Learning Analytics is not simply about collecting data from learners, but about finding meaning in the data in order to improve future learning.
Predictive Analytics is a statistical method that utilizes algorithms and machine learning to identify trends in data and predict future behaviors.
With increasing pressure to show a return on investment (ROI) for implementing learning analytics, it is no longer enough for a business to simply show how learners performed or how they interacted with learning content. It is now desirable to go beyond descriptive analytics and gain insight into whether training initiatives are working and how they can be improved.
Predictive Analytics can take both past and current data and offer predictions of what could happen in the future. This identification of possible risks or opportunities enables businesses to take actionable intervention in order to improve future learning initiatives.
The software for predictive analytics has moved beyond the realm of statisticians and is becoming more affordable and accessible for different markets and industries, including the field of learning & development.
For online learning specifically, predictive analytics is often found incorporated in the Learning Management System (LMS), but can also be purchased separately as specialized software.
For the learner, predictive forecasting could be as simple as a dashboard located on the main screen after logging in to access a course. Analyzing data from past and current progress, visual indicators in the dashboard could be provided to signal whether the employee was on track with training requirements.
At the business level, an LMS system with predictive analytic capability can help improve decision-making by offering in-depth insight to strategic questions and concerns. This could range from anything to course enrolment, to course completion rates, to employee performance.
Because predictive analytics goes beyond sorting and describing data, it relies heavily on complex models designed to make inferences about the data it encounters. These models utilize algorithms and machine learning to analyze past and present data in order to provide future trends.
Each model differs depending on the specific needs of those employing predictive analytics.
Some common basic models that are utilized at a broad level include:
For businesses who want to incorporate predictive analytics into their learning analytics strategy, the following steps should be considered:
Here are a few key benefits that businesses can expect to find when incorporating predictive analytics into their overall learning analytics strategy:
Many businesses are beginning to incorporate predictive analytics into their learning analytics strategy by utilizing the predictive forecasting features offered in Learning Management Systems and specialized software.
Here are a few examples:
Descriptive Analytics is focused solely on historical data.
You can think of Predictive Analytics as then using this historical data to develop statistical models that will then forecast about future possibilities.
Prescriptive Analytics takes Predictive Analytics a step further and takes the possible forecasted outcomes and predicts consequences for these outcomes.