Indiana University (IU) Professor of Music Peter Miksza has been using U.S. National Science Foundation ACCESS-allocated resources to teach graduate students the connection between music and mathematics. With the assistance of the Jetstream2 supercomputer at IU, Miksza designed several applications for his graduate-level classes that allow students to master statistical concepts while connecting their musical talents with mathematical skills.
“Grappling with statistics can often be overwhelming for those whose most recent formal mathematics education experiences may have been many years in the past,” Miksza said. “In my courses, I have found that while clear explanations and practice examples are good – live manipulable demonstrations using ACCESS allocations on Jetstream2 can be an extremely powerful way for developing insight in regard to statistics.”
“I am very fortunate to work with some amazing colleagues involved in the Jetsream2 project, like Jeremy Fischer who has been instrumental in helping me acquire and apply these resources,” he said.
Miksza specifically utilizes Jetstream2 in two courses: Quantitative Research in Music Education (MUS-E631) and Advanced Quantitative Research in Music Education (MUS-E632). He said that MUS-E631 teaches scientific inquiry, research design, statistical analysis, data critique and proposal development to support long-term research goals in music education. Meanwhile, MUS-E632 builds on MUS-E631 – covering intermediate and advanced statistical methods for complex research designs while involving students in research critique, data analysis and the completion of an original empirical study.
We use our ACCESS allocations on JetStream2 to support graduate-level courses aimed at teaching statistics to music students.
–Peter Miksza, professor of music, Indiana University
Miksza said that these courses are aimed at graduate students from all music-related disciplines interested in pursuing empirical research. The courses introduce students to fundamental quantitative research design types and concomitant data analysis procedures useful when addressing basic and advanced descriptive, correlational and experimental research questions.