Efforts to improve education are non-stop. Whether it is a nationwide initiative or a classroom-based approach for a handful of students, data can be used to inform decisions. There are many pieces of data that are or could be collected in educational settings. These include:

  • Demographic data on students, teachers, administrators, and other employees
  • Students’ prior and current academic performance
  • Teachers’ credentials, training, and experience
  • Teaching and classroom management strategies
  • Assessment and evaluation methods
  • Budgets and expenditures on various aspects of the educational system, including personnel, transportation, supplies, books and other teaching materials, and sports and other extracurricular activities
  • Amount and type of hardware and software used in schools and administrative offices
  • Attendance records
  • Services provided to teachers and students, including services to specific populations of students
  • Students’ involvement with other public service agencies

Given the huge amount of data that can be collected, it is a big challenge to analyze and use the data to change educational practices for the better. To get the most out of data analytical efforts, teachers – the front line of education – must be trained to understand data analysis and interpret the results in terms of what they do in the classroom. They need to be able to take the data, such as on the relationship between their teaching methods and student outcomes for various types of students, and make adjustments, as needed, to the learning environment.

Understanding how to analyze the information that can be gathered can help to create better student learning outcomes

However, while the potential to collect, analyze, and use data is real, there are barriers to data collection in educational settings that hinder the effective use of the data. These barriers are limited access to the data by teachers and even administrators, slow or untimely collection of data, low quality of manually-collected data, and lack of time and support.

Data in Online Education

Online learning, however, is one area in which data analytics is more readily applicable. Not only is it easier to collect data – because everything is already digitized – but it is also easier to make adjustments for individual learners. With some of today’s online learning systems, the level of difficulty of questions or problem scenarios that are given to students can vary depending on their answers to previous questions, the number of attempts at answering a question, and the time it takes them to answer a question. Online learning systems also allows schools and larger entities, such as school districts and states, to collect assessment and demographic data.  All of these data can be used to make decisions as to what learning systems are used or where adjustments to learning systems are needed.

If manual data, such as scores on writing assignments and projects, and opinion data, such as what aspects of the learning system students like and dislike, are added to the automatically-collected data, schools can get a robust picture of where things are going well and where they are not. Because the cost of online learning systems is great, schools and school systems need to assure that the money being spent on technology is bringing the expected results in student learning.

Research & the Future

Educational research can also benefit from the use of online learning systems and data analytics. By combining and comparing even larger data sets, researchers can gain great insight into what works and what does not work in terms of providing a good educational experience.  For independent researchers, such as those at universities, their findings might result in recommendations to schools and school systems or in government policies. For commercial researchers, modifications to existing learning systems or the creation of new learning systems may result. In addition, the life of the company for which they work may ride on the research and the changes implemented based on the results.

To find out how to start your career in data analysis and make an impact in the educational field, click here.


Sampson, D. G. (2016).  Educational Data Analytics Technologies for Data-Driven Decision Making in Schools.

U. S. Department of Education. (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief.

Author: Neil Starr