Data Analytics Education

The Future of K-12 Analytics

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.


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. 


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.

Data Analytics Education

How Data Analytics is Transforming Student Achievement

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.


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. 

Big Data Education

How Is Data Analysis Affecting the Education Sector in 2021?

When people think about Big Data, most of the ideas that come to mind involve businesses or governments trying to deal with large-scale issues. Many use cases involving Bid Data and analytics boil down to questions that can be answered if you happen to have a massive number of data points to work with. This means that the education sector is, in fact, one of the ripest areas for new analytics work to be done.

Why the Education Sector?

In the simplest terms, education and analytics go together well because of the need to teach hundreds of millions of both children and adults each year. From preschools to doctoral programs, most people on the planet now receive at least some formal education.

This means that there is data about people across many cultures, and that has the potential to act as a filter in performing analysis. One of the biggest concerns in the world of Big Data is that datasets are imputing biases. Organizations sharing data at the global level have access to information that can be used to filter out biases, normalize what performance should be and devise better class plans based on hard data science.

Similarly, academia has a much better tradition of sharing than other areas where analytics has made major inroads, especially finances. While folks on Wall Street are often worried about keeping their findings tightly guarded, educators generally want to share their discoveries as far and wide as possible.

What Are the Potential Use Cases?


Intervention is an important job at all ages in the education process. A classic case where Big Data has been in use longer than the term “Big Data” has been around is in tracking high school and college dropout rates. While plenty of data on the subject has been available for decades, the opportunity to apply analytics has made an old use case into a fresh one.

Detecting wobbliness in student performance, for example, can be a challenge for a human being to do. It’s difficult for a single teacher to spot a student whose grades are starting to slip. At a macro scale, though, potential dropouts can be identified from the larger pool by matching them to previous students who matched the pattern. Schools can then direct resources such as study aid, financial help and even counseling toward students who might be at risk.

Career Paths

Even the best and most stable performers can feel challenged by picking a career path. Students can be tracked using multiple datasets and questionnaires to determine what career paths match their interests and what courses they should be taking. If a student wants to get into a STEM field, for example, a model can be worked up that will guide them in their high school course selection. This can ensure that they’ll be better prepared when they get to college. Similarly, students who are a bit tepid can be directed toward courses that will help them find their paths.


While it’s important to note that analytics systems aren’t oracles, there’s something to be said for trying to forecast students’ grades based on demographics, curricula, institutions and other factors. If the forecast for an elementary school student looks worrisome, interventions can then be arranged to make up for gaps that might only appear years or even decades down the road.

Likewise, the same approach can be used to intervene at schools that may be on the brink of trouble. This can be especially helpful when looking at problems like budgeting, teacher allotments, deploying resources and even shutting down schools. A district may be able to run multitudes of simulations to drill down to what is the ideal composition for a district. The goal should ultimately be to make the best use of the dollars available for each student.

Teaching Styles

One of the hardest issues to address in education is getting teachers who may be laggards to perform as the best educators do. While it’s tempting to tag these folks as “bad” teachers, the reality is often that they don’t quite have the magic formula for controlling a classroom and engaging with students. It’s also easy to dismiss the best teachers as talented, but it is possible to track what they do well. With this template in hand, interventions can be done during their time in universities and during teacher training to adapt their skills to what works.

How to Become Data-Centric

Every organization that moves toward analytics has to embrace a new culture. On one end, this means embracing data and its use. In some cases, this even means letting go of administrators who can’t get on board with analytics. Conversely, it’s also important to make sure that happy adopters appreciate the importance of dealing competently with issues like data privacy, biases, anonymization, errors and the limitations of analytics-driven decision-making.

It will take time to build an educational system that used data to improve life for both educators and students. The field, however, is ripe with available data. Education is a sector that is fertile ground for analytics, and there is also an installed based of interested educators who can plow the ground. With direction and resources, virtually any school can benefit from adopting Big Data and analytics.

Data Analytics Education

How Data Analytics is Transforming Higher Education

Institutions of higher education are among the best potential adopters for analytics platforms and big data methods. They oftentimes have thousands of students, and their total enrollment numbers over many decades or even centuries can sometimes number more than a million. Students also frequently take more than 40 classes to complete a single bachelor’s degree. Without getting into more granular individual data, these estimations alone represent an abundance of data to work with.

Putting this data into action, however, has required a commitment to ongoing digital and data transformations. These efforts often have been aimed at improving institutional efficiency. While this is a big target, there are several ways to hit it. Let’s take a look at how data analytics systems are transforming higher education.

What’s Being Used?

Traditionally, a good analytics package has to be backed by solid infrastructure. This means deploying database servers, oftentimes cloud-based ones, that can securely store large amounts of raw data. Likewise, these servers have to be designed with privacy and security in mind to protect sensitive student data.

Most data scientists then use a variety of solutions to prep the data for analysis. It’s not uncommon to write bespoke code for this purpose in order to correct minor issues. The data then has to be checked against to make sure nothing is:

  • Altered
  • Lost
  • Place in the wrong column or row in the database

Big Data services can then be connected to analytics packages to conduct research, develop models, generate reports, and produce dashboards. From these, insights can be generated that decision-makers can utilize. Likewise, long-term data warehousing is used to maintain and share the information accumulated from these efforts.

How Can an Institution Use Data Insights?

Student retention is one of the most difficult challenges that higher education institutions face on a yearly basis. Every semester, thousands of students will decide that completing college just isn’t in reach. Frustration will eventually sabotage their academic efforts, and dropping out becomes a real risk.

Predictive and prescriptive analytics are needed to address this problem. First, predictive analytics packages allow researchers to model patterns regarding which students are most likely to have academic trouble as well as when these concerns may become critical. Once a student’s issues have been identified, prescriptive analytics will provide administrators with a list of potential solutions to apply.

Suppose a student comes from a specific geographic background that has a history of running into learning difficulties within the first two years of college. The university might have all students take certain core classes that provide a solid baseline for identifying these students. For example, a section of writing might be included as a core class to distinguish students who struggle with the basic skills needed to produce academic-quality papers.

Upon finishing the first semester of these classes, underperformers might be flagged based on the challenges that similar students have faced. A prescriptive analysis can then be used to assign them to classes or provide them with academic resources that will provide appropriate remediation to close their skills deficits. Academic support may be provided in the form of tutoring, mentoring, and other various resources.

A number of problems in higher education can be handled this way. A university, for example, might use analytics to address:

  • Faculty retention rates
  • The allocation of budgets and supplies
  • Campus crime
  • Sports team performance
  • Frictions between the school and the surrounding community
  • Regulatory compliance issues

What Has to be Done

Higher education is a sector that has a bit of reputation for keeping with traditional or conventional practices. However, adopting data analytics is a bit like quitting smoking: there’s is no better day to get started than today. 

While institutional review processes need to be preserved, that should not stand in the way of aggressively rolling out the use of analytics. Decision-makers have to be onboarded with a data-centric culture, even if that means offering severance to folks who can’t get on board. Appropriate measures have to be taken to acquire machines, adapt existing networks, and integrate the university’s trove of data. With a long-term commitment to becoming more data-driven, an institution can achieve greater efficiency in achieving its goals and service stakeholders.

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