You Need to Understand Learning Analytics to Personalize Learning

Personalized learning refers to the effort to teach each individual student. Historically, most teachers have had little choice save to teach to the average. Of course, no one student is perfectly average. This means that teaching was often not appropriate for many students, who struggled as a result. But personalized learning reframes instruction by using the most appropriate tools, approaches, and content for each individual student. Of course, this is extremely difficult to accomplish without edtech tools. And one of the keys to implementing a quality personalized learning approach—one that is more likely to result in improved student learning outcomes—is a focus on data analytics. In other words, you need to understand learning analytics in order to personalize learning.

Learning analytics refers to the fine-grained analysis of student performance in order to shape future instruction. A teacher focused on learning analytics is not content to see that a student earned a B grade on an exam. Rather, that teacher will closely assess what the student got wrong in order to ensure that the student can master that material. An important component of learning analytics is the analysis of wrong answers in order to determine what misconceptions a student has. Odd as it may sound, analysis of incorrect answers may actually be more important to the student’s future success. This approach requires a radical reorientation away from grades as a final statement of a student’s performance toward focused analysis of performance as a guide to future instruction.

While some teachers have access to sophisticated systems to use for learning analytics, not all districts make that available. But teachers can use free tools in order to use learning analytics to personalize learning. A student response system such as Socrative can be used to, for example, assess a student’s knowledge–before a new unit begins–with a formative assessment.

Teachers can then make assignments targeted at correcting misconceptions and filling in gaps—based on the personalized learning needs of individual students—before continuing with the material. Similarly, a tool as simple as Google Forms can be used to assess student learning; based on their results, students can be directed to instructional activities that are appropriate for them.

Personalized learning is a powerful concept. But, absent focused learning analytics, it can be difficult to implement. In fact, assignments might, in that case, stem more from teacher bias than from student need. But a few simple tools used to conduct learning analytics can make personalized learning a reality in every classroom.




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