The application of Simulation-Assisted Learning Statistics (SALS) for correcting misconceptions and improving understanding of correlation

Simulation-based computer assisted learning (CAL) is recommended to help students understand important statistical concepts, although the current systems are still far from ideal. Simulation-Assisted Learning Statistics (SALS) is a simulation-based CAL that is developed with a learning model that is based on cognitive conflict theory to correct misconceptions and enhance understanding of correlation. In this study, a mixed method (embedded experiment model) was utilized to examine the effects of SALS-based learning compared with lecture-based learning. The sample was composed of 72 grade-12 students, who were randomly assigned to either the experimental group or the comparison group. The findings reveal that the SALS-based learning approach is significantly more effective than lecture-based learning terms of correcting students' misconceptions and improving their understanding of correlation. The study also uses quantitative and qualitative data to examine how the learning model of the SALS-based learning approach contributes to the enhanced learning outcomes. Finally, practical suggestions were made with regard to directions for future studies.