APPLICATION OF COGNITIVE PRINCIPLES WITHIN AN ONLINE STATISTICAL LEARNING ENVIRONMENT

Abstract

Three experiments were conducted in order to further investigate optimal learning procedures within an online statistical learning environment. Experiment 1 exposed learners to retrieval practice learning conditions with or without segmentation. Retrieval practice formats included; multiple choice, open ended, multiple evaluation, or instruction only type manipulations. Experiment 2 explored the impact of added immediate feedback in conjunction with retrieval practice and segmentation. Experiment 3 further investigated how the benefits of optimal learning procedures transfer to novel situations / examinations. Within all experiments a series of metacognitive questions were administered to learners in order to measure their metamemory over the statistical knowledge that was taught. In alignment with our hypotheses and previous research it was found that retrieval practice (experiment 1) and retrieval practice with immediate feedback (experiment 2) tended to boost memory retention. However, the data trend for all experiments tended to suggest that segmentation has little or no impact on statistical learning, such a finding was support against our hypotheses as well as the findings within previous studies. Though the results are somewhat mixed, the benefits associated with retrieval practice and retrieval practice with feedback did not seem to transfer to novel instances. Individuals that learned within an open ended or multiple evaluation type format tended to have greater insight into their own metacognitive knowledge.