Biases non-native English-speaking students encounter

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Biases non-native English-speaking students encounter

Written by JJ Cloutier

Classrooms across Manitoba are filled with a variety of students, and a solid percentage of them have a first language other than Canadian English, whether they are French Canadian, Indigenous speaker, or first-generation, second or even third-generation immigrants from various non-English predominate speaking countries.  

Modern (and inclusive) pedagogies stress the importance of providing ample time for students to complete formative (activities) and summative assessments. A widespread tool for estimating student task time is the Rice University Course workload estimator. Within the notes accompanying the calculator, the data on student needs for reading and writing in academia and the assumptions used for the calculator are explained. What is unclear from the notes is what, if any, consideration is taken for readers whose first language is not English (non-native speakers). 

In the July 2023  article “Non-native English speaking scientists work much harder just to keep up global research reveals,” the difference between English first language speakers and non-native English speakers is clearly shown. Non-native English speakers need: 

  • 91% more time for reading a scientific paper 
  • 51% more to write a paper 
  • 94% more time for preparing a presentation 

These recently released numbers are based on surveying over 900 researchers in environmental sciences who will have even more experience with reading and writing in English than undergrads or students just entering post-secondary. 

At the same time, instructors are faced with determining if students use Ai (Artificial Intelligence) to help them with assessments.  Those GPT detectors, to put it mildly, are problematic. “Most GPT detectors use text perplexity to detect AI-generated text, which might inadvertently penalize non-native writers who use a more limited range of linguistic expressions.”  (Liang, W., Yuksekgonul , M., Mao, Y., Wu, E. and Zou, J., 2023), based on their high rate of misclassification of TOEFL essays as written by Ai. 

So, what can instructors and post-secondary institutions do? The main goal is to provide effective student writing feedback directly from instructors and campus services.

Specifically, instructors can: 

  • “Push our students to write early and often so they can identify weaknesses in their thought and use the resulting discussions to guide further research” (Zhao, K., 2023) discussion or expression. 
  • Give more time for students to read, write and prepare for presentations. (Amona, T., 2023) 
  • Evaluate and give feedback on the central argument before focusing on an odd word choice or problematic grammar and paragraph length. (Zhao, K., 2023) 
  • Provide good writing advice that points out specific problems and discusses strategies to resolve them. (Zhao, K., 2023) 
  • Connect the student to training opportunities on your campus for academic English reading and writing. You can provide this training information in your course outline or syllabus. (Amona, T., 2023) 

What post-secondary institutions can do 

  • Provide training opportunities for academic English reading. (Amona, T., 2023) 
  • Provide training opportunities for academic English writing. (Amona, T., 2023) 
  • Financially support English editing and translation (through grants or similar means) for academic writing for publication purposes. (Amona, T., 2023) 
  • Begin discussions with various stakeholders on how best to use Ai as a legitimate and accepted tool for non-native English speakers as a linguistic aid to enhance their writing. (Liang, W., Yuksekgonul , M., Mao, Y., Wu, E. and Zou, J., 2023) 

Partner supports for student academic English writing 

References 

Amano, T. (2023, July 18) Non-native English speaking scientists work much harder just to keep up, global research reveals. The Conversation. https://theconversation.com/non-native-english-speaking-scientists-work-much-harder-just-to-keep-up-global-research-reveals-208750 

Liang, W., Yuksekgonul , M., Mao, Y., Wu, E. and Zou, J., (2023) GPT detectors are biased against non-native English writers. Patterns. https://doi.org/10.1016/j.patter.2023.100779 

Zhao, K., (2023, August 18) “Native Speaker” Fallacy: Stop telling students to have their essays checked by a native English speaker. Inside Higher Ed. https://www.insidehighered.com/opinion/views/2023/08/18/stop-native-speaker-writing-advice-opinion 

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