- Pay special attention toensuring that all students feel supported in participating in computational work, in light of pervasive stereotypes of computation as a white and male activity.
- Encourage to engage students in the design of instructional materials, assessments, and other projects, and to ask students what they need to be able to learn and demonstrate their understanding. For example, students may be able to provide input on grading and assessment structures to better support their learning.
- Train your instructional staff to notice and respond to the marginalization of students during group work, recognizing that the nature of this marginalization may be different during computational activities than during other kinds of activities. For example, students with more experience in computer science may dominate computational group work, so instructional staff should be aware of students’ backgrounds and strategies and include all students.
- Use the results of incoming student surveys to intentionally design courses and experiences that are inclusive and promote equity within the program.
- Provide computational learning opportunities for and celebrate achievements of all students, particularly those from historically .
- Invite women and people of color whose work includes computational physics to share their experiences with students in classes or departmental seminars.
- Discuss historical contributions to computation by women and people of color, e.g., Katherine Johnson, Dorothy Vaughan, Grace Hopper, and Ada Lovelace.
- See the section on Equity, Diversity, and Inclusion for other strategies for supporting students from marginalized groups.
FAQs
What are the 4 computational skills? ›
BBC outlines four cornerstones of computational thinking: decomposition, pattern recognition, abstraction, and algorithms. Decomposition invites students to break down complex problems into smaller, simpler problems.
What are the 3 A's of computational thinking? ›The "three As" Computational Thinking Process describes computational thinking as a set of three steps: abstraction, automation, and analysis.
Is there value in teaching computational skills? ›Improves problem-solving skills.
Computational thinking teaches students to be diligent and organized in their work, to plan from the outset how they want to solve a problem but to embrace the fluidity of the process as they come to more and more understanding of the data and information they're navigating.
The topics include decomposition (Lesson 1), pattern recognition (Lesson 2), abstraction (Lesson 3), generalization (Lesson 4), algorithmic thinking (Lesson 5), evaluation (Lesson 6), relationships (Lesson 7), and everywhere (Lesson 8).
What are the 4 C's computing? ›The four C's (communication, collaboration, creativity, and critical thinking) are extremely interconnected, especially in computer science curriculum.
What are the basic computation skills? ›Computational abilities include a broad range of abilities. But, in general, these skills allow employees to solve mathematical problems, such as using multiplication, division, addition, subtraction, and more foundational skills related to mathematics.
What are examples of computational skills? ›Computational skills encompass computational physics skills (e.g., translating models into code, choosing scales and units, choosing appropriate algorithms and tools, extracting physical insight, understanding the limitations of computers and computer models), the use of a variety of computational tools (e.g., ...
Is computational thinking math? ›Thus, computational thinking skills such as pattern recognition and decomposition, design and the use of abstraction, the use of appropriate computational tools and the definition of algorithms have been identified as part of the mathematical problem-solving process (Wilensky, 1995).
Is computational thinking literacy? ›We hold that computational thinking is a new literacy, with a programmatic logic that drives new media production. Future research should focus on gaining a better understanding of the material, cognitive, social, and creative processes involved in the learning of computational thinking.
What are 3 characteristics of a computational thinker? ›Computational thinking requires: exploring and analysing problems thoroughly in order to fully understand them. using precise and detailed language to outline both problems and solutions. applying clear reasoning at every stage of the process.
What are the 3 major computational thinking methods? ›
Three important elements of computational thinking are:
- decomposition.
- abstraction.
- algorithmic thinking - read more about this in the algorithm production guide.
Younger students may recognize computational thinking in how they organize their toys or share with a friend or family member. Older students may recognize this process in how they plan or execute a bike route, organize their schedule, complete homework, set goals or solve real-life problems.
What are the 4 computational methods? ›There are four key techniques (cornerstones) to computational thinking:
- decomposition. - breaking down a complex problem or system into smaller, more manageable parts.
- pattern recognition. – looking for similarities among and within problems.
- abstraction. ...
- algorithms.
These include:
- Decomposition. Decomposition is the process of breaking down a problem or challenge – even a complex one – into small, manageable parts.
- Abstraction. ...
- Pattern recognition. ...
- Algorithm design. ...
- What are some examples of computational thinking?
Sep 1, 2022
Demystify Computational Thinking
- Abstraction: Look at relevant and important details only.
- Algorithms: Use steps and sequencing to solve problems.
- Decomposition: Break things down into smaller manageable parts.
- Patterns: Find similarities and trends.
This broad problem-solving technique includes four elements: decomposition, pattern recognition, abstraction and algorithms. There are a variety of ways that students can practice and hone their computational thinking, well before they try computer programming.