Using Google Scholar, EBSCO and ERIC, we searched the terms “AES”, “Automated Essay Scoring”, “Automated Essay Grading”, or “Automatic Essay” for essays written in English language. The purpose of this paper is to review the AES systems literature pertaining to scoring extended-response items in language writing exams. Recent advances in the deep learning approach have shown that applying neural network approaches to AES systems has accomplished state-of-the-art results ( Page, 2003 Valenti, Neri & Cucchiarelli, 2017) with the additional benefit of using features that are automatically learnt from the data. Typically, AES models exploit a wide range of manually-tuned shallow and deep linguistic features ( Farag, Yannakoudakis & Briscoe, 2018). They focus on automatically analyzing the quality of the composition and assigning a score to the text. Many AES systems have been developed over the past decades. As the process of human scoring takes much time, effort, and are not always as objective as required, there is a need for an automated essay scoring system that reduces cost, time and determines an accurate and reliable score.Īutomated Essay Scoring (AES) systems usually utilize Natural Language Processing and machine learning techniques to automatically rate essays written for a target prompt ( Dikli, 2006). It occurs that the score of an essay scored by different human-raters vary substantially because human scoring is subjective ( Peng, Ke & Xu, 2012). Human-raters score these essays based on specific scoring rubrics or schemes. In language tests, test-takers are usually required to write an essay about a given topic. Extended-response items, such as essays, problem-based examinations, and scenarios, are like restricted-response items, except that they extend the demands made on test-takers to include more complex situations, more difficult reasoning, and higher levels of understanding which are based on real-life situations requiring test-takers to apply their knowledge and skills to new settings or situations ( Isaacs et al., 2013). However, considerable skill is required to develop test items that measure analysis, evaluation, and other higher cognitive skills ( Stecher et al., 1997).ĬR items, sometimes called open-ended, include two sub-types: restricted-response and extended-response items ( Nitko & Brookhart, 2007). SR questions are commonly used for gathering information about knowledge, facts, higher-order thinking, and problem-solving skills. The SR items, such as true/false, matching or multiple-choice, are much easier than the CR items in terms of objective scoring ( Isaacs et al., 2013). Test items (questions) are usually classified into two types: selected-response (SR), and constructed-response (CR). Finally, we draw a set of discussions and conclusions. Second, we present a structured literature review of the available Automatic Featuring AES systems. First, we present a structured literature review of the available Handcrafted Features AES systems. We reviewed the systems of the two categories in terms of system primary focus, technique(s) used in the system, the need for training data, instructional application (feedback system), and the correlation between e-scores and human scores. On the other hand, the systems of the latter category are based on the automatic learning of the features and relations between an essay and its score without any handcrafted features. The systems of the former category are closely bonded to the quality of the designed features. Two categories have been identified: handcrafted features and automatically featured AES systems. We have reviewed the existing literature using Google Scholar, EBSCO and ERIC to search for the terms “AES”, “Automated Essay Scoring”, “Automated Essay Grading”, or “Automatic Essay” for essays written in English language.
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