Over the past decade, the education sector has been flooded with an influx of high volumes of data in varying formats. To mine this data and visualize actionable insights from it, organizations need to consolidate it and leverage education analytics to derive insights.
Data warehouses help achieve this by acting as a single source of truth for educational organizations. They minimize the time required to consolidate data, extract insights, and build real-time visualizations. An integral component of the data pipeline architecture, data warehouse not only helps build automated data pipelines, but can also be easily connected to BI software to create dashboards for reporting and analytics.
In this blog, we will look at the different types of data analytics used in the education sector and how they can be leveraged. We will also explore how a robust data warehousing architecture can help institutions track student progress, empower students and teachers through data-backed course content, and modernize administrative operations in an organization.
Types of Data Analytics in Education
Managing and administrating an institute is tied to collecting large amounts of education data coming from disparate sources. This is important as schools and colleges need to recruit the most appropriate talent for their classes and design curriculum that can be understood by students coming from ethnically and geographically diverse backgrounds.
From an administrative perspective, registrar files and financial records must be maintained to guide managerial decisions and ensure that all students have paid their dues. Often, universities have limits on the number of students that can enroll in each class, so admissions and course allocations must be recorded and monitored as well.
These institutes can optimize the learning experiences of students by implementing an agile data warehouse that provides information about the seat caps and the number of seats filled, and available seats for each class. A data warehouse can store aggregated information over the years to guide faculty members and registrar office professionals in designing courses, setting the class size for enriched learning experiences, and making the curriculum accessible to students from diverse educational backgrounds.
As shown in the image above, we can divide the types of data and analytics in the education sector into four broad categories:
- Descriptive Analytics: Descriptive analytics in education refer to metrics that tell us what has happened historically. These can include attendance records, mark sheets, teacher performance evaluations, and budget sheets.
- Diagnostic Analytics: Diagnostic analytics refers to the interpretation of sets of descriptive metrics to uncover patterns and make inferences. An example of diagnostic analytics is the relative grading system and subsequent GPA calculation for students.
- Predictive Analytics: Predictive education analytics focuses on forecasting what will happen in the future. For example, the total number of students enrolled in a particular course for the past five years can be used to predict how many students will enroll in the following years.
- Prescriptive Analytics: Prescriptive learning analytics and education data can help optimize administrative practices and teaching agendas for future classes based on the existing data. For instance, classes with larger cap sizes might be reduced in size if the average student performance is not up to par.
In the next section, we will go over how a combination of different types of analytics can help professionals, staff, faculty, and the students at educational institutions.
Tracking Student Progress
Developing a progress tracking system enables organizations to gauge the effectiveness of their course content and measure results in the classroom. This information allows them to adjust the difficulty and comprehensibility of the taught material to ensure that students can learn new skills and gain knowledge.
Tracking also benefits students as they can identify patterns and trends in their performance and see which approaches to studying have worked for them in the past. For example, a student might learn that they score better on exams when they study with a partner. Therefore, they might prefer to study in groups to increase their probability of achieving a good grade.
Of course, the metrics that are measured to track student learning analytics vary for each course, depending on the nature of the curriculum and teachers’ preferred mode of instruction. Typically, institutions can collect test and quiz scores, behavioral assessments, and homework completion rates to assess students’ learning.
Adjusting Curriculum and Teaching Methods
According to a study on personalized learning, organizations that leverage higher education analytics report greater achievement rates from their students. In addition, catering to the individual learning needs of students has been shown to accelerate learning and improve mathematical and analytical skills.
Also, instructors should develop an awareness of their students’ learning requirements to optimize teaching methods and course content. As each class comprises of individuals with varying skillsets and levels of conceptual understanding, it can be helpful for teachers to divide large groups into smaller subgroups and personalize learning experiences based on descriptive education analytics.
Instructors and college professors need to be mindful of several factors when designing a curriculum. They are primarily concerned with:
- Time Restraints: Teachers need to consider the total number of classes they will be teaching and the amount of content that can be taught effectively within that duration.
- Cost Restraints: Course components such as film screenings, site trips, and online modules might cost more, and therefore, instructors might have to assess which works best for them.
- Holistic Data: Instructors can use student data across the organization, such as ethnicity, race, religion, and academic records, to understand the different cross-sections that exist within their classrooms.
Education data provides an ideal solution for teachers looking to adapt their teaching according to the requirements of class students as well as the objectives they want to achieve from their curriculum. As learning becomes more modern and advanced, designing more flexible and malleable courses can virtually guarantee higher achievement rates. For example, certain universities in the United States conduct research surveys to assess the proficiency of incoming students in certain skills and then teach at a pace that is acceptable for the majority.
Modernizing Administrative Operations
Lastly, data can provide a mechanism for educational institutes to modernize their administrative operations and be prepared for the desired outcomes. This can involve optimizing enrollment procedures, finances, bill payments, teacher evaluation, and feedback systems, and managing employee salaries.
This eliminates the need for hours of clerical work and the cost of maintaining file management systems as information is updated into the organization’s data warehouse as soon as it is recorded in any of the source systems. In addition, the data becomes easily accessible to key decision makers (superintendents, deans, etc.) allowing them to plan initiatives based on a bird’s eye view of their operations.
Modernizing data infrastructures in educational organizations helps in deriving insights faster through interactive visualizations in business intelligence software in contrast to manually sorting through individual records. Some of the most notable institutes that have been able to modernize their IT landscape include Texas A&M University, Monash University, Vanderbilt University, Touro College of Dental Medicine, and Henrico County Public Schools.
Fast-track Your Education Analytics with Astera ADWB
A customized data warehouse architecture for your educational organization can help streamline education analytics and business intelligence processes to drive more revenue and facilitate teaching as well as learning experiences.
Astera Data Warehouse Builder is a state-of-the-art ETL tool, designed to supplement enterprise data needs for all types of institutions. It can help users automate their data pipelines from disparate sources, consolidate information for a holistic analysis, apply transformations to clean data, and connect to BI software such as Microsoft PowerBI or Tableau for dashboarding and visualization.
If you have made it this far in the blog, we highly recommend you see a demo of the product in action to see how ADWB works. Click here to schedule a demo now!