Sampling Distributions and the Central Limit Theorem-Applying the Active Learning Method POGIL combined with Technology

Abstract

Abstract—This research presents an innovative approach encompassing the development, execution, and evaluation of an interactive learning technique known as Process Oriented Guided Inquiry Learning,(POGIL), merged with advanced technology, to enhance students' grasp of the intricate notions concerning sampling distributions of means and the Central Limit Theorem. Within this exercise, students engage in the act of drawing samples and embarking on an exploration of the diverse shapes exhibited by sampling distributions across varying sample sizes. To gauge the efficacy of this technologyinfused POGIL activity, a comparative analysis was performed. While one group received conventional lecturestyle instruction on the Central Limit Theorem, another cohort experienced an alternative approach combining student-driven active learning through POGIL with technology integration. The primary objective of this inquiry was to ascertain whether the activity-centered POGIL strategy, enriched with simulation-based methodologies, yielded heightened comprehension of the Central Limit Theorem among students. Upon meticulous statistical comparison of pre and post test performance between the two groups, it became evident that the amalgamation of guided inquiry learning and simulation significantly bolstered the experimental group's performance (at a significance level of p=0.05) in comparison to the control group. This enhancement was particularly pronounced in their grasp of concepts related to sampling distributions and the Central Limit Theorem, as manifested by the experimental group's substantially elevated mean achievement scores. Keywords—Active-Learning; Central Limit Theorem; POGIL; Sampling Distributions.