Categorizing students into groups according to their learning attitudes based on cluster analysis - Janchiv Shinebayar; Badarch Jadamba; Ochirbat Altangoo; Raash Namjildagva; Tumurbaatar Ganbaatar; Ravdandorj Togoo; Sukhbaatar Batchuluun (Lavai)
Tests are frequently used in any field of science education to assess students’ knowledge and skills. In this paper, we briefly introduce the results of the analysis on the test data of twelve grade students in Mongolia. These analyses are based on two approaches- classical test theories and cluster analysis. It is less important to measure learners’ attitudes through only one subject. We believe that students’ attitudes towards learning academic subjects and acquiring scientific education can be defined through their achievement data on their knowledge and skills on multiple subjects. According to learning attitudes, most researchers analyze the data using a survey that includes “Likert” scale statements and questions. In this paper, we have categorized students' learning attitudes based on their results of academic performances that assess only students’ knowledge and skills. Students are categorized into five groups according to their learning attitudes based on the two-step clustering components.