Predicting the Efficiency and Success Rate of Programming Courses in MOOC Using Machine Learning Approach for Future Employment in the IT Industry

Abstract

Modern businesses and jobs in demand have witnessed the requirement of programming skills in candidates, for example, business analyst, database administrator, software engineer, software developer, and many more. Programming courses are a very influential and important part of forming the future of the IT industry. Throughout the recent years, a substantial amount of research has been conducted to improve the programming novices, but the problems are returning in every new generation and reporting high failure rates. The dataset used in this study is the ‘CodeChef competition' dataset and the ‘Coursera' dataset. Firstly, this research work conducts the preview analysis to understand the performance of learners in programming languages. Secondly, this work proposes a clear rationale between the popularity of MOOC courses and low completion rates. There is increasingly high enrolment in MOOC courses but with non-ideal completion rates. Finally, it builds the machine learning model and validates the accuracy of the trained model.