CS4Impact: Measuring Computational
Thinking Concepts Present in CS4HS
Participant Lesson Plans
Heather Bort and Dennis Brylow
SIGCSE 2013
Marquette University
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Outline
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Problem
Solution
Workshop Structure
Rubric
Results
Future Work
Marquette University
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The Problem
 Many current K-12 outreach efforts attempt to
increase the number of students interested in
majoring in computer science and related
fields
 Assessing these efforts has proven to be
challenging
 Most prior work on examining the impact of
professional development interventions for K12 CS teachers stops with indirect measures
Marquette University
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Indirect vs Direct
 Measuring Knowledge
• Before and After workshop attitudinal
survey (indirect)
• Concept Quiz (direct)
 Measuring Concept Integration
• Surveying attitudes about using the
concepts in their classrooms (indirect)
• Ability to integrate workshop material into
lesson plans for the classroom (direct)
Marquette University
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Measuring Impact
 Workshop structured around Computational
Thinking (CT) lesson plan building and
sharing
 Designed a rubric to measure how CT
concepts were used in the lesson plans
 Applied the rubric during the sharing phase of
the workshop
Marquette University
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Workshop Structure
A: basic
Combined
• Exploring CS and
CT
• Boolean Building
Blocks
• HPC and
Sciences
• CT and the
Sciences
• Alice
• Algorithms
• Scratch
• State and Curriculum
Issues
• Problem/Project-Based
Learning and
Computational
Thinking
• Careers Panel
• Google Keynote
Marquette University
B: advanced
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AP CS Principles
Creativity
Big Data
Scratch
Impact and the
Internet
• TechSpots
• Lesson Planning
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Data Collection
 Each participant presented their lesson plan
to the group
 Presentations were video taped for later
analysis
 4 hours video data with full text of written
plans coded with rubric
Marquette University
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Rubric
 Computational Thinking Concepts
 Level of Inquiry
Marquette University
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Computational Thinking
 Jeannette Wing states that computational
thinking “represents a universally applicable
attitude and skill set everyone, not just
computer scientists, would be eager to learn
and use”
 a problem solving method that uses
algorithmic processes and abstraction to
arrive at a answer
 showcase concepts over programming skill or
computational tools in the classroom
Marquette University
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Computational Thinking Concepts
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Data Collection
Data Analysis
Data Representation
Problem Decomposition
Abstraction
Algorithms & Procedures
Automation
Simulation
Parallelization
Marquette University
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Why Inquiry based learning?
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We learn by inquiry from birth
Important skill set
Central to science learning
Right answer versus appropriate resolution
Marquette University
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Traditional Approach to
Learning
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Focused on mastery of content
Teacher centered
Teacher dispenses “what is known”
Students are receivers of information
Assessment is focused on the importance of “one right
answer”
Marquette University
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Inquiry Approach to Learning
• Focused on using and learning content to develop
information processing and problem solving skills.
• More student centered
• Teacher is the facilitator of learning
• More emphasis on “how we come to know”
• Students are involved in the construction of
knowledge
Marquette University
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Sage on the Stage
Versus
Guide on the Side
Marquette University
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Levels of Inquiry
Inquiry Level
Question Procedure Solution
1- Confirmation Inquiry
Students confirm a principle through an
activity when the results are known in
advance.
X
X
2- Structured Inquiry
Students investigate a teacherpresented question through a
prescribed procedure.
X
X
3- Guided Inquiry
Students investigate a teacherpresented question using student
designed/selected procedures.
X
X
4- Open Inquiry
Students investigate questions that are
student formulated through student
designed/selected procedures.
Marquette University
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5 Characteristics Of Inquiry Based Learning
Marquette University
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1. Bloom’s taxonomy
•Inquiry based learning asks questions that come
from the higher levels of Bloom’s Taxonomy
Marquette University
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Evaluation
Synthesis
Analysis
Application
Comprehension
Knowledge
Marquette University
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2. Asks questions that
motivate
•Inquiry based learning involves questions that are interesting
and motivating to students
Marquette University
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Types of questions
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Inference
Interpretation
Transfer
About hypotheses
Reflective
Marquette University
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3. Utilizes wide variety of
resources
•Inquiry based learning utilizes a wide variety of resources so
students can gather information and form opinions.
Marquette University
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4. Teacher as facilitator
• Teachers play a new role as guide or facilitator
Marquette University
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5. Meaningful products come
out of inquiry based learning
•Students must be meaningfully engaged in learning activities
through interaction with others and worthwhile tasks.
Marquette University
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Inquiry based learning in
Computer science
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Cooperative Learning
Teamwork
Collaboration
Project-oriented learning
Authentic Focus
Marquette University
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Rubric
Concept
Data Collection
Data Analysis
0
1
2
not incorporated
provides the data the
the student will use
students are
required to collect
their own data
an interpretation of the
students will analyze
not incorporated
data is given to the
the data
student
the student is given a
Data Representation not incorporated
specific method to use
Problem
Decomposition
Abstraction
Marquette University
students are able to
choose their own
method
students are
an outline or similar
required to break the
not incorporated structure is provided to
problem down on
the student
their own
not incorporated
provides an expected student arrives at an
outcome
outcome
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Rubric
Concept
0
1
2
not incorporated
the basic steps for an
algorithmic solution are
provided
students develop an
algorithm or
procedure
not incorporated
students are provided
with a program or some
other technology that
automates their process
students are able to
automate their
process
Parallelization
not incorporated
students will decide
students are instructed
how to distribute their
to work in parellel
workload
Simulation
not incorporated
Algorithms and
Procedures
Automation
Connecton to Other
Fields
Marquette University
not incorporated
students are shown a
simulation
students will produce
their own simulation
students are required
the connection is given
to make a connection
to the student
to another field26
Results
Concept
0
1
2
Data Collection
7
6
3
Data Analysis
9
4
3
Data Representation
8
6
2
Problem Decomposition
5
10
1
Abstraction
5
9
2
Algorithms and Procedures
4
9
3
Automation
3
12
1
Parallelization
12
2
2
Simulation
0
13
3
Connection to Other Fields
10
6
0
Marquette University
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What We Learned
 Many of the participants did not effectively
integrate the CT core concepts into their
lessons
 A large number of lesson plans scored 0 in
some sections of the rubric
Marquette University
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What We Learned
 Among the experienced CS teachers, some
are firmly entrenched in a pedagogical style
that still emphasizes conveying facts and
programming language syntax, not in focusing
on skill building
 Large number of participants were able to
produce lesson plans with level 1 or level 2
components, sometimes in multiple core
areas.
Marquette University
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Follow Up
 One third of participants volunteered feedback
for six month follow up survey.
 All but one respondent has been incorporating
concepts from the workshop in their
classrooms
Marquette University
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Moving Forward
 Link CS4HS content to Common Core
Standards
 Better lesson plan development and
assessment
 Continued multi track structure
Marquette University
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Our Thanks To:
 Google
 Wisconsin Department of Public Instruction
 The Leadership of the Wisconsin Dairyland
CSTA
 The many teachers that participated
Marquette University
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