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Ahmet Can Uyar; Dilek Büyükahiska – International Journal of Assessment Tools in Education, 2025
This study explores the effectiveness of using ChatGPT, an Artificial Intelligence (AI) language model, as an Automated Essay Scoring (AES) tool for grading English as a Foreign Language (EFL) learners' essays. The corpus consists of 50 essays representing various types including analysis, compare and contrast, descriptive, narrative, and opinion…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Teaching Methods
Heidari, Nasim; Ghanbari, Nasim; Abbasi, Abbas – Language Testing in Asia, 2022
It is widely believed that human rating performance is influenced by an array of different factors. Among these, rater-related variables such as experience, language background, perceptions, and attitudes have been mentioned. One of the important rater-related factors is the way the raters interact with the rating scales. In particular, how raters…
Descriptors: Evaluators, Rating Scales, Language Tests, English (Second Language)
Ahmadi Shirazi, Masoumeh – SAGE Open, 2019
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…
Descriptors: Foreign Countries, Language Tests, Test Bias, Essay Tests
Müller, Amanda – Higher Education Research and Development, 2015
This paper attempts to demonstrate the differences in writing between International English Language Testing System (IELTS) bands 6.0, 6.5 and 7.0. An analysis of exemplars provided from the IELTS test makers reveals that IELTS 6.0, 6.5 and 7.0 writers can make a minimum of 206 errors, 96 errors and 35 errors per 1000 words. The following section…
Descriptors: English (Second Language), Second Language Learning, Language Tests, Scores