The growth of education in the globalisation era demands a paradigm change in the learning process, especially in terms of assessment that emphasises the importance of higher-order thinking skills (HOTS), which are no longer only focused on memorising and understanding basic concepts. This study aims to evaluate HOTS tests on renewable energy materials using the Rasch model as the main analysis technique. This research uses an evaluative design with a quantitative approach that aims to analyse the psychometric quality of HOTS test instruments objectively and systematically. The research subjects consisted of 71 learners selected through a purposive sampling technique from two classes in senior high school. The research instrument used was 10 multiple-choice questions with five answer options and analysed using Ministep software. The analysis results through the Wright map show that the distribution of students' abilities is close to a normal distribution. In contrast, the distribution of item difficulties has an uneven pattern, so that it does not cover the entire range of students' HOTS abilities. In addition, most of the items had adequate psychometric quality, but two items did not fit and needed special attention. Nevertheless, further analysis using the Item Characteristic Curve (ICC) revealed that item S3 indicated misfit, bringing the total to three items that did not fit the Rasch Model predictions. In addition, item S5 is included as a bias item. Overall, the instrument shows that the hierarchy of item difficulty is consistent and reliable, but it has limitations in sensitivity in distinguishing ability levels between learners. In addition, ICC analysis provides more sensitive detection of response pattern discrepancies, indicating the need for multiple analyses for comprehensive validation. Additional items are needed to comprehensively cover the spectrum of student abilities and improve the precision of identifying individual ability differences.
Rahmatillah et al. (Wed,) studied this question.