Native Language and Dialect

Naismith et al. (2018) pdf

• Model that measures L2 learners’ lexical sophistication with the frequency list based on the native speaker corpora
• Arabic-speaking learners are rated systematically lower across all levels of human-assessed English proficiency than speakers of Chinese, Japanese, Korean, and Spanish
• Level 5 Arabic-speaking learners are inaccurately evaluated to have similar level of lexical sophistication as Level 4 learners from China, Japan, Korean and Spain
• When used on the ETS corpus, essays by Japanese-speaking learners with higher human-rated lexical sophistication are rated significantly lower in lexical sophistication than Arabic, Japanese, Korean and Spanish peers

Loukina et al. (2019) pdf

• Models providing automated speech scores on English language proficiency assessment
• L1-specific model trained on the speaker’s native language was the least fair, especially for Chinese, Japanese, and Korean speakers, but not for German speakers
• All models (Baseline, Fair feature subset, L1-specific) performed worse for Japanese speakers

Rzepka et al. (2022) pdf

• Models predicting whether student will quit spelling learning activity without completing
• Multiple algorithms have slightly better false positive rates for second-language speakers than native speakers, but equivalent performance on multiple other metrics.

Sha et al. (2021) pdf

• Models predicting a MOOC discussion forum post is content-relevant or content-irrelevant
• MOOCs taught in English
• ABROCA varied from 0.03 to 0.08 for non-native speakers of English versus native speakers
• Balancing the size of each group in the training set reduced ABROCA