A Predictive Model for Student Employability Using Deep Learning Techniques

Abstract

Education in the present scenario is outcome based and focuses mainly on the skill sets a student acquires on completion of the studies. The society is increasingly concerned about the quality of programs, international rankings, and placement statistics of HEI (Higher Education Institutions). This study concentrates on how demographic data, scholastic and co-scholastic abilities of students, faculty characteristics, and teaching practices contribute to the student learning. Dataset pertaining to the study were collected from the same institution for which the placement prediction needs to be calculated. The study models the problem as a sequential event prediction problem and employs deep learning techniques. The proposed model extracts data from dataset with 18 attributes. This predictive approach evaluates the performance of lower level and higher order skills and provide the enhancement methods by which a student can be on the path to full-time employment before leaving the campus.