• Project Title: Using Machine Learning to Predict Student Performance

  • BASIS Advisor: Dr. Brown

  • Internship Location: Texas State University

  • Onsite Mentor: Dr. Jelena Tesic

Education is extremely important in the modern world. Students who do well in school tend to do well later in life, and students who do poorly in school tend to have problems later in life. Today’s students will eventually make up the workforce of the nation, and it has been shown that nations with better education systems also tend to perform better economically. However, it can be difficult to detect the signs that a student will face trouble, which can be a problem as early intervention is essential in helping a student do better in school and gain the full benefits associated with education. Machine learning techniques can be very useful for analyzing data and can sometimes find correlations that are difficult for humans to notice. Along with Professor Tesic from Texas State University, I will create a program that can learn from student test data to detect, at a higher-than-chance rate, students who might be at risk in the future. This can be used to provide help before it is too late and the students are already failing.