Assessing Students’ Learning Ability in a Postgraduate Statistical Course: A Rasch Analysis

AbstractIn an effort to change the assessment paradigm from the traditional method of assessment, this paper will suggest a different assessment approach focusing on learning of statistics at the postgraduate level. As a case study, a course taught to Universiti Teknologi MARA postgraduate students in Applied Statistics, namely Categorical Data Analysis was chosen as the agent of assessment where students perceived ability based on an entrance-exit survey are assessed using Rasch analysis. This analysis is able to classify students’ perceived learning ability and identify learning difficulty more precisely. The study had shown that through Rasch measurement tools, students’ learning in a statistical course can be accurately measure based on the logit scale measurement derived from the Rasch model. This model has provided an excellent alternative tool for measuring postgraduate students’ actual ability in learning statistical concepts