Automated essay scoring can produce reliable scores that are highly correlated with human scores, but is limited in its evaluation of content and other higher-order aspects of writing. The increased use of automated essay scoring in high-stakes testing underscores the need for human scoring that is focused on higher-order aspects of writing. This…

Generalizability of writing scores has always been a longstanding concern in L2 writing assessment. A number of studies have been conducted to investigate this topic during the last two decades. However, with the introduction of new test methods, such as reading-to-write tasks, generalizability studies need to focus on the score accuracy of…

In this paper, we describe and evaluate two state-of-the-art systems for identifying and correcting writing errors involving English articles and prepositions. Criterion[superscript SM], developed by Educational Testing Service, and "ESL Assistant", developed by Microsoft Research, both use machine learning techniques to build models of article…

The present study employed multi-faceted Rasch measurement (MFRM) to explore the rater bias patterns of native English-speaker (NES) raters when they rate EFL essays. Forty NES raters rated 40 essays written by female Japanese university students on a single topic adapted from the TOEFL Test of Written English (TWE). The essays were assessed using…

The assessment of writing ability is notoriously difficult. Different facets of the assessment seem to influence its outcome. Besides the writer's writing proficiency, the topic of the assignment, the features or traits scored (e.g., content or language use) and even the way in which these traits are scored (e.g., holistically or analytically)…