Imagine keeping track of each student’s digital footprint, class attendance, and individual needs. Essentially, this is what learning analytics does. Learning analytics is defined as the measurement and dissection of data about learners used to optimize their learning experience.

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

While there is much excitement about learning analytics and the ways it can shape the classroom and transform education, it is not without flaws. As with any technological tool, we must assess potential risks.

Does Not Leave Room to Answer Why

First and foremost, learning analytics is not personal. While it can provide a university or a professor with data about an individual student, it cannot answer the big questions. For instance, while it should make learning personalized, it does not mean that the school or the teacher will be able to read past the data to see the person. Learning analytics takes away the humanness and turns students into collections of data.

Failed Interventions

Along these same lines, whereas learning analytics is being used to identify “at risk” students and predict failure, it is not capable of solving the problem. For example, learning analytics can predict failure based on a student’s lack of class attendance. However, sending out warning emails will not change the situation. The teacher will not know based on the learning analytics why the student is missing class, nor will the student be able to improve his or her grade simply based on a software’s notifications of potential failure.

Additionally, the correlation between attendance and achievement is not always accurate. For instance, a student may attend every class and still be far behind or vice versa. Therefore, it is imperative for schools and educators to use the learning analytics to speak to the individual student rather than relying on the software alone.

Misuse of Student Data

The final major concerns of learning analytics are the misuse of student data and ethical issues. Niall Sclater explains, “Anyone thinking about deploying learning analytics at their institution is likely to encounter ethical objections from some of their colleagues, and perhaps some of their students too. There is no doubt that there are many possibilities for the misuse of student data.” He’s even identified 86 separate ethical, legal and logistical issues in various articles and research papers about learning analytics.

Ultimately, learning analytics can be helpful; however, they should be used with caution and not to replace interpersonal communication between educators and students.