A Web-Based Platform for Assessing Critical Reading Competence to Support Deep Learning

Abstract

Critical reading skills are essential for supporting deep learning, but assessments in schools are still often conducted manually and separate reading speed from comprehension. This study aims to develop and evaluate a web-based platform for measuring critical reading competence that integrates Effective Reading Speed (ERS), reading comprehension, text readability, and immediate feedback for students from elementary through high school. The study employed a research and development design consisting of six stages: needs analysis, planning, initial product development, expert validation, limited and large-scale pilot testing, and revision and implementation of the final product. The needs analysis involved 30 Indonesian language teachers. The limited pilot test involved 90 students, while the large-scale pilot test involved 210 students evenly distributed across grades 5, 8, and 11. The results showed that 93% of teachers experienced difficulties in conducting critical reading assessments and 87% needed an automated platform. All aspects of validation scored above 85%. In the large-scale pilot, Critical Reading Skills (KEM) improved descriptively by 35.3% in Grade 5, 41.3% in Grade 8, and 33.8% in Grade 11; comprehension ranged from 83–88%, while student satisfaction reached 4.5–4.7 on a 5-point scale. The novelty of this research lies in the integration of time measurement, comprehension, cross-grade readability calibration, and immediate feedback into a single Indonesian-language platform. The platform has the potential to support data-driven formative assessment, although the causal effectiveness and reliability of the instrument still require further testing.