Sustainable Development of Employability and Skill Development in Outcome-based Education: A Machine Learning Approach

The contribution of technological enhancement has played a vital role in strengthening the country’s economy. The Higher Education lnstitutions (HEI) are providing placement assistance to the students by employing various measures as a part of their curriculum. A few of the attempts that HEIs have adopted to enhance soft skills as well as hard skills of the students are, course structure modification, student-mentoring, industry-academia collaboration etc. In spite of all the noble efforts, many unemployed engineers are witnessed in the society. Therefore, this research aims to develop an effective and accurate framework that would be highly beneficial for both students as well as teachers in HEIs. A Semi-Supervised-Sequence-Prediction (SSSP) machine learning framework has been designed to predict students’ academic achievement towards the completion of a four-year degree course in an outcome-based education system. A time series analysis of students’ data has been conducted to analyze the progression of final cumulative GPA and employability prospect in sequential manner. The results show the effectiveness of the proposed framework in predicting employability of students along with the areas where students need improvement in order to be employable. This research provides an inclusive roadmap, empowering the use of machine learning for employability and skill development in outcome-based education.