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Geoffrey M. Draper Aaron M. Curtis
Faculty of Math and Computing Faculty of Math and Computing
Brigham Young University–Hawaii Brigham Young University–Hawaii
55-220 Kulanui Street 55-220 Kulanui Street
Laie, HI USA Laie, HI USA
DOI: 10.1145/3350745 ©2019 ACM 2153-2184/19/09 $15.00
Two students who did not go to graduate school nevertheless
found aspects of the course useful in their respective employment:
I am working … as a algorithm engineer now, and I am
doing exactly what I was doing in CS 490R - reading papers
and trying to apply it in the project I am currently working
on. … 490R definitely [helped] me get used to reading
research papers and implement ideas into real projects.
I remember taking the information visualization class and
it did get me thinking about possibly getting a master’s
degree. Although my decision to go straight into working
full time and not pursue a masters was made even before I
took the class. … It helped me with my Java programming
which I get to use in my job.
These students’ comments seem very much in line with our
hoped-for outcomes of this course as outlined in the Introduction.
Because our focus is preparing Computer Science students for
grad school, we give several fairly heavy programming assignments requiring students to implement algorithms by hand, without the aid of visualization-specific APIs. However, we may relax
this policy over the next few years. Commercial tools like Tableau,
domain-specific languages like R or Processing, and high-level
APIs like D3 are growing in popularity, and are increasingly at
home in both pure visualization research and applied data science. Although we still plan to retain the course’s focus on grad
school preparation, in the future we may grant more flexibility in
the way the programming assignments are implemented.
As another improvement, the next time we teach the course,
we plan to survey the students about their plans for graduate
school, both before and after the course. In this way, we can
better measure whether students’ attitudes about grad school
change as a result of taking this class.
Finally, to familiarize students with the concept of peer review, we also plan to incorporate a system of peer critiques on
programming assignments and oral presentations, specifically
in the final project.
Having a diverse professoriate in the future requires that we
attract and encourage more students—especially those who
might not otherwise consider a career in academia—to pursue
graduate studies. Although the topic of our course is data visualization, the motivating theme is to expose students to the
daily routines of graduate school: academic reading and writing, implementing projects, and oral presentations. By the end
of the course, students will have completed the equivalent of a
first-year literature search in the domain of data visualization.
Regardless of what students choose to do after they graduate,
this course will help them make an informed choice about
whether grad school is for them.