An educational innovation in its own right, learning analytics is the use of educational data to identify trends and patterns about learners to optimize the learning experience. Measuring, collecting, analyzing, and reporting are all companion aspects to the field.
Big data has been an important topic over the past decade across multiple industries, including education. Below we look at different areas where learning analytics has a noticeable impact.
Online learning analytics
Online learning platforms provide a host of data that can be further analyzed. The more fine-grained the analytics are, the more insights teachers and administrators can access.
“With this information, teachers can reduce bias in classroom evaluations. The reduction in bias is due to more objective, individualized results that are available, instead of simply looking at standardized student progress,” says Peter Luntz, director of studies at International Language School.
Luntz provides an example in the form of online language learning. The metrics from language learning platforms often focus on learning objectives in terms of improving or maintaining a language level. So an initial online assessment sets the baseline for the student, regular assessments track progress, and a final assessment can determine whether the objective was reached.
The tailored nature of these tools is key. Luntz notes that the best assessment tools are computer adaptive, which means they react to the learner. Adaptive assessment allows the testing tool to home in on a student’s level of knowledge more quickly and accurately. “Compare this to standardized tests that are the same for each student — these are less effective and more time-consuming,” he explains.
Luntz contends that any successful digital learning resource must have detailed metrics on the learner’s usage. And the metrics must have parameters that are meaningful for final evaluation of the learner’s progress. “In addition, online tracking that provides aggregate data can be very useful for getting an overall view of a certain learner population,” says Luntz.
Classroom analytics
For years, teachers have been performing a tech-less version of learning analytics by examining data in the form of student attendance, grades, and other areas. Technology has simply made the collection and assessment of this data quicker and more robust.
In this post on classroom analytics, we go into detail about what teachers have learned over the years from their fundamental data-collection practices, how technology is helping teachers create better learning outcomes, and how teachers can track their students’ needs.
School and community analytics
Beyond the classroom is the community where students live — which is also relevant to the learning experience. (Remember the old adage “It takes a village to raise a child”?)
Schools need the support of their surrounding communities to help students learn, but the communities need to have a clear idea about school and student progress, among other things, to understand how they can help.
Check out this post on how schools can use data to engage their communities and get the support they need for improving students’ learning experiences. It covers what data schools should be gathering, how this data can help engage community stakeholders, and what steps to take to encourage feedback and get help from the community at large.
Self-guided learning analytics
While a teacher’s job is critical to guiding students to educational success, students should also be active participants in their educational journey.
For this to happen, students need to know more than just their grades at the end of the reporting period. They need quantitative and qualitative feedback about the work they’re doing, as quickly as possible.
Luntz points out that self-guided learning is another area where AI can help. Assuming the school chooses a student feedback tool that has an AI component, the tool would be able to give students the information they need, when they need it, based on past performance.
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