1 Marquette University Heather Bort and Dennis Brylow SIGCSE 2013 CS4Impact: Measuring Computational Thinking Concepts Present in CS4HS Participant Lesson Plans
Feb 24, 2016
1Marquette University
Heather Bort and Dennis Brylow
SIGCSE 2013
CS4Impact: Measuring Computational Thinking Concepts Present in CS4HS Participant Lesson Plans
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Problem Solution Workshop Structure Rubric Results Future Work
Outline
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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 K-12 CS teachers stops with indirect measures
The Problem
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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)
Indirect vs Direct
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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
Measuring Impact
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A: basic • Exploring CS and
CT• Boolean Building
Blocks• HPC and
Sciences• CT and the
Sciences• Alice
Combined• Algorithms• Scratch• State and Curriculum
Issues• Problem/Project-Based
Learning and Computational Thinking
• Careers Panel• Google Keynote• TechSpots• Lesson Planning
B: advanced
• AP CS Principles• Creativity• Big Data• Scratch• Impact and the
Internet
Workshop Structure
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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
Data Collection
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Computational Thinking Concepts Level of Inquiry
Rubric
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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
Computational Thinking
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Data Collection Data Analysis Data Representation Problem Decomposition Abstraction Algorithms & Procedures Automation Simulation Parallelization
Computational Thinking Concepts
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Why Inquiry based learning?• We learn by inquiry from birth• Important skill set• Central to science learning• Right answer versus appropriate resolution
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Traditional Approach to Learning• 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”
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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
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Sage on the Stage VersusGuide on the Side
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Levels of InquiryInquiry Level Question Procedure Solution1- Confirmation InquiryStudents confirm a principle through an activity when the results are known in advance.
X X X
2- Structured InquiryStudents investigate a teacher-presented question through a prescribed procedure.
X X
3- Guided InquiryStudents investigate a teacher-presented question using student designed/selected procedures.
X
4- Open InquiryStudents investigate questions that are student formulated through student designed/selected procedures.
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5 Characteristics Of Inquiry Based Learning
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1. Bloom’s taxonomy•Inquiry based learning asks questions that come from the higher levels of Bloom’s Taxonomy
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Evaluation
Synthesis
Analysis
Application
Comprehension
Knowledge
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2. Asks questions that motivate
•Inquiry based learning involves questions that are interesting and motivating to students
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Types of questions• Inference • Interpretation• Transfer• About hypotheses• Reflective
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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.
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4. Teacher as facilitator
• Teachers play a new role as guide or facilitator
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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.
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Inquiry based learning in Computer science• Cooperative Learning• Teamwork• Collaboration• Project-oriented learning• Authentic Focus
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Concept 0 1 2
Data Collection not incorporated provides the data the the student will use
students are required to collect
their own data
Data Analysis not incorporatedan interpretation of the
data is given to the student
students will analyze the data
Data Representation not incorporated the student is given a specific method to use
students are able to choose their own
method
Problem Decomposition not incorporated
an outline or similar structure is provided to
the student
students are required to break the
problem down on their own
Abstraction not incorporated provides an expected outcome
student arrives at an outcome
Rubric
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Concept 0 1 2
Algorithms and Procedures not incorporated
the basic steps for an algorithmic solution are
provided
students develop an algorithm or procedure
Automation not incorporatedstudents are provided
with a program or some other technology that
automates their process
students are able to automate their
process
Parallelization not incorporated students are instructed to work in parellel
students will decide how to distribute their
workload
Simulation not incorporated students are shown a simulation
students will produce their own simulation
Connecton to Other Fields not incorporated the connection is given
to the studentstudents are required to make a connection
to another field
Rubric
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Concept 0 1 2Data Collection 7 6 3Data Analysis 9 4 3
Data Representation 8 6 2
Problem Decomposition 5 10 1Abstraction 5 9 2
Algorithms and Procedures 4 9 3Automation 3 12 1
Parallelization 12 2 2
Simulation 0 13 3Connection to Other Fields 10 6 0
Results
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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
What We Learned
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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.
What We Learned
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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
Follow Up
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Link CS4HS content to Common Core Standards
Better lesson plan development and assessment
Continued multi track structure
Moving Forward
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Google Wisconsin Department of Public Instruction The Leadership of the Wisconsin Dairyland
CSTA The many teachers that participated
Our Thanks To: