Data-Driven Decision Making in Education: Best Tips for Teachers and Administration

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Data storytelling, which stands for the process of interpreting the results of the data analysis, is one of the most powerful techniques used for data-driven decision-making. According to Gartner, by 2025, data stories will become the sources of the most valuable insights for any industry. The field of education, which is on the way to becoming data-driven, isn’t an exception. 

Making data-supported decisions is the best way to achieve the ultimate efficiency of learning and teaching, improving the learners’ experience, increasing the satisfaction of both students and teachers, and even cutting the operational costs of an educational institution.

Let’s find out how data-driven decision-making in schools changes the way we teach and learn. 

What Is Data-Driven Teaching?

Data-driven teaching refers to the process of using data collected from multiple sources to improve the process of knowledge delivery, students’ performance, and the approaches to education as such, innovating the way of knowledge transfer and making it more tailored to the needs of modern learners. 

Data-driven decision-making in education, in turn, means using the insights extracted from the data to develop better teaching and learning tactics and approaches. 

But to have the best picture, the educational institutions have to use data arrays gathered from multiple sources to take a look at the same problem from different angles. 

The types of data to use in data-driven decision-making are listed below. 

Types of Data to Use for Decision-Making in Education

  • Academic achievement data. This data gathers the statistics on students’ performance for a certain period of time, depending on the discipline.
  • Non-academic data. This data refers to the information about students’ attendance and their socio-economic status. 
  • Program and system data. This data array unites all the kinds of standards and regulations on data transfer quality and the best practices to do so. 
  • Perception data. This is the data gathered from the learners directly, and this feature makes it pretty valuable. By analyzing the surveys and opinion polls, the teachers and administrators can unlock the feedback and better tailor their teaching strategies to the students’ expectations. 

The Benefits of Data-Driven Decision Making in Education

Let’s talk about the benefits of data-driven decision-making in education which allow the educational institutions to reach a new level of quality of education delivered, student performance, and experience of both teachers and learners. 

  • Personalization. While the ultimate personalization is still impossible, analyzing the data and making data-driven decisions allows for better personalization, at least within a group of students. For example, the teachers can decide to focus on a specific type of information delivery. This can be educational videos if the perception data suggests that a given group of students perceive knowledge better in the video format. 
  • Performance. A personalized learning process is more encouraging and interesting for students. Their deeper engagement results in improved performance, improving attendance as well. 
  • Satisfaction. A good level of personalization leads to improved students’ performance. This contributes to the bigger satisfaction of students for their success and of teachers for their work. The issue of teachers’ satisfaction was researched in 2015, and according to this study, student performance is one of the most important factors for it, along with the school environment where the “teachers’ satisfaction is both an aspect and a consequence”. 

How Сan Teachers and Administrators Use Data-Driven Decisions in Education?

Below are some ways to make data-driven decisions in education, building a holistic strategy for education delivery improvement. 

  1. Analyze Previously Taken Decisions

This is the step making data-driven decisions in education should start with. Analyzing the actual efficiency of a certain decision can become a good foundation for further strategies development supported by the data. 

  1. Find the Patterns and Anomalies

Artificial intelligence models can help spot patterns and anomalies in student performance that otherwise might have gone unnoticed by the human eye, and bring them to the attention of decision-makers.

For example, an educational institution can gather data on student attendance and align it with students’ socio-economic status (for instance, the need to combine the full-time job and studies). With this reason in mind, the institution can develop an evening education program or create remote learning opportunities for this group of students. 

  1. Create Data-Inspired Lessons

This is the point where data-driven decision-making in education meets personalization. Being inspired by the insights gathered from perception data, the teachers can create more engaging, effective, and personalized lesson plans.  They make them more tailored to how students perceive new information and deliver them the knowledge they expect to be the most useful in their future careers. 

  1. Visualize the Data

Data-driven decision-making in higher education works better when the insights extracted from the data are shared with stakeholders, administrators, and other teachers. 

Visualizing the data patterns is one of the best ways to represent them and show a clear picture. AnalyticVue’s customizable dashboards make it easy to display data and filter it for enhanced data analysis, reporting, and ultimately, decision making. 

  1. Plan for the Future

Educational data mining and predictive analytics also open up an opportunity to make forecasts based on existing data patterns and trends. This allows educators to adjust teaching strategies accordingly or craft an individual approach to help prevent a particular student from dropping out. 

Tools for Data-Driven Decision Making in K12 Education

There are a lot of data visualization tools to support data-driven decision-making, however, K12 data has its own specifics, such as the need to integrate and align the data sources, privacy concerns and more. Besides, it is important that non-technical users be able to use this solution too, without a steep learning curve. AnalyticVue conveniently accommodates these challenges as this is a solution built specifically for K12 data integration and analytics. Implementing AnalyticVue can definitely help  integrate data-driven teaching into the educational process and leverage its benefits.

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