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Bringing Big Data to your School’s Analytics

Date posted: September 3, 2013

Every day, we create multiple terabytes of data. It comes from every dimension: Social media, email communication, digital graphics, online transaction and many more. This data is really big and getting bigger and bigger! Like any other niche, the education system produces a tremendous amount of data as well. For example: Student grades, Campus ID, Homework, Class syllabus, campus activates etc. When this data is combined and analyzed properly, It can reveals astonishing insights about user behavior patterns (or user interactions to school’s systems)

Big Data is a term to describe such a mammoth amount of structured, semi structured and unstructured data-set, which is so complex and big that it’s almost impossible to handle easily by traditional database management systems. Big data spans over 4 dimensions. I.e. Volume, Velocity, Variety and Veracity.

Volume: One of the fundamental characteristic of Big Data is the amount of data. Volume refers the amount of data that schools are trying to analyze to improve decision-making and define scope for further initiatives.

Velocity: The speed at which new data is being created, captured and analyzed by the school, in our current digitized world, data is continually being created at a speed that is almost impossible for traditional database systems to capture and process.

Variety: Variety is about managing the complexity of multiple data sources i.e. structured, semi-structured and unstructured. With the continuous innovation and development of technologies and social collaboration, data is being created in countless forms e.g. text, sensor, graphics, log, web, etc. In our traditional database management systems, the data that captured and processed, is generally well structured and ordered.

Veracity: It refers the degree of continuity and reliability of data. In order for the data to be useful it has to be clean and reliable. For example: data collected from Facebook post and student activities board may offer clues about user sentiments and the reliability of such data is very much questionable.

Source: Source: IBM

Educational datasets:

Schools and universities gather an astonishingly big amount of data from various sources and activates. E.g. tracking grades, attendance, swiping campus ID, textbook purchases, library activities and more. But a very little is actually being used to analyze behavior pattern to improve decision making. For the sake of simplicity, we divide educational data into three major categories.

Identity Data: It includes the personal data of the end user. E.g. student ID, Student Name, grades etc.

Interaction Data: Interaction data includes engagement metrics and dimensions. E.g. Student attendance, swiping the campus ID etc.

Content Data: It includes content related data. Schools and universities generate and process a really big amount of content every year. E.g. class syllabus, Course content, Homework, schoolwork and more.

Recent advancement in technologies and data science made it very much feasible to capture and process all these different varieties of data. Big is one of them, It can your schools in a number of ways. Here are the few main ones:

  • Determine student’s sentiments and behavior towards school.
  • Identify the student’s core skills.
  • Determine student performance and interaction with class and course.
  • Determine the effectiveness of classes and courses.
  • Determine School reputation.

To compete in a global economy, it is increasingly essential that organizations need a comprehensive understanding of market and user behavioral pattern. These understandings require proper information, intelligence and analytics setup. Now, with the growing adoption of big data, many schools are discovering entirely new ways to compete and gain behavioral insights but in order to get significant and measureable business values from big data your school need to put into place an information foundation that supports the rapidly increasing volume, variety and velocity of the captured data.