The Effects of Culturally Congruent Educational Technologies on Student Achievement
By Samantha L. Finkelstein, Evelyn Yarzebinski, Callie Vaughn, Amy E. Ogan, Justine Cassell. 2013
73 citations recorded by the source corpus.
Subfields: Developmental and Educational Psychology, Education, Language and Linguistics, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Developmental and Educational Psychology
In Education
In Language and Linguistics
In Natural Language Processing
- Shrimai Prabhumoye
- Iason Gabriel
- John F. J. Mellor
- Ryan Shaun Joazeiro de Baker
- Alan W. Black
- Borja Balle
- Geoffrey Irving
- Amelia Glaese
Citations in the corpus
- Ethical and social risks of harm from Language Models (2021, background): “Such ‘code-switching’ can lead to lower utility and worse outcomes for these users, as has been shown for language technologies in education (Finkelstein et al., 2013).”
- Taxonomy of Risks posed by Language Models (2022, background): “The detection of lower performance for some linguistic groups is complicated by users who typically speak in vernacular, ”code-switching” in order to improve the technology’s performance [55].”
- Equity Beyond Bias in Language Technologies for Education (2019, background): “Incorporating representation improves embod-ied tutors, with improved student outcomes (Finkelstein et al., 2013).”
- Algorithmic Bias in Education (2021, background): “However, we were unable to identify any work on algorithmic bias in terms of learner dialect, although there has been work to develop learning systems that are appropriate for speakers of non-traditional dialects (e.g. Finkelstein et al., 2013).”