Comparison of Abstraction in Computer Coding and Critical Thinking Christine Liebe, PhD Candidate

Overview Significance Definitions Comparisons / Contrasts Educational Applications

Monet Abstractionist (Impressionist)

Reality – many details

Why study abstraction in computer science education? Answer: INFORG

Object= data + code from Object-oriented Programming

Levels of abstraction in computer systems

Abstraction is a process and a thing representation or simplification Use of simplified code whenever possible to reduce repetition

Recursion, variables, event handlers, reals

Perrenet, Groote, Kassenbrood (PKG) Hierarchy Execution level – algorithms Program level – bigger picture, understanding of most efficient elegant programming language Object level – algorithm is a distinct form Problem level – computer is a thing and a process offering a solution

Critical thinking is… The ability to process information in a variety of ways that include, for example synthesis, analysis, and metacognition.

Bloom’s Taxonomy

Marzano & Kendall’s New Taxonomy Retroduction

Computer Science specific learning taxonomy

Fuller et al applied to Critical Thinking Producing   Create Apply None Interpreting Remember Understand Analyze Evaluate

Teaching Takeaways Direct Instruction Indirect Instruction Deduction Induction Theory Modeling Independent Collaborative Online In person Andragogical Pedagogical Learning Styles Self-system, motivation, beliefs

Educational implications Curriculum Instruction Assessment Professional Development

Resources and References Abelson, H., Ledeen, K., & Lewis, H. R. (2008). Blown to bits: Your life, liberty, and happiness after the digital explosion. Upper Saddle River, NJ: Addison-Wesley. Armoni, M. (2013). On teaching abstraction in Computer Science to novices. Journal of Computers in Mathematics and Science Teaching. (32) 265-284. Brenan, K., Resnick, M. (AERA, 2012). New frameworks for evaluating and discussing the development of computational thinking. White paper. MIT Medial Lab. Brookshear, J. G. (1997). Computer science: an overview. Paul Muljadi. College Board. (2015). AP Computer science principles. Retrieved from https://secure-media.collegeboard.org/digitalServices/pdf/ap/ap-computer-science-principles-curriculum-framework.pdf Dale, N. B., & Lewis, J. (2007). Computer science illuminated. Jones & Bartlett Learning. Colburn, T. (2015). Philosophy and computer science. Routledge. Retrieved from https://books.google.com/books?hl=en&lr=&id=w5xzCQAAQBAJ&oi=fnd&pg=PP1&dq=what+is+abstraction+in+computer+science&ots=_FhNkACXGJ&sig=AsmBZIsdDDi6oBJHSkTDxA5xo50 Colburn, T., & Shute, G. (2007). Abstraction in computer science. Minds and Machines, 17(2), 169-184. Fuller, U., Johnson, C., Ahoniemi, T. et al (2007). Developing a computer science specific learning taxonomy. ITiCSE working group report on innovation and technology in computer science education. doi: 10.1145/1345443.1345438 Gardner, H. (2011). Frames of mind: The theory of multiple intelligences. Basic books. Gobbo, F., & Benini, M. (2014). The minimal levels of abstraction in the history of modern computing. Philosophy & Technology, 27(3), 327-343. Kolb, D. A. (1981). Learning styles and disciplinary differences. The modern American college, 1, 232-255. Marzano, R. J., & Kendall, J. S. (Eds.). (2006). The new taxonomy of educational objectives. Corwin Press. Perrenet, J.C., J.F. Groote & E. Kaasenbrood (2005). Exploring Students’ Understanding of the Concept of Algorithm: Levels of Abstraction; In: Proceedings of the 10th annual SIGCSE-conference on Innovation and technology in computer science education, 64–68; Caparica, Portugal. © ACM 1-59593-024-8/05/0006. Retrieved from http://acm.org/10.1145/1070000/1067467 Perrenet, J.C. & E. Kaasenbrood (2006). Levels of Abstraction in Students’ Understanding of the Concept of Algorithm: the Qualitative Perspective; In: Proceedings of the 11th annual SIGCSE-conference on Innovation and technology in computer science education, 270–275; Bologna, Italy. © ACM 1-59593-055-8/06/0006. Retrieved from http://acm.org/10.1145/1150000/1140196 Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.

PhD Candidate Education – Curriculum, Instruction, Assessment I would like to extend gratitude to all computer scientists and educational scholars, my mentor Dr. Wade Smith, friends & family, and the Lord. My scholarship is dedicated to the prosperous compassionate futures all digitally literate students deserve. Christine@christineliebe.com Christineliebe.com PhD Candidate Education – Curriculum, Instruction, Assessment Walden University