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, in 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.