K12 Analytics to Prevent Students from Dropping Out

The average dropout rate of US high-school students is 9%, as of 2019. Though it decreased quite substantially over the last decade, this is still high. How can educational institutions lower this number, encouraging students to proceed with their studies? 

Applying K12 data analytics can be a useful solution for dropout prevention in schools. Let’s find out what drives students to drop out and how high schools can leverage the power of AI, big data and machine learning to keep their learners motivated and engaged. 

The Main Reasons of Students’ Dropping Out

Here are some of the main reasons why students drop out of high school:

• Individual Factors. Student’s abilities, talents, desires and inclinations are ultimately different. This is the core reason behind education personalization. When an educational program is poorly matched with students’ actual interests and life goals, the risk of dropping out increases because of the low academic performance, lack of motivation and lack of understanding how learning a specific set of subjects will help them in the future. Some students also need a special approach to study successfully. For example, 36% of students with a disability drop out of high school. 
• Family Factors. 5% of US students who dropped out of high school have parents without a high-school education. In this case, the students are more likely to follow their parents’ path since parents are the most powerful role models for kids, according to different studies. 
• School Factors. According to a study, 75% of students have negative feelings about the high school they are studying in. The reasons for such an attitude are pretty diverse — the students may have communication and socialization issues, misunderstandings with teachers and even face bullying. 
• Community Factors. The issue of dropping out is even more relevant for students from low-developed communities. They are at a higher dropout risk by default because of their social and cultural environment. They also have a higher risk of developing alcohol or drug addiction. They may lack literacy and local authorities support, a learning-centered mindset, and an understanding of the importance of education. 

Read more on K12 Data Analytics for Dropout Prevention.