Big learning data will be a powerful and potentially disruptive tool for learning professionals.
The businesses that we support are already leveraging big data for business intelligence and are going to draw the connection between learning and customer satisfaction. Big learning data also has benefits for the learner, designer, manager or the organization that enable each to do things better, faster, cheaper, more strategically and more persuasively.
When we use the term “big learning data,” it details three things:
• Volume: Big learning data enables an organization to access and analyze a volume of data, including multiple data points, for a richer perspective.
• Velocity: Big learning data enables learners and organizations to have rapid, real-time data access. Velocity instantly provides a learner with remedial and enrichment options based on historical learning patterns and successful strategies from thousands of others.
• Variety: Big learning data connects the dots on a variety of information from talent, performance, demographics and business metrics.
The problem with learning data is historically we’ve always gone for the low-hanging fruit. Learning professionals have collected inexpensive, easily acquired data from within our domain: usually the classroom or program. We need to rethink those sources for:
• Depth of measurement: More valuable data might include not just exam answers, but how long it took to answer questions and whether their mouse hovered over a wrong answer for a while.
• Expense: We have relied on inexpensive data. Some big learning data will be more expensive because what we collect easily tends to be superficial. Collecting data through interviews with learners’ managers, for example, costs more but yields more data.
• Types of data: We have looked at how learners answer a question. But more valuable would be their confidence in answering the question.
We must honestly consider the risks that big learning data raises, including:
• Organizational change: An interactive process of planning, feedback and disclosures.
• Privacy, security and transparency issues: Strategic, legal and codes of conduct elements.
• New skill, competency and leadership dimensions: Learning interventions to build, assess and develop skills.
• Externally referenced phenomena: Greater context for how big data issues evolve externally, including governmental, consumer and judicial elements.
• Values sensitive: Alignment to an individual’s beliefs about privacy, openness and individuality.
• Globally sensitive: An approach based on culture and governmental regulations and expectations.
It is difficult to be prescriptive. Part of the big data innovation process is active and open dialogue and collaboration on these risks. Here are a few approaches to better align big learning data with these concerns:
• Transparency: Learners have the right to know how data will be used. Develop a policy.
• Privacy: Organizations may want to define areas where privacy levels are different.
• Value: Big learning data can provide great value to the learner. Individuals may want to know what others who have taken the same program found most difficult.
• Silly data be gone: Managers will be tempted to provide high-definition big data analysis. This shouts “silly data.” Data must have context, trust and reliability to be effective.
If big learning data create an environment of rich information, the learner will be better informed, and it would allow for greater personalization. It also could be informative from a feedback and context arena, because somebody might fail at a topic but not know why. It becomes interesting when the learner can look at other people who have had that experience.
We are in the early stages of considering the possibilities for big learning data in the workplace. While exciting, this means that we have a lot of work to do as learning professionals. Going forward, let’s approach big learning data as a new world with great potential but also real risks and new challenges.
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