What is Learning Analytics? Key Trends & Use Cases

Emerging technologies continue to influence the education industry, and learning analytics helps instructing and learning.

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 a 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.

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 is 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, visualization, statistics and computer or data sciences.

These complex elements converge around human-centered design elements of sociotechnical 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, but never have these disciplines been powered by digital date 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.

What Are the Best Ways to Use Learning Analytics?

Even though e-learning has created new opportunities for learning, there is a need for accountability and evaluation of effectiveness with 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

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 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 ofthe 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.

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 the personal preference 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 organization and learner.

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.

Sources

https://steinhardt.nyu.edu/learning-analytics-101

https://www.getmagicbox.com/blog/learning-analytics-trends-driving-education-in-2021/

https://www.solaresearch.org/about/what-is-learning-analytics/

https://www.northeastern.edu/graduate/blog/learning-analytics/

https://at.doit.wisc.edu/guides/what-are-the-pedagogical-uses-of-learning-analytics/

https://link.springer.com/article/10.1007/s40692-020-00155-8

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