Data analytics in education

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Big data analytics’ impact has been far-reaching across an array of industries, concepts, and communities worldwide. Impacted sectors range from the obvious (scientific research, healthcare) to the unexpected, benefiting mutually from enhanced data collecting, monitoring, and analyzing.

Depending on the angle, education may fall into either one of these categories, as its implementation of data analytics has ranged, in itself, from obvious uses to lesser known ones.

Here are a few ways in which big data has impacted education.

 

A closer look

 

Enhanced student monitoring represents a clear use of modern data analytics. Some districts have explored new and innovative ways of track countless aspects of a student’s day, ranging from time spent between meals to classroom activity. While some of these factors may seem too minuscule to monitor, proponents of this analysis argue that they signify key aspects of a student’s behavior, including general motivation levels, behavioral habits, and levels of attention and social interaction. By better understanding these characteristics, educators may gain a closer look into what teaching and engagement methods will favor specific students, rather than implementing a blanket approach and potentially overlooking key nuances (a spike in enthusiasm for math following a period of physical exercise, for example).

 

 

A better understanding

 

This data-driven insight does not stop at exterior student habits and tendencies, however; they can also reveal quite a lot about a student’s academic performance. Data-centric education has gravitated towards a mostly individualized model in which student learning is centered on a personalized process. This approach is observed in everything from digital curricula to adaptive assignments allowing students to collaborate and integrate their unique learning approaches.

 

Furthermore, grading and scoring have benefited immensely from spikes in big data usage. A variety of data-driven applications and programs have streamlined the student evaluation process, focusing on what students learn, when they learn it, and at what level they learn it. Many of these resources allow instructors to efficiently monitor student growth while quickly comparing said growth to that reported in national averages.

 

 

A clearer future

 

Data-based education is only just beginning to catch fire, and a variety of exciting possibilities currently sit on its horizon. For example, programs such as iParadigms are emerging as tools to combat plagiarism and improper sourcing. Other fledgling concepts like BubbleScore continue to simplify test distribution and scoring, adding a layer of fluidity to the process for both faculty and students.

 

Regardless of the forms it takes in the coming years, big data in the classroom is sure to provide a much clearer image in terms of student behavior and academic achievement.