Language Technology (Computational Linguistics) Identifiers URN: urn:nbn:se:uu:diva-459058 OAI: oai:DiVA. 41Īutomated Essay Scoring, neural network models, linguistic features National Category Place, publisher, year, edition, pages2021. ![]() In addition, TextRNN_LSTM performs best, with an accuracy of 54.79%, an F1 score of 0.55, and a Quadratic Weighted Kappa of 0.59, which beats the statistically-based baseline models. The experimental results show that models trained only with count-based features outperform the models trained using other feature combinations. The models are evaluated via three measurements: Accuracy, F1 score, and Quadratic Weighted Kappa. Those features are divided into four groups: count-based, morphological, syntactic, and lexical features, and the four groups of features can form a total of 14 combinations. Each essay is represented with linguistic features measuring linguistic complexity. The models studied in this experiment include TextCNN, TextRNN_LSTM, Tex- tRNN_GRU, and TextRCNN trained with the essays from the Automated Student Assessment Prize (ASAP) from Kaggle competitions. It also proves that the same linguistic features are applicable to more than one language. This thesis work makes a comparative study among various neural network models with supervised machine learning algorithms and different linguistic feature combinations. There have been many successful models with supervised or unsupervised machine learning algorithms in the eld of Automated Essay Scoring. We can form a vector with BoW and Word2vec, TF-IDF. Then Automated Essay Scoring may be a helpful tool in the decision-making by the teacher. First, to train a Machine Learning algorithm with essays, all the essays are converted to vector form. However, the task for teachers is quite daunting when it comes to essay scoring. Consequently, academic writing is a common part of university and college admissions applications, standardized tests, and classroom assessments. Written skills are an essential evaluation criterion for a student’s creativity, knowledge, and intellect. Rather than simply scoring essays according to a standard rubric, the EASE software can mimic the grading styles of particular professors."Īt the same time, the report noted, Perelman used to teach a course with Anant Agarwal, chief executive of edX, back in the 1990s, and said "they have been talking about running some experiments to see if the Babel Generator can be used to inoculate EASE against some of the weaknesses the generator was designed to expose.2021 (English) Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits Student thesis Abstract "Essentially," wrote Kolowich, "the edX software tries to make its machine graders more human. The engine is defined as a library that allows for machine learning-based classification of textual content, for tasks such as scoring student essays. This system is called the Enhanced AI Scoring Engine, or EASE. ![]() Kolowich observed that interesting work in automated essay grading is also taking place at MIT, where computer scientists at edX, co-founded by the university, is developing an automated essay-scoring system. With further development, machines giving student feedback may become increasingly useful. Still, not everyone falls into Perelman's camp. Kolowich said Babel Generator is turning "the concept of automation into a farce: machines fooling machines for the amusement of human skeptics." ![]() Essay Language" Generator.Ī detailed report on Perelman's work appeared in Monday's The Chronicle of Higher Education, where Steve Kolowich wrote that Perelman's fundamental problem with essay-grading automatons is that they are not measuring any of the real constructs that have to do with writing. The Babel Generator stands for "Basic Automatic B.S. The Babel program delivers essays that are intentionally gibberish to prove the weaknesses of automated essay-grading software. Perelman is concerned over the idea of using software to grade essays. The program was fed one keyword: "privacy." The key players behind this software, called Babel, are Les Perelman, former director of undergraduate writing at the Massachusetts Institute of Technology, together with Harvard and MIT students. For the curious, the essay had sentences such as "Privateness has not been and undoubtedly never will be lauded, precarious, and decent.".
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