issues, such as learning progressions
and integration of computing content and computational thinking into
math, science, and non-STEM classes.
Tensions in CER around research
methods mirror what is going on in
other STEM education research areas.
Traditionally, much of CER focused
on social science methodologies. New
researchers to CER are using big data
analytic techniques, and borrowing
approaches from other computing
disciplines, most notably machine
learning. How can these “newer” techniques (for example, looking at click-through rates, intermediate forms
of student artifacts, and other forms
of “big data”) be used to answer our
questions? Do they substitute for our
other research approaches, or do we
use them in addition to more traditional approaches?
Computing education research is
unique among the STEM education
research fields. The economic value
of knowing computing is greater than
any other STEM field, but computing is
the least diverse of all the STEM fields.
Thus, too many people are losing out
on the advantages of computing. We
have to figure out how to fix that, while
still answering the fundamental questions of our discipline. How do people
come to understanding computing,
and how can we make it better?
1. Cuny, J. Finding 10,000 teachers. CSTA Voice 5, 6
2. Grover, S. and Pea, R. Computational thinking in
K–12: A review of the state of the field. Educational
Researcher 42, 1 (Jan./Feb. 2013), 38–43.
3. National Research Council. Discipline-based Education
Research: Understanding and Improving Learning in
Undergraduate Science and Engineering. The National
Academies Press, Washington, D. C., 2012.
4. Shulman, L. Those who understand: Knowledge
growth in teaching. Educational Researcher 15, 2 (Feb.
5. Wing, J. Computational thinking. Commun. ACM 49, 3
(Mar. 2006), 33–35.
Steve Cooper ( firstname.lastname@example.org) is an associate
professor in the computer science department at Stanford
University, Stanford, CA.
Shuchi Grover ( email@example.com) is a research
scientist at SRI International and a visiting lecturer in
the computer science department at Stanford University,
Mark Guzdial ( firstname.lastname@example.org) is a professor
in the College of Computing at Georgia Institute of
Technology in Atlanta, GA.
Beth Simon ( email@example.com) is the senior
associate director of Learning Sciences and Technology
the Center for Teaching Development and tenured faculty
in the Computer Science and Engineering Department at
the University of California, San Diego.
Copyright held by authors.
to the ranks of discipline-based education research. Mathematics education researchers know a lot about
how students come to understand
numbers and sets. Physics education researchers have detailed research into the misconceptions that
students develop about the physical
world from living in it without measuring it. But we know relatively little
about the foundations of computing knowledge, and these are more
critical issues as we move computing
into primary and secondary schools.
Foundational questions include
“How do students learn to program
and what does that development
look like?” “What are successful and
unsuccessful mental models of challenging computing concepts?” “How
do we support successful transfer
from beginner programming environments to real-world ones?” and
“What are common challenges in
conceptual understanding in computing course?” were listed among
the many key questions the learning sciences can help address. Even
though these topics have been studied extensively since the 1980s, many
questions remain unanswered and
would benefit from contemporary
research in the learning sciences in
socio-cultural and situated learning,
distributed and embodied cognition,
as well as activity, interaction and
Researchers are working to define
and validate learning progressions for
K– 12 computing. Some researchers are
studying how people “become computer scientists” and to investigate the
broader meanings of the computing
discipline. The use of design-based
research and design-based implementation research is common in the
community to support research and
assessment in naturalistic settings.
A more critical issue for CER to-
day is how to develop enough teach-
ers to support computing education
in primary and secondary schools.
We need to support the professional
growth of computing teachers, and
in parallel, build a robust commu-
nity of computing teachers. How,
for example, will the U.S. meet the
CS10K challenge1 by 2016? A ma-
jor focus for research in comput-
ing education is, “What do we teach
computing teachers?” What do com-
puting teachers need to know that’s
different from other STEM teachers?
That discipline-specific knowledge
of teachers is called Pedagogical
4 Defining PCK
for computing and figuring out
how to teach it are major thrusts in
CER today. Computing education
researchers are developing just-in-
time teacher professional develop-
ment to help teachers communicate
and learn best practices, robust as-
sessments, and the latest introduc-
Sally Fincher’s keynote focused on
the needs of teachers in order to improve computing education. How do
we make our computing education research matter and influence practice?
She pointed out that many of the innovations in teaching reading failed
because they changed reading in ways
that was dissimilar to practice. We
need to ensure that we in computing
education are focusing on what the
students actually need to know, or we
risk becoming ineffective and useless.
Like other research areas, the CER
community is asking itself if “big data”
can help with our issues too. Some
researchers are calling for data and
evidence-driven analyses of student
learning as part of adding rigor to drive
practice in CER, including the use of
big data to develop scalable methods
for automated assessments (along the
lines of emergent forms of assessment
in MOOCs). The hope is to leverage big
data to understand better the nature of
learning programming and computational problem solving, the problems
learners encounter, and the pedagogies
and strategies that can be used to address them. Researchers are using evi-dence-centered design and data mining
to study both cognitive and non-cogni-tive aspects of computational thinking.
Moving Forward with CER
The summit exposed some of the tensions between research groups in CER
about what are the most important research areas to be addressing. A large
contingent whose research focused on
issues of equity argued that all research
in CER, whether content pedagogy, culture, and so forth needed to be viewed
through an equity lens. Others argued
for CER to focus on more foundational