Want more successful digital learning journeys? Look at the data

Companies can use digital learning data to drive recommendations for personalization and adaptive learning that will help them – and their burgeoning leaders – make targeted improvements that yield long-term success

13Feb

Innovations in digital learning technologies have been a huge leap forward for business acumen training and leadership development programs. But until quite recently, these technologies have been little more than missed opportunities. They had limited data tracking capabilities, allowing them to collect a bare minimum of data, therefore yielding few valuable insights for organizations.

Previously, program owners could get only a rudimentary picture of a learner's journey: When someone started and stopped a course, which answers were correct, and which were incorrect and the duration of the test. Now, thanks to the advent of the experience API standard (xAPI) and learning record stores, activities can be tracked in-depth and stored in a consistent data format. Studying that data reveals what is working about digital learning and what is not, leading to effective improvements and more consistently positive – and predictable – outcomes.

Data paints a picture

Today's technology makes it possible to track anything and everything related to digital learning journeys. In business simulation programs, for example, observers can track how long test-takers took to make a decision, when they revisited questions, and what information they accessed in order to make choices. Collectively, this data provides deep insights into how people make decisions, establish fact sets, evaluate risk and define success. Instead of focusing on whether someone got the "right" answer, data reveals how the learner arrived at the answer in the first place.

Such insights go far to help companies execute their strategies. First and foremost, data can reveal where learning has turned strategy into action and where it hasn't, plus what specific concepts people struggle with the most. Armed with these analytics, companies find it easier to revise programs to improve results. Over time, companies can spend time and money on the right development programs, taking advantage of learning journeys that build stronger workforces.

Learning data also paints a picture of employees that would be impossible to discern otherwise. Business acumen is an essential professional skill but is difficult to track and quantify. Using empirical data to analyze how people work through problems reveals something unique about their aptitude. Professional development becomes much more effective once individuals (and their employers) understand their strengths and weaknesses.

Applying data in the flow of work

While collecting data from digital learning journeys is important, applying that knowledge is the entire point. For example, imagine that someone underperforms in a specific area of a simulation or assessment. This could suggest that the person needs more focus in that area, as a result, that person can be automatically given further leadership coaching and practice. On the other hand, if the test reveals an area in which the individual excels, then that person can be given progressively harder questions. Data can drive recommendations for personalization and adaptive learning, maximizing impact while expending as few resources as possible.

This type of dynamic development is important in light of how careers are evolving today. It is estimated that most professionals will work a 60-year career, during which they will need to adopt new skill sets many times over. One survey showed that 90% of professionals believe they need to update their skills yearly, yet only 34% feel supported by their organizations to do so. Data-driven learning journeys allow professionals to continuously update their skills: overcoming weaknesses, building on strengths, and constantly adapting to new developments.

Data-driven learning has applications for organizational development as well as employee development. Creating business simulations of strategic scenarios allows decision makers to test different outcomes and practice their leadership skills and behaviors in a risk-free setting. This same process can be applied in the C-suite or at any level in an organization. In practice, running business simulations empowers companies and individuals alike to make targeted improvements to help future-proof their strategies.

Enhancing digital learning journeys

Digital learning journeys yield a wealth of data but turning all of that data into actionable insights is easier said than done. The following strategies will help you transform learning journey data into a long-term asset:

Start with the end: What is your end goal? For example, do you want to improve how well employees understand the strategy and direction, or study how executives approach risk management? Asking specific questions is important because it sharpens your focus on specific data. Once you have established goals, design a program to track, collect, and then analyze only the applicable data. That way you're not overwhelmed trying to make across-the-board improvements.

Store learning records: You can benefit from learning journey data only if it's accessible. Only a functioning depository can capture the rich xAPI data you need. To that end, you'll need to store learning records either in-house or through a third-party provider. Otherwise, the process could be time-consuming and unproductive.

Do more with the data: Information is worthless without actions. Data reporting is a good first step. But it's analytics you glean from that data that leads to insights and action, making the data truly valuable. If transforming large amounts of raw data into clear insights isn't easy, try what many companies are doing: either hire analytics experts or find an outside partner. Turning strategy into action is the difference between digital learning journeys that work and ones that are pointless exercises. Diving into the data and using it to make targeted improvements puts companies in control of their own futures, allowing them to stay ahead of twists and turns in the market.


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