A high school program in computer science

Gal‐Ezer, Judith; Beeri, Catriel; Harel, David; Yehudai, Amiram

doi:10.1109/2.467599

Cited by 118 publications

(80 citation statements)

References 5 publications

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“…(For example, Michael Bloomberg, the mayor of New York, has publically endorsed a course on learning to program that he is taking, and which is targetted at school children.) We welcome these recent events, even if we strongly believe that computer science in schools needs to be broader than just learning to program [9]; it needs to be about computational thinking [23], algorithmic thinking [8] and algorithmic learning [12]. Programming in schools is important, but teaching about abstraction may be even more so [18].…”

Section: Introductionmentioning

confidence: 99%

Teaching graph algorithms to children of all ages

Gibson

2012

Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education

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We report on our experiences in teaching graph theory and algorithms to school children, aged 5 to 17. Our objectives were to demonstrate that children can discover quite complex mathematical concepts, and are able to work with abstractions and use computation reasoning from quite an early age. We provide details of our incremental approach, which can be used with students of a wide range of abilities. Also, we comment on the importance of problem based learning where the algorithms are presented as possible solutions to games or puzzles. Finally, we conclude with a number of important observations with regard to the introduction of computer science into schools.

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Section: Introductionmentioning

confidence: 99%

Teaching graph algorithms to children of all ages

Gibson

2012

Proceedings of the 17th ACM Annual Conference on Innovation and Technology in Computer Science Education

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“…The current high school CS program is based on a relatively new curriculum (Gal-Ezer, Beeri, Harel, & Yehudai, 1995;Gal-Ezer & Harel, 1999). One of the elective theoretical units of the curriculum is a unit on computational models (CM), for which a textbook and a teachers' guide were written (Armoni, Kaufman, & Bargury, 1998).…”

Section: Introductionmentioning

confidence: 99%

Solving Problems Reductively

Armoni

Gal‐Ezer

Tirosh

2005

Journal of Educational Computing Research

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Solving problems by reduction is an important issue in mathematics and science education in general (both in high school and in college or university) and particularly in computer science education. Developing reductive thinking patterns is an important goal in any scientific discipline, yet reduction is not an easy subject to cope with. Still, the use of reduction usually is insufficiently reflected in high school mathematics and science programs. Even in academic computer science programs the concept of reduction is mentioned explicitly only in advanced academic courses such as computability and complexity theory. However, reduction can be applied in other courses as well, even on the high school level. Specifically, in the field of computational models, reduction is an important method for solving design and proof problems. This study focuses on high school students studying the unit "computational models"-a unique unit, which is part of the new Israeli computer science high school curriculum. We examined whether high school students tend to solve problems dealing with computational models reductively, and if they do, what is the nature of their reductive solutions. To the best of our knowledge, the tendency to reductive thinking in theoretical computer science has not been studied before. Our findings show that even though many students use reduction, many others prefer non-reductive solutions, even when reduction can significantly decrease the technical complexity of the solution. We discuss these findings and suggest possible ways to improve reductive thinking.

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“…This evaluation of algorithmic processes, including their power and limitations, is foreshadowed by Gal-Ezer, et al [16]. Application of the term to user interfaces is evidenced in the second objective of the New Zealand proposed curriculum, as part of designing programs [1].…”

Section: Defining Computational Thinkingmentioning

confidence: 99%

“…Inclusion of algorithmic thinking in a curriculum for high schools appears prior to Wing's contribution. In the Israeli computer science curriculum, Gal-Ezer, et al [16] placed an emphasis on inclusion of the study of algorithmic processes.…”

Section: Defining Computational Thinkingmentioning

confidence: 99%

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Selby

2015

Proceedings of the Workshop in Primary and Secondary Computing Education

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This study explores the relationship between computational thinking, teaching programming, and Bloom's Taxonomy. Data is collected from teachers, academics, and professionals, purposively selected because of their knowledge of the topics of problem solving, computational thinking, or the teaching of programming. This data is analysed following a grounded theory approach. A computational thinking taxonomy is developed. The relationships between cognitive processes, the pedagogy of programming, and the perceived levels of difficulty of computational thinking skills are illustrated by a model. Specifically, a definition for computational thinking is presented. The skills identified are mapped to Bloom's Taxonomy: Cognitive Domain. This mapping concentrates computational skills at the application, analysis, synthesis, and evaluation levels. Analysis of the data indicates that abstraction of functionality is less difficult than abstraction of data, but both are perceived as difficult. The most difficult computational thinking skill is reported as decomposition. This ordering of difficulty for learners is a reversal of the cognitive complexity predicted by Bloom's model. The plausibility of this inconsistency is explored. The taxonomy, model, and the other results of this study may be used by educators to focus learning onto the computational thinking skills acquired by the learners, while using programming as a tool. They may also be employed in the design of curriculum subjects, such as ICT, computing, or computer science.

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A high school program in computer science

Cited by 118 publications

References 5 publications

Teaching graph algorithms to children of all ages

Teaching graph algorithms to children of all ages

Solving Problems Reductively

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