Nevertheless, automated writing-evaluation systems might provide precisely the platforms we need to elucidate many of the features that characterize good and bad writing, and many of the linguistic, cognitive, and other skills that underlie the human capacity for both reading and writing. So it comes as no surprise that attempts to devise computer programs that evaluate writing are often met with resounding skepticism. Furthermore, we hold our ability to express ideas in writing as a pinnacle of this uniquely human language facility-it defies formulaic or algorithmic specification. The ability to communicate in natural language has long been considered a defining characteristic of human intelligence. In conclusion, the paper proposes hybrid framework standard as the potential upcoming AES framework as it capable to aggregate both style and content to predict essay grades Thus, the main objective of this study is to discuss various critical issues pertaining to the current development of AES which yielded our recommendations on the future AES development. In addition, we presented and compared various common evaluation metrics in measuring the efficiency of AES and proposed Quadratic Weighted Kappa (QWK) as standard evaluation metric since it corrects the agreement purely by chance when estimate the degree of agreement between two raters. ![]() To generalize existing AES systems according to their constructs, we attempted to fit all of them into three frameworks which are content similarity, machine learning and hybrid. This critical review examines various AES development milestones specifically on different methodologies and attributes used in deriving essay scores. Despite the strong appeal, its implementation varies widely according to researchers' preferences. It has gained a lot of research interest in educational institutions as it expedites the process and reduces the effort of human raters in grading the essays as close to humans' decisions. Also, the loss function from Root Mean Square Error (RSME) showed value of 0.620 which is very small and thus signifies closeness to the line of best fit from the regression equation.Īutomated Essay Scoring (AES) is a service or software that can predictively grade essay based on a pre-trained computational model. Results of performance evaluation of iNLPEGS showed accuracy of 89.03% and error of 10.97% connoting that there is very little difference between scores from the developed intelligent essay grading system and a human grader. Web based application was developed using Django, Gensim, Jupyter Notebook and Anaconda as the development tools due to availability of several Python libraries with SQLite as the database. ![]() An Intelligent Natural Language Processing Essay Grading Model was designed based on Enhanced Latent Semantic Analysis using Part of Speech n-gram Inverse Document Frequency. ![]() Assemblage of Computer Science questions and answers were collected from Babcock University Computer Science Department to create a more robust dataset to ensure high reliability. Secondary dataset collected from Kaggle provided by The Hewlett Foundation was used to aid semantic analysis and Part of Speech tagging. This study presents an Essay Grading System called Intelligent Natural Language Processing Essay Grading System (iNLPEGS) with high accuracy percentage and minimal loss function for scoring assessment that can accommodate more robust questions. Marking theoretical essay questions which involves thousands of examinees can be biased, subjective and time-consuming, leading to variation in grades awarded by different human assessors. Educational Institutions are facing enormous tasks of marking and grading students at the end of every examination within the shortest possible time.
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