Multidimensional Assessment Performance Analysis: A Framework to Advance Multilingual Learners’ Scientific Equity in K-12 Contexts

Evaluating science learning through written assessments for Multilingual Learners (MLs) can offer a critical source of information for educators aiming to adopt culturally and linguistically sustaining practices. However, without careful planning and design, written assessments for MLs can yield unreliable data. This conceptual article addresses this challenge by first examining the complexities of the written language of science and its impact on student learning. It then explores current literature and presents the Multidimensional Assessment Performance Analysis (MAPA) framework, a multimodal model for analyzing writing answers in multilingual educational contexts. MAPA integrates Systemic Functional Linguistics (SFL) to evaluate language use and Topic Models to allocate students based on their cognitive reasoning and thinking patterns. The article concludes with recommendations for classroom teachers and researchers to enhance assessment practices in science education for MLs.