Data analytics is the talk of just about every industry. As Peter Sondergaard, former executive vice president of Gartner Research, said in a 2011 speech, “Information is the oil of the 21st century, and analytics is the combustion engine.” In learning and development (L&D), one of the primary reasons for the buzz around analytics is the need to evaluate the effectiveness of training programs.
Conventional data sources, such as focus groups, instructors, surveys and learning management system (LMS) reports, continue to offer valuable information to help learning leaders make data-driven decisions. However, they need more than data; they need intelligent and actionable insights to enable forward-thinking actions. Wouldn’t it be nice if your LMS harnessed data to recommend training initiatives for the next quarter or to predict whether specific groups of learners would be successful in a given course?
Advanced analytics systems can perform these tasks. They’re predictive and prescriptive in nature, not only predicting the future but also recommending the best course of action. Artificial intelligence (AI) and automation enable such deep insights. Let’s delve into the impact of AI and automation on L&D and particularly on training evaluation.
Artificial Intelligence and Automation in L&D
AI refers to the capacity of machines to learn and process data and then make decisions on their own. For instance, ridesharing apps use AI to predict information such as the price of a ride and travel time.
In L&D, AI can analyze data and optimize training programs accordingly. Owing to this capability, the adoption of AI has gained momentum. According to Gartner, the number of enterprises using AI has grown 270% in the past four years and tripled in the past year.
Automation has one purpose: to perform repetitive and monotonous tasks using preprogrammed rules. Unlike AI, you must configure the automated system to work the way you want it to. Essentially, with automation, the machine is intelligent enough to follow human orders but not to make decisions. In L&D, automated authoring tools can help instructional designers develop e-learning courses more rapidly and with fewer resources.
AI and Automation in Training Evaluation
There are several models and frameworks that support the measurement of training effectiveness. The most notable is Kirkpatrick’s model, whose four levels — reaction, learning, behavior and results — offer insights on training effectiveness.
You can gather data for the first level using feedback forms, surveys and “smile sheets,” while you can use pre- and post-assessments for the second level. It is the third and fourth levels where predictive and prescriptive analytics (backed by AI and automation) come in. AI and automation can minimize the amount of human intervention needed to analyze data and identify subsequent actions.
With AI, the machine gathers data over a period of time and from sources beyond the LMS, such as the human resources information system (HRIS), the enterprise resource planning (ERP) platform, 360-degree assessments and performance evaluations. Data gathered from these sources enables systems to make better predictions and recommendations.
AI-enabled analytics tools can also predict learners’ future performance based on existing data about job roles, preferences, learning history and performance. This capability enables you to create necessary learning interventions and support for the areas where learners are likely to struggle.
In addition, with e-learning automation, algorithms offer detailed instructions that enable the LMS to determine business and individual needs in an organization. Regardless of the frequency of change, the system will continue to adjust learning initiatives automatically. As a result, instead of your trying to plan for every possibility, the automated LMS analyzes data and tells you which possibility is most likely.
According to a report by Reuters, the global predictive and prescriptive learning analytics market is projected to reach a value of $16.84 billion by the end of 2023 — a compound annual growth rate of 20.43%. These advanced analytic technologies are here to stay. Moving from conventional data analysis to advanced analytics will provide training organizations with much-needed speed and accuracy in decision-making. If you haven’t thought about making that move yet, it’s not too late!
