Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…

Using item-response theory to model rater effects provides an alternative solution for rater monitoring and diagnosis, compared to using standard performance metrics. In order to fit such models, the ratings data must be sufficiently connected in order to estimate rater effects. Due to popular rating designs used in large-scale testing scenarios,…

Manually scoring and revising student essays has long been a time-consuming task for educators. With the rise of natural language processing techniques, automated essay scoring (AES) and automated essay revising (AER) have emerged to alleviate this burden. However, current AES and AER models require large amounts of training data and lack…

Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score…

We investigated the relationship between the scores assigned by an Automated Essay Scoring (AES) system, the Intelligent Essay Assessor (IEA), and grades allocated by trained, professional human raters to English essay writing by instigating two procedures novel to written-language assessment: the logistic transformation of AES raw scores into…

Studies on automatic scoring systems in writing assessments have also evaluated the relationship between human and machine scores for the reliability of automated essay scoring systems. This study investigated the magnitudes of indices for inter-rater agreement and discrepancy, especially regarding human and machine scoring, in writing assessment.…

Creativity is highly valued in both education and the workforce, but assessing and developing creativity can be difficult without psychometrically robust and affordable tools. The open-ended nature of creativity assessments has made them difficult to score, expensive, often imprecise, and therefore impractical for school- or district-wide use. To…

The advent of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, has significantly impacted the educational landscape, offering unique opportunities for learning and assessment. In the realm of written assessment grading, traditionally viewed as a laborious and subjective process, this study sought to…

Purpose: This study builds a new system for automatically assessing learners' speech elicited from an oral discourse completion task (DCT), and evaluates the prediction capability of the system with a view to better understanding factors deemed influential in predicting speaking proficiency scores and the pedagogical implications of the system.…

Automated scoring based on Latent Semantic Analysis (LSA) has been successfully used to score essays and constrained short answer responses. Scoring tests that capture open-ended, short answer responses poses some challenges for machine learning approaches. We used LSA techniques to score short answer responses to the Consequences Test, a measure…

The rating of proficiency tests that use the Inter-agency Roundtable (ILR) and American Council on the Teaching of Foreign Languages (ACTFL) guidelines claims that each major level is based on hierarchal linguistic functions that require mastery of multidimensional traits in such a way that each level subsumes the levels beneath it. These…

Purpose: The aim of this study was to advance the use of structured, monologic discourse analysis by validating an automated scoring procedure for core lexicon (CoreLex) using transcripts. Method: Forty-nine transcripts from persons with aphasia and 48 transcripts from persons with no brain injury were retrieved from the AphasiaBank database. Five…

Managing crowded classes in terms of classroom assessment is a difficult task due to the amount of time which needs to be devoted to providing feedback to student products. In this respect, the present study aimed to develop an automated essay scoring environment as a potential means to overcome this problem. Secondarily, the study aimed to test…

Recent advances in machine learning have made automated scoring of learner speech widespread, and yet validation research that provides support for applying automated scoring technology to assessment is still in its infancy. Both the educational measurement and language assessment communities have called for greater transparency in describing…

Automated essay scoring engines (AESEs) are becoming increasingly popular as an efficient method for performance assessments in writing, including many language assessments that are used worldwide. Before they can be used operationally, AESEs must be "trained" using machine-learning techniques that incorporate human ratings. However, the…