An Innovative Strategy to Anticipate Students’ Cheating: The Development of Automatic Essay Assessment on the “MoLearn” Learning Management System

A quick and effective learning assessment is needed to evaluate the learning process. Many tools currently offer automatic assessment for subjective and objective questions; however, there is no such free tool that provides plagiarism detection among students for subjective questions in a learning management system (LMS). This article aims to create an automatic essay assessment in MoLearn LMS that can check students’ answers. At the same time, the LMS can also anticipate online cheating. This article employed two methods comprising the system development life cycle waterfall model and the latent semantic analysis method. The former was used to design and create essay assessment applications, and the latter was employed to check the essay's answer and automatically detect students’ plagiarism. The result showed that MoLearn LMS was successfully working on detecting students’ plagiarism, leading to better LMS innovation. Automatic essay assessment accelerates grading time by about 8.04 times that of manual grading, which takes 5.52 s per question.