Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…

The ability to think critically is an important and valuable skill that students should develop to successfully solve problems. The process of writing requires critical thinking (CT), and the subsequent piece of text can be viewed as a product of CT. One of the strategies educators may use to develop CT is modelling. Given ChatGPT's ability to…

Automated Essay Scoring (AES) tools aim to improve the efficiency and consistency of essay scoring by using machine learning algorithms. In the existing research work on this topic, most researchers agree that human-automated score agreement remains the benchmark for assessing the accuracy of machine-generated scores. To measure the performance of…

In automated essay scoring (AES), scores are automatically assigned to essays as an alternative to grading by humans. Traditional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks to obviate the need for feature engineering. Those AES models generally require training on a…

WriteRightNow (https://writerightnow.com) is an innovative digital platform meticulously crafted to enhance writing instruction across various curricula. Central to its design is the customization of instructional content, allowing for a multi-faceted approach that caters to diverse student needs, including those with special educational…

Automated essay scoring (AES) has emerged as a secondary or as a sole marker for many high-stakes educational assessments, in native and non-native testing, owing to remarkable advances in feature engineering using natural language processing, machine learning, and deep-neural algorithms. The purpose of this study is to compare the effectiveness…

This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…

The Attention, Relevance, Confidence, and Satisfaction (ARCS) model provides a basis for integrating motivational dynamics and technological affordances into the design and implementation of instructions to maintain learner motivation and interest. Little attention has been paid to this potential in teaching the complex and often demotivating…

Automated Essay Scoring (AES) can reliably grade essays at scale and reduce human effort in both classroom and commercial settings. There are currently three dominant supervised learning paradigms for building AES models: feature-based, neural, and hybrid. While feature-based models are more explainable, neural network models often outperform…

Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in…

In this research, we investigated the feasibility of implementing the "e-rater"® scoring engine as a check score in place of all-human scoring for the "Graduate Record Examinations"® ("GRE"®) revised General Test (rGRE) Analytical Writing measure. This report provides the scientific basis for the use of e-rater as a…

In this paper, I describe the design and evaluation of automated essay scoring (AES) models for an institution's writing placement program. Information was gathered on admitted student writing performance at a science and technology research university in the northeastern United States. Under timed conditions, first-year students (N = 879) were…

This study explored relationships between writing sample features and LMS usage patterns for 366 college students who enrolled in Theology courses, junior-level courses cross-listed with theology courses, or Senior Perspective Program courses in the fall semester of 2012. These hybrid courses were managed inside the Canvas(TM) learning management…

In this paper we present a system for automatic correction of errors made by learners of English. The system has two novel aspects. First, machine-learned classifiers trained on large amounts of native data and a very large language model are combined to optimize the precision of suggested corrections. Second, the user can access real-life web…

The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…