1. 1Marquette University Heather Bort and Dennis Brylow SIGCSE 2013 CS4Impact: Measuring Computational Thinking Concepts Present in CS4HS Participant Lesson Plans

2. 2Marquette University  Problem  Solution  Workshop Structure  Rubric  Results  Future Work Outline

3. 3Marquette University  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

4. 4Marquette University  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

5. 5Marquette University  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

6. 6Marquette University 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

7. 7Marquette University  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

8. 8Marquette University  Computational Thinking Concepts  Level of Inquiry Rubric

9. 9Marquette University  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

10. 10Marquette University  Data Collection  Data Analysis  Data Representation  Problem Decomposition  Abstraction  Algorithms & Procedures  Automation  Simulation  Parallelization Computational Thinking Concepts

11. 11Marquette University Why Inquiry based learning? We learn by inquiry from birth Important skill set Central to science learning Right answer versus appropriate resolution

12. 12Marquette University 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”

13. 13Marquette University 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

14. 14Marquette University Sage on the Stage Versus Guide on the Side

15. 15Marquette University Levels of Inquiry Inquiry LevelQuestionProcedureSolution 1- Confirmation Inquiry Students confirm a principle through an activity when the results are known in advance. XXX 2- Structured Inquiry Students investigate a teacher- presented question through a prescribed procedure. XX 3- Guided Inquiry Students investigate a teacher- presented question using student designed/selected procedures. X 4- Open Inquiry Students investigate questions that are student formulated through student designed/selected procedures.

16. 16Marquette University 5 Characteristics Of Inquiry Based Learning

17. 17Marquette University 1. Bloom’s taxonomy Inquiry based learning asks questions that come from the higher levels of Bloom’s Taxonomy

18. 18Marquette University Evaluation Synthesis Analysis Application Comprehension Knowledge

19. 19Marquette University 2. Asks questions that motivate Inquiry based learning involves questions that are interesting and motivating to students

20. 20Marquette University Types of questions Inference Interpretation Transfer About hypotheses Reflective

21. 21Marquette University 3. Utilizes wide variety of resources Inquiry based learning utilizes a wide variety of resources so students can gather information and form opinions.

22. 22Marquette University 4. Teacher as facilitator Teachers play a new role as guide or facilitator

23. 23Marquette University 5. Meaningful products come out of inquiry based learning Students must be meaningfully engaged in learning activities through interaction with others and worthwhile tasks.

24. 24Marquette University Inquiry based learning in Computer science Cooperative Learning Teamwork Collaboration Project-oriented learning Authentic Focus

25. 25Marquette University Concept012 Data Collectionnot incorporated provides the data the the student will use students are required to collect their own data Data Analysisnot incorporated an interpretation of the data is given to the student students will analyze the data Data Representationnot 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 Abstractionnot incorporated provides an expected outcome student arrives at an outcome Rubric

26. 26Marquette University Concept012 Algorithms and Procedures not incorporated the basic steps for an algorithmic solution are provided students develop an algorithm or procedure Automationnot incorporated students are provided with a program or some other technology that automates their process students are able to automate their process Parallelizationnot incorporated students are instructed to work in parellel students will decide how to distribute their workload Simulationnot incorporated students are shown a simulation students will produce their own simulation Connecton to Other Fields not incorporated the connection is given to the student students are required to make a connection to another field Rubric

27. 27Marquette University Concept012 Data Collection763 Data Analysis943 Data Representation862 Problem Decomposition5101 Abstraction592 Algorithms and Procedures493 Automation3121 Parallelization1222 Simulation0133 Connection to Other Fields1060 Results

28. 28Marquette University  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

29. 29Marquette University  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

30. 30Marquette University  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

31. 31Marquette University  Link CS4HS content to Common Core Standards  Better lesson plan development and assessment  Continued multi track structure Moving Forward

32. 32Marquette University  Google  Wisconsin Department of Public Instruction  The Leadership of the Wisconsin Dairyland CSTA  The many teachers that participated Our Thanks To: