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# Computer Science 12

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### Big Ideas

### Grandes idées

Decomposition and abstraction help us to solve difficult problems by managing complexity.

abstraction

- reducing complexity by representing essential features without including the background details or explanations
*Sample questions to support inquiry with students:*- How do we decide when an object should be abstracted?
- How do we choose public features?
- How do we choose which features are advertised?
- How does hiding background detail simplify the problem-solving process?

Algorithms are essential in solving problems computationally.

Algorithms

*Sample questions**to support inquiry with students:*- When comparing algorithms, how do we determine which one is most efficient?
- Can an elegant algorithm be efficient?
- How is an algorithm formulated?
- What makes one algorithm better than another algorithm?
- What is the relationship between elegant algorithms and efficient algorithms?
- Can all problems be solved through a series of predefined steps?

Programming is a tool that allows us to implement computational thinking.

computational thinking

- a thought process that uses pattern recognition and decomposition to describe an algorithm in a way that a computer can execute
*Sample questions**to support inquiry with students:*- How do we decide which programming language to use in solving a specific problem?
- Why is code readability important?
- What factors affect code readability?
- How much source code documentation is enough?
- Are there patterns in the solution that can be generalized?
- How do we recognize patterns?

Solving problems is a creative process.

Solving problems

*Sample questions**to support inquiry with students:*- How many different ways can this problem be solved?
- How do we determine which solution is better?
- How do we approach solving a problem in different ways?
- Without knowing a solution, how do we start to solve a problem?

Data representation allows us to understand and solve problems efficiently.

Data representation

- a method of storing and organizing information in a container
*Sample questions**to support inquiry with students:*- When should we create our own data type?
- How do computers use electricity to represent data?
- How can we organize our data types more efficiently?
- How do we decide which data types to use?

## Learning Standards

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### Curricular Competencies

*Students are expected to be able to do the following:*### Reasoning and modelling

Develop fluent, flexible, and strategic thinking to analyze and create algorithms

fluent, flexible, and strategic thinking

- understanding the efficiency of different algorithms in solving the same problem, balancing performance and elegance

Explore, analyze, and apply mathematical ideas and computer science concepts using reason, technology, and other tools

analyze

- examine the structure of and connections between mathematical ideas (e.g., big-O analysis)

reason

- inductive and deductive reasoning
- predictions, generalizations, conclusions drawn from experiences (e.g., with coding)

technology

- graphing technology, dynamic geometry, calculators, virtual manipulatives, concept-based apps
- can be used for a wide variety of purposes, including:
- exploring and demonstrating mathematical relationships
- organizing and displaying data
- generating and testing inductive conjectures
- mathematical modelling

other tools

- integrated development environments (IDE)
- IDE debugger to inspect memory at run-time
- third-party libraries
- visual code comparison tools to view code differences (e.g., Meld)
- memory analyzers to discover memory leaks
- version control systems to share source code among team members (e.g., git)

Model with mathematics in situational contexts

Model

- use mathematical concepts and tools to solve problems and make decisions (e.g., in real-life and/or abstract scenarios)
- take a complex, essentially non-mathematical scenario and figure out what mathematical concepts and tools are needed to make sense of it

situational contexts

- including real-life scenarios and open-ended challenges that connect mathematics with everyday life

Think creatively and with curiosity and wonder when exploring problems

Think creatively

- by being open to trying different strategies
- refers to creative and innovative mathematical thinking rather than to representing math in a creative way, such as through art or music

curiosity and wonder

- asking questions to further understanding or to open other avenues of investigation

### Understanding and solving

Develop, demonstrate, and apply conceptual understanding through experimentation, inquiry, and problem solving

inquiry

- includes structured, guided, and open inquiry
- noticing and wondering
- determining what is needed to make sense of and solve problems

Visualize to explore and illustrate computer science concepts and relationships

Visualize

- visualize data structures pictorially
- use flow charts
- use code visualization tools or websites (e.g., http://pythontutor.com/)

Apply flexible and strategic approaches to solve problems

flexible and strategic approaches

- using different algorithms to solve the same problem
- designing algorithms that solve a class of problems rather than a single problem
- deciding which programming patterns and well-known algorithms to use to solve a problem
- choosing an effective strategy to solve a problem (e.g., guess and check, model, solve a simpler problem, use a chart, use diagrams, role-play)

solve problems

- interpret a situation to identify a problem
- apply mathematics to solve the problem
- analyze and evaluate the solution in terms of the initial context
- repeat this cycle until a solution makes sense

Solve problems with persistence and a positive disposition

persistence and a positive disposition

- not giving up when facing a challenge
- problem solving with vigour and determination

Engage in problem-solving experiences connected with place, story, cultural practices, and perspectives relevant to local First Peoples communities, the local community, and other cultures

connected

- through daily activities, local and traditional practices, popular media and news events, cross-curricular integration
- by posing and solving problems or asking questions about place, stories, and cultural practices

### Communicating and representing

Explain and justify computer science ideas and decisions in many ways

Explain and justify

- use mathematical arguments to convince
- includes anticipating consequences

decisions

- Have students explore which of two scenarios they would choose and then defend their choice.

many ways

- including oral, written, pseudocode, pictures, use of technology
- communicating effectively according to what is being communicated and to whom

Represent computer science ideas in concrete, pictorial, and symbolic forms

Represent

- using pseudocode (e.g., with models, tables, flow charts, words, numbers, symbols)
- connecting meanings among various representations
- using concrete materials and dynamic interactive technology

Use computer science and mathematical vocabulary and language to contribute to discussions in the classroom

discussions

- partner talks, small-group discussions, teacher-student conferences

Take risks when offering ideas in classroom discourse

discourse

- is valuable for deepening understanding of concepts
- can help clarify students’ thinking, even if they are not sure about an idea or have misconceptions

### Connecting and reflecting

Reflect on mathematical and computational thinking

Reflect

- share the mathematicaland computational thinking of self and others, including evaluating strategies and solutions, extending, posing new problems and questions

Connect mathematical and computer science concepts with each other, other areas, and personal interests

Connect mathematical and computer science concepts

- to develop a sense of how computer science helps us understand the world around us (e.g., daily activities, local and traditional practices, popular media and news events, social justice, cross-curricular integration)

Use mistakes as opportunities to advance learning

mistakes

- include syntax, semantic, run-time, and logic errors

opportunities to advance learning

- by:
- analyzing errors to discover misunderstandings
- making adjustments in further attempts (e.g., debugging)
- identifying not only mistakes but also parts of a solution that are correct

Incorporate First Peoples worldviews, perspectives, knowledge, and practices to make connections with computer science concepts

Incorporate

- by:
- collaborating with Elders and knowledge keepers among local First Peoples
- exploring the First Peoples Principles of Learning (http://www.fnesc.ca/wp/wp-content/uploads/2015/09/PUB-LFP-POSTER-Princip... e.g., Learning is holistic, reflexive, reflective, experiential, and relational [focused on connectedness, on reciprocal relationships, and a sense of place]; Learning involves patience and time)
- making explicit connections with learning mathematics
- exploring cultural practices and knowledge of local First Peoples and identifying mathematical connections

knowledge

- local knowledge and cultural practices that are appropriate to share and that are non-appropriated

practices

- Bishop’s cultural practices: counting, measuring, locating, designing, playing, explaining (http://www.csus.edu/indiv/o/oreyd/ACP.htm_files/abishop.htm)
- Aboriginal Education Resources (www.aboriginaleducation.ca)
*Teaching Mathematics in a First Nations Context*, FNESC (http://www.fnesc.ca/resources/math-first-peoples/)

### Content

*Students are expected to know the following:*access variables in memory

access variables

- pass by value versus by reference, or mutable/immutable data types

ways in which data structures are organized in memory

data structures

- vectors, lists, queues, dictionaries, maps, trees, stacks

uses of multidimensional arrays

uses

- board games, image manipulation, representing tabular data or matrices

classical algorithms, including sorting and searching

sorting and searching

- sorting (e.g., bubble, insertion, selection, quick merge)
- searching (e.g., binary search, data structure traversal)

use of Big-O notation to help predict run-time performance

performance

- analyzing algorithms to predict and compare run-time complexity
- working with large data sets

recursive problem solving

recursive problem solving

- recognizing recursive problems or patterns
- Fibonacci sequence, exponents, factorials, palindromes, combinations, greatest common factor, fractals

persistent memory

persistent memory

- read from/write to a file

encapsulation of data

encapsulation

- creating your own data type, class, or structure as well as public, private, static/class variables

ways to model mathematical problems

model mathematical problems

- estimate theoretical probability through simulation
- represent finite sequences and series
- solve a system of linear equations, exponential growth/decay
- solve a polynomial equation
- calculate statistical values (e.g., frequency, central tendencies, standard deviation) of a large data set

**Note:**Some of the learning standards in the PHE curriculum address topics that some students and their parents or guardians may feel more comfortable addressing at home. Refer to ministry policy regarding opting for alternative delivery.