What is Predictive Analytics?
Predictive analytics uses data, machine learning, and statistical algorithms to estimate the probability of future outcomes by analyzing historical data trends.
Although data may seem complex, the process itself is straightforward.
Organizations and institutions use predictive analytics to identify trends in historical data and make informed decisions for the future. This process helps establish clear benchmarks for different types of data and create models that anticipate and understand risks. By understanding the relationship between datasets and outcomes, businesses can predict the impact of their decisions.
Examples of Predictive Analytics
Predictive analytics is often perceived as a new technology, but this is not the case. It has been used for years across various industries. Most organizations collect data, whether related to product logistics, customer transactions, or student performance. By highlighting trends in existing data, businesses can predict how similar customers are likely to behave.
For example, in the healthcare sector, predictive analytics can determine the likelihood of a patient canceling or missing an appointment. In the insurance industry, it can help prevent fraud. Another example is in financial services, where predictive analytics can estimate whether a loan applicant is likely to repay their loan based on specific characteristics collected during the application process.
Predictive analytics relies on “big data”, which consists of complex datasets that are often difficult to manage manually. This is why business intelligence software is used to make predictive analytics more accessible. These tools allow institutions to visualize data and easily access insights that may otherwise be highly complex. Thanks to this technology, there is no longer a need to build technical scripts or complex algorithms, enabling decision-makers to directly access data and make informed choices.
The Evolution of Predictive Analytics in Education
Many associate big data analytics with digital, marketing, finance, or healthcare sectors. However, predictive analytics is increasingly being used to improve education, with institutions leveraging student data to better understand their current and future performance.
Predicting student outcomes has always been a priority in the education sector. Schools, colleges, and universities have historically used exam results and attendance rates to anticipate academic success. Traditionally, these data were collected and analyzed manually, and any anomaly required human intervention. This often meant that data remained siloed within different departments, delaying interventions.
As institutions and technologies evolve, data has become more complex. Today, the rise of digital learning and educational technologies has significantly increased the volume of collected data. A wealth of useful information can now be extracted, improving student performance and optimizing decision-making on both academic and operational levels.
A student management system contains various academic and administrative data. But beyond these elements, institutions also track student engagement in digital learning, library usage, and participation in extracurricular activities. Whether on campus or through online platforms, students leave numerous digital traces. Predictive analytics utilizes this big data to identify trends and provide a comprehensive view of institutional performance.
The Importance of Predictive Analytics in Education
By obtaining insights through data modeling, educational institutions (colleges, universities, and training centers) can anticipate potential issues and address them proactively to enhance the student experience while optimizing resources. However, as the number of datasets increases, analysis becomes more complex.
The advantage of data is that it constantly evolves. Once an institution has implemented a predictive analytics model to understand student outcomes, the model adapts and updates based on new data received.
For example, an institution with a predictive model capable of estimating whether a student will pass or fail a course can use this model to assess a new student’s likelihood of success.
Predictive analytics plays a crucial role in improving institutional efficiency. By integrating data from various sources (attendance, academic performance, enrollment, etc.), institutions can identify the key factors impacting student success. In a context of limited resources, predictive analytics becomes a powerful tool for improving student attendance, performance, and enrollment rates.