Difference between revisions of "Native Language and Dialect"

From Penn Center for Learning Analytics Wiki
Jump to navigation Jump to search
(Added Sha et al (2021))
 
(2 intermediate revisions by 2 users not shown)
Line 1: Line 1:
Naismith et al. (2018) [http://d-scholarship.pitt.edu/40665/1/EDM2018_paper_37.pdf pdf]
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
* 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.
* 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 .
* 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.
* 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




Line 12: Line 12:
* 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
* 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
* 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 12:01, 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