A synthesis of mathematical word problem‐solving instructions for English learners with learning disabilities in mathematics

Abstract

Mathematics problem‐solving is a fundamental aspect of school mathematics that requires an integrated set of skills, such as comprehending the problems and mathematics computation. Providing useful problem‐solving interventions and instructions is essential for students who are English learners (ELs) with learning disabilities or at risk for learning disabilities in mathematics (LDM). This study aims to synthesise current word problem‐solving intervention studies targeted at improving the mathematic problem‐solving performance of ELs with LDM. Fourteen studies were reviewed under three instructional categories: culturally responsive scaffolding, linguistic scaffolding, and mathematical model‐based visual scaffolding. The limitations and implications for future research and practice are also discussed. Context and implicationsRationale for this studyThe task of teaching mathematics word problems to English learners (ELs) with learning disabilities in mathematics (LDM) is challenging due to the additional cultural and language barriers that may coexist with disability‐related factors. This review was conducted to identify effective instructional strategies that can support ELs with LDM in overcoming these challenges and succeeding in solving mathematical word problems.Why the new findings matterThe findings from this study matter because they provide educators with valuable insights into effective instructional scaffolding that can assist ELs with LDM in solving mathematical word problems. Knowing which scaffolds are effective can help teachers provide better support to ELs with LDM in their classrooms.ImplicationsImplications for teachers are that providing multi‐component instructions during word problem solving, such as combining culturally responsive scaffolding with linguistically relevant instruction and domain‐specific instruction, can benefit ELs with LDM in solving mathematical word problems. For researchers, further studies are necessary to explore the effectiveness of incorporating non‐linguistic visual representations into interventions as a means of helping ELs make sense of mathematical tasks and express their ideas. Understanding the effectiveness of various instructional strategies can inform the development of more effective interventions and better support ELs with LDM in their academic pursuits.

Manual Label and Machine Learning in Clustering and Predicting Student Performance

“…For instance, students who fall into the high-high quadrant may benefit from challenging and rigorous academic programs that demand a high level of both result-oriented and process-oriented investment. Conversely, students in the low-low quadrant may require scaffolded support (Lei & Xin, 2023) and motivational strategies to enhance their engagement and investment in academic activities. Our study has theoretical implications in the following aspects: (a) AIM model introduces a novel approach to student performance prediction within the field of educational analytics.…”