Evaluating the quality of university exam papers is crucial for universities seeking institutional and program accreditation. Currently, exam papers are assessed manually, a process that can be tedious, lengthy, and in some cases, inconsistent. This is often due to the focus on assessing only the formal specifications of exam papers. This study…

Automated writing evaluation (AWE) systems are developed based on interdisciplinary research and technological advances such as natural language processing, computer sciences, and latent semantic analysis. Despite a steady increase in research publications in this area, the results of AWE investigations are often mixed, and their validity may be…

Student plagiarism has challenged educators for decades, with heightened paranoia following the advent of the Internet in the 1980's and ready access to easily copied text. But plagiarism will look like child's play next to new developments in AI-based natural-language processing (NLP) systems that increasingly appear to "write" as…

This paper presents a multidimensional model of variation in writing quality, register, and genre in student essays, trained and tested via confirmatory factor analysis of 1.37 million essay submissions to ETS' digital writing service, Criterion®. The model was also validated with several other corpora, which indicated that it provides a…

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…

Background: This exploratory writing analytics study uses argumentative writing samples from two performance contexts--standardized writing assessments and university English course writing assignments--to compare: (1) linguistic features in argumentative writing; and (2) relationships between linguistic characteristics and academic performance…

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…

Summarization is an effective strategy to promote and enhance learning and deep comprehension of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation of students' summaries requires time and effort. This problem has led to the development of automated models of summarization quality. However,…

With the exponential increase of digital assessment, different types of data in addition to item responses become available in the measurement process. One of the salient features in digital assessment is that process data can be easily collected. This non-conventional structured or unstructured data source may bring new perspectives to better…

We used multivariate generalizability theory to examine the reliability of hand-scoring and automated essay scoring (AES) and to identify how these scoring methods could be used in conjunction to optimize writing assessment. Students (n = 113) included subsamples of struggling writers and non-struggling writers in Grades 3-5 drawn from a larger…

In writing studies research, automated writing evaluation technology is typically examined for a specific, often narrow purpose: to evaluate a particular writing improvement measure, to mine data for changes in writing performance, or to demonstrate the effectiveness of a single technology and accompanying validity arguments. This article adopts a…

In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This…

This article demonstrates how recent advances in technology allow fine-grained analyses of candidate-produced essays, thus providing a deeper insight on writing performance. We examined how essay features, automatically extracted using natural language processing and keystroke logging techniques, can predict various performance measures using data…

In writing studies research, automated writing evaluation technology is typically examined for a specific, often narrow purpose: to evaluate a particular writing improvement measure, to mine data for changes in writing performance, or to demonstrate the effectiveness of a single technology and accompanying validity arguments. This article adopts a…

Automated Writing Evaluation (AWE) has been considered a potential pedagogical technique that exploits technology to assist the students' writing. However, little attention has been devoted to examining students' perceptions of Grammarly use in higher education context. This paper aims to obtain information regarding the writing process and the…