A Storied Approach to Learning Data Analytics in a Graduate Data Analytics Program

Purpose

If you think back to when you learned how to ride a bike, you probably didn't master this skill by listening to a series of riveting lectures on bike riding. Rather, you tried it out for yourself, made mistakes, fell down a few times, picked yourself back up, and tried again. When mastering an activity, there's no substitute for active learning - the interaction and feedback that comes from practice.

Would we see the same level of mastery if classroom learning was a little more active? Would university instruction be more effective if students spent some of their class time on active forms of learning like activities, discussions, or group work, instead of spending all of their class time listening? And would this even be possible with a completely online program? What would be the effect if an entire program was built around this idea instead of isolated courses?

Theresa is typical of most online graduate students I meet in the first statistical course of their program. Sounding uneasy as we make phone introductions, Theresa starts out with basic information about herself, and then exclaims “To be honest, I have tried avoiding this class for as long I could! I’m scared to death of statistics!” She isn’t alone.

Students often consider statistics as the “worst” course they take while in college (Hogg, 1991). For instructors, there is often a struggle with how best to reach students, to help them learn statistics, and to help them become practical consumers of the knowledge – especially when students enter statistics courses with negative self-images. As some of this negative imagery comes from the massive amounts of formulas students can face while in the course, one solution is to structure an introductory statistics course (possibly all statistical courses) around data analysis versus mathematical technique. Another solution is found in innovative instructional paradigms in which the traditional lecture, with students passively listening, is replaced with more hands-on activities.

Yet in graduate statistical education, the actual implementation of these different approaches into a classroom setting can be quite challenging and confusing. This is amplified when it comes to online learning. Many of these approaches involve unique learning opportunities which have not customarily been incorporated in traditional graduate-level statistics classes. Moreover, because most research has been conducted on undergraduate statistics classes (see next section), one might ask “Would the same techniques of active or cooperative learning actually work in a graduate introductory statistics class? Or possibly in more advanced classes?”

The purpose of this research is to consider alternative instructional methods in the teaching of an online graduate-level data analytics program. Based upon personal informal surveying of graduate instructors, many have only seen graduate programs where courses might be prerequisites for others, yet the courses feel very independent of each other. Indeed, for many graduate instructors I have interviewed, an integrated storied program is an idea never considered. Thus, I created a graduate-level data analytics program around projects where the students become part of the “story.”

The “storied” approach to teaching (with both projects and active learning) of interest in this study would allow many graduate programs the opportunity to explore the advantages of active learning in a manner that is both fun to the student and also progressive in learning concepts. Thus, the purpose of this research is to explore an instructional model that involves projects and active/cooperative learning in a graduate statistics program. Comparisons were made with previous terms in which these approaches were not undertaken. Specifically, this study attempted to answer the following research questions:

• 1.

  Can active or cooperative learning be successfully implemented and accepted in a graduate-level data analytics program? Can these strategies be combined with a story to create a cohesive instructional approach?

• 2.

  Does more active student involvement help graduate students learn complex statistics?

• 3.

  What benefit in affective and cognitive measures is seen by introducing active or cooperative learning throughout a graduate statistics program? As males and females may gravitate toward different teaching approaches, do these benefits differ by gender?

• 4.

  Does a particular story work better than others with graduate students?