What is Learning Analytics? Turn Data Into Meaningful Lessons

At first glance, it might seem as if data and empathy have nothing in common. 

However, when put in the context of analytics and extracting information to draw meaningful conclusions, data takes on a whole new meaning.

Whether it’s to get a well-rounded view of customers, innovate new solutions to problems, or improve the education system, institutions worldwide use data to better understand the people they interact with. 

And while plenty of arguments work against establishments and software tapping into our personal data, learning management systems are a blessing in the realm of learning analytics. 

 Universities, standardized testing organizations, and online education platforms use learning analytics to collect data about their student base and how they learn so they can make adjustments to their approach if necessary. 

Learning analytics closely mirrors data mining, which typically has a negative connotation. However, there is a moral difference between data mining that intends to sell you something and data mining that works toward improving education systems.

In the context of learning analytics, this collection of information is referred to as educational data mining. 

What is educational data mining?

Educational data mining refers to the field of research concerned with gathering and applying data, learning, and statistics cultivated from learning systems. 

Educational data mining is a relatively new method being implemented to identify areas of improvement in education systems. By gathering information on students and the settings in which they learn using education technology, institutions can better understand their current learning situation and identify the best way for them to learn within it, opening doors for those students to advance. 

Information typically gathered includes how many students accessed a certain piece of information, when it was viewed, and how long it was displayed on their computer screens. Educational data mining can also collect information on student assessments, such as when someone submits a response to a problem and whether or not it was correct. 

While these might seem like minuscule details regarding a student’s overall potential, when learning analytics is applied, these details can be analyzed to draw meaningful conclusions. These practices have done wonders for educational psychology and learning analytics. 

How is learning analytics used? 

Instructors have been keeping track of student information since educational institutions were first established.

Graded assessments have acted as the cornerstone for educators to understand where students are progressing and where they need additional support. When applied to the digital era we currently live in, those timeless practices can live on, and new ones like learning analytics can be adapted at the same time. 

One of the most common uses of learning analytics is predicting the success of students, including determining if a particular student is on the course of passing, failing, or dropping out. Beyond that, there are a couple of other reasons educational organizations implement learning analytics. 

Supporting student development 

Besides offering support to students who are at risk of performing poorly or dropping out, learning analytics can also offer educators the information they need to assist students in their overall development.

This might include finding the right skills and strategies that correspond to a particular student’s preferred learning style or helping students adapt to their current learning environment. 

Offering feedback

Based on the information collected with learning analytics, educators can offer positive feedback and constructive criticism to students. These pieces of advice can be delivered immediately, giving students fast opportunities to improve. 

Developing students’ self-awareness

As students receive feedback and understand proper personal development methods, their self-awareness increases, enabling them to find unique ways to help themselves with their own learning. 

Providing quality education

Overall, the reason learning analytics is used is to increase the quality of education being provided to students. By learning more about what works and what doesn’t, educators can improve the experience. 

Learning analytics approaches 

Students show their progress, interest, and skill levels in a variety of ways, meaning there are a few different avenues for collecting data for learning analytics.

Before we go over some of the approaches that learning analytics professionals can take when gathering information, it’s important to note that focusing in on just one of these might limit the conclusions you can draw from learning analytics. 

Performance can’t be based solely on test scores or participation -  different people learn better under different circumstances. To get an accurate picture of a student’s situation, more than one area of input should be analyzed. 

• Content analysis: Looking at how students perform on essays, tests, and other assessments. Some materials for content analysis are objective, while others are a bit more subjective, so it’s important to use both types of content. 
• Discourse analysis: Analyzing student interactions and languages used and relating it back to the context of education. 
• Social learning analysis: Examining the social interactions and learning networks that occur in educational settings. 
• Disposition analysis: Understanding the way students look at their relationship with learning and curiosity levels. 

Learning analytics methods

With all of those approaches listed above in mind, there are four key methods used to gather data for learning analytics. Each method can be applied to content analysis, discourse analysis, social learning analysis, and disposition analysis. 

To clarify, the approaches for learning analytics (listed above) refer to student actions educators look at to measure certain areas of performance, and the methods are the means of analysis. Essentially, the approaches are the information they gather, and the methods are how they analyze it. 

A crucial part of any learning analytics method is finding patterns and trends in the data. By making a constructive and informed generalization about students and their learning situations while also recognizing outliers, these tendencies are key in finding ways to improve the student experience. 

Descriptive analytics

Descriptive analytics refers to the collection of historical data to draw conclusions. In the context of education, descriptive analytics is used to gather information on a student’s past performance.

This can be done at any stage of a student’s involvement with an educational institution - from the moment they enroll to when they start taking exams and graduate. 

Descriptive analytics is also a key tool in gathering feedback from a large group of students to understand how the masses feel about their experience while being educated. A general consensus on things like course material, learning environment, and interaction levels can offer valuable insights for educators looking to improve those processes and experiences for students. 

Diagnostic analytics

Diagnostic analytics collects data to understand the reasons why something happened. In the educational realm, diagnostic analytics is used to establish why a student performed well or performed poorly.

It’s all about relationships: X happened because Y occurred. Kim performed well because she had more hands-on attention. Scott didn’t perform well because he wasn’t seeing a tutor when he should’ve been. 

Educators will take a deep dive into the strategies to see which ones supported students and which ones could’ve pulled their weight a bit more. Understanding why a certain educational tactic worked or didn’t work is a critical component in making adjustments. 

Predictive analytics

Predictive analytics does exactly what anyone would assume it does - predicts the future. In the field of education, predictive analytics identifies patterns in student behavior and performance to identify any risks or opportunities.

By gathering historical data and factoring in changing conditions that will affect students and the educational institution in the future, educators can predict the outcomes of taking or avoiding certain actions. 

Prescriptive analytics

Prescriptive analytics takes the other methods of analysis a step further by using those predictions to offer advice on the possible outcomes of a situation. Because of its complex nature and goal of accurately predicting the future, this method uses advanced means of analysis, like machine learning, algorithms, and computational modeling. 

Examples of prescriptive analytics in education include determining if a change in course work will affect the performance and engagement levels of students before actually making those alterations or giving educators metrics that will offer an idea of how they can expect students to perform. 

Impact of learning analytics 

Educational institutions have been collecting data since they were first established. Simple things like a student’s address, test responses, and grade point averages are all examples of educational data. However, it wasn’t until learning analytics was applied that this valuable data reached its true potential. 

Online learning platforms, learning management systems, and technology used to assess student performance can collect hundreds of data points. Using the various learning analytics methods, educational institutions can unlock patterns and trends regarding the reasons why something occurred, the likelihood of an event, and advice on how to handle certain situations. 

This opens a world of possibilities for educators. Imagine if you could predict which students are going to pass, which ones are going to fail, and the reasons why. What about the point at which most students drop out of school and when that turning point occurs? This information allows teachers to be proactive by taking particular actions (and avoiding others) to help students perform to the best of their ability. 

Here are the key beneficiaries of learning analytics and what they gain: 

• Students: receive feedback and constructive criticism in the hopes of improving performance 
• Faculty: find ways to offer course material and guidance in the most effective way possible
• Administrators: understand the outcomes of programs, curriculum, and approaches 
• Online course providers: gain perspective on the impact of having classes online compared to in-person meetings 

Overall, learning analytics provides educators with the information they need to offer students the greatest possible chance of succeeding in their education. 

Learning analytics FAQs

Let's take up a few questions about learning analytics before we sum up this educational content.

Q: What is learning analytics in higher education?

A: Learning analytics in higher education allows institutions to understand how students are doing in their studies. It helps figure out the best ways to teach and support students. The process relies on technology and data-driven approaches for effective decision-making. Ethical considerations, including data privacy, are crucial in implementing learning analytics.

Q: What is learning analytics used for?

A: Application of learning analytics in educational settings includes:

• Tracking student progress.
• Identifying at-risk students.
• Personalizing instruction.
• Assessing the effectiveness of teaching methods.

Q: What kind of data is collected in learning analytics?

A: Learning analytics collects various data types, like academic performance data (grades, test and assessment scores), engagement metrics (time spent on tasks, attendance, interaction with course material), social interactions, and more. 

Q: What are the benefits of learning analytics?

A: Learning analytics benefits both educators and students. Where institutions can provide timely support using data-driven insights and refine teaching methods, students are offered personalized feedback and enhanced learning experiences. 

Q: What are the challenges of learning analytics?

A: Common challenges with learning analytics include:

• Privacy concerns.
• Data security.
• Data interpretation.
• Ensuring data quality.
• And potential biases in the analysis process.

Improve with data

The idea of big data and mining information can be seen in a negative light. However, when applied to the context of education, it’s tricky to see the downsides.

It’s an unfortunate truth that education isn’t prioritized in many places, and finding ways to improve learning strategies can often be overlooked. As frustrating as that might be, sometimes answers lie directly in the data. 

Looking for other ways to empower your students? Check out our list of free remote learning software to make education happen from just about anywhere.