What is learning analytics?

Learning analytics describes the use of data to understand learners’ needs and improve educational services accordingly. It can be used to test the effectiveness of different learning techniques, track students’ progress and identify areas for improvement, and give educators insight into the most successful tactics. Learning analytics uses technologies such as data mining, analytics, and artificial intelligence (AI) to improve the learning experience and better support students with data-informed approaches. Through learning analytics, institutions can see an improvement in student grades, retention, and graduation rates.

Learning analytics deals with collecting school and university data, measuring students’ understanding and success in different areas or subjects, and assessing the resources and follow up strategies that work best for educators. Analytics can support educators and help them understand students’ problem areas and opportunities for learning. It’s a data-driven approach to educating because not every student has the same issues or learns in the same way. Learning analytics can help educators individualize their plans to address those specific needs and challenges. Many platforms provide personalized results and recommendations for students which can help both teachers in developing future lesson plans but also give the students better insight into where they should focus their studies.

Learning analytics can use big data collected by schools, universities, online sources, governments, and other educational institutions. These massive sources of data when mined provide a great opportunity to improve educational outcomes. Unfortunately, these resources are often untapped or underutilized as the application of big data analytics to learning environments is fairly new. But with more institutions hiring analysts for this specific purpose and more educators engaging with self-service analytics, learning analytics continues to grow and use data to improve education.

Types of learning data to analyze

Learning analytics can be used to understand a variety of educational data, including but not limited to the following:

  • Student Feedback Surveys: Survey data from students and graduates can be extremely valuable in how educational institutions assess educator performance, address problem areas, and improve student satisfaction
  • Admissions Data: Universities are often interested in the number of applicants they receive, the percentage that is accepted, and how many accepted students attend
  • Exam Scores: From a basic performance level, what trends can educators see in exam scores as the result of different learning strategies
  • Graduation Rates: Graduation rates are also often used as a baseline for institutions to understand their performance from a high level
  • Student Engagement: What content students are engaging with the most and how educational content can be improved to raise that engagement
  • Key Performance Indicators (KPIs): Measurements of student success and educational effectiveness

Learning analytics benefits

Learning analytics benefits both educators and students. Analytics can help provide research on the best tools and strategies for teaching and assist in developing new, better ways of learning that significantly improve students’ experience. The impact of these changes can have far-reaching effects on society as more young people benefit from a personalized education, increasing graduation rates and spreading knowledge.

As the field of learning analytics grows, Institutions will come to rely on these practices to make the most of their resources and deliver on promised results for students’ success. It would be a missed opportunity for institutions to not invest in analytics because the return in terms of student development and improved effectiveness is significant.

Overall, the main benefits of learning analytics are:

  • Make the Most of Limited Resources: Analytics can help educational establishments with limited resources manage those resources in a smarter, data-driven way. This can also help maximize the impact of those resources, ensuring that the most valuable resources are available and deprioritizing other less necessary items.
  • Increase Accountability: Educators are often held accountable either by the state, by parents, or by the students themselves who want to get the most value out of their education. Analytics can help quantify that value and measure improvement over time so that educators can provide support for their impact on students’ performance.
  • Share Results: Another benefit of learning analytics is in the increased transparency it provides around education. This openness can lead to better methods of learning not just at the institution but across many educational centers that implement the findings from such analytic programs.
  • Data-Informed Education: Learning analytics helps take the guesswork out of education. It can help educators fully understand their students and make informed decisions on how to address their greatest challenges and areas for growth.
  • Empower Students: One of the most beneficial and yet overlooked benefits of learning analytics is the way it puts learning back into the hands of students. It can empower students to understand what they don’t understand. They can then use that information to revise their own study strategies inside and outside of the classroom.

How to implement learning analytics

There are several factors to consider when implementing learning analytics, including:

  • Self-service Analytics: If an educational institution is looking to roll out an analytics program for educators to track student progress, the solution must be truly self-service and educators should be properly trained on all its components. Furthermore, for such a program to be successful, the organization must promote a data-driven culture where educators and leaders of the institution are committed to using data often and well to drive change. Ensuring widespread adoption of the analytics program is essential to the success of the initiative.
  • Analyst/Educator Communication: If the institution is hiring dedicated analysts or even outsourcing analysis work, there must be open channels of communication between those analysts and the educators using the data. Educators will still need instruction and training on how to do this. Educators need to understand how to successfully leverage the information given to them and how to ask further questions for the analysts to investigate.
  • Data Privacy: Educational institutions are not exempt from data privacy regulations and must abide by government guidelines for collecting, analyzing, using, and sharing student and faculty data. It is important that educators handling data are aware of privacy guidelines and properly trained on how to maintain privacy of personal information. For example, when such data is shared externally, institutions should ensure that the data is anonymized so that student and faculty personal information is protected.
  • Automation with Machine Learning: Some institutions may even consider automating the process of analyzing student data and reporting on those findings. With machine learning, analytic solutions can dig into predetermined questions, make recommendations for best next actions for the student or educator, and even send automated alerts or notifications if an area of learning needs special attention.
Learning analytics diagram

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