This is the second installment in our four-part series exploring the synergy between Content Modernization, Intelligence, Mobility and Performance Support as key factors driving the development and innovation of eLearning today. In this article, we will cover Intelligence and instructional data.
In addition to introducing content modernization, which engages new resources and delivery methods designed to bolster learning in the workplace, employers must also link courses to backend systems that facilitate the collection and analysis of instructional data.
But before data can be effectively leveraged, employers must first improve and build instructional data literacy. In fact, according to an article, ‘ … with all the talk about big data, one thing is very clear: the vast majority of us have very little insight into how to actually find insight in it.’
Because there is literally more data than anyone could possibly make sense of. How, much, exactly?
According to the same article, ‘We are dealing with massive … scales of data. Umbel’s Digital Genome alone collects, analyzes and visualizes 18,446,744,073,709,600,000 data points per person in less than one second.’
That’s 18 quintillion. Go ahead – look that up; we had to.
So it stands to reason that while data is critical and infinitely useful, it’s all for naught if no one can actually process, analyze or apply any of the intelligence gleaned from it.
Once instructional data literacy is addressed, the benefits of such intelligence are nearly infinite. For eLearning, data-driven instruction boasts solutions to some of its greatest inherent challenges – here’s how.
First, training and development staff can use these insights to evaluate program efficacy. Secondly, instructional data serves as the basis for personalization, giving organizations the power to pinpoint learning tendencies for every employee and use these findings to develop customized training tracks and ultimately increase productivity.
In fact, according to a survey conducted by Accenture and GE, 73 percent of companies are already investing more than 20 percent of their overall technology budget on Big Data analytics – and more than two in 10 are investing more than 30 percent.
Such collections and analyses of instructional data are forecasted to make a major contribution to employee training by dramatically improving the experience for both employees and employers. Further, the same survey suggests that 84 percent of businesses expect that data analytics will change the landscape of their industry in the coming year, especially as the development of more effective and innovative corporate learning programs emerge.
Integrating raw performance data into training programs is another effective approach, as organizations can collect insights from third-party production platforms to see if corrective instruction might be necessary.
Unfortunately, this kind of data-backed method is rare, even today. Most businesses use qualitative feedback from employees and managers to evaluate their training efforts. However, those looking to develop a competitive edge through sustainable, effective employee training programs should exchange these rudimentary methods and implement data collection systems that create actionable feedback – the kind required to drive nimble performance-enhancement and support efforts.
In addition to leveraging instructional data to solve operational problems and offer remedial training, firms can mine analytics to pinpoint staffing trends and skills gaps.
This analysis further optimizes the operation, strengthening both efficiency and performance simultaneously. Surprisingly, many modern workers are comfortable with this macro analytical approach, especially young millennials and Generation Z who perceive learning as the key to career development and therefore embrace objective, data-backed performance evaluation.
In our next installment, we’ll address the third of the four key tenets, mobility, and its role in further advancing workplace training programs.