Improving Reading Comprehension Ability Through Optimizing Metacognition (Metcomprehension) and Inferential Ability Using Bigbook Digital

Abstract

This research is motivated by elementary school students' less-than-optimal reading comprehension skills. This is caused by the lack of teachers' understanding of how to improve students' reading skills and the lack of attention to measuring students' reading results, which causes students to need more information on their reading results. This study analysed the correlation between reading comprehension ability, metacognitive components, and inferential ability using digital big books. This research method uses a factorial analysis design to see the role of metacognitive variables and inferential ability on reading comprehension. This study involved 200 elementary school students from 4 schools. The sample was taken randomly. The data collection method used was a test. In contrast, the data collection instrument for students' metacomprehension skills was measured using the ESCOLA reading awareness scale, commonly used in students aged 10-15. The reading awareness measurement scale consists of 55 multiple-choice questions to assess three dimensions of metacomprehension, namely planning, monitoring, and evaluation. The data analysis used in this study was the Bonferroni analysis. The researcher selects the data obtained from the reading awareness scale, reading comprehension test, and its acquisition before analysis. The results showed that individual reading comprehension ability can predict metacomprehension accuracy. Based on these findings, it can be concluded that students' metacomprehension accuracy on textual question types is consistently better than their metacomprehension accuracy on inferential question types. This study implies that teachers can consider these aspects while optimizing the role of these variables to improve students' reading comprehension skills.