Understanding Students' Computational Fluency: Synechistically Using Test Scores and Interviews for a Richer Picture

Abstract

Mathematics education researchers are tasked with solving practical research problems involving complex constructs in complex settings. The effective integration of quantitative and qualitative data allows researchers to draw more nuanced conclusions about these complex phenomena. This article describes the use of a convergent parallel mixed methods design to integrate two seemingly conflicting data sources that measured six second-grade students’ development of computational fluency. The mixed methods analysis of students’ computational fluency assessments and interviews showed that there was variation in students’ assessment scores, strategy use, and engagement of number sense. Within these variations, the quantitative and qualitative data converged or diverged at various measurement points, and the results highlight the importance of merging the two data sets to capture a richer picture of students’ computational fluency. Implications for using mixed methods in understanding how mathematics learning occurs in classrooms are discussed.

Published
2018-09-01