Data, an essential part of improving skills
Skills upgrading has been in the spotlight over the past 18 months as organizations have made significant changes in the workplace to keep their employees safe, healthy and happy. Employees had to rapidly evolve and diversify their skills and knowledge in order to thrive after the transition to a fully virtual work environment.
As skills development efforts have grown, many organizations have turned to various virtual teaching methods to help employees develop quickly. In many cases, the reactive nature of these development efforts has led many organizations to overlook the importance and necessity of collecting, organizing and analyzing development performance data, let alone integrating it into developmental data. pre-existing learning ecosystems.
Data is needed
One of the most common mistakes organizations make is not realizing how critical data is in development efforts. Why? Because data helps improve training fidelity by helping trainers and instructors objectively identify skill gaps, supporting their subjective and intuitive assessments. It also provides the information administrators need to make sound organizational decisions that can help improve the efficiency and effectiveness of their development programs.
When implementing skills improvement in your organization, the proper collection, organization and analysis of performance data should not be negotiable.
Data is everywhere
What type and how much data should you collect? A good rule of thumb is: if you can measure it, then collect it! But don’t go too far.
When designing a data collection plan for skills improvement, make sure you are clear about the types of performance issues you are trying to solve. Collecting data unique to each person will provide you with actionable insights and help you individualize employee skills improvement, saving you time and money in the future.
It can be difficult to collect certain types of data on skills improvement. Often, skill enhancement occurs organically in the workplace, as employees often learn as they go and then share their knowledge with their peers. Data is typically not formally collected when employees share industry best practices or lessons learned in side conversations with each other.
But when data is not collected from these informal skills development events, there is no way to know whether employee skills have improved, leveled off, or declined after implementing new skills and knowledge. . Finding ways to collect and record this dynamic development can provide opportunities for personalizing formal development events, such as the annual recertification training.
The data is unique
The specific type of data you need to capture depends on your organization’s educational ecosystem. Your results will be unique, so your data reporting tools and capabilities will also be unique.
The Air Force’s Pilot Training Next (PTN) initiative collects treasure trove of data from student pilots who virtually record flight hours through extended reality systems. Biometrics, performance reviews, eye tracking, flight maneuver parameters, and other data sets help the instructor and additional technologies tailor each student’s learning experience to help them master more. quickly so they can get to work sooner. The Air Force is experiencing a pilot shortage and is currently exploring innovative ways to train more pilots faster, and the types of data collected are helping to inform their ultimate training solution.
However, the data collected for PTN is significantly different from that collected for the Federal Aviation Administration. They have different problems to solve and different questions to answer in their learning environment.
Make the data useful
The data you collect is actually not the most important aspect of using data to accelerate skills improvement. Most important is data analysis which helps you determine how that data will work for you.
You need to collect and analyze multiple types of data to be useful and free from misinterpretation, as unmistakable relationships between data points begin to emerge. Additionally, your data analysis methods should provide a way to prove that your development is delivering a measurable return on investment. This can be difficult, especially given the difficulty of standardizing and coding data within learning ecosystems that incorporate a variety of different technologies. Inflated data sets and limitations with software or hardware can create redundant and unnecessary work when analyzing data. It is essential to integrate and define data standards in development environments.
One-on-one development within an organization is the “holy grail” for increasing the effectiveness and efficiency of training, and data plays a critical role in this. Capturing and analyzing performance data that includes both skills and confidence, as well as feedback on employee experience as they improve their skills, is the best way to personalize the job. training. The goal of improving skills quickly is to avoid wasting time on things students already know and to spend their time filling skills gaps. It is impossible without data.
Data lightens the burden
When data is analyzed with precision and transformed into actionable information, it can help reduce the burden on all parties involved. A development solution that automatically establishes an individual baseline, clearly illustrates skill challenges and gaps, and provides detailed feedback for better instruction and decision making.
Kathryn Thompson is the Leader of the Learning Engineering Focused Training Analyst Team in SAIC’s Research and Development Department.