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Learning Analytics as an Effective Assessment Tool

Data-driven systems have been incorporated in different industries, including education. They change the nature of learning and teaching, providing efficient assessment tools that capture trends in students’ behavior and performance. Yet, collecting data is useless without proper analysis. Therefore, learning analytics is what makes the transformation possible.

What Is Learning Analytics?
Learning analytics refers to the collection, analysis, and interpretation of data about one’s academic progress. It serves to improve the learning experience and outcomes with the help of effective assessment.

More and more teachers advocate the use of learning analytics in the educational process. Indeed, it brings massive benefits to students and educational entities. Schools, colleges, and universities collect information on students’ performance. Yet, without technologies, this data cannot be used properly as it is poorly organized and scattered across different locations.

Learning analytics makes it possible to deal with large amounts of data as it applies computational analysis methods. As a result, educators get information on the progress and level of each student. The analysis allows them to make quick and informed decisions, shaping the educational process to meet students’ needs.

How Learning Analytics Works in Education
The analytics process starts with a learning activity. Then, analysts collect quantitative and qualitative data and process it. At this stage, data should be organized so it can be used to solve specific problems. The next step involves the actual analysis that combines several procedures, such as clustering, prediction, and discovery with models. Once data is analyzed, it should be presented in the form of graphics. It helps educators to visualize complex trends.

In an educational environment, learning analytics functions within three frameworks: descriptive, diagnostic, and prescriptive analytics.

- Descriptive analytics concerns students’ data from admission to graduation. Analysis of data at different stages supports more detailed reports on one’s progress, showing patterns and trends. It promotes better decision making, molding teaching strategies according to the needs of students.

- Diagnostic analytics is focused on determining future trends. Just like descriptive analytics, it takes into account past data, adding analysis of current information. Diagnostic analytics helps to assess student performance, identifying low-engaging students. Predicting what is likely to happen next, teachers can help such students improve their learning strategies.

- Prescriptive analytics is closely connected to decision making. It provides insights regarding alternative solutions that can improve educational resources and tools. The prescriptive analysis applies algorithms, machine learning, and modeling to suggest changes that improve student outcomes.

The combination of assessment tools that learning analytics provides helps to create a customized learning environment. Data-driven courses improve content delivery in educational institutions, increasing student satisfaction.

How Learning Analytics Facilitates Assessment
Learning analytics provides tools to improve teaching and training, promoting detailed and profound assessment. It is a source of reliable evidence about students’ grades and engagement. Aside from capturing and analyzing data, it enables further visualization and improvement strategies. Learning analytics offers actionable insights in the following areas.

Timely Feedback
Access to up-to-date metrics gives teachers the possibility to provide timely feedback. Individualized interventions prevent students from lagging behind or being isolated from the learner community. The timely evidence-based assessment also teaches learners the importance of reflection, promoting self-directed education. Learning analytics tools can generate insights or warnings for students automatically when their performance is below their potential.

Improved Retention
Learning analytics can influence retention rates as well. As educators have better tools to detect unwanted learning behaviors and assess students’ risks of performing poorly, they can take timely actions to prevent dropout. Proper assistance, counseling, and individual learning plans are the central factors that improve student retention. Besides, they make the learning experience more individualized and engaging for students.

Better Teaching Practices
The data-empowered assessment brings benefits to both students and teachers. It encompasses much more than test results, grades, or essay reviews. Analyzing behaviors, characteristics, emotions, and use of resources, teachers see the results of their work and can make conclusions about its effectiveness. For example, if some pedagogical practices or reading materials are not useful for students, they can replace or improve them, offering better course design for students.

Eliminated Knowledge Gaps
Empowered by learning analytics, the assessment helps to identify and address knowledge and skill gaps. If there is a discord between a student’s performance and potential, a teacher needs to develop timely interventions. In this case, learning analytics will show what a student needs and suggest approaches to help him or her meet objectives. Thus, it answers questions about productivity and comprehension.

Increased Effectiveness
Educational institutions deal with mountains of data. Yet, it is challenging to reach meaningful conclusions if the information is not processed and organized properly. Learning analytics automatizes work with data, delivering results to educators and students. It increases the cost-effectiveness of the whole assessment process. Besides, it also supports efficiency in resource allocation among courses. As analytics assesses the popularity of courses and materials, educators can identify ways to improve the content to meet students’ needs.

To Sum Up
Learning analytics is here to make a difference in education. It provides relevant evidence that helps to assess students’ performance and, as a result, support their learning. Analytics can organize data processing to generate valuable insights for institutions. However, the data itself does nothing. The main role in the process of improvement belongs to people. The willingness of educators to adapt teaching styles and strategies is what empowers students to reach their full potential.
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