In an increasingly digital age, one of the most sought-after resources is data. Our everyday actions, no matter how unimportant they may seem to us, can generate data that reflects our routines, preferences, and lifestyles. This data can be leveraged in such a way that it can even help predict what we might choose to do in the future.

Making the most of data is a task that comes with its fair share of pros and cons. When used responsibly, it can help aid people make informed decisions, be it in the workplace, educational institutions, or just personal life. To do so requires the use of Artificial Intelligence (AI) and Machine Learning in predictive analytics tools. 

As mysterious as the term predictive analytics sounds, the concept behind it is in fact, simple and straightforward. In this article, we will explore the use of predictive analytics in eLearning, starting with an understanding of the term itself.

Understanding Predictive Analytics

Predictive Learning Analytics (PLA) uses past individual data to forecast the future performance of the individual. It indicates whether learners will leverage the information they learn in a useful manner. The analysis is done by applying data mining, predictive modeling, and AI tools to historical data to predict future performance.

In eLearning, the two major applications of predictive analytics are workplace training and higher education. Reporting tools in LMS platforms can help collect useful data and leverage predictive analytics tools to determine whether a learner is enrolled in the right courses, is performing consistently, whether the training received is being applied in practice, and more. The resulting benefits, both in higher education and in the workplace, are enormous. 

Why Use Predictive Analytics in Workplace Learning?

Using Predictive Analytics in Workplace Learning

 

With many corporations investing in top-of-the-line workplace learning programs, it is important to ensure that the right data collection mechanisms are in place. Using predictive analytics on this data, it is possible to identify individual employees’ needs and address them.

Identify Employee Training Needs

A one-size-fits-all training solution is not conducive to effective learning. It is important that organizations realize that each individual has their own specific approach to learning, and unless they are allowed to learn in their preferred way, the training would deliver sub-optimal results. Fortunately, in today’s data-centric world, getting to know employee preferences is not a very difficult task.

Having seen the way certain employees react to certain kinds of training programs, predictive analytics can help determine which future training a certain employee would be most likely to excel in. For example, if an employee has previously been unable to complete video lectures, but has attempted all interactive exercises, chances are that they prefer a hands-on approach to learning. Consequently, enrolling them in courses featuring long videos would be a mistake.

Ensure Retention

The importance of allocating the right training resources to the right personnel cannot be emphasized enough. If a company is dedicating its resources to train an employee who is showing signs of leaving, it is likely that their efforts are in vain. Predictive analytics help identify employees who are actively engaged in training, learning on-the-job skills, and showing initiative that would indicate that they are going to stay for the long haul. The company should therefore allocate more training resources to these employees.

On top of that, the analysis can also reveal workers with declining performance. Knowing this will help the company address the difficulties these employees are facing and come up with learning solutions that would help boost their performance. This, in turn, could help reduce the turnover and keep them invested in the company.

Offer Professional Growth

One of the biggest things prospective employees look for in their jobs is the opportunity to grow. This is especially true for millennials who form the majority of the workforce in most fields today. If an employee is offered training courses that help them grow in their roles, learn soft skills, and hone their strengths, the result does not only benefit the learners themselves but also the company.

What does this have to do with predictive analytics? The answer is pretty simple. Based on employee preferences and training data, it is possible to create and track their professional paths and work on course correcting, should the employees veer off track. Knowing that a company is leveraging the latest predictive analytics tools to help the professional growth of their employees is likely to attract the best talent out there.

Improve Employee Satisfaction

Receiving personalized training with the help of predictive analytics is likely to keep online learners motivated. More motivated employees mean more productivity. In addition to performing well in training programs, these employees are also likely to perform well in their roles. 

Knowing which kind of training modules are positively received by an employee ensures that they don’t have to sit through those boring and time-consuming mandatory courses that might actually be detrimental to their performance. The result is a motivated and satisfied workforce.

The Need for Predictive Analytics in Higher Education

Using Predictive Analytics in Higher Education

 

The need for predictive analytics in learning institutions can be attributed to the increased adoption of online learning in higher education. With both in-person, but especially distance learning programs, it is important to keep track of student performance in order to ensure their learning is on track.

Implementing Adaptive Learning

The great thing about being able to learn online is the degree of flexibility and personalization that comes with it. Online courses with good instructional design take into account the unique ways in which individuals learn. This allows institutions to develop adaptive learning measures via predictive analytics, which involves studying past student data to determine the content, assessments, and learning paths that are most effective for a particular student.

Predictive analytics in adaptive learning also allows instructors to learn the deficiencies of their students and address them in a timely manner. Knowing what mode of instruction, type of assessment, and learning pace a student performs best in allows for future courses to be created in a way that makes their learning most efficient. 

Adaptive learning also gives learners a measure of control over their own learning. Students can choose their own learning paths, skip over the content they already know, and spend extra time where they feel they are lacking. Having an active role in determining the course content also makes the learners more invested in their learning.

Identifying Strengths and Interests

With the help of past student data, higher education institutions can determine what strengths and interests their students have. Based on the nature of past courses in which they are enrolled and their respective grades, it is possible to determine which future courses a student can be expected to excel in. Armed with this information, institutions can help students plan their future courses in line with their career aspirations. In this way, predictive analytics can play a major role in targeted student advising.

Addressing Performance Issues

If institutions are keeping up with student performance data, they have the unique opportunity to identify students at risk of performing poorly. Predictive analytics makes it convenient for colleges to leverage huge chunks of student data in order to determine, with a fair amount of accuracy, if a student is on track, at-risk, or performing at a moderate level. 

An application of this can be seen at Purdue University where a ‘Signals’ project mimics traffic signals to indicate to learners how well they are progressing in their course. Another insightful application can be seen at Georgia State University, where early student intervention, done by an analytics alert, has seen a marked improvement in graduation rates.

Managing Enrollment

It’s not just the students who benefit from predictive analytics, the institution does too. With each passing year, colleges see a marked increase in new enrollments. With so many students vying for courses with limited capacity, it is imperative that institutions make sure that they make accurate enrollment decisions.

By leveraging predictive analytics, it is possible to determine the size of incoming batches, which students are most likely to apply to certain schools, and how much funding would be required to fulfill the financial needs of certain students. It also helps institutions tailor their recruitment and marketing efforts to a select audience that is most likely to take an interest in the majors they have to offer.

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Leverage Predictive Analytics Tools with Edly

As we discussed above, both higher education and workplace learning can make use of predictive analytics to make their approach to learning more fruitful. The results are not only beneficial to the learners but ultimately also contribute to organizational success. Knowing that an organization is leveraging the latest digital technologies to empower its learners automatically attracts bright and talented individuals.

With so much to gain from predictive analytics, it is essential to invest in an LMS platform that gathers the right kind of data. Since data is generated for every single user action, it is important to distinguish useful data from noise. Edly’s LMS gathers this much-needed data under the Edly Insights tab, where detailed user and course information can be accessed. That, combined with other helpful features, makes Edly a widely successful learning platform. To learn more about our product, be sure to request a free demo!

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