Automated essay scoring (AES), where natural language processing is applied to score written text, can underpin educational resources in blended and distance learning. AES performance has typically been reported in terms of correlation coefficients or agreement statistics calculated between a system and an expert human examiner. We describe the…

Human essay grading is a laborious task that can consume much time and effort. Automated Essay Scoring (AES) has thus been proposed as a fast and effective solution to the problem of grading student writing at scale. However, because AES typically uses supervised machine learning, a human-graded essay corpus is still required to train the AES…

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…

Scoring essays is generally an exhausting and time-consuming task for teachers. Automated Essay Scoring (AES) facilitates the scoring process to be faster and more consistent. The most logical way to assess the performance of an automated scorer is by measuring the score agreement with the human raters. However, we provide empirical evidence that…

The growth of Artificial Intelligence (AI) chatbots has created a great deal of discussion in the education community. While many have gravitated towards the ability of these bots to make learning more interactive, others have grave concerns that student created essays, long used as a means of assessing the subject comprehension of students, may…

The current study explores the ability to predict argumentative claims in structurally-annotated student essays to gain insights into the role of argumentation structure in the quality of persuasive writing. Our annotation scheme specified six types of argumentative components based on the well-established Toulmin's model of argumentation. We…

The current study examined the extent to which the cohesion detected in readers' constructed responses to multiple documents was predictive of persuasive, source-based essay quality. Participants (N=95) completed multiple-documents reading tasks wherein they were prompted to think-aloud, self-explain, or evaluate the sources while reading a set of…

Peer feedback is an effective instructional strategy for improving students' argumentative essay writing in higher education. However, little is known how do differently or similarly bachelor's and master's students perform in their peer feedback activities for essay writing. This study sought to identify the role of education level in students'…

According to the literature, students' motivation and satisfaction can influence their perceived learning outcomes. However, little is known about what kind of a role do motivation and satisfaction play in the context of online peer feedback. This exploratory study aims to examine the relationship between students' motivation and satisfaction with…

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of…

In this paper, we extracted content-based and structure-based features of text to predict human annotations for claims and nonclaims in argumentative essays. We compared Logistic Regression, Bernoulli Naive Bayes, Gaussian Naive Bayes, Linear Support Vector Classification, Random Forest, and Neural Networks to train classification models. Random…

In the literature, little is known regarding the role of students' perceived motivation and perceived fairness of peer feedback for their learning satisfaction, particularly in the context of argumentative essay writing in online learning environments. This study explores the effects of students' perceived motivation and perceived fairness of peer…

It is of great importance to identify students' negative emotions so as to avoid accidents. However, most of the students with mental problems seldom express their emotions in some ways, which makes it more difficult for the emotion recognition system to obtain the emotional data of these students. To solve the problem of the lack of data…

In Automated Essay Scoring (AES) systems, many previous works have studied group fairness using the demographic features of essay writers. However, individual fairness also plays an important role in fair evaluation and has not been yet explored. Initialized by Dwork et al., the fundamental concept of individual fairness is "similar people…

The aim of this study was to explore how students perceive their learning outcomes and satisfaction during an online peer feedback activity in the context of argumentative essays. In this study, 135 undergraduate Argumentative Essay Writing course on the Brightspace platform. In this module, students wrote an argumentative essay for the first…