This project dataset contains data for seven selected courses, the students registered for the courses, and the students’ interactions with the Virtual Learning Environment (VLE) for those courses. The Open University currently collects similar data on an on-going basis as input to algorithms they developed to identify students at risk for failing a course. Identification of at-risk students then triggers automated intervention measures to encourage behavior that would create success. For example, the algorithm might identify a student with low grades on intermediate assessments (quizzes). That student may be sent an automated email reminder about available tutoring options. The goal of the data collection effort is to maximize student success, which has numerous benefits for the University. This subset of anonymized data was made available to the public for educational purposes on Machine Learning approaches. For the purpose of this project, the data will be used to determine if socio-economic and/or behavior-based data can be used to predict a student's performance in a course. Performance is determined by the final result of the student’s effort and is characterized by completing the course with a passing score, either with or without Distinction. The specific Questions of Interest are: Can we predict a student's final status in a course based on socio-economic factors and/or patterns of interaction with the VLE?

Desired Targets:

• Prediction of student pass/ no pass the course after course completion (goal: 90% accuracy)
• Prediction of student pass/ no pass the course after 30 days of commencement (goal: 75% accuracy)

• If there are socio-economic factors impacting success, can the univeristy adopt strategies to mitigate those factors?
• To identify behaviors that maximize the opportunity for success and generally promote those behaviors with students
• To identify students at risk based on general patterns and perform outreach to drive awareness of recommended study behaviors and available resources to assist the student
• To indentfy students based on current behavior patterns in the first 30 days of class and conduct intervention efforts to increase opportunity for a positive outcome in that class