Open AccessAN EMPIRICAL CONSIDERATION OF A BALANCED AMALGAMATION OF LEARNING STRATEGIES IN GRADUATE INTRODUCTORY STATISTICS CLASSES
Author(s) -
Brandon K. Vaughn
Publication year - 2022
Publication title -
statistics education research journal
Language(s) - English
Resource type - Journals
ISSN - 1570-1824
DOI - 10.52041/serj.v8i1.458
Subject(s) - mathematics education , class (philosophy) , active learning (machine learning) , graduate students , teaching method , computer science , cognition , psychology , pedagogy , artificial intelligence , neuroscience
This study considers the effectiveness of a “balanced amalgamated” approach to teaching graduate level introductory statistics. Although some research stresses replacing traditional lectures with more active learning methods, the approach of this study is to combine effective lecturing with active learning and team projects. The results of this study indicate that such a balanced amalgamated approach to learning not only improves student cognition of course material, but student morale as well. An instructional approach that combines mini-lectures with in-class active-learnin activities appears to be a better approach than traditional lecturing alone for teaching graduate-level students.First published May 2009 at Statistics Education Research Journal: Archives