Difference between revisions of "Native Language and Dialect"
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Naismith et al. (2018) | Naismith et al. (2018) [http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf 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) [[https:// | Loukina et al. (2019) [https://aclanthology.org/W19-4401.pdf 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) [https://www.insticc.org/node/TechnicalProgram/CSEDU/2022/presentationDetails/109621 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) [https://angusglchen.github.io/files/AIED2021_Lele_Assessing.pdf 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 | |||
Latest revision as of 11:01, 4 July 2022
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