02 February 2022

The advancement of artificial intelligence and machine learning has made it possible to obtain, clean, and analyze more data than ever before. With endless stores of data at our fingertips, we are in a position to utilize it to improve our products and services for the people who rely on them. For example, for the world of online learning, predictive analytics powered by artificial intelligence and machine learning can help improve e-learning.

How is Predictive Analytics Being Used in Other Industries?

Predictive analytics is already being used in various industries, including banking and finance, retail, oil, gas and utilities, government, the public sector, health insurance, and manufacturing. The power of predictive analytics has brought about much change and innovation for these industries, allowing them to streamline their processes, mitigate future risks, improve customer service, and optimize their industries. In addition, as e-learning gains in popularity, there has been a push to utilize the power of predictive analytics to enhance the online educational experience. Predictive analytics is a valuable tool because it allows us to “predict the future” by utilizing advanced data analytics. By studying vast amounts of data produced every day online, data analysts learn to find patterns in people’s behavior and habits. These observed patterns in the data can be translated into valuable insight about people, such as their spending habits or what media they consume. The insights uncovered by data analysts can then be used to create preemptive strategies to address future needs.

For example, if a retail company is looking to increase sales and decrease spending on marketing their products, they can use predictive analytics to analyze their audience. They can obtain data on all sorts of things, from what clothing they tend to purchase to how much time they spend browsing on the retail company’s website. They can also get insight into the demographics that tend to frequent their company and what channels they use. With this data, they can create a ‘persona’ of their typical customer. The ‘persona’ acts as a stand-in for the customer and can predict that customer’s future behavior. With a solid idea of who their customer is and how they interact with their company, the retail company can then create an effective marketing campaign tailored to that customer. For example, if the customer is a young woman who tends to purchase sneakers that are priced under $50 and this customer likes to utilize Instagram to view the sneakers, the retail company would be able to use the data on that customer to predict what will most likely result in a sale from this customer. With this information in mind, the retail company would invest in an Instagram ad campaign featuring sneakers under $50 and target that ad towards that customer. Targeted advertisement is much cheaper and much more effective than broad marketing, as it reaches customers where they are. Predictive analytics allows for more effective targeted marketing because data paints a more accurate picture of the customer. The same principles of data analysis to “predict the future” can be applied to e-learning. And the insights drawn from that data can be used to improve online education vastly.

Applying Predictive Analytics to E-learning

With the vast amounts of data present on e-learning platforms, online learning providers can use that data to draw insights on their users and turn those insights into valuable changes that can vastly improve the industry. Many e-learning courses collect data from users on a variety of different things. For example, there is data on the kind of e-learning content they consume, the amount of time they spend on a course, the types of courses they like to take, and their grades and test scores. With this information in mind, e-learning providers can do a host of things to improve the e-learning experience for learners and providers.

Predictive Analytics for Learners

Using predictive analytics, e-learning course creators can make content to help learners reach their personal learning goals faster. Predictive analytics can be used to study a learner’s preferred habits and the materials they like to engage with. It can also be used to examine their skill set and where they may be lacking. With this information, content with that learner’s learning style and courses that address their needs can be created and adjusted to suit that learner. For example, if learners enjoy engaging with short-form content like video clips, their e-learning course can be changed to utilize more video clips. Likewise, if there is a learner who excels in English but struggles with Math, an e-learning course can be created to target their weaker math skills and adjusted to help them focus and improve on their math skills. Personalized learning can improve focus and increase motivation to complete course material, as it can be designed to be more engaging. Learners can be matched with mentors who are skilled in the areas they wish to improve on, giving them access to a reliable resource. With personalized learning, learners can also receive more relevant feedback to help them stay focused and ultimately reach their learning goals.

Predictive Analytics for Providers

E-learning course creators and administrators can also benefit from the power of predictive analytics. Resource allocation is a significant factor for creating an effective e-learning experience. E-learning course creators and administrators must know what resources are needed and how they should be allocated and how much these resources may cost. Data on learners is essential for understanding and disseminating the required help. Before predictive analytics, e-learning providers had to pour a lot more time into analyzing metrics. Predictive analytics can streamline this process, automatically generating insight, which can then customize resource allocation to suit their needs. Providers can then spend more time on the quality of the content that they create for learners. With the ability to forecast learning outcomes, providers can also spend less money deploying the resources needed while still helping to meet overall learning objectives. Resources can be applied when and where they are required, and ineffective assets can be discarded.