Advanced Learning Concepts: The Application of Data Analytics in Training

There are over 2.7 zettabytes of data in the world today, and the amount of business data is more than doubling annually.

Although leading companies around the world are leveraging this data, the training community could be doing more with analytics to improve the learner experience.

The world we live in is becoming more complicated, and so are the associated training requirements. However, the demands we place on our learners continue to increase, often while organizations are managing reduced training budgets, shorter preparation times, and a relatively new type of workforce.

Creating “better training” is no longer good enough. Training efficiency and overall improvements in training effectiveness are required to meet these increasing demands.

Applications for Improved Learning

Learning analytics and educational data mining are two ways the training community can use data to do more for their learners.

Learning Analytics:

• Focuses on the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing training and the environments in which it occurs.
• Gets to the heart of data collection with a focus on the specifics of each individual training event.
• Predicts and influences student performance, personalizes the learning experience and adapts course materials when we identify areas for improvement.

Educational Data Mining:

• Is an emerging discipline leveraging large-scale data analytics, machine learning and statistics to better understand students and their learning environments.
• Broadens the analytic scope to include other, non-traditional, data. It looks at the breadth of data across a learning population, including data at an institutional level that has not traditionally been incorporated into training solutions.
• Allows us to understand the trends associated with the training while helping to identify key feature, sequences and patterns that are working or that may need to be adjusted.

Both applications feed predictive analytics and contribute to better efficiency and effectiveness. 

Training Focused on Outcomes

Pivoting training requirements to focus on achieving desired end-user outcomes allows us to take a more holistic approach to how we prepare our learners.

• Comprehensive training solutions that incorporate communications strategies, change management, fully integrated solutions and other reference materials all contribute to a more effective and efficient outcome.
• Training modality selection that maps to the requirements of the task to be accomplished is critically important, whether it leverages more traditional approaches, like instructor led and computer based training, or focuses on new strategies such as microlearning and gamification.
• Big data and data analytics contribute to both how we evaluate the effectiveness of the holistic strategy we implement and how we measure the achievement of our final outcome.

As learners are forced to adapt to an increasingly complicated training environment, embedding data analytics and metrics to customize the learning experience in real-time can create a more efficient and effective training experience. Ultimately, this process can lead to reduced costs, increased retention, and improved outcomes. 

Jeff Raver is Vice President and Training Manager for SAIC within the Engineering, Integration and Mission Solutions Market Segment.

Follow @SAICinc and @raverja1 on Twitter.