Standards in this Framework
Standards Mapped
Mapped to Course
| Standard | Lessons |
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ALG.1.a.m.1
Represent algorithms visually using structured formats such as flowcharts, pseudocode, or block-based tools to solve computational problems. |
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ALG.1.a.m.2
Model decision-making and repetition in algorithm representations to reflect problem-solving strategies. |
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ALG.1.b.m.1
Describe how data is taken in (as input), stored, processed, and then produced as a result (as output) in a computational solution. |
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ALG.1.b.m.2
Represent and explain data transformations using visual or textual formats such as flowcharts, pseudocode, or structured diagrams. |
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ALG.1.c.m.1
Write algorithm steps using sequence, iteration, and selection to solve a task involving at least one data element. |
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ALG.1.c.m.2
Create and refine algorithms using structure formats (e.g. pseudocode, or flowcharts), incorporating inputs, logics, and outputs. |
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ALG.2.a.m.1
Design algorithms, using human-centered design principles such as empathy, user needs and requirements, and accessibility. |
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ALG.2.a.m.2
Refine algorithms iteratively through user feedback to improve usability, accessibility, and user experience. |
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ALG.3.a.m.1
Compare different sets of steps to see which works better or faster. |
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ALG.3.a.m.2
Compare two or more algorithms to decide which is more accurate or efficient. |
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ALG.3.a.m.3
Suggest improvements to make an algorithm work better or faster based on what the problem needs. |
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ALG.3.b.1.m
Explain how a system gives outputs even if the parts inside aren't fully visible (like a black box). |
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ALG3.b.2.m
Use patterns or structure in problems to help predict how algorithms work and where they can be improved. |
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ALG.4.a.m.1
Describe how algorithms - including deterministic and probabilistic types - can impact people or groups unfairly, and explain why accessibility and bias matter. |
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ALG.4.b.m.1
Modify an algorithm to address a specific societal impact, ethical issue, or bias. |
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PRO.1.a.m.1
Analyze how a segment of code works, identifying the roles of variables, selection, and iteration. |
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PRO.1.a.m.2
Use procedures without parameters to structure code for clarity and readability. |
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PRO.2.a.m.2
Use variables and fundamental data types (e.g., integers, strings, Booleans) to represent and organize data. |
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PRO.2.b.m.1
Use appropriate data types and variables to store, update, and manage data in programs. |
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PRO.2.c.m.1
Use appropriate data structures to store related data and iterate them to process elements. |
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PRO.3.a.1.m
Use testing and debugging methods to ensure program correctness and completeness. |
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PRO.5.a.m.1
Define clear project objectives and develop a step-by-step plan or timeline to guide workflow. |
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PRO.4.b.m.1
Document design choices and development process through notes, journals, or basic version tracking. |
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PRO.4.b.m.2
Communicate progress and challenges to peers or instructors. |
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PRO.4.c.m.1
Reflect on project outcomes and process, identify areas for improvement. |
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PRO.4.c.m.2
Revise code or plan based on testing or peer feedback. |
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DA.1.a.m.1
Collect both quantitative and qualitative data relevant to a problem or question using computational tools (e.g., spreadsheets, sensors, forms). |
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DA.1.a.m.2
Generate and interpret metadata to describe the purpose, structure, source, and collection of methods of datasets, supporting accurate data interpretation and reuse. |
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DA.1.b.m.1
Represent and interpret data using standard and student-created encoding systems (e.g., binary, Unicode, Morse code), recognizing how encoding supports communication and data processing. |
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DA.1.b.m.2
Explain and convert between basic number systems (such as binary and decimal) and describe how digital systems use these to represent various types of data (e.g., text, images, sound). |
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DA.1.c.m.1
Evaluate how precision and granularity (e.g., rounding, sampling rates, image resolution) affect data accuracy, storage, and interpretation in computational analysis. |
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DA.2.a.m.1
Use computational tools (e.g., spreadsheets, apps) to gather, organize, sort, filter, group, and aggregate quantitative data, including calculating summary statistics (e.g., totals, averages, frequencies, measures of center and spread). |
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DA.2.a.m.2
Manipulate and transform data to support analysis by creating new variables or attributes, selecting appropriate data, and generating visualizations (e.g., charts, graphs) to answer specific questions or investigate patterns. |
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DA.2.a.m.3
Compare data storage formats (e.g., image, text, and music files) and explain tradeoffs between file size, resolution, and quality in terms of computational efficiency and use. |
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DA.2.b.m.1
Apply appropriate strategies to address missing, out-of-range, or anomalous data (e.g., using imputation, removing outliers, or marking invalid entries), and explain the impact on data quality. |
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DA.3.a.m.1
Pose data-driven questions that anticipate patterns or variability and guide purposeful investigations. |
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DA.3.a.m.2
Collect, organize, and analyze data using computational tools (e.g., spreadsheets, databases) to identify relationships, classify information, and make predictions or informed decisions. |
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DA.3.a.m.3
Evaluate datasets and digital information sources by analyzing their reliability, accuracy, bias, format, and relevance for answering specific data questions. |
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DA.3.b.m.1
Analyze data using computational tools (e.g., spreadsheets, databases) to identify relationships, classify information, and make predictions or informed decisions. |
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DA.3.c.m.1
Create and refine data visualizations to clearly and accurately communicate findings, considering how design choices affect clarity, accessibility, and interpretation. |
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DA.3.c.m.2
Communicate the results of a data investigation by explaining the question, data collection methods, analysis process, and evidence that supports conclusions. |
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DA.4.a.m.1
Explain the benefits and risks of collecting personal data and incorporating it into datasets, including impacts on privacy and individual rights. |
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DA.4.a.m.2
Explain how automated decisions and AI influence individuals and society by exploring these effects through simple computational models. |
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DA.4.b.m.1
Explain how decisions made during data collection, processing, analysis, and presentation can introduce bias, lead to misleading conclusions, and affect the fairness of AI models. |
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DA.4.b.m.2
Evaluate the credibility of data from AI and machine learning sources by assessing the source, accuracy, potential biases, and relevance of the information. |
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DA.4.c.m.1
Explain how data-driven algorithms and AI systems impact society, including issues of privacy, fairness, and ethics. |
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DA.4.c.m.2
Use data investigations and basic models to propose solutions to real-world problems, considering potential benefits and ethical impacts. |
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CSS.1.a.m.1
Explain how computers organize and store information, including how files are named, grouped, and protected with settings that control who can use them. |
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CSS.1.a.m.2
Describe how different types of computing devices (such as smartphones, tablets, or cloud-based systems) connect and share information through networks. |
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CSS.1.a.m.3
Explain how software and hardware interact to perform tasks, including how updates or settings affect device performance and user experience. |
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CSS.1.b.m.1
Use instructions, help guides, or online resources to learn how to complete computer tasks or solve problems. |
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CSS.1.b.m.2
Explain the differences between apps that run on the internet and ones installed on a device, and describe when and why someone might use one over the other. |
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CSS.1.c.m.1
Use basic troubleshooting methods to identify and fix issues such as a frozen screen or apps that won't open. |
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CSS.2.a.m.1
Compare the strengths and challenges of wired vs. wireless networks in places like homes or schools. |
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CSS.2.a.m.2
Describe how the parts of the Internet work (servers, routers, and cables) keep it working reliably and fix problems. |
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CSS.2.b.m.1
Show how the Internet sends data in packets - breaking it up, choosing different paths, and reassembling on the receiving end. |
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CSS.3.a.m.1
Identify and describe common types of cyber-attacks - like tricking people (social engineering) or installing harmful software (malware) and explain how they can affect users and devices. |
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CSS.3.a.m.2
Explain how computers and data centers can be at risk from both natural events (storms, earthquakes) or intentional harm (hacking to steal data). |
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CSS.3.b.m.1
Compare ways to protect computers and information - using both digital tools (like passwords, firewalls) and physical tools (like locks, cameras) and describing how protections impact ease of use. |
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CSS.3.b.m.2
Describe how using multiple layers of protection (Defense in Depth) keeps systems safer and explain steps to take if a system is attacked. |
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CSS.2.b.m.3
Describe how encryption helps keep digital information private and secure and give examples of where it's used - like in messaging apps, websites, or online banking. |
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CSS.2.b.m.4
Describe safe practices for accessing systems, including recognizing secure sites, managing passwords, and avoiding risky downloads or pop-ups. |
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CSS.4.m.1
Analyze how widely used technology helps individuals solve problems, (education, communication) while also creating challenges (cyberbullying, privacy issues, screen time). |
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CSS.4.b.m.1
Investigate how access to computing systems varies by socioeconomic status, location, ability, and age - and analyze how these differences affect individuals and communities. |
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CSS.4.b.m.2
Design a user interface (e.g., web pages, app, animation) to be more inclusive and accessible, minimizing the impact of designer bias. |
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FUT.1.a.m.1
Compare early and modern computing devices and describe how changes in society have influenced their development. |
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FUT.1.b.m.1
Analyze intended and unintended impacts of historical computing technologies on society and the natural environment. |
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FUT.1.c.m.1
Compare roles of individuals, communities, organizations, and governments in advancing technology. |
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FUT.1.d.m.1
Identify and discuss how historical computing technologies have raised ethical questions or unintended societal issues. |
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FUT.2.a.m.1
Use the internet ethically and safely to critically evaluate and redesign a computational artifact to remove barriers to universal access. (e.g. using captions on images, high contrast colors, and/or larger font sizes) |
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FUT.2.a.m.2
Evaluate how design decisions in emerging technologies influence user experiences differently across different communities. |
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FUT.2.b.m.1
Provide examples of how computational innovations and devices impact health and wellbeing, both positively and negatively, locally and globally and create new solutions that depend on expertise from multiple fields (e.g. effects of globalization and automation). |
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FUT.2.b.m.2
Contrast the features, functionality, and characteristics of emerging technologies with technologies that came before. |
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FUT.2.b.m.3
Explain how emerging technologies create new solutions that depend on expertise from multiple fields |
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FUT.2.c.m.1
Explain the impact of the digital divide (e.g. uneven access to computing, computing education, and interfaces) on access to critical information. |
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FUT.2.d.m.1
Explain the role of licenses and permissions in using or sharing technology and digital content and compare how different types of intellectual property (like open source vs. proprietary) affect creators and users. |
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FUT.2.e.m.1
Analyze and present beneficial and harmful effects of personal electronic communication and social electronic communication. |
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FUT.2.e.m.2
Describe ways in which the Internet impacts global communication and collaboration. |
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FUT.2.f.m.1
Build and test a basic machine learning model and describe how the model makes predictions. |
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FUT2.f.m.2
Students analyze how the choice of training data affects artificial intelligence outcomes and can recognize examples of biased outputs. |
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FUT.3.a.m.1
Compare how professionals in different careers use computational thinking to solve real-world problems and identify how your own interests and strengths might connect to those applications. |
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FUT.3.a.m.2
Examine how changes in technology can create new jobs or change how people work. |
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FUT.3.b.m.1
Engage in real-world computational thinking projects that reflect professional, diverse CS problem-solving approaches, and integrate skills with expertise from diverse fields. |
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FUT.3.b.m.2
Investigate how professionals in computing careers address ethical dilemmas. |
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