Exploring the role of self-regulation in young learners' writing assessment and intervention using BalanceAI automated diagnostic feedback
Eunice Eunhee Jang, Melissa R. Hunte, Christine Barron, Liam Hannah · 2023
Since the introduction of DIALANG, technological advances have made possible timely, dynamic, and multi-agent implementation of language assessment and feedback for learners. In particular, new horizons have opened with performative task designs infused with multi-modal stimuli, automated data processing and scoring, and adaptive scaffolding during an assessment. Although the COVID-19 pandemic has profoundly impacted students’ learning and language development, it also propelled the integration of technologies into teaching, learning, and assessment. This chapter examines the AI-infused feedback mechanism through which diagnostic scaffolding was provided for young writers during their BalanceAI assessment. Specifically, we closely examine their latent profiles that describe the distinctive relationship between their writing ability and self-regulating ability. Based on qualitative and quantitative data from one-to-one BalanceAI tutoring sessions over three to four weeks, we pay close attention to ways in which diagnostic feedback serves as reciprocal scaffolding, facilitating human–human and human–machine interactions as well as self-scaffolding, which supports learners’ metacognitive control.