How Training Technology Can Boost Employee Performance

Learning is the cornerstone of any successful business, and being able to improve how you learn can take your business from average to booming. Yet, many decisions in employee training and development aren’t data-driven. Often, this is because employee learning and performance data can be difficult to capture — without the right training technology, at least.

Learning measurement and analytics have changed the game when it comes to improving employee performance. Simply saying “we had a 90% completion rate” doesn’t mean compared to data that can draw a line between custom eLearning activities and your organization’s revenue growth. Beyond that, insight into performance within the learning experience offers vital data for making improvements to training assets and future curriculum.

As training technology continues to evolve, it will become easier to improve upon the learning experience

Using the Data

The Kirkpatrick Model might be the best known method for analyzing the results of educational programs by measuring both formal and informal training styles to determine the effectiveness of a program based on four criteria. Those four criteria are:

• Reaction- measures how participants engage with the training course.
• Learning- this measures if the participants understood the training and learned new skills.
• Behavior- quantifies if learned material is being used at work.
• Results- calculates if the training had a positive effect on business

With Kirkpatrick’s Model in mind, part of what makes learning analytics so useful is the ability to improve your company’s learning experience either in real-time, or after the fact with tweaks made based on data gathered after a course’s completion.

Forms of Measurement and Evaluation

Progress Measurement can be tracked through learning analytics to keep tabs on employees enrolled in your training, due dates, and completion statuses. This information is valuable because it allows you to understand which subjects are more or less challenging and where certain learners are falling behind on their training. Data like this can trigger follow-up activities or direct subsequent conversations with mentors. Essentially, it allows learners the opportunity to receive extra help before it is too late to catch-up. These kinds of measurements are more closely aligned with levels 1 & 2 of the Kirkpatrick model.

Impact Measurement dials into what employees have taken from the learning experience (i.e., knowledge, critical thinking skills, and behaviors). To be able to connect the learning experience to your organizational goals, you must show that these behaviors influence the business. For example, if the skills acquired during a training simulation are applied to work, leading to key performance improvements. These measurements are more closely aligned with the levels 3 & 4 of the Kirkpatrick model.

The Role of AI in eLearning

AI has been huge in developing and improving training technology.

Though a more recent development, the emergence of AI has been huge in developing and improving training technology. AI’s main use is to analyze a learner’s behaviors, cognition, engagement, and performance throughout a course to predict and optimize future learning endeavors. This is accomplished through gathering data around various activities, creating baselines, and making comparisons to expectations, as well as the data from other learners. AI’s has been particularly useful in taking that data and feeding it back into training technology to make eLearning more personalized.

With AI still being relatively green in the eLearning realm it has the potential to make much more of a splash in the future. A report by Raytheon concludes that only 7% of businesses are currently using this technology to their benefit when it comes to their eLearning programs and training.

Low-Tech Evaluation Methods

Artificial Intelligence isn’t the only way to gather valuable information on the effectiveness of the type of your training methods. Using exit interviews, questionnaires, surveys, focus groups, and general observations are other ways to analyze what is working well with your training and what could use improvement.

Interviews, though time-consuming, allow you to get more qualitative data from employees. This method is particularly effective when you are looking for more detailed information around complex ideas and perspectives.

Assessments are a more scalable way to capture more quantitative data. Whether you are using multiple-choice answers, written responses, or simulations to gauge responses, assessments can capture data around knowledge and skills acquisition. Consider expanding upon the traditional pre/post-test model and take measurements throughout the learning experience to see how certain activities contribute to employee development.

Observation allows you to watch and take notes as employees go through their training, as well as their subsequent performance on-the-job. Short of performance measurement tools integrated into your business operations, this is an effective way to capture data after the initial training event is over. Moreover, it allows you to more effectively gauge whether training is having an impact on performance.

Conclusion

As training technology continues to evolve, it will become easier to improve upon the learning experience. AI-enabled analytics will surely see more use in eLearning design and development. However, employee training isn’t limited by technology. Methods like observations and interviews can still make a contribution to the effectiveness of your training strategy.