Stepping from service-learning to SERVICE-LEARNING pedagogy


Authors: 
Amy L. Phelps
Year: 
2012
URL: 
http://ww2.amstat.org/publications/jse/v20n3/phelps.pdf
Abstract: 

Service-learning can mean different things and look quite different in varying statistics curricula
that may include undergraduates, graduates, majors and non-majors across a wide array of higher institutions. The terms community engagement, volunteerism, community-based projects and service-learning are tossed around on various institutions‟ websites. The purpose of this article is two-fold. First is to provide an historical review of the evolution of service-learning activities to try to unify and define the terminology as one might use this pedagogy for statistics
instruction. Second is to present some examples of how a first and second course in business
statistics can step up from service-learning and move up the continuum towards reaping the
reciprocal benefits of SERVICE-LEARNING (SL). In this article, service learning (note the
omission of a hyphen) is a valued classroom service activity that separates the activity from the
learning goals of the class, while service-learning (note the presence of a hyphen) is a teaching
methodology in which the service and learning goals are carefully given equal weight in the
development of the project so that classroom goals and service outcomes enhance each other
providing a reciprocal experience for all participants (Sigmon 1994). When this careful design is
a “method of teaching through which students apply newly acquired academic skills and
knowledge to address real-life needs in their own communities” (ASLER 1994), SL unifies what
students are currently learning in the classroom with the service they are simultaneously
providing in the community. Careful design opens the door to provide opportunities of SL in an
introductory, non-majors statistics class.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education