Definition of predictable analysis
Predictive Analytics is a statistical method that uses algorithms and machine learning to identify trends in data and predict future behavior.
With increasing pressure to show a return on investment (ROI) to implement learning analytics, it is no longer enough for a company to simply show how students performed or how they interacted with content. It is now desirable to go beyond descriptive analyzes and gain insight into whether educational measures work and how they can be improved.
Predictive Analytics can take both past and present data and offer predictions of what might happen in the future. This identification of potential risks or opportunities enables companies to take actionable action to improve future learning initiatives.
How does Predictive Analytics work?
The predictive analytics software has moved beyond the realm of statistics and is becoming more affordable and accessible to various markets and industries, including learning and development.
Specifically for online learning, predictive analytics is often incorporated into 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 placed on the main screen after logging in to access a course. By analyzing data from past and present progress, visual indicators in the dashboard could be provided to signal whether the employee was on track with the training requirements.
At the business level, an LMS system with predictable analytical capability can help improve decision-making by offering in-depth insights into strategic issues and concerns. This can vary from everything to course registration, to course completion rates to employee performance.
Predictive analytical models
Because predictive analytics goes beyond sorting and describing data, it relies heavily on complex models designed to draw conclusions about the data it encounters. These models use algorithms and machine learning to analyze past and present data to provide future trends.
Each model differs depending on the specific needs of those who use predictive analytics.
Some common basic models used at a broad level include:
Decision trees use branching to display options that result from each result or selection.
Regression techniques help to understand relationships between variables.
Neural networks use algorithms to find out possible connections within datasets.
What does a business need to know before using predictive analytics?
For companies wishing to incorporate predictive analytics into their learning analytics strategy, the following steps should be considered:
Set a clear direction
Predictive analytics relies on specifically programmed algorithms and machine learning to track and analyze data, all of which depend on the unique questions being asked. For example, it is a specific question to want to know if employees will complete a course; the software must analyze the relevant data in order to formulate possible trends for completion rates. It is important that companies know what their needs are.
Be actively involved
Predictive analysis requires active input and involvement from those who use the technique. This means deciding and understanding what data is being collected and why. The quality of data should also be monitored. Without human involvement, the data collected and models used for analysis may make no beneficial sense.
