Application of Machine Learning Techniques to Evaluate the Performance of Students in an Academic Environment

Identifying the most influential factors affecting the student’s performance plays a vital role in improvising student’s academic results. The conventional counseling is a time consuming process to understand students’ performance. Machine Learning techniques play a major role in educational institutions to estimate the students, performance leading to better performance in placements. The major objective is to find behavior patterns of students in a timely and accurate manner. We find out the groups of students who need to be counseled in time. The system uses parameters such as attendance status, extra circular activities, grade, technical skills, previous semester results, grasping capability, aptitude grade, interaction with lecturers etc. This also helps faculty members to identify the most influential factors affecting the students’ performance. Analyzing student mental issues for low academic performances is a complex task in the current education sector. The system uses data science technique called as "Association Learning" to find the patterns. The Eclat algorithm is used to find patterns. The proposed system builds as real time application useful to educational institutions to understand students’ behavioral patterns. The system helps faculty to identify the most influential factors affecting the students’ performance. A web browser in the future can be developed and used as an application. More number of parameters can be added to predict the students’ performance.