Organizations do care about training, but if you are responsible for the learning and performance of your team or organization then you know how important it is to measure training effectiveness. According to the American Society of Training and Development1 overall spending on employees training in US organizations is $164 billion and the average employee receives 30.3 hours of learning per year. The average cost to train each employee is $1,195. And these statistics mean that you can’t afford to spend time or money on training that doesn’t produce results.
In the past, most attempts at calculating and reporting on the actual learning program results was an exercise in futility. Using quizzes and surveys, it was possible to assess the effectiveness of training within the learning management system (e-learning). Testing could also be used to determine to what degree the employee acquired the intended knowledge and skills based on their participation in a training session. But without the means to correlate business process performance with learning program results it simply wasn’t possible to get trackable, quantifiable results from within the work environment – the data was just not there.
Do trainees put any of their learning to actual use on the job?
So this is really the 164 billion dollar question. Upper management and stakeholders have acknowledged that education and training are vital to the success of the organization and consequently put a learning and support ecosystem into place to change and improve the business process. However, there is still no hard data to measure employee performance once training is completed and employees are back at their desks. This ‘missing data’ has been at the center of many heated discussions between L&D and upper management.
Performance support is a component of the learning ecosystem that was developed to provide real-time guidance and automation to help employees navigate the jungle of technologies and applications necessary in order to succeed in our technology era. With a performance support system in place, employees seek out assistance within the business application, in their moment of need. This relieves the pressure to provide all learning in a classroom environment or even in an e-learning environment, as both scenarios mean time spent away from the task at hand. From the workers’ perspective, the goal is to get the job done. From the organization’s perspective, the goal is to reduce errors, reduce costs, and improve productivity. Performance support systems ensure that all these goals are accomplished.
But even a large investment in the latest and greatest learning and performance support technologies did not include a way to correlate learning program results and business process performance; there was no way to truly measure learning effectiveness – until now.
The Missing Link in the Learning Performance Ecosystem
Recently there has been a lot of buzz in the learning industry regarding the Tin Can API (also known as the Experience API or xAPI). In a nutshell, the Tin Can API is a specification for learning technology that captures data about a person’s behavior and activities (online and offline). For the first time, learning data can be collected in a consistent format, analyzed, and used to generate reporting.
Leo Performance Support by Kryon Systems is the first and only performance support platform that integrates the Tin Can API. By integrating the Tin Can API into Leo software, a feedback loop is created between learning program results and employee performance on business applications. This learning-performance feedback includes: employees’ business process performance (be it successful/unsuccessful task completion, errors and common mistakes or deviation from a regulated business process) on any enterprise application. And because Leo works on a live business environment, the system delivers feedback in real-time.
Evidence of Effectiveness
For the first time, Learning and Development departments can readily identify and measure where learning programs are effective. They can see if training is producing the desired business outcome and also identify the processes where errors are commonly occurring. Similar to a system of checks and balances, they now have the knowledge to adjust their learning and training programs to guarantee that optimal results are achieved.
And the best part is? Solid, quantifiable learning-performance reporting that you can take to management to get the budget you need and deserve!
“Thanks to the integration of the Tin Can API into Leo performance support, organizations can truly measure the effectiveness of their training within a live business environment. The result is the ability of L&D to demonstrate the impact of training on business results while learning where gaps exist and improving programs accordingly. This capability was the missing link in the learning-performance ecosystem and we are proud that Leo now closes this loop.” -Bibi Rosenbach, CEO of Kryon Systems. Read the full press release here.