Category: Education

Secondary education is a students’ last stop before either entering the workforce or continuing to higher education. Regardless of whichever path they choose, it is crucial to ensure thorough preparation for professional success. Using predictive analytics can increase a student’s likelihood of achieving this success and help continually improve upon their learning experience.

How is Data Analytics Being Used?

Primary and secondary education share many analytics use cases when it comes to improving student outcomes. Both are required to meet criteria based on standardized testing, English language learner proficiency, and additional nonacademic measures. However, secondary education places a much heavier weight on graduation and completion rates. Let’s explore how data is being used to influence the path to completion and advancing these student outcomes.

Attendance

Data analytics can be used to closely examine factors beyond grades such as attendance and the amount of time spent outside the classroom. Schools often have hundreds or even thousands of students, which can make identifying absence trends of individual students challenging. 

Attendance and out-of-school suspension metrics, for example, can be monitored to highlight chronic absenteeism and potential at-risk students. This allows educators to decipher what factors might be supporting or hindering individual students. Reviewing this aggregate data can also bring determinants not typically associated with attendance, such as school climate, to light.

Graduation Rates

Graduation is the goal of every educator and student in secondary education. Though, pinpointing the advancing indicators that a student might drop out can be extremely difficult. Based on historical data, analytics tools can detect complex patterns and insights into signs a student might be in danger of not graduating. Recognizing initial warning signs and taking action early on can make all the difference in the long run.

Curriculum Adjustments

Continuously evaluating and improving upon instruction is another way data analytics is changing secondary education. Curriculum differences amongst feeder schools are an area of concern when it comes to a student’s success within secondary education. For example, say a nontraditional math course is offered at the traditional feeder school to align with the high school’s curriculum. If this course is not offered at the other middle schools in the area, this could position students from other areas to struggle with this topic at the high school level.

Data analytics would enable educators to monitor the performance of this target student group and highlight which students need additional support. This not only assists in keeping students on track with their peers but also maximizes student learning opportunities. 

Conclusion

Big data is transforming the education sector through an increased focus on data-driven decision-making. Promoting these variables of student achievement is a fraction of the core benefits that come with adopting analytics in education. By taking a data-driven approach, any school can enhance student outcomes through actionable insights.

As the combined use of technology becoming more prevalent in education, the volume of this data has been rapidly increasing along with it. States collect information regarding learning, testing, and demographics from hundreds of students and schools each year. But how exactly are school districts supposed to use all of this data?

To explore how data analytics is transforming primary education, let’s take a look at how it’s currently being used to enhance the key variables of student achievement.

Why Primary Education?

The mission of primary education is to provide students with foundational learning skills and to ultimately promote student success. Along with this mission, it’s also important to keep in mind that primary education consists of a student’s core developmental years. Their success here is critical in preparing them for their journey into secondary education and beyond.

If assessment and student success are not properly monitored at this stage, learning gaps can easily be overlooked. This can have a negative impact on their foundational learning as well as their future achievement outcomes. 

How is Data Analytics Being Used in Education?

Data analytics tools help schools use their data to satisfy state-mandated accountability requirements and identify areas for internal improvement. One of the key functions of Big Data and analytics in education is measuring and providing insights for the various determinants of student achievement. 

While many important factors go into a student’s performance, educators are working with data to improve assessments, ESSA status, and teaching effectiveness.

Assessments

Combining various sources of student assessment data helps teachers and administrators measure performance on multiple levels. Schools can set and monitor education goals for an entire school, a specific class, an individual student, or even by subject. Additionally, the use of these metrics goes beyond the tracking value to administrators. Making assessment and success metrics visible to students also opens up the possibility for students to develop skills in monitoring their individual learning. 

Teaching Effectiveness

Teachers can use this collected data to gain a deeper understanding of how they should tailor future assignments or adapt their teaching style.

Comparing historical assessment data can assist teachers in identifying any possible learning gaps. These insights can then be used to evaluate the design of lesson planning and teaching methods. For instance, a teacher might decide to allocate more time to topics that students have historically struggled with or try a new instructional approach.

ESSA Status

The Every Student Succeeds Act, referred to as ESSA, requires that schools meet a certain degree of academic performance and assigns them a status based on the identified need for support. Accountability here is predominantly focused on the requirements for subgroups of students and other academic measures.

Data analytics empowers schools to convey the performance of these subgroups in real-time. This increases accessibility to measured criteria for both educators and administrators. Schools can then easily communicate this information to stakeholders to not only inform but also spark additional conversations regarding areas of needed improvement.