists?

The conversation around data scientists was very reminiscent of those from a few years ago regarding eLearning developers: in the absence of formal academic programs and degrees to produce data scientists, the emerge from a variety of disciplines that have similar competencies. Much in the way that eLearning developers emerge from music, teaching, and creative arts programs, data scientists are emerging from IT, engineering, and business analytics areas. 

Because each of these disciplines offers something unique to the learning data perspective, it is the pull toward the convergence that is the most valuable. As the individual picks up skills from the various disciplines or expands her personal network to include individuals from these disciplines, they become a more well-rounded data scientist. 

As I try to think of examples that buck the trend, I find it to hold up fairly well. Individuals with a statistical, data-driven mindset who have worked to apply their craft to business typically come though manufacturing or business experiences that allow them to hone their analytical craft in real world situations (thank you six sigma). 

Data and Human Capital

While we looked at the bog learning data implications, we acknowledged that learning data doesn't exist in isolation. In fact, when combined with human capital and business metrics, is value is exponentially increased. With learning's tight integration with human capital, it can influence and/or provide feedback on the recruiting, onboarding, and promotion/expansion portions of the human capital life cycle. 

I have seen learning influence human capital processes and human capital influence learning. As learning teams are able to clearly define the characteristics of new hires that make them successful, they can provide that information to hiring managers and recruiters to help identify potential pluses and pitfalls with candidates before an offer is made. The flip side of that equation is that recruiters spend a great deal of time with candidates, oftentimes helping them weigh real life decisions as they decide to leave their current role and take the risky move to a new environment.  As recruiters work closely with candidates, they have the opportunity to 'get in their head' and see how they make these decisions. This insight can be shared with hiring managers to identify a candidates strengths and weaknesses to help develop that individual if they make the leap to work for e company. 

Who are the stakeholders?

The short answer: nearly everyone. While we traditionally think of busk ess units and learning teams, the true beneficiaries if big learning data are everyone from the CEO (understanding of succession pool and organizational competencies) to the learner herself (how do I compare? What's my trajectory?). 

In an age of tremendous data creation, the learner herself is e most interesting to me. Just as Wolfrum Alpha can show metrics about your Facebook usage, learning data should he.p you to better understand how and what you are learning. By understanding that you learn better in e morning, you can tailor your learning registration to best meet your personal needs. Additionally, by understanding others who have similarities to yourself (demographics, experiences, interests) you can see where they have headed to identify if it is the oath best suited for you. If not, what can or should you modify to craft your own path?

The biggest fights will be about what to share, not what is collected

While we are poised to discuss the benefits of certain metrics that are tracked that may seem invasive (the company reads all my IMs?!?!) the biggest battles may come over what data is shared, not what is collected. The example discussed was someone who is not achieving or learning at a level that would indicate they would ever be eligible for a promotion - should they have that analysis shared with them? 

Personally, I'm torn on that example. I believe that by sharing not the fact that they aren't eligible for a promotion, but the indicators, it gives the individual the ability to adjust in order to achieve that next highest level. Of course, the caveat is that there is an understanding that meeting the criteria would put a candidate into a pool of possibilities, not guarantee a promotion. 

What's all this mean?

At this point, these are just some of the many points of interest I found from the conversation. As the event was designed with robust dialogue in mind and table breakouts were utilized, even as an attendee, I don't have a full view into everything that occurred that day. The Masie Consortium is working to pull together the information, findings, and exploratory paths from the event in order to share in the near future. 

For me, I'm excited about the possibilities of big learning data. While there will be many pain points as we sift through the mountains of data to find what is truly valuable, the experience overall should be one that helps us leverage more data in our designs and decisions. This data will help us better target for business results that are relevant and meaningful. It will also provide the learner with her own data, allowing her to take control of her learning path and outcomes in new and exciting ways.