Abstract:

Two large cohorts of business undergraduate students (N=307) participated in separate interactive statistics classes with a single university professor. The large size of the two classes (n1=172, n2=135) was necessary due to availability of professors and increases in university enrollment. To compensate for the large class size and maintain an interactive environment, the classes incorporated cellular and laptop polling technology provided by PollEverywhere. PollEverywhere provided a mechanism for gathering real-time student data (both qualitative and quantitative) as well as attendance data. Student data gathered through PollEverywhere ported directly to comma separated value (.csv) files for use in near real-time analysis. Students learned to clean data from these files (as some of their polling responses contained extra characters or were otherwise not as expected) and to generate descriptive analysis appropriate for the level of measurement. As the course progressed, students conducted inferential analysis using the data gathered in class, making it particularly relevant to their own cohort. In addition to the polling technology, Hawkes Learning System, an automated statistics learning system, enforced a homework regiment that included a certification component. At the end of the semester, students' comments revealed significant support for the use of interactive polling using cellular technology (84% reported its use to be “valuable”); however, 69% would still have preferred class sizes of 40 or fewer. Students who did not find the polling technology valuable were 1.4 times as likely to prefer smaller class sizes; however, this result was not statistically significant (p>.05). About 88% of students reported that the use of Hawkes Learning System forced them to learn the material. Of the 12% that reported Hawkes Learning System did not force them to learn the material, 100% found the polling system valuable. The results seem to indicate significant student support for the selected technological interventions in this undergraduate business statistics course; however, the technological interventions did not fully offset the value of smaller class sizes.

Keywords:

Cellular polling, large classes, technological interventions.