Â鶹´«Ã½

Hannah, L., Jang, E. E., Shah, M., & Gupta, V. (2023). Validity arguments for automated essay scoring of young students’ writing traits. Language Assessment Quarterly, 20(4–5), 399–420.

Hannah, L., Kim, H., & Jang, E. E. (2022). Investigating the effects of task type and linguistic background on accuracy in automated speech recognition systems: Implications for use in language assessment of young learners. Language Assessment Quarterly, 19(3), 289–313.

Hunte, M.R., Jang, E. E., & Ignacio, A. (In press). Cross-coupling effects of cognitive diagnostic assessment and dynamic assessment. In D. Leontjev, L. M. Poehner, & A. Huhta (Eds.), Dynamic and diagnostic language assessment: Learning across frameworks to support L2 education. De Gruyter.

Hunte, M. R., McCormick, S., Shah, M., Lau, C., & Jang, E. E. (2021). Investigating the potential of NLP-driven linguistic and acoustic features for predicting human scores of children’s oral language proficiency. Assessment in Education: Principles, Policy & Practice, 28(4), 477–505.

Ignacio, A., Jang, E. E., Heywood, A., Hunte, M. R., & Lai, H. (2024). Fostering young students’ writing skill development and self-regulation through dynamic assessment during pandemic disruptions. Studies in Language Assessment, 13(2), 147-186. 

Jang, E. E., Vincett, M., van der Boom, E. H., Lau, C., &Yang, Y. (2017). Considering young learners’ characteristics in developing a diagnostic assessment intervention. In M. K. Wolf & Y. G. Butler (Eds.), English language proficiency assessment for young learners (pp. 193-213). Routledge.

Jang, E. E., Hunte, M. R., Barron, C., & Hannah, L. (2023). Exploring the role of self-regulation in young learners’ writing assessment and intervention using BalanceAI automated diagnostic feedback. In K. Sadeghi & D. Douglas (Eds.), Fundamental considerations in technology mediated language assessment (pp. 31-48). Routledge.

Maplethorpe, L., Kim, H., Hunte, M. R., Vincett, M., & Jang, E. E. (2022). Student-generated questions in literacy education and assessment. Journal of Literacy Research, 54(1), 74–97.

Sinclair, J., Jang, E. E., & Rudzicz, F. (2021). Using machine learning to predict children’s reading comprehension from linguistic features extracted from speech and writing. Journal of Educational Psychology, 113(6), 1088–1106.