What Is Learning Analytics? Key Trends & Use Cases

Whether you run a corporate training program or work in an academic setting, using data to better understand the learning process from all perspectives achieves better long-term recall and develops stronger learners and teachers. Learning analytics is just a part of a larger existing education structure but one that holds tremendous possibilities for the future.

John Whitmer, Ed.D., learning analytics expert, says: "It’s long been accepted that, when it comes to long-term learner success, one of the most important elements is how engaged learners are. This is a topic that I spend a lot of time thinking about and researching in my work...asking questions that will help us to identify behaviors and course materials that are especially effective. Through my work, I’ve found that Learning Analytics, when incorporated in your LMS, can provide a key tool to helping figure these out and put tools in the hands of people can make a difference, while there’s still time to make a difference."

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What Is Learning Analytics?

A learning analytics definition requires defining the aspects of the term. Analytics refers to the collection, measurement, reporting, and analysis of information, but within the context of learning, data is gathered about both the learner and the environment where instruction and learning are taking place.

The goal of this tool is to understand the learning process and identify the factors that influence how learning takes place. Learning deals with areas of educational technology, learning and assessment sciences, and educational research, where analytics is associated with artificial intelligence (AI), visualization, statistics, and computer or data sciences.

These complex elements converge around human-centered design elements of socio-technical systems thinking, participatory design, and usability.

While the research community has long studied student progress, teaching methods, school or training data, and assessment or curriculum design, never have these disciplines been powered by digital data and computational analysis through AI and data science.

The greater insight advanced technology brings to the fields will move past the traditional predictions of student success or failure. The use of analytics supports lifelong learning and skill development, creates personalized feedback in student learning, and advances personal awareness and self-reflection.

It can also evaluate instructional methodologies for effectiveness and inform pedagogical innovation.

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What Are the Best Ways to Use Learning Analytics?

Even though eLearning has created new opportunities for learning, there is a need for accountability and evaluation of the effectiveness of each course or program to ensure learning is taking place.

Analytics provides solid data upon which training or education-related decision-making can take place. The results of both instructional methods and student efforts are more transparent, guiding curriculum or programs through analysis of performance patterns, evaluating relationships between elements, and predicting factors of success.

Program or course developers and instructors can use learning analytics for many things. These are just a few of the categories explored in the Learning Analytics Functional Taxonomy.

  • Evaluate social learning
  • Access learning behavior
  • Improve individualized learning
  • Predict student performance
  • Improve learning tools and materials
  • Visualize learning activities

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The 21st-century learning environment values personalized learning and EdTech tools, but the COVID-19 pandemic put a new spotlight on flexible learning environments and the need for effective online learning opportunities.

These aren’t just expectations for the academic community but for the corporate business world as well. Finding ways to educate in concepts, theories, or skills beyond the traditional classrooms has become a key task for learning analytics, and the digital trails left by learning management systems are ripe for data mining. These are just a few of the trends of analytics in education.

K-12 Education

Digital learning environments are relying on Big Data and AI to mine insight from student efforts and behaviors. Student data is collected from completion times, quiz results, browsing activities, log on/off times, and more to determine course elements that need adjustment or that are successful in developing engagement and retention.

As many instructors are moving toward a flipped classroom strategy, this data encourages informed decision-making.

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Self-Regulated Learning

Guiding students through the digital learning experience is no longer left up to chance.

Advanced digital learning platforms will employ analytics to evaluate individual learning behaviors and give actionable insight into how to personalize the learning pathway and make it more relevant to the individual. This will create a unique learning environment that considers personal preferences and needs in learning.

Teaching Strategies

Educators and trainers will be able to see learner engagement and evaluate responses across learning objectives. This will help teachers:

  • Track engagement according to content
  • Create customized lessons
  • Adapt teaching methods

Teachers can fine-tune their strategy and curriculum to achieve optimum results for both the organization and the learner.

When It Comes to Engagement, What Can Learning Analytics Do for You?

Let's circle back to analytics expert John Whitmer for more on this. He says: "For one thing, it can provide instructors a means to better understand where and how their teaching methods impact learner engagement and success. What course elements do the most successful learners interact with? Is this what you expected, or not? Where are learners at risk of failure spending their time? Are they ignoring some of the course materials and activities that the A+ students are working with? Why might that be? Armed with the right information, instructors can fine-tune their courses. And they can check in with students who, based on their engagement level, seem to be at risk of dropping or failing a course. Early intervention can help a student who’s floundering get back on the right path.

"In order to capture the data that’s needed for analysis, Learning Analytics should ideally be closely integrated with your LMS. It’s where your students are already spending their time, and it’s where there’s a rich store of raw data that, with Learning Analytics can be turned into powerful insights that instructors can act on."

How Does Learning Analytics in Education Impact Your Work?

To cultivate a successful learning environment, you can’t afford to ignore learning analytics!

Data-driven decisions are more effective and cost-efficient, as your time and resources are spent addressing the specific needs of your students or organization. Use an LMS to get your content delivered well, but rely on learning analytics to evaluate the results.

Open LMS not only provides you with excellent tools to improve your learners’ experience, but it also gives you detailed learning analytics that will make your course management more efficient and effective. Contact our team of experts to know more or request a trial!

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