Education Industry Data

What Is Education Industry Data?

The education industry covers instruction and training for students and educators in pre-kindergarten to post-graduate levels. Education industry data analyzes students, teachers, unions, local and national policies, funding, school type, teaching methodology, and more.

Ed-tech, a subset of the education industry, focuses on technology and software that enables or enhances education. In the light of Covid-19, more and more classes have moved online, making ed-tech particularly important.

Where Does Education Industry Data Come From?

Most education data comes from individual schools, teacher’s unions, parent organizations, and local and national governments. Even government departments which track related information, like demographics and local regulations, provide important data.

NGOs also track this information, especially to compare student proficiency across nations.

What Types of Columns/Attributes Should I Expect When Working with This Data?

The majority of this data tracks student performance and progress, so historical student test scores and student demographics are a major feature. Additionally, this category tracks educator and administrator data, especially employment numbers, union membership, and school type or methodology.

No matter the purpose of the education dataset, however, researchers should add external data, especially economic and demographic data.

What Is Education Industry Data Used For?

Individual schools may use this data in a large number of ways. For example, they measure teachers effectiveness, whether their budgetary decisions were productive, and whether they need to set up after-school programs for students.

District and national researchers also use this data to measure student proficiency and progress as well as teacher and union effectiveness. They compare student test results across districts and by school type (for instance, comparing public schools to charter schools).

Finally, of course, parents and students use this data to decide whether to enroll in, apply to, or leave a certain school.

How Should I Test the Quality of This Data?

As mentioned above, most data set should use a wide variety of sources. However, if the question being investigated is very specific, like whether a certain distance learning AI program is useful to a class, the range of data sources will be narrow.

Beyond this, however, researchers should simply ensure that the data sources used are comprehensive and up-to-date. Once researchers collect all the sources, it becomes a simple matter to standardize and cleanse the data. Any discrepancies at that point can be investigated.

Interesting Case Studies and Blogs to Look Into

Frontiers: Educational Psychology
Stanford: Urban Charter School Study

Tangible Examples of Impact

The budget-friendly, hi-tech smartphones and tablets can project the contents of the screen on any flat surface and enable educators to deliver engaging lectures in remote areas.

The 5G smartphones and tablets are equipped with several groundbreaking features, including nanotechnology, which offers high-speed connectivity and enhances its capabilities… The phone and tablets also feature a powerful battery and nano-heat dissipation technology that allow it to perform immaculately when running heavy applications.

Digital Journal: Aasim Saied is Revolutionizing the Education Industry With Projector Tablet Technology