With the technology available today, how do educational institutions make sure that actual learning takes place? That is an incredibly challenging task. There are many tools and solutions intended to help schools achieve this goal, and things like standards and objectives that must be met in order to demonstrate that students are acquiring the knowledge that they need. So how can predictive analytics and by extension, machine learning, help achieve this objective?
What Is Predictive Analytics in Education?
Schools typically collect massive amounts of information, everything from demographic information about each student, to their performance, disciplinary records, attendance rates, and a lot more. Traditionally, this has been used for reporting – tracking progress and seeing whether or not expectations are being met and, where they are not, taking corrective action.
Predictive analytics is a forward-looking approach. It takes a look at historical data, and backed by tested research, applies patterns from that data to make predictions about new data. Based on a metric like absenteeism for instance, you can predict certain likely outcomes. This enables decision-makers to be proactive in their approach and implement measures that tackle problems very early on.
Examples of Predictive Analytics in Education
Some of the biggest problems that predictive analytics is used to tackle in education are issues like graduation and dropout rates. By applying advanced analytics to parameters like absenteeism, course completion, and overall grade point average, schools can determine whether or not a student that fits a certain pattern ends up graduating or not.
This kind of predictive capability has actually been around for quite a long time. However, with recent innovations, like AI and Machine Learning, predictive analytics has become even more powerful. The result is that predictions are more accurate, and patterns for certain likely outcomes can be identified much earlier and with accuracies as high as 90-95%.
How Predictive Analytics Can Help In The Education Sector
Problem identification is an important part of policy and decision-making at both the school and district level. If a district decides, for example, to increase the number of students that pass state assessments by 5 percent, they will want to focus on the group of students that is the most likely to make the needed improvement to achieve this goal.
Let’s take this example further. If the cut-off score for a state exam is, for instance, 260, the students who scored between 250 and 270 in a preparatory exam would be what are called the bubble students. This is the group of students most at risk of slipping back below the cut-off point, and those who have the least ground to cover in order to make it to the cut-off point. Using predictive analytics and working with bubble students, schools can identify in which areas these students need the most help and support.
Predictive analytics harnesses the latent power of the historical data that all schools typically have. The capabilities of predictive analytics are not limited to analyzing patterns from past data to predict future outcomes. You can also use them to glean more information from past successful students. Schools can apply research-based models to determine the critical success factors and use those findings in policy-making so that future students can also benefit.
More than just diagnosing problems, or predicting success, predictive analytics can also be used to ascertain the value of different interventions. Looking at past data, and extrapolating the expected outcomes based on certain parameters, it is possible to see which interventions are most effective at preventing negative outcomes. These could be things like weekend online classes, or quick reviews during exam periods. Schools can then continue to monitor these results and iteratively improve their policies to achieve better and better outcomes.
How Does Machine Learning Help Predictive Analytics?
The value of predictive analytics in education is clear. What we haven’t touched on is how computers actually get to recognize patterns, make meaningful conclusions, and accurately make predictions. The short answer? Using machine learning.
A common demonstration of machine learning is image recognition. If you show a computer a sufficiently large number of images of horses and ducks, for example, it can pick out the similarities in those groups of pictures (the training dataset) and be able to accurately identify a horse in a picture it has never seen before. This is a subset of machine learning called deep learning. It is made possible by neural networks, so let’s take a look at those.
Neural networks are modeled after the learning process inside the human brain. Human beings learn and retain information by creating unique pathways between neurons in the brain.
Computers will do the same thing. You give them a set of data where the correct outcome is known, and they create possible logical reasoning pathways between nodes to predict the outcome. Based on whether or not the conclusion is right, the computer reevaluates these pathways until it is satisfied with the accuracy of its conclusions.
In practice, this is done mathematically using inputs, weights, a bias or threshold, and an output in a process that is similar to linear regression. Repeated enough times with enough training data, this will produce an algorithm that is capable of making highly accurate predictions.
How Can Machine Learning Help In The Education Sector?
The education sector, with the mountains of data that is routinely collected, is a prime candidate for machine learning. You can use records of past students with known outcomes as training data and develop algorithms that will make valuable predictions for new students. These insights can then be used to improve not only to improve student outcomes, but also to enhance the operations of your institutions. The application scenarios are diverse.
Examples of Machine Learning in Education
Machine Learning can be applied from the very get go to ensure you enroll the right profile of students based on certain risk factors. You can also identify at a very early stage, which students are struggling and implement targeted programs to combat this.You can determine which specific factors to prioritize in order to give students the best chance of graduating. Other examples include virtual teaching assistants, automated test grading, and much more.
AnalyticVue For Predictive Analytics
One of the biggest challenges educational institutions face today is organizing data. The different parameters needed for a Machine Learning model to work are usually scattered across different platforms. Implementing predictive analytics is almost impossible.
A system like AnalyticVue will help you bring all of this together. The system will help you pull fragmented data from sources like i-ready, NWEA, Star, and different Student Information Systems so you can have allof your data in one place.
AnalyticVue takes the administrative burden of making basic assessments, and offers you the opportunity to gain even more insights from your data. Once you have your data – state assessments, classwork, i-readys, and so on – in one system, you can then start to make it work for you. What is an administrative headache becomes a powerful asset that can be used to achieve objectives at all levels for the different stakeholders involved.