Changing Instructional Tides: Four Critical Concepts Bolstering Effective Training Programs – Part 2 of 4: Intelligence
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.
Part 2 of 4: Intelligence
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.
How Data-Driven Instruction Benefits eLearning
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.
- Differentiate Curriculum. As every learner is unique in how they process information and at what speed, eLearning leveraging intelligence can intuitively suggest different means of learning, taking into consideration the learner’s level of mastery and individualized pace.
- Individualized Tutoring. Unlike traditional classrooms, eLearning leveraging intelligence accommodates just-in-time questions, at a learner’s exact point of need. This not only facilitates learning, but also makes the process more fluid and effective as questions can be addressed in real time.
- Discreet Clarification. While it’s been said that there’s no such thing as a silly question, the fear of embarrassment still plagues classroom learners. So much like individualized tutoring, eLearning courses allow students to ask questions privately or in a group of students who share the same level of mastery or pace.
- Reduced Cost. Often, the price of higher or continued education at a brick-and-mortar school can be cost-prohibitive but with data-driven instruction, costs associated with faculty and administrative salary, supplies and facilities would be far less burdensome, leveling the financial playing field for receiving an education.
- Engaging, Relevant Content. Whereas traditional courses are often rigid in structure, set long before the first student enters the classroom, intelligent eLearning boasts the ability to adapt and adjust its content in real time, reflective of changing events, interests, needs and topicality.
How Intelligence Translates To The Workplace
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.