Enhancing Advanced Level Students’ Achievement in Motion Concepts in Physics through the Integration of Digital Simulations

Abstract

This study investigated the effect of integrating digital simulations on students’ achievement in motion concepts in Advanced Level Physics. The main research question was "Does using digital simulation improve the understanding of motion concepts in Advanced Level students?" The objectives of the study were to assess students’ existing achievement levels, design and implement simulation-integrated lesson activities, and evaluate their effectiveness in improving conceptual understanding. A Design-Based Research (DBR) approach was used throughout four cycles. Six Advanced Level Physics students were chosen using census sampling. PhET simulations were integrated into lessons, along with teacher guidance and worksheets. Each DBR cycle lasted 80 minutes, which included post-tests, feedback collection, and semi-structured student interviews. Quantitative data were analyzed using descriptive statistics, normalized gain scores, and the Wilcoxon Signed-Rank Test (p = 0.05), while qualitative insights from researcher reflection journals, senior teacher observations, and student feedback were analyzed thematically. Results showed significant improvement between cycles 2 and 3 with all students reaching at least 80% competency by the fourth cycle. Students made notable progress in areas they initially found challenging, including graph interpretation and multi-stage motion comparisons. Qualitative evidence showed increased engagement, confidence, and independent use of simulations. The findings suggest that revising simulation-based lessons along with scaffolding and reflective assessment, can improve understanding in complex physics topics. The study concludes that structured integration of digital simulations can sustainably improve learning outcomes in motion concepts in Advanced Level Physics. Future research should explore long-term retention, application to other physics concepts, and generalizability across larger student samples.