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Predictive Analysis – The Road Ahead

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.

For education to catch up to technology trends, it takes time. But, effective use of data can shorten the time lag. The use of pertinent data to resolve educational problems, quality, inform academic practices, and refine applicable policies should be of the utmost importance in efforts to reach national and state education goals.

As per studies, although the United States is graduating more students from high school than anywhere else in the world, their students are not performing as highly on some education outcomes as students elsewhere. Data analytics can help determine why some schools fail while others thrive and then guide the recommendations and planning throughout the process of school improvement. To use current data and analytics for timely student intervention and improved outcomes, educators and administrators alike need access to data dashboards.

Many states need to address multiple measures of progress on performance as they move toward performance-related funding models, from the student level to the teacher, school, district, system and state levels, through post secondary performance, and into the workforce.

Oracle
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