Abstract
Open science practices are now at the forefront of discussions in the applied linguistics research community. Proponents of open science argue for its potential to enhance research quality and accessibility while promoting a collaborative and equitable environment. Winke advocates for integrating open science into language assessment research to enhance research quality, accessibility, and collaboration. This response introduces two additional perspectives to support open science practices. The first is a framework, which identifies five schools of thought on open science that emphasize understanding the various goals of open science and the scientific methods and tools that are used to pursue them. Second, I highlight two additional characteristics of open science: the need for community and the costs of open science. These additional perspectives underscore the significance of making research processes transparent and inclusive, extending beyond traditional academic boundaries to engage the public and industry stakeholders. By integrating these considerations, this response aims to offer a nuanced view of the challenges and opportunities that open science presents in the field of language assessment, suggesting ideas for how researchers outside and inside the language assessment industry can work toward improving open science practices in language assessment research.
Reference17 articles.
1. Belzak W. (2024, February 19). New research on proctors: How to reduce variability in decision making. Test Center: Official blog of the Duolingo English Test. https://blog.englishtest.duolingo.com/new-research-variability-in-proctors-decision-making/
2. Measuring Variability in Proctor Decision Making on High‐Stakes Assessments: Improving Test Security in the Digital Age
3. TOEFL11: A CORPUS OF NON-NATIVE ENGLISH
4. Second language speech comprehensibility and acceptability in academic settings: Listener perceptions and speech stream influences
5. Duolingo. (2020). Data for the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE) (Draft version) [Data set]. Harvard Dataverse. https://doi.org/10.7910/DVN/38OJR6