Imagine keeping track of each learner’s digital footprint, class attendance, and individual needs. This is what learning analytics does. It is defined as the measurement and analysis of data about learners used to optimize their learning experience. 

Learning analytics makes learning personal. With learning analytics, educators can assess where learning gaps have occurred. In universities, learning analytics is being used to predict learner success and identify learners who are “at risk.” 

Although there is excitement about learning analytics and how it can shape the classroom, it is not without flaws. As with any technological tool, we must assess potential risks. 

It’s Not Personal 

Learning analytics is not personal. Although it can provide a university or a professor with data about an individual learner, it cannot answer the big questions. For instance, although learning should be personalized, it does not mean that the school or the educator will be able to read past the data to see the person. Learning analytics takes away humanity and turns learners into collections of data. 

Failed Interventions

Although earning analytics can identify “at-risk” learners and predict failure, it cannot solve the problem. For instance, learning analytics can predict failure based on a learner’s lack of class attendance. Sending out warning emails won’t change the situation. Based on the learning analytics, the educator will not know why the learner is missing class, nor will the learner be able to improve their grade based on a software’s notifications of potential failure. 

The correlation between attendance and achievement is not always accurate. For instance, a learner may attend every class and still be far behind, or vice versa. Therefore, schools and educators must use learning analytics to speak to individual learners rather than rely on the software alone.