News & Publications
ROAR Development Newsletter
Computer adaptive assessment & new dashboard system
January 15th, 2025
Curious to hear more about the ROAR? Visit this page to see the latest media and research on the ROAR and its’ development. Click on the cards below to read the full articles!
- All
- Research
- Media
Stanford team builds tool to keep young readers from falling through the cracks
ROAR was selected by the Reading Difficulties Risk Screening Selection Panel as an approved screener for the state of California. Learn more about the journey from Stanford News or Ed Source.
ROAR-CAT: Rapid Online Assessment of Reading ability with Computerized Adaptive Testing
Ma, W. A., Richie-Halford, A., Burkhardt, A., Kanopka, K., Chou, C., Domingue, B., & Yeatman, J. D. (2025). ROAR-CAT: Rapid Online Assessment of Reading ability with Computerized Adaptive Testing. Behavior Research Methods, 57(56).
A Fair Lexical Decision Task for Monolingual and Multilingual Spanish-speakers
Siebert, J. M., Fuentes-Jimenez, M., Ma, W. A., Saavedra, A., Townley-Flores, C., & Yeatman, J. D. (2024, December 28). A Fair Lexical Decision Task for Monolingual and Multilingual Spanish-speakers. https://doi.org/10.31234/osf.io/qfdpb
Development and validation of a rapid and precise online sentence reading efficiency assessment
Yeatman, J. D., Tran, J. E., Burkhardt, A. K., Ma, W. A., Mitchell, J. L., Yablonski, M., Gijbels, L., Townley-Flores, C., & Richie-Halford, A. (2024). Development and validation of a rapid and precise online sentence reading efficiency assessment. Frontiers in Education, 9, 1494431.
California Dyslexia Initiative Keynote Talk
Professor Jason Yeatman gives a keynote talk on the ROAR to the California Dyslexia Initiative. The ROAR project is presented @ 33:14.
Rapid online assessment of reading and phonological awareness (ROAR-PA)
Gijbels, L., Burkhardt, A., Ma, W.A., & Yeatman, J.D. (2024). Rapid online assessment of reading and phonological awareness (ROAR-PA). Scientific Reports, 14(10249). https://doi.org/10.1038/s41598-024-60834-9
Can a New Reading Assessment Tool Help Improve Literacy Rates?
“ROAR is going to partner with an expert in Black American English to undertake a rigorous and innovative item analysis to reduce the tests’ bias. Too often, standardized literacy assessments unreasonably penalize dialect speakers and underestimate their language and reading skills. This will be a game changer, showing the wider reading assessment field that linguistic bias reduction can and should be done.”
Rapid Online Assessment of Reading (ROAR): Evaluation of an online tool for screening reading skills in a Developmental-Behavioral Pediatrics Clinic
Barrington, E., Sarkisian, S. M., Feldman, H. M., & Yeatman, J.D. Rapid Online Assessment of Reading (ROAR): Evaluation of an online tool for screening reading skills in a Developmental-Behavioral Pediatrics Clinic. Journal of Developmental & Behavioral Pediatrics, 44(9). https://doi.org/10.1097/dbp.0000000000001226.
Development and validation of a rapid online sentence reading efficiency assessment
Yeatman, J. D., Tran, J. E., Burkhardt, A., Ma, W. A., Mitchell, J., Yablonski, M., Gijbels, L., Townley-Flores, C., & Richie-Halford, A. [Pre-print]. Development and validation of a silent sentence reading efficiency online assessment.
Generative AI work for Sentence Reading Efficiency work presented at the AI+Education Summit
Zelikman, E., Ma, W. A., Tran, J. E., Yang. D., Yeatman, J. D., & Haber, N. (2023). Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency [Poster presentation]. 2024 AI+Education Summit: AI in the Service of Teaching & Learning.
Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency
Zelikman, E., Ma, W. A., Tran, J. E., Yang. D., Yeatman, J. D., & Haber, N. (2023). Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency [Conference proceeding]. 2023 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.48550/arXiv.2310.06837
