Communications of the ACM

Follow us on Twitter at http://twitter.com/blogCACM The Communications Web site, http://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications, we’ll publish selected posts or excerpts.

Mark Guzdial

14 Years of a Learner-Centered Python IDE http://bit.ly/2aNXAnC August 10, 2016

Early September saw the 2016 International Computing Education Research (ICER) conference in Melbourne, Australia. The conference was terrific, as is usual for ICER.

Two of its papers were meta-papers studying the ICER community itself. They found the community has healthy levels of newcomers and collaboration, and features methodological rigor and strong theoretical foundations.

I will report on three papers: two award recipients, and one of my favorite papers at the conference. ICER has two paper awards: a “people’s choice” ( voted on by attendees) John Henry award for innovation and new directions, and a “Chairs” award selected by the conference chairs based on the paper reviews.

The people’s choice award was won
by Elizabeth Patitsas (with Jesse Berlin,
Michelle Craig, and Steve Easterbrook)
from the University of Toronto for “Evi-
dence that Computer Science Grades
are not Bimodal” ( http://bit.ly/2dsBo3L).
ming), they saw bimodality, even if it
was not there.
The provocative explanation is that
CS teachers see bimodality because
they do not teach well. Patitsas used a
social defense theory to explain “seeing-
bimodality.” Patitsas suggests our over-
confidence in CS teaching leads to see-
ing bimodality when there is none.
The Chairs award was particularly ex-
citing because it was won by a team led
from a School of Education. Comput-
ing education research has been domi-
nated by CS researchers, and it’s terrific
to see the Education side playing a more
prominent role. The paper was “Learn-
ing to Program: Gender Differences and
Interactive Effects of Students’ Motiva-
tion, Goals, and Self-Efficacy on Perfor-
mance” ( http://bit.ly/2d3UKJA) by Alex
Lishinski, Aman Yadav, Jon Good, and
Richard Enbody from Michigan State
University. Self-efficacy is one’s own
rating of their ability to succeed or per-
form in a particular discipline. We knew
from prior work women tend to have
low self-efficacy ratings at the start of
CS classes, while men have high ratings.
What hasn’t been studied previously
was how these changed with feedback.
As students get grades back on home-
work and exams, what changes? Lishin-
ski showed women more quickly adapt
self-efficacy ratings compared to men;
the scores rise dramatically. It takes a
long time (more feedback) to get men to
downgrade their overestimated skills to
match their performance.
One of my favorite papers at ICER
2016 was “Some Trouble with Trans-
parency: An Analysis of Student Errors
Elizabeth’s paper had two studies in it
and a provocative discussion section.
Many CS teachers believe grades in
CS classes are bimodal: some students
have innate ability and just “get it”; oth-
ers don’t. There are research papers pre-
senting explanations for the bimodality
effect, but is the effect really there?

In her first study, Patitsas did an analysis of 18 years’ worth of grade data from a large university CS department, and found less than 5% of the courses had signs of non-normality. Her second study was a “deception study” (debriefed at http://bit.ly/2dKM48u). She asked 60 CS teachers (mostly from the SIGCSE members list) to judge if a number of grade distributions were bimodal; the reality was that none of them were. She also asked teachers if they agreed with the statements “Some students are innately predisposed to do better at CS than others” and “ Nearly everyone is capable of succeeding in computer science if they work at it.” Both of these statements strongly correlated with “seeing bimodality” in the distributions, the first positively and the second negatively. If teachers believed in a “Geek Gene” (that some students are innately gifted at program-