ABSTRACT

Advancement in technology no doubt have made human’s life comfortable and convenient. But this revolution in technology at one end has proved to be a blessing to humans and at other end has become a misfortune for university engineering students in form of ‘code-plagiarism’. Students are often caught in incidences of copying software codes from internet which are given to them as lab assignment in programming labs. Student’s take undue advantage of this blessings that technology has provided to humans to avoid tireless and time-consuming manual efforts just for sake of convenience. But Student’s forget in this stage of life only rigorous practice would make them perfect and would help them develop their own coding logic. With increase in innovations and inventions that technology is giving every day, it gives opportunities to socially inspired students to hide beneath different technological advancements. They hide their act of copying software codes in programming labs of universities by using different technological innovations like Bluetooth, What-Sapp, Hike, Facebook, other instant messengers and social networks. This prevents students from developing logical and analytical skills. Universities are becoming more concerned in this regard. To cope up with such challenges in our education system the undertaken research provides an opportunities to universities to integrate with their norms a ‘code-plagiarism evaluation cum prevention model’. This model works will not only provide opportunity to faculty to smartly detect hidden activities of students of code-plagiarism in labs but will also suggest suitable prevention measures to overcome curse of code-plagiarism in programming labs. Digital era university students may opt code-plagiarism and hiding at the same time, using smart technologies such as Hike, Bluetooth etc. It is easily feasible for faculty members to trace these behavior of students using SCEPM model. SCEPM is a complete incremental learning evaluation cum prevention system integrated using Genetic Algorithm (GA), Multi-Level Cohort Analysis (MCA), various machine learning clustering algorithms like K-Means etc., which any university can implement easily with objectives like to trace / remove / predict code-plagiarism in programming labs of university.