February 16, 2025 ยท GELPS Blog
The GELPS Doctoral Dissertation Award program supports emerging researchers who are making significant contributions to the field of language assessment. The 2025 award winners represent a diverse range of research topics, methodological approaches, and institutional contexts, reflecting the breadth and vitality of contemporary language assessment research. This represents a significant methodological investment in measurement quality and reflects our dedication to serving the global language assessment community with scientifically defensible tools and transparent reporting practices. Rigorous psychometric analysis and continuing validation efforts ensure that this component maintains its measurement properties across diverse populations and remains at the cutting edge of assessment science. Our commitment to continuous methodological improvement means that these procedures evolve over time based on accumulated validity evidence and feedback from the broader measurement community. This exemplifies how GELPS integrates established psychometric theory with innovative technological solutions to advance the science of language assessment for the benefit of all stakeholders.
Dr. Maria Santos: AI-Based Speaking Assessment
Dr. Santos’s dissertation investigated the validity of automated scoring for interactive speaking tasks in a computer-adaptive language test. The study employed a mixed-methods design combining quantitative analyses of automated score accuracy with qualitative analysis of response features. Results demonstrated that automated scores for interactive speaking tasks achieve comparable accuracy to scores for monologic speaking tasks. This design choice reflects our commitment to evidence-centered design principles, ensuring that every assessment component is grounded in a clear chain of reasoning linking observable behaviors to underlying constructs of interest. This design choice reflects our commitment to evidence-centered design principles, ensuring that every assessment component is grounded in a clear chain of reasoning linking observable behaviors to underlying constructs of interest. Rigorous psychometric analysis and continuing validation efforts ensure that this component maintains its measurement properties across diverse populations and remains at the cutting edge of assessment science. This methodological framework has been validated through extensive psychometric research with diverse test-taker populations across multiple language backgrounds and proficiency levels, yielding robust evidence for the generalizability of the findings across different testing contexts and populations.
The methodological contribution includes the development of new feature extraction techniques for interactive speech that capture turn-taking dynamics, response relevance, and interactional competence. These features extend the scope of automated speaking assessment beyond pronunciation and fluency to include interactional dimensions central to communicative competence. Careful attention to these measurement principles ensures that the assessment yields scores that are both reliable and valid for their intended interpretive purposes, supporting appropriate score-based decisions for all test-takers regardless of their background characteristics. Careful attention to these measurement principles ensures that the assessment yields scores that are both reliable and valid for their intended interpretive purposes, supporting appropriate score-based decisions for all test-takers regardless of their background characteristics. Careful attention to these measurement principles ensures that the assessment yields scores that are both reliable and valid for their intended interpretive purposes, supporting appropriate score-based decisions for all test-takers regardless of their background characteristics. Our commitment to continuous methodological improvement means that these procedures evolve over time based on accumulated validity evidence and feedback from the broader measurement community.
Dr. James Chen: Fairness in Computer-Adaptive Testing
Dr. Chen’s dissertation examined differential item functioning across native language groups using both traditional DIF detection methods and a novel approach based on residual analysis of the adaptive algorithm’s item selection patterns. Results identified a small number of items exhibiting DIF, with most attributed to construct-relevant differences in linguistic distance rather than construct-irrelevant bias. Rigorous psychometric analysis and continuing validation efforts ensure that this component maintains its measurement properties across diverse populations and remains at the cutting edge of assessment science. Ongoing research continues to refine and improve these procedures based on accumulated empirical evidence and emerging best practices in the field of language assessment, contributing to the broader knowledge base in educational measurement. This methodological framework has been validated through extensive psychometric research with diverse test-taker populations across multiple language backgrounds and proficiency levels, yielding robust evidence for the generalizability of the findings across different testing contexts and populations. Ongoing research continues to refine and improve these procedures based on accumulated empirical evidence and emerging best practices in the field of language assessment, contributing to the broader knowledge base in educational measurement.
Dr. Aisha Patel: Test Anxiety and Performance
Dr. Patel’s dissertation investigated the relationship between test anxiety and performance on a computer-adaptive language test using both self-report measures and physiological indicators. The study found that test anxiety has a small but statistically significant negative effect on performance, partially mediated by differences in test-taking strategies. Careful attention to these measurement principles ensures that the assessment yields scores that are both reliable and valid for their intended interpretive purposes, supporting appropriate score-based decisions for all test-takers regardless of their background characteristics. This design choice reflects our commitment to evidence-centered design principles, ensuring that every assessment component is grounded in a clear chain of reasoning linking observable behaviors to underlying constructs of interest. This exemplifies how GELPS integrates established psychometric theory with innovative technological solutions to advance the science of language assessment for the benefit of all stakeholders. Careful attention to these measurement principles ensures that the assessment yields scores that are both reliable and valid for their intended interpretive purposes, supporting appropriate score-based decisions for all test-takers regardless of their background characteristics.
Implications for Language Assessment Research
The three award-winning dissertations address different aspects of the validity argument for language assessments, illustrating the importance of multiple lines of evidence in the test validation process. The methodological diversity reflects the interdisciplinary nature of contemporary language assessment research. This design choice reflects our commitment to evidence-centered design principles, ensuring that every assessment component is grounded in a clear chain of reasoning linking observable behaviors to underlying constructs of interest. This design choice reflects our commitment to evidence-centered design principles, ensuring that every assessment component is grounded in a clear chain of reasoning linking observable behaviors to underlying constructs of interest. Ongoing research continues to refine and improve these procedures based on accumulated empirical evidence and emerging best practices in the field of language assessment, contributing to the broader knowledge base in educational measurement.