Alignments

Create programs using a programming language that utilize sequencing, repetition, conditionals, and variables to solve a problem or express ideas both independently and collaboratively. [See: Method Chaining; Grouped Samples.]

Use an iterative process to plan the development of a program while solving simple problems. [See: Functions Make Life Easier!; Solving Word Problems with the Design Recipe; Defining Table Functions.]

Communicate and explain your program development using comments, presentations and demonstrations. [See: Solving Word Problems with the Design Recipe.]

Collect and organize data in various visual formats. [See: Collecting Data.]

With guidance, utilize data to make predictions and discuss whether there is adequate data to make reliable predictions. [See: Linear Regression; Threats to Validity.]

Compare and refine multiple algorithms for the same task. [See: Method Chaining.]

Decompose large problems into smaller, manageable subproblems to facilitate the program development process. [See: Method Chaining.]

Analyze, create, and debug a program that includes sequencing, repetition, conditionals and variables in a programming language. [See: Method Chaining.]

Communicate and explain your program development using comments, presentations and demonstrations. [See: Solving Word Problems with the Design Recipe.]

Organize and present collected data visually to highlight comparisons. [See: Collecting Data.]

Determine how the accuracy of conclusions are influenced by the amount of data collected. [See: Linear Regression.]

Decompose large problems into smaller, manageable subproblems and then into a precise sequence of instructions. [See: Method Chaining.]

Analyze, create, and debug a program that includes sequencing, repetition, conditionals and variables in a programming language. [See: Method Chaining.]

Communicate and explain your program development using comments, presentations and demonstrations. [See: Solving Word Problems with the Design Recipe.]

Organize and present collected data to highlight comparisons and support a claim. [See: Collecting Data.]

Use data to highlight or propose cause and effect relationships, predict outcomes, or communicate an idea. [See: Introduction to Data Science.]

Describe, classify and construct triangles, including equilateral, right, scalene, and isosceles triangles. Recognize triangles in various contexts. [See: Contracts.]

Use and evaluate variables in expressions, equations, and inequalities that arise from various contexts, including determining when or if, for a given value of the variable, an equation or inequality involving a variable is true or false. [See: Simple Data Types.]

Use an existing algorithm in natural language or pseudocode to solve complex problems. [See: Solving Word Problems with the Design Recipe.]

Break down tasks and follow an individual timeline when developing a computational artifact. [See: Probability, Inference, and Sample Size.]

Document text-based programs in order to make them easier to follow, test, and debug. [See: Solving Word Problems with the Design Recipe.]

Create and analyze box and whisker plots observing how each segment contains one quarter of the data. [See: Bar and Pie Charts; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Grouped Samples.]

Collect data using computational tools and transform the data to make it more useful. [See: Box Plots; Standard Deviation.]

Identify how the same data can be represented in multiple ways. [See: Bar and Pie Charts.]

Develop and use the fact that the sum of the interior angles of a triangle is 180° to determine missing angle measures in a triangle. [See: Contracts.]

Explain how computing impacts people’s everyday activities. [See: Computing Needs All Voices.]

Select and modify an existing algorithm in natural language or pseudocode to solve complex problems. [See: Simple Data Types; Table Methods; Solving Word Problems with the Design Recipe.]

Decompose problems into parts to facilitate the design, implementation, and review of increasingly complex programs. [See: Method Chaining.]

Distribute tasks and maintain a project timeline when collaboratively developing computational artifacts. [See: Probability, Inference, and Sample Size.]

Document text-based programs of increasing complexity in order to make them easier to follow, test, and debug. [See: Solving Word Problems with the Design Recipe.]

Use reasoning with proportions to display and interpret data in circle graphs (pie charts) and histograms. Choose the appropriate data display and know how to create the display using a spreadsheet or other graphing technology. [See: Bar and Pie Charts; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Grouped Samples.]

Collect data using computational tools and transform the data to make it more useful and reliable. [See: Box Plots; Standard Deviation.]

Create multiple representations of data. [See: Bar and Pie Charts.]

Know that every rational number can be written as the ratio of two integers or as a terminating or repeating decimal. [See: Simple Data Types.]

Compare and order rational numbers expressed in various forms using the symbols <, >, and =. [See: Simple Data Types.]

Recognize and generate equivalent representations of rational numbers, including equivalent fractions. [See: Simple Data Types.]

Design algorithms in natural language, flow and control diagrams, comments within code, and/or pseudocode to solve complex problems. [See: Solving Word Problems with the Design Recipe.]

Develop programs that utilize combinations of nested repetition, compound conditionals, procedures without parameters, and the manipulation of variables representing different data types. [See: Simple Data Types.]

Decompose problems and subproblems into parts to facilitate the design, implementation, and review of complex programs. [See: Method Chaining.]

Incorporate existing code, media, and libraries into original programs of increasing complexity and give attribution. [See: Functions Make Life Easier!.]

Explain how effective communication between participants is required for successful collaboration when developing computational artifacts. [See: Solving Word Problems with the Design Recipe.]

Document text-based programs of increasing complexity in order to make them easier to follow, test, and debug. [See: Solving Word Problems with the Design Recipe.]

Develop, implement, and refine a process that utilizes computational tools to collect and transform data to make it more useful and reliable. [See: Introduction to Data Science; Grouped Samples.]

Analyze multiple methods of representing data and choose the most appropriate method for representing data. [See: Bar and Pie Charts; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Box Plots; Standard Deviation; Grouped Samples.]

Communicate and publish key ideas and details individually or collaboratively in a way that informs, persuades, and/or entertains using a variety of digital tools and media-rich resources. Describe and use safe, appropriate, and responsible practices (netiquette) when participating in online communities (e.g., discussion groups, blogs, social networking sites). [See: Choosing Your Dataset; Threats to Validity.]

Use knowledge of solving equations with rational values to represent and solve mathematical and real-world problems (e.g., angle measures, geometric formulas, science, or statistics) and interpret the solutions in the original context. [See: Functions Make Life Easier!.]

Describe a data set using data displays, describe and compare data sets using summary statistics, including measures of central tendency, location, and spread. Know how to use calculators, spreadsheets, or other appropriate technology to display data and calculate summary statistics. [See: Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Grouped Samples.]

Collect data and use scatterplots to analyze patterns and describe linear relationships between two variables. Using graphing technology, determine regression lines and correlation coefficients; use regression lines to make predictions and correlation coefficients to assess the reliability of those predictions. [See: Collecting Data.]

Select and apply counting procedures, such as the multiplication and addition principles and tree diagrams, to determine the size of a sample space (the number of possible outcomes) and to calculate probabilities. [See: Table Methods; Method Chaining; Defining Table Functions.]

Identify the dependent and independent variables as well as the domain and range given a function, equation, or graph. Identify restrictions on the domain and range in real-world contexts. [See: Contracts.]

Write linear functions, using function notation, to model real-world and mathematical situations. [See: Contracts; Functions Make Life Easier!.]

Given a graph modeling a real-world situation, read and interpret the linear piecewise function (excluding step functions). [See: Contracts; Custom Scatter Plots.]

Graph piecewise functions with no more than three branches (including linear, quadratic, or exponential branches) and analyze the function by identifying the domain, range, intercepts, and intervals for which it is increasing, decreasing, and constant. [See: Custom Scatter Plots.]

Construct logical arguments to prove triangle congruence (SSS, SAS, ASA, AAS and HL) and triangle similarity (AA, SSS, SAS). [See: Contracts.]

Create a prototype that uses algorithms (e.g., searching, sorting, finding shortest distance) to provide a possible solution for a real-world problem. [See: Grouped Samples.]

Break down a solution into procedures using systematic analysis and design. [See: Custom Scatter Plots; Method Chaining; Defining Table Functions.]

Create computational artifacts by systematically organizing, manipulating and/or processing data. [See: Custom Scatter Plots; Table Methods; Method Chaining; Defining Table Functions.]

Evaluate and refine computational artifacts to make them more user-friendly, efficient and/or accessible. [See: Histograms; Visualizing the “Shape” of Data.]

Use tools and techniques to locate, collect, and create visualizations of small- and largescale data sets (e.g., paper surveys and online data sets). [See: Choosing Your Dataset.]

Show the relationships between collected data elements using computational models. [See: Scatter Plots; Correlations; Linear Regression.]

Evaluate the ways computing impacts personal, ethical, social, economic, and cultural practices. [See: Ethics, Privacy, and Bias.]

Test and refine computational artifacts to reduce bias and equity deficits. [See: Probability, Inference, and Sample Size; Choosing Your Dataset; Grouped Samples; Checking Your Work; Threats to Validity.]

Explain the privacy concerns related to the large-scale collection and analysis of information about individuals (e.g., how businesses, social media, and the government collects and uses data) that may not be evident to users. [See: Ethics, Privacy, and Bias.]

Evaluate the social and economic consequences of how law and ethics interact with digital aspects of privacy, data, property, information, and identity. [See: Ethics, Privacy, and Bias.]

Create programming solutions by reusing existing code (e.g., libraries, Application Programming Interface (APIs), code repositories). [See: Table Methods.]

Develop programs for multiple computing platforms. [See: Functions Make Life Easier!.]

Develop and use a series of test cases to verify that a program performs according to its design specifications. [See: Solving Word Problems with the Design Recipe.]

Use data analysis tools and techniques to identify patterns from complex real-world data. [See: Linear Regression.]

Generate data sets that use a variety of data collection tools and analysis techniques to support a claim and/or communicate information. [See: Box Plots; Standard Deviation.]

Evaluate the beneficial and harmful effects that computational artifacts and innovations have on society. [See: Ethics, Privacy, and Bias.]

Debate laws and regulations that impact the development and use of software. [See: Ethics, Privacy, and Bias.]

Develop a productive mathematical disposition. [See: Functions Make Life Easier!.]

Develop the ability to make conjectures, model, and generalize. [See: Functions Make Life Easier!.]

Develop the ability to communicate mathematically. [See: Solving Word Problems with the Design Recipe; Linear Regression.]

Recognize that a function is a relationship between an independent variable and a dependent variable in which the value of the independent variable determines the value of the dependent variable. [See: Contracts; Functions Make Life Easier!.]

Use linear functions to represent and explain real-world and mathematical situations. [See: Functions Make Life Easier!; Solving Word Problems with the Design Recipe.]

Identify a function as linear if it can be expressed in the form y = mx + b or if its graph is a straight line. [See: Solving Word Problems with the Design Recipe.]

Recognize linear functions in real-world and mathematical situations; represent linear functions and other functions with tables, verbal descriptions, symbols, and graphs; solve problems involving linear functions and interpret results in the original context. [See: Solving Word Problems with the Design Recipe.]

Represent linear functions with tables, verbal descriptions, symbols, and graphs; translate from one representation to another. [See: Solving Word Problems with the Design Recipe.]

Identify, describe, and analyze linear relationships between two variables. [See: Probability, Inference, and Sample Size; Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Grouped Samples.]

Display and interpret data in a variety of ways, including using scatterplots and approximate lines of best fit. Use line of best fit and average rate of change to make predictions and draw conclusions about data. [See: Choosing Your Dataset.]

Describe the impact that inserting or deleting a data point has on the mean and the median of a data set. Know how to create data displays using a spreadsheet and use a calculator to examine this impact. [See: Choosing Your Dataset; Histograms; Visualizing the “Shape” of Data; Grouped Samples.]

Explain how outliers affect measures of central tendency. [See: Measures of Center.]

Collect, display and interpret data using scatterplots. Use the shape of the scatterplot to informally estimate a line of best fit, make statements about average rate of change, and make predictions about values not in the original data set. Use appropriate titles, labels and units. [See: Scatter Plots; Correlations; Linear Regression.]

Determine how samples are chosen (random, limited, biased) to draw and support conclusions about generalizing a sample to a population. [See: Probability, Inference, and Sample Size.]