Pedagogical Content Knowledge and Its Implications for Improving Quality of
Computer Science Education Community
CE21 PI and Community Meeting, January 21-23, Baltimore, MA.
Moderator: Dr. Mehmet Aydeniz
This paper is a report of the discussion that took place at NSF’s CE21 PI and Community
Meeting, Baltimore, MA. The purpose of this paper is to summarize the content of
group’s discussions and consider its implications for computer science education mostly
drawing from science and mathematics education research.
What is PCK?
PCK has been defined in various ways over the years but central to the idea is that
teaching a particular concept to a group of students in an effective and meaningful way
(Shulman, 1986). More specifically, it refers to the knowledge of where students struggle,
common misconceptions students may have about the concept of interest, ability to
decompose chunks of knowledge for beginners to they can relate, engage in and
effectively learn the topic and for the experts to provide the challenge needed so they can
deepen their current understanding. This includes using the best relevant examples to
“hook” and motivate students, asking good questions to ask students to clarify
misconceptions, to engage students in critical thinking and to guide them to engage in
more cognitively demanding and intellectually challenging concepts (Nilsson &
Loughran, 2012; Park & Oliver, 2008; Schneider & Plasman, 2011). While science and
mathematics educators have used Shulman’s initial definition to inform the design of
their professional development activities both for in-service and pre-service teachers,
they have critiqued and refined the initial PCK conception mainly to give it a precise
language so that they can most effectively measure and promote its growth. Nevertheless,
the concept has received great attention from science and mathematics educators and
through the language of PCK science and mathematics education scholars have been able
to help teachers develop professional knowledge base that is needed to engage students in
learning in an effective and meaningful way.
PCK and Computer Science
After defining and discussing various definitions of PCK, the group discussed the
possibility of adopting the PCK language for improving computer science teachers’
professional knowledge base to teach computer science concepts and practices. While the
majority of colleagues embraced the idea of using PCK to study computer science
teachers’ professional knowledge base, one colleague raised the question that “computer
science is too broad, therefore, PCK may not be a useful concept for studying computer
science teacher’s PCK. However, the other members of the group challenged this
perspective and argued the utility of PCK for helping computer science teachers to
develop a unique knowledge base that is germane to the teachability of core computer
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science concepts. This discussion led to the question of whether PCK has to be specific to
particular content? Or is there anything general about it? As a result of our discussion the
group came to the conclusion that while PCK has some universal attributes, is subject
specific (Hume & Berry, 2011; Hill, Rowan, & Ball, 2005; Loughran, Berry, & Mulhall,
2006; Nilsson & Loughran, 2012) and context-dependent (Abell, 2008; Grossman, 1990).
While general idea of making connections explicit is pedagogy, the knowledge needed
for teachers to help students to make specific connections between relevant concepts is
PCK. There were some concerns that Teacher professional development providers
sometimes have an assumption that the teachers already know the content they are
teaching- but over time, have realized that we should always be teaching the teachers
content; there should always be new content or reinterpretation of the existing content.
Teachers teach content in a way that is divorced from the history of the subject. For
example, science is often taught by presenting finished models rather than giving students
agency to construct those models and going through the same processes that people did
throughout history to create the models. The PCK that would be needed to teach this way:
teachers would need to know how these models were developed historically, would need
to know different representations and see connections. The challenge remains how we as
a community develop PCK models for effective measurement and growth of computer
science teachers’ PCK.
The group also engaged in productive discussions about answers to the following
questions:
 Which is more important to have: pedagogy or content? Can you be good at
pedagogy without content, or good at the content without pedagogy?
Several colleagues referred to Deborah Ball’s work in mathematics education which
suggests that content knowledge in math is important, but what is more important is
being open to new ways of seeing patterns in student reasoning and knowledge. PCK
research in science education resonates with PCK literature in math education in that
PCK is about being knowledgeable of content, about students’ misconceptions related to
a specific science topic, ability to notice fallacies in student reasoning, gaps in their
knowledge. Teacher’s role then becomes developing responsive instruction to address
students’ learning needs, support them in pursuit of new knowledge, empower them with
knowledge and skills that can be transferable to other relevant contexts.
While computer science education community still has a way to go in terms of
understanding the concept of PCK and using it both to measure and improve computer
science teachers’ professional knowledge base. There are currently efforts going on
within CS community that attempts to help teachers’ develop pedagogical content
knowledge. For instance, Barbara Erikson’s EBooks provide teachers with the
opportunity to develop PCK,there is a teacher layer under the student layer that allows for
teachers to respond to student difficulties.
CSNYC works with code academy, code.org, to build teacher layer in. Code.org so we
can measure teacher PCK. They are looking into data points for where students struggle
and use that as a context to capture and improve PCK.
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Melissa Koch of SRI talked about her experiences with preparing teachers and after
school educators to teach computer science within the context of after school programs
and how it related to PCK. Our curricula (Build IT and ICT4me) focuses on teaching
educators the big ideas in the curriculum, how those ideas connect, and providing more
background information specifically for the educator so that they can better understand
the CS content and the associated big ideas. We anticipate that we need to teach
educators content and pedagogical knowledge in the PD and that the curriculum should
support and reinforce teacher learning. All of their curricula are educative curriculum
materials (Ball & Cohen, 1996; Davis & Krajik, 2005).
Colleen Lewis and her colleagues at Harvey Mudd College has developed an excellent
website to help computer science teachers to develop PCK or to use a set of best practices
informed by teacher PCK to address their students’ learning needs. The website
www.csteachingtips.org provides a set of CS teaching tips suggested by experts “to help
teachers anticipate students’ difficulties and build upon students’ strengths”. They
attempt to instill PCK related knowledge into twitter-sized bites that are implementable in
the classroom.
Folks at the University of Colorado are also working on a project that looks at teacher
PCK. We focus on teacher recognition of Computational Thinking Patterns as an
organizing principle for curriculum and teaching. We make these CTPs explicit in our
professional development, in our materials, and we have similarly observed teachers use
these with students. Not surprisingly, students also use these CTPs as they articulate
different aspects of coding within their projects. This meta-level perspective invites
teachers and students to make connections across programming projects and supports
students as they apply previously learned coding structures in new contexts. (see:
http://sgd.cs.colorado.edu/wiki/Category:Computational_Thinking_Patterns)
While some members of computer science education community have been using PCK,
still more work needs to be done both to understand teacher PCK and to develop more
rigorous interventions to help computer science teachers develop sophisticated PCK.
While this discussion was going on, some folks pointed out that it is not only CS teachers
that need CS specific PCK but also math and science teachers who are being asked to
integrate computational thinking and CS concepts into their curricula need to develop
PCK. This started a conversation about teachers’ beliefs. We spent quite sometime
discussing answers to the following questions:
o What helps mathematics or science teachers to believe or buy into
integrating computational thinking into their curricula?
o What types of experiences to they themselves need to have in order to
integrate computational thinking into their curricula in an effective and
meaningful way?
The group discussed teachers’ beliefs about teaching and learning as it relates to
computer science. Folks pointed out that that openness to learning new ideas is the most
important quality of a teacher more so than the content.
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We discussed that if we want science and mathematics teachers to develop positive
attitudes towards integration, first, they will need to have a solid understanding of what
computational thinking is as most confuse it with programming. Then, they themselves
need to go through experiences that will help them 1) to experience conceptual change
about computational thinking and 2) provide them with the type of experiences that will
empower them both with knowledge of computational thinking concepts, practices, tools
and of pedagogy of computational thinking.
Alexander Reppening and David Webb talked about how they provide such experiences
to the teachers through their projects at the University of Colorado. It is their belief that
to deepen teachers’ PCK teachers need first hand experiences as learners with similar
materials (or materials at their level, not for students) so that they understand the value of
representations, metaphors, and other strategies to make sense of computer programing.
The group came to the conclusion that PCK is a useful construct for computer science
education community, not only at the K-12 level but also at the college level, challenge
remains, how to move forward in conceptualizing it for CS community, developing
contexts and tools to measure it and developing effective programs to improve teacher
PCK.
Measuring Teachers’ PCK
While science and mathematics educators have developed methods and tools to measure
science and mathematics teachers’ PCK, a discussion of which method or tool can most
effectively capture a teachers’ PCK is far from settled (Abell, 2008; Aydeniz &Kirbulut,
2013). Science and mathematics educators have used paper-and-pencil assessments (e.g.,
Park, Chen, & Jang, 2008), observations and interviews (Magnusson, Krajcik, & Borko,
1994)) and some have used a combination of these methods (Adadan, 2014; Aydin et al.,
2013; Park et al., 2012) to capture a teachers’ PCK. While a combination of multiple
methods can provide a clearer picture and an in-depth understanding of teachers’ PCK,
this may not be a feasible method or method of preference because of the limitations
placed on the researchers because of the context of the study or the available resources
and time. Therefore, science and mathematics educators have used diverse methods to
capture teachers’ PCK.
While until recently researchers had used observations of classroom teaching to make
decisions about sophistication level of a teachers’ PCK, this method has its own
limitations. Alonzo et al (2012) state because “Teachers are often unaware of knowledge
they use to make instructional decisions, and day-to-day discussions of teaching tend to
center around practices, rather than the knowledge and reasoning underlying them.”(p. 5),
reliance on observations alone may not provide accurate picture of a teachers’ PCK. As a
result, science educators have recently become interested in measuring teachers’ PCK
using such tools as CoRes and PaPers (Hume & Berry, 2011; Loughran, Berry, &
Mulhall, 2006; Nilsson & Loughran, 2012). These tools ask teachers a set of questions
that allow the researchers not only to measure how teachers connect content with
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pedagogy but also how teachers go about justifying instructional and assessment methods
that they use. Moreover, some of the prompts in these tools explore how teachers’
instructional decisions are informed by what they know about their students’ prior
experiences and knowledge as well as common misconceptions reported in the literature.
Implications for Computer Science Education
As it is the case with other constructs related to a teachers’ professional knowledge base
developing valid and reliable instruments to measure teacher PCK is important.
Colleagues contributed to discussion by drawing from the literature in their own field of
expertise and their experiences. We discussed that we need to develop frameworks for
assessing CS teachers’ PCK. A discussion focusing on the difference between PCK and
TPCK surfaced towards the end of our meeting. One colleague argues that TPCK
(Technological Pedagogical Content Knowledge) (Mishra & Koehler, 2006), may be
more responsive to the nature of computer science teaching. Since there is a technology
component in teaching CSM, TPCK also becomes relevant but where we draw the line?
Or whether we should explore the contributions of each framework for effective and
meaningful teaching of computer science. Nevertheless, the conversation immediately
focused on tools and assessment methods that we could use to measure CS teachers’ PCK
and/or TPCK.
Leigh Ann talked about the project that she is involved with some other colleagues called
“Developing a Validated Assessment for Teacher Pedagogical Content Knowledge:” This
is a framework is being developed for PCK based on TPACK (Technology PCK). They
started with TPACK categories and refined and revised it for computer science.
o They built an assessment (piloting now) that has questions based on the
new framework.
o The assessment would be administered after a workshop to gauge or
evaluate teacher PCK.
o Using the population from the TEALS program, to observe the changes in
teacher PCK over time.
o The score is a “heat map” for strengths and weaknesses across the
framework.
We discussed that this framework may be useful for large-scale studies, but we also need
to see what teachers actually do in the classrooms, how they interpret classroom
situations on the fly and interact with students to more effectively and meaningfully
engage their students in learning of core computational thinking skills and concepts of
computing. We discussed that surveys are important, but may not correlate to what
actually happens in classrooms but it is no surprise to anyone that seeing what happens in
classrooms is expensive and difficult because of labor and time involved in capturing
teacher practice. However, if our goal is to understand how teachers enact their espoused
PCK (Aydeniz & Kirbulut, 2013) in the classrooms, we need to focus on classroom
practices as well in our measurement of teacher PCK.
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Up to now, most assessments of PCK at least in the context of computer science have
been self-reported by teachers, typically reporting their confidence on Likert-scale type
items. The TPCK framework developed by Leigh Anne and her colleagues contains
questions that ask the teachers to take an action. For example, a question provides a
scenario and asks the teacher what their next step would be, or a video is shown and the
teacher explains where they would insert break points for additional instruction. This is
good practice because teachers are being asked to suggest an instructional strategy to
address students’ specific needs as they arise while they are learning. However, even
then, you do not get to their reasoning. A PCK assessment tool should measure teachers’
metacognitive ability to notice what mistake students are making, and reasoning why and
suggest specific instructional strategy that may effectively address students’ learning
needs on the fly but also the reasoning behind that suggestion/decision (Park & Oliver,
2008). We cannot explore teacher reasoning through observations therefore, we need to
conduct follow up interviews and explore teacher reasoning behind good or bad
instructional moves that they make to address students’ learning needs. However, it is no
surprise to anyone that using multiple methods and especially going into the classrooms
and conducting interviews with teachers takes significant resources and time that many
folks may not have access to.
We discussed that some folks in science education have developed very structured selfreporting measures called CoRes and Papers to measure teacher PCK. While these
instruments are far more powerful than the surveys, they do provide an in-depth
understanding of a teacher’s PCK. Reports from science education literature suggest that
these tools are very powerful especially for helping pre-service teachers to develop a
strong foundation for developing a robust PCK that typically comes with experience. In
fact, colleagues have used CoRes and PaPers constructed by expert teachers to help
novice teachers to develop sophisticated PCK. However, again, we will not have a solid
understanding about teacher PCK until it is tested against classroom practice.
Finally, we talked about the need to study PCK from a learning progressions perspective
(Schneider & Plasman, 2011). A possible learning progression will include multiple
anchor points for a teachers’ PCK (e.g., levels:Naïve, developing and sophisticated). The
learning progressions framework helps us track the growth in teacher PCK over time and
with experience or as a result of short interventions.
The purpose of the session was to define some of the interesting questions in the space,
seeing our gaps and holes in the knowledge and also build collaborations across
campuses and expertise (email addresses of participants were collected) moving forward.
Overall, this was a very interesting and productive conversation in terms of defining and
clarifying PCK as it relates to computer science. We look forward to continuing this
conversation with folks through email and hope that this email exchange will result in
possible collaborations around PCK.
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Conclusions
Computer Science education has been experiencing significant challenges in attracting
and retaining students. While lack of emphasis on computer science in K-12 classrooms
is one of the main contributors to the problems of computer science education, it is not
the field’s only problem. The issues range from curriculum reform, teacher capacity, lack
of research base to inform policy and pedagogy, student attrition and failure to attract
students from historically marginalized underrepresented populations such as girls and
African Americans (Cuny, 2012).
Like all other subjects, the teaching of computer science, too requires use of a range of
instructional strategies to make curriculum relevant and understandable by the students.
One type of professional knowledge base that can make the curriculum comprehensible
to the students is pedagogical content knowledge (PCK). Shulman (1987) defined PCK as
“the form of knowledge that embodies the aspects of content most germane to its
teachability” (p. 9). These include “the most useful forms of representation of scientific
ideas, the most powerful analogies, illustrations, examples, explanations and
demonstrations- in a word, the ways of representing and formulating the subject that
make it comprehensible to others” (p. 9). Shulman (1986) argued “Although the
knowledge of theories and methods of teaching is important, it plays decidedly a
secondary role in the qualifications of a teacher” (p. 5).
In the context of computer science education, we often feel the need use models,
examples and analogies to help our students develop conceptual understanding of core
concepts in our courses. However, we lack a theoretical model that can help us assess and
improve computer science teachers’ and professors’ PCK. This suggests that we need to
devote some of our resources to develop a theoretical model to capture computer science
teachers’ PCK, design assessments based on this framework to effectively measure
teachers/professors’ PCK and empirically test if and how PCK impacts student learning.
Finally, there is need for professional development models across contexts that can
improve teachers’ PCK. We hope that our colleagues can use the content of this
discussion to think about new questions, new interventions and new assessments as they
focus their efforts on improve CS teachers’ PCK or PCK specific to computational
thinking in other STEM contexts.
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