Calibration of Mathematics Reading Assessment and Students’ Performance based on MIRT and Cluster Analysis

  • Yuan-Horng Lin
  • Hsin-Ying Huang


The purpose of this study aims to calibrate mathematics reading assessment based on multidimensional item response theory (MIRT) and analyse students’ performance. The mathematics reading assessment is mathematics reading texts related to real-life statistics and the sample is 789 sixth graders. In this study, mathematics reading includes three dimensions which are based on the content reading components provided by M. C. McKenna and R. D. Robinson.  Firstly, the researcher develops the mathematics reading assessment with respect to real-life statistics. This study calibrates the mathematics reading assessment by multidimensional item response theory. Item fit indicators show this assessment conforms to the assumption of the above three dimensions on mathematics reading. Namely, there are three latent traits with respect to the mathematics reading assessment. Secondly, this study adopts cluster analysis of k-means to classify students based on their three latent traits on mathematics reading.  Results show that are five clusters in which each cluster reveals homogeneity in three latent traits. Characteristics of latent traits among clusters are distinct and each cluster shows its own cognitive characteristics on mathematics reading. The mathematics reading is an important issue with regard to mathematics literacy but little is known in terms of psychometrical analysis. This study could provide references for instruction and assessment of mathematics reading. Finally, based on the findings of this study, some recommendations and suggestions for future researches and methodologies are also discussed.


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How to Cite
LIN, Yuan-Horng; HUANG, Hsin-Ying. Calibration of Mathematics Reading Assessment and Students’ Performance based on MIRT and Cluster Analysis. International Journal of Advanced Research in Education and Society, [S.l.], v. 2, n. 2, p. 47-54, june 2020. ISSN 2682-8138. Available at: <>. Date accessed: 11 aug. 2020.