Usage of Predictive Analysis in Edtech

Only recently, education has begun to adjust to the culture of big data. Big data refers to data from a wide variety of sources including learning management systems, student information systems, enterprise resource planning systems, data warehouses, longitudinal data systems, vendor systems and countless devices and sensors. Education leaders and policymakers often don’t know how to make sense of it all the data they are steeped in so much. They not only spend resources on education but also need accountability and return on investment to justify additional funding. They generally don’t know what works, what has limited results and what would work better with modifications unless they can gather and interpret data from a variety of sources.

These leaders can find what each student and teacher needs to grow toward better outcomes through the help of systematic mining of data. For instance, good data analysis can point teachers toward timely professional development to help them help their students. Owing to the fact that no two students are alike and each one has unique gaps in knowledge or understanding, they benefit from predictive modelling and artificial intelligence to guide their paths to a better education, just like their teachers. Schools and districts not using these tools effectively squander both time and dollars in guessing what students need.

To choose educational technology systems that would better serve their goals for educational improvement, education leaders can also use predictive models based on descriptive data and diagnostics. These models often show trends in data that would otherwise not be apparent and can help direct decisions toward educational improvement and efficiency.

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