This scope-review presents the milestones of how Hierarchical Rater Models (HRMs) become operable to used in automated essay scoring (AES) to improve instructional evaluation. Although essay evaluations--a useful instrument for evaluating higher-order cognitive abilities--have always depended on human raters, concerns regarding rater bias,…

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…

With the generative artificial intelligence (AI) tool's remarkable capabilities in understanding and generating meaningful content, intriguing questions have been raised about its potential as an automated essay scoring (AES) system. One such tool is ChatGPT, which is capable of scoring any written work based on predefined criteria. However,…

This chapter reports on a study investigating non-expert raters' scoring behavior and cognitive processes involved in evaluating speech acts and pragmatic routines in L2 Chinese. Pragmatic production data were collected from 51 American learners of Chinese, who completed a 12-item oral Discourse Completion Test (DCT). The learners were divided…

The validity of the Kappa coefficient of chance-corrected agreement has been questioned when the prevalence of specific rating scale categories is low and agreement between raters is high. The researchers proposed the Lambda Coefficient of Rater-Mediated Agreement as an alternative to Kappa to address these concerns. Lambda corrects for chance…

Unfolding models are frequently used to develop scales for measuring attitudes. Recently, unfolding models have been applied to examine rater severity and accuracy within the context of rater-mediated assessments. One of the problems in applying unfolding models to rater-mediated assessments is that the substantive interpretations of the latent…

Efficient constructed response (CR) scoring requires both accuracy and speed from human raters. This study was designed to determine if setting scoring rate expectations would encourage raters to score at a faster pace, and if so, if there would be differential effects on scoring accuracy for raters who score at different rates. Three rater groups…

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,…

Practical constraints in rater-mediated assessments limit the availability of complete data. Instead, most scoring procedures include one or two ratings for each performance, with overlapping performances across raters or linking sets of multiple-choice items to facilitate model estimation. These incomplete scoring designs present challenges for…

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…

This introductory article describes how constructed response scoring is carried out, particularly the rater monitoring processes and illustrates three potential designs for conducting rater monitoring in an operational scoring project. The introduction also presents a framework for interpreting research conducted by those who study the constructed…

The emergence of ChatGPT as one of the most advanced chatbots and its ability to generate diverse data has given room for numerous discussions worldwide regarding its utility, particularly in advancing medical education and research. This study seeks to assess the performance of ChatGPT in medical biochemistry to evaluate its potential as an…

Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming…

The Canadian Agility and Movement Skill Assessment (CAMSA) is a valid and reliable circuit-based test of motor competence which can be used to assess children's skills in a live or recorded performance and then coded. We aimed to analyze the intrarater reliability of the CAMSA scores (total, time, and skill score) and time measured, by comparing…

Rater training is necessary to accurately evaluate fundamental motor skills among children with developmental disabilities (DD). The purpose of this pilot study was to examine the impact of an online rater training program for novice raters on scoring accuracy of the run and two-hand strike skills on the Test of Gross Motor Development-Third…