Big data in education: Reshaping learning with analytics

With the evolving crisis in education, it’s becoming more and more important for schools, colleges, and universities to stay competitive, and here’s where the burning topic of data analytics in education emerges. As proof of the growing trend, it is estimated that big data analysis in the education market will reach $36.12 billion by 2027. 

Data analytics in education means the systematic and automated collection, processing, and analysis of educational data to gain insights that improve teaching and administrative processes and, subsequently, their results.

With our extensive experience in the EdTech industry, including data collection and personalized learning, we’ll guide you through the most important aspects of data analytics in education and help you find out how it can help your university or school stay competitive and succeed in the industry today. 

Benefits of data analytics in EdTech

Traditional education systems currently face significant challenges. Many students have become disengaged, and teachers aren’t sure why or how to re-engage them—the system can no longer meet the educational needs of younger generations.

The problem is the world has become faster, more diverse, and more supportive of our unique needs and features, but the traditional approach to education hasn’t. 

Traditional teaching methods expect all students to follow exactly the same path and pace, which isn’t working. 

• Some students are lagging, others are on track, and some have gone further and are ready for more challenging content. 

• Some students are okay with large textbooks, while others can’t digest information this way and need a different approach, so inconsistencies may occur. 

These differences in students’ progress are only visible to educators when they become significant. In other words, the best time to intervene has already passed. This old way of addressing the issues only after they become apparent is more difficult and less effective.

So, what can schools do? 

They can replace their reactive approach with a proactive one through education data analytics. With real-time access to educational data, there will be fewer gaps in identifying struggling students, refining teaching methods, and optimizing resource allocation.

So, that’s a sneak peek of how big data in education can help you reinvent learning environments. By using education data analysis, you can make informed, data-driven decisions and address challenges before they become burning issues.

7 benefits of big data in education

Recognizing a problem in the learning process is the first step to solving it. However, the role of data analysis in education goes far beyond tracking outcomes and engagement parameters. It gives you actionable insights to help shape personalized learning experiences and ensure efficient resource allocation.

Here are the seven ways you can use education data analysis to modernize your education systems and the benefits you can expect:

1. Enabling personalized and adaptive learning. Machine learning helps expose student strengths and weaknesses. Then, your students will get tailored lessons to help them progress at their own pace. Adaptive learning platforms will provide targeted resources for each student. The result: personalized learning paths based on each student’s needs. 

2. Improving engagement and retention. With personalized learning paths, your students will be more motivated to engage with the content. As an added benefit, this will improve their retention and learning outcomes. So, your students will want to learn more and be able to remember and apply more of what they learned, which is the basis of student and school success.

3. Streamlining curriculum development. With insights into performance trends and evolving student needs, you can create an iterative approach to curriculum development. So, your courses will stay relevant and practical for each student cohort.

4. Optimizing resource use. Using data analytics in education can help your institution distribute resources where they’re most needed, including teacher time, technology, and materials. For example, if students are struggling with algebra, you can devote more teacher time and tools to this topic.

5. Enabling proactive interventions. Early problem detection helps a school identify and support struggling students, improving engagement and retention rates. So, fewer students will drop out, and all of them will feel supported in each step of their educational journey.

6. Empowering collaboration and continual improvement. Big data analytics in higher education can spark collaboration among teachers, administrators, and students. When you share learning insights with everyone involved, you can drive continual improvement through shared goals.

7. Streamlining processes. Big data analytics for schools can help you with complex administrative tasks. This includes enrollment management, resource planning, and scheduling. By automating routine tasks, your institution can ensure smooth operations, as well as high staff and student satisfaction. So, your processes will run smoothly, and your staff and students will feel empowered to pursue higher goals instead of being overwhelmed by the administration.

In the age of big data in higher education, educators and administrators can’t ignore its transformative potential. From improving student outcomes to creating streamlined processes, learning data is revolutionizing the market. The future of education is adaptive, efficient, and highly personalized. And it’s already here.

Applications of big data in education: 5 compelling examples

Big data and higher education have converged to drive educational innovation. There is a diverse range of use cases that show how analytics can take your institution to the next level. So, let’s look at some examples.

1. Personalizing learning based on student data

You can use data analytics to create customized learning paths. Here are the steps you should follow:

1. Data collection and integration. You can collect data from big learning management systems, online quizzes and tests, classroom performance records, and interaction with digital tools.

2. Data processing with big data technologies. This includes preparing and analyzing diverse data formats, such as text logs, videos, or structured data.

3. Employing advanced analytics and AI algorithms. This enables pattern recognition, performance prediction, and content recommendation.

4. Creating adaptive learning systems. This is where the magic starts to happen. These systems will dynamically adjust what content is shown to a student based on their actions.

5. Employing data visualization tools. These tools will give you insights into student performance, engagement, trends, and metrics across subjects and problem areas.

6. Continual improvement. You should establish feedback loops to ensure consistent refinement of recommendations and curricula.

The tech behind data analysis in education combines big data technologies, AI, and user-friendly interfaces. This combination creates a school ecosystem that evolves alongside its students.

Example: adaptive learning platforms powered by student data

Dreambox Learning started offering adaptive math curriculums for grades six through eight during 2014. All schools in the United States can utilize the programs. The software tracks how students interact with lessons and challenges them appropriately. The result was students’ math performance improved by up to 60% in one year.

On the other hand, Knewton Alta offers adaptive learning for higher education. It combines practice with advanced personalization. This includes detailed explanations, prerequisite skills, and remediation instruction. And it’s all based on student performance.

2. Improving student retention and engagement

Once you have personalized learning paths, you can boost your student engagement and retention. How? Learning analytics will help you identify patterns, trends, and behaviors. With this information, you can assess which students are engaged and doing well versus which students might be at risk of failing or dropping out. Then, your organization can reshape educational experiences to suit each student and encourage their progress.

Here’s what you can do to improve student retention and engagement:

1. Monitor student interaction with learning platforms (number and frequency of logins, time spent, participation in discussions, assignment completion)

2. Analyze academic performance (identify trends: a drop in grades, poor test scores, incomplete assignments)

3. Track attendance (identify irregular attendance)

4. Identify social and emotional factors (surveys, peer feedback, behavioral data, extracurriculars)

5. Consider external factors (socioeconomic background, family support, commute times)

6. Use predictive models (analyze historical data to predict outcomes for similar profiles of students)

7. Enable real-time alerts and dashboards (display student risk levels, empowering teachers and administrators to act)

8. Suggest personalized interventions (data helps determine the most effective intervention—tutoring, counseling, or financial assistance)

Example: data-based interventions that boost engagement

One example of data-based interventions comes from Purdue University and its Signals app. Course Signals uses many big and small factors to predict student performance. These factors include demographics, past academic history, and interactions with the LMS. 

Once data is analyzed, faculty and students can see a specific color on a stoplight to indicate how the student is doing. The majority of students (89%) reported a positive experience with Signal. Similarly, students and staff said it improved engagement and motivation.

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3. Optimizing curriculum development

As the learning process evolves, so will your curriculum contents and delivery methods. Data insights can give you real-time, evidence-based feedback on student performance and preferences. You can use this to adjust the curriculum based on those performance metrics and student feedback.

Here’s what you can do to improve your curriculums:

• Identify knowledge gaps and adjust content (based on assessments, interactions, and time spent)

• Incorporate student feedback (surveys, course evaluations)

• Improve content relevance (based on most accessed and/or least completed materials)

• Align them with industry needs (based on alumni/employer data and job requirements)

• Differentiate instruction based on performance (e.g., beginner, intermediate, advanced tracks)

• Predict and respond to trends (over longer periods)

• Test and iterate curriculum changes (analyze impact before making new curriculum elements permanent)

• Address diverse learning styles (offer different content formats)

Example: analytics-driven curriculum improvement

Maryland schools improved their math and reading curriculums. They identified gaps in algebra readiness in middle school students. Then, they adapted the curriculum to include targeted interventions for underperforming students. As a result, their test scores improved in these areas.

4. Streamlining administrative efficiency

Data analytics in education offers applications beyond academics. They can also delight your administrators by making their jobs easier. Here’s how.

By integrating data analysis into your administrative workflow, your institution can reduce workloads while improving operational efficiency and decision-making processes.

Example: predictive analytics for tracking student performance

Georgia State University employed predictive analytics to improve graduation rates by 22%. Their system tracks 800 risk factors, including student grades and attendance. Without AI-powered systems, the school would have to hire excess administrative staff to collect and analyze this amount of data.

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5. Supporting data-driven decision-making for educators

Finally, you can directly support your educators with data analytics. Educators can improve their decision-making through data dashboards. These powerful tools will aggregate and visualize data from various sources. Then, teachers can use them to inform effective teaching strategies through:

• Monitoring individual student progress (grades, attendance, engagement);

• Tracking classroom trends (test scores, participation, comprehension);

• Tailoring instructions for different learning styles (visual, auditory, etc.);

• Identifying at-risk students (academic and/or behavioral issues);

• Facilitating data-driven lesson planning (performance of past lessons);

• Simplifying parent-teacher communication (data compilation/summary);

• Real-time adjustments during lessons (track activity and responses in polls).

Based on these data points, your teachers can become proactive in their lesson plans and student and parent communication. They can adjust their teaching to resonate with students and address critical areas first.

Example: student insights guide teaching strategies

Los Angeles Unified School District (LAUSD) has launched My Integrated Student Information System (MiSiS). In essence, this system compiles different student information in one place. With role-based access, teachers, administrators, parents, and others can see relevant information for students. For example, a teacher may have access to multiple student records. This helps them track attendance, behavior, and academic performance. Based on that, they can personalize lessons for students or provide extra resources where necessary.

So, why is data important in education? The role of big data in transforming education institutions is immense. Personalized learning experiences and streamlined processes are just the beginning. Ultimately, big data analytics for higher education will help create smart learning environments that adapt even better to diverse student needs.

Genuisee’s role in EdTech data analytics solutions

With so many options and factors to track, it can be difficult to get started with EdTech data analysis. But, as long as you know what you want to achieve, our experts can help you achieve your goals. Our EdTech services include custom LMS, e-learning platform development, school management software, virtual classrooms, apps, and more.

Here are some of our projects that use data analytics in education:

• E-learning platform for aspiring med students. The interactive platform offers custom learning paths that help medical school applicants prepare for the next step in their education. It features video lessons, standardized tests, practice questions, and progress tracking.

• SciQuiry is an AI-powered e-learning platform. It focuses on engagement and learning personalization in STEM subjects. Gamification, animation, and challenges are some of the engagement boosters that make the platform successful.

• MyTutor is an online tutoring platform for personalized on-demand learning. It was built based on user insights for maximum convenience and security.

These are just a few of our projects as a snippet of potential inspiration. Whatever your plans, our dedicated teams are ready to support your unique needs.

Harnessing the power of big data in education

Educators and institutions are realizing the immense potential of big data in education. It is transforming all areas of modern education. Starting from enrollment and administrative tasks, it can optimize resource use and automate workflows. Most importantly, it streamlines processes in all stages of the educational process. 

Curriculum development, personalization, engagement, and academic interventions are empowered by data analytics in education.

If you’re still not sure how to start, browse through our data analytics solutions for EdTech. We’d be thrilled to help your organization harness the power of big data analytics and optimize your educational processes.