An official website of the United States government
I describe the implementation of a class wiki in an introductory linguistics class. There were two pedagogical goals: (1) facilitate asynchronous student engagement and collaborative learning; (2) provide opportunities for students to engage with various linguistic issues having to do with justice, equity, diversity, and inclusion. Assessment for the wiki was done using a version of specifications grading (Nilson 2015), so that students could choose their level of engagement with the wiki. A full description of the wiki is available at https://cbjorndahl.github.io/CMUNoLWiki/, which includes detailed descriptions, learning objectives, and prompts given to students for each wiki cate-gory. The present paper focusses primarily on the pedagogical motivations, design of the pedagogical intervention, and a reflection of its effectiveness.
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Linguistic Variation and Linguistic Inclusion in the US Educational Context
This article examines linguistic variation in relation to the critical social institution and social domain of education, with an emphasis on linguistic inclusion, focusing on the United States. Education is imbued with power dynamics, and language often serves as a gatekeeping mechanism for students from minoritized backgrounds, which helps create, sustain, and perpetuate educational inequalities. Grounded in this context, the article reviews intersecting factors related to linguistic variation that affect student academic performance. Empirical and applied models of effective partnerships among researchers, educators, and students are presented, which provide road maps to advance linguistic inclusion in schools within the broader social movement for equity in education.
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- PAR ID:
- 10559588
- Publisher / Repository:
- Annual Reviews
- Date Published:
- Journal Name:
- Annual Review of Linguistics
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2333-9683
- Page Range / eLocation ID:
- 37 to 57
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT This article calls attention to the saliency of secondary education within the community and its utility in constructing social categories, in order to consider how it affects linguistic variation. Older St. Louisans draw on secondary education to construct a divide between those who attended Catholic high schools and those who attended public schools. I show that speakers in a sample of older St. Louisans differ in production of the thought vowel based on education type. This effect is weakened in apparent time when we consider a larger sample that includes both older and younger speakers. I draw on Brubaker's (2004) view of groups as events and actions to argue that these categories were indexed only while they had a high degree of groupness, and suggest that social changes that led to diminished groupness between Catholics and Publics also resulted in the loss of a linguistic distinction between the groups. (Education, groups, Northern Cities Shift, Catholicism)*more » « less
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Abstract As use of artificial intelligence (AI) has increased, concerns about AI bias and discrimination have been growing. This paper discusses an application called PyrEval in which natural language processing (NLP) was used to automate assessment and provide feedback on middle school science writing without linguistic discrimination. Linguistic discrimination in this study was operationalized as unfair assessment of scientific essays based on writing features that are not considered normative such as subject‐verb disagreement. Such unfair assessment is especially problematic when the purpose of assessment is not assessing English writing but rather assessing the content of scientific explanations. PyrEval was implemented in middle school science classrooms. Students explained their roller coaster design by stating relationships among such science concepts as potential energy, kinetic energy and law of conservation of energy. Initial and revised versions of scientific essays written by 307 eighth‐grade students were analyzed. Our manual and NLP assessment comparison analysis showed that PyrEval did not penalize student essays that contained non‐normative writing features. Repeated measures ANOVAs and GLMM analysis results revealed that essay quality significantly improved from initial to revised essays after receiving the NLP feedback, regardless of non‐normative writing features. Findings and implications are discussed. Practitioner notesWhat is already known about this topicAdvancement in AI has created a variety of opportunities in education, including automated assessment, but AI is not bias‐free.Automated writing assessment designed to improve students' scientific explanations has been studied.While limited, some studies reported biased performance of automated writing assessment tools, but without looking into actual linguistic features about which the tools may have discriminated.What this paper addsThis study conducted an actual examination of non‐normative linguistic features in essays written by middle school students to uncover how our NLP tool called PyrEval worked to assess them.PyrEval did not penalize essays containing non‐normative linguistic features.Regardless of non‐normative linguistic features, students' essay quality scores significantly improved from initial to revised essays after receiving feedback from PyrEval. Essay quality improvement was observed regardless of students' prior knowledge, school district and teacher variables.Implications for practice and/or policyThis paper inspires practitioners to attend to linguistic discrimination (re)produced by AI.This paper offers possibilities of using PyrEval as a reflection tool, to which human assessors compare their assessment and discover implicit bias against non‐normative linguistic features.PyrEval is available for use ongithub.com/psunlpgroup/PyrEvalv2.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-linguistics-031120-121546
