Big data is any very large data set that can be computationally analyzed to reveal patterns, trends, and insights about a specific user group or demographic. It’s typically used as a predictive tool to anticipate human behavior, spot business trends, prevent diseases, combat crime, and perform other large-scale statistical assessments. In schools, we use it to gather data on learning styles and outcomes, predict career paths, identify problem areas like unequal opportunity, and understand motivation. But how well does it really work as a tool for improving education?
Yaz El Hakim, Director of Learning Experience at Kortext, calls big data adoption a “smarts race,” not an arms race. Schools shouldn’t be using it just for the sake of using it, but rather using it wisely, applying it where necessary. Useful applications include simultaneously advancing and assessing student learning with machine learning algorithms, continuously gauging performance, identifying problem areas before tests are taken, evaluating where students need deeper understanding or haven’t grasped concepts fully, helping teachers decide when to spend more time on material or move on, creating customized lesson plans and curricula for optimal educational outcomes, reducing dropout rates, and recruiting specific types of students if you’re a university. Many institutions are already making progress in improving outcomes by leveraging big data in this way.
Here’s a bit more about how big data is affecting these areas:
1. Improving Student Outcomes
Over the course of her educational career, each student leaves a unique paper trail of her learning data. Much of this is left behind unless schoolwork is saved. The main measure we have of progress is exams. But big data can help us keep track of a student’s educational career over time in order to improve overall outcomes, help her choose the right career, and optimize lifelong learning. Collecting big data in such a way can offer us insight into creating the right test questions and environment to enhance learning for each student.
2. Personalizing Curricula
Big data can help educators create customizable curricula and programs based on feedback from machine learning algorithms. Leveraging a blended learning approach (both offline and online learning), these programs allow students to work at their own pace and pursue topics they are interested in. Massive online open courses (MOOCs), for example, provide educational opportunities to a much wider pool of students, allowing them to have more control over how and when they learn.
3. Designing Recruitment Strategies
Educational institutions can use big data to anticipate how many students will apply, which student profiles may be most likely to apply, where there may be gaps in diverse representation, and more. Once schools are aware of which variables affect the application process, with the help of big data they can adjust their recruitment process accordingly.
4. Reducing the Dropout Rate
Big data provides schools with predictive analyses to anticipate the likelihood of dropouts and to take action against it accordingly. Based on outputs like these, they can even predict how students might perform in a certain job market and readjust the student’s educational path to prevent dropout if, for instance, the data suggests the student would fare better in a different market.
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Have you heard of other uses of big data in education, or used it yourself? Please share your experiences or thoughts in the comments section below.