Analysis of item difficulties and students’ computational thinking skills assessment bias on electrolyte and non electrolyte solutions: An applications of Many Facets Rasch Model

Abstract

Skills are latent in nature, but all this time the measurement uses a ranking scale with certain criteria called ordinal data. Ordinal data is only counting, does not have a unit or distance between scores in a definite manner, and does not have zero absolute values as in the ratio data generated from physical measurements. Therefore, ordinal data is a raw score that cannot properly show one’s skills. Ordinal data can be converted into ratio data using the Rasch model analysis. This study aims to analyze the difficulty of items and bias towards the assessment of students’ Computational Thinking (CT) skills conducted with more than one observer on electrolyte and non electrolyte solutions. The method used was descriptive quantitative, with participants as many as 3 observers and 186 of tenth grade students in 3 high schools in Surakarta with high, medium, and low categories. The measurement uses observation assessment sheet at each meeting during the learning process which is then analyzed by the Many Face...