In the PISA 2022 creative thinking test, students provide a response to a prompt, which is then coded by human raters as no credit, partial credit, or full credit. Like many large-scale educational testing frameworks, PISA uses the generalized partial credit model (GPCM) as a response model for these ordinal ratings. In this paper, we show that…

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,…

Principles of skill acquisition dictate that raters should be provided with frequent feedback about their ratings. However, in current operational practice, raters rarely receive immediate feedback about their scores owing to the prohibitive effort required to generate such feedback. An approach for generating and administering feedback responses…

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…

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…

With the rapid development of generative artificial intelligence (AI) frameworks (e.g., the generative pre-trained transformer [GPT]), a growing number of researchers have started to explore its potential as an automated essay scoring (AES) system. While previous studies have investigated the alignment between human ratings and GPT ratings, few…

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.…

This study describes the development of a special education teacher observation protocol detailing the elements of effective decoding instruction. The psychometric properties of the protocol were investigated through many-facet Rasch measurement (MFRM). Video observations of classroom decoding instruction from 20 special education teachers across…

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…

In language performance tests, raters are important as their scoring decisions determine which aspects of performance the scores represent; however, raters are considered as one of the potential sources contributing to unwanted variability in scores (Davis, 2012). Although a great number of studies have been conducted to unpack how rater…

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…

Background: A strong interest in researching World Englishes (WE) in relation to language assessment has become an emerging theme in language assessment studies over the past two decades. While research on WE has highlighted the status, function, and legitimacy of varieties of English language, it remains unclear how raters respond to the results…

Threats to construct validity should be reduced to a minimum. If true, sources of bias, namely raters, items, tests as well as gender, age, race, language background, culture, and socio-economic status need to be spotted and removed. This study investigates raters' experience, language background, and the choice of essay prompt as potential…

Through Many-Facet Rasch analysis, this study explores the rating differences between 1 computer automatic rater and 5 expert teacher raters on scoring 119 students in a computerized English listening-speaking test. Results indicate that both automatic and the teacher raters demonstrate good inter-rater reliability, though the automatic rater…

This paper reports findings from a project called "The National Panel of Raters" (NPR) that took place within a writing test programme in Norway (2010-2016). A recent research project found individual differences between the raters in the NPR. This paper reports results from an explorative follow up-study where 63 NPR members were…