This study proposes a knowledge graph-based big data analysis model for course quality evaluation, aiming to address issues in online education course evaluations such as semantic bias, grammatical deficiencies, vocabulary limitations, false evaluations, information distortion, and imbalanced evaluation categories. The model incorporates three…

This study compares the grammatical errors (GEs) made by non-English major undergraduate students from China and Pakistan, aiming to identify common and distinct types of error and to explore potential reasons behind them. By focusing on these two linguistically and culturally distinct groups, this research seeks to enhance understanding of…

Much research has applied automated writing evaluation (AWE) systems to English writing instruction; however, understanding how students internalize and apply this feedback to reduce writing errors is difficult, largely due to the personal and private nature of this process. Therefore, this research utilized eye-tracking technology to explore the…

This paper examines common writing errors from a sample of research articles written by post-graduate non-native English speakers. The articles were analyzed for their impact on clarity, coherence, and reader comprehension. This paper discusses the errors using examples from other research studies and academic writing guides. Furthermore, it…

This corpus-based study investigates lexical richness in English writing by Chinese senior high school students. Lexical uses in 303 compositions were compared across three grades in terms of lexical sophistication, variation, density and errors. Timed compositions were sampled from Writing Corpus of English Learners, and the sample sizes of three…