Standards in this Framework
| Standard | Description |
|---|---|
| DLCS25.HS.1 | Compare and contrast a generalized algorithm in pseudocode and its concrete implementation in a programming language. |
| DLCS25.HS.2 | Translate pseudocode, flowcharts, or other planning tools into multiple programming languages. |
| DLCS25.HS.3 | Explain the characteristics of algorithms, including speed, accuracy, and storage requirements. |
| DLCS25.HS.4 | Model and adapt classic algorithms, including sorting and searching, to solve computational problems. |
| DLCS25.HS.5 | Decompose problems into component parts, extract key information, and model levels of abstraction in complex systems. |
| DLCS25.HS.6 | Compare different data compression algorithms by analyzing their main features, including their compression speed and whether they preserve data exactly (lossless) or reduce data quality for higher compression (lossy). |
| DLCS25.HS.7 | Create software solutions using libraries and application programming interfaces (APIs) that demonstrate code reuse. |
| DLCS25.HS.8 | Compare and contrast the major categories of machine learning, including supervised, unsupervised, and reinforcement learning. [AI] |
| DLCS25.HS.9 | Compare and contrast fundamental data structures and their uses. |
| DLCS25.HS.10 | Develop and use a series of test cases to verify that a program performs according to its design specifications. |
| DLCS25.HS.11 | Utilize an iterative and incremental software design process, including learning from mistakes, to improve a program. |
| DLCS25.HS.12 | Improve existing code by restructuring (refactoring) it to enhance readability and/or increase efficiency without changing its overall behavior. |
| DLCS25.HS.13 | Select and utilize effective debugging techniques to correct problems in software. |
| DLCS25.HS.14 | Create a complete program to solve a problem or explore personal interests, using a text-based programming language. |
| DLCS25.HS.15 | Design and implement a program that processes user input, applies relational and logical operators within conditional logic, maintains program state, and produces appropriate responses. |
| DLCS25.HS.16 | Create interactive data visualizations to help others understand real-world phenomena. [AI] |
| DLCS25.HS.17 | Verify the validity of a dataset by identifying missing, out-of-range, inconsistent, or invalid data and distinguishing these from statistical outliers using basic measures such as range, mean, or standard deviation. |
| DLCS25.HS.18 | Correct or remove entries containing missing, out-of-range, inconsistent, or invalid data from a dataset to prepare it for analysis. |
| DLCS25.HS.19 | Utilize data analysis tools and statistical methods on a dataset to discover useful information, identify patterns, or make an informed decision. |
| DLCS25.HS.20 | Create and utilize models and simulations to help formulate, test, and refine a hypothesis. |
| DLCS25.HS.21 | Update an existing model to address flaws and improve precision. |
| DLCS25.HS.22 | Analyze how network infrastructure impacts the speed, reliability, and scalability of services. |
| DLCS25.HS.23 | Explain how security protocols in networked systems protect or expose data and assess the risks associated with IoT devices and cloud services. |
| DLCS25.HS.24 | Explain the tradeoffs when selecting and implementing cybersecurity recommendations, balancing cost, performance, usability, and security. |
| DLCS25.HS.25 | Summarize the mechanisms and purposes of various tracking technologies and identify strategies to manage them. |
| DLCS25.HS.26 | Investigate the purpose of and relationship among various computer security measures. |
| DLCS25.HS.27 | Create a personal cybersecurity plan incorporating the CIA Triad *(confidentiality, integrity, and availability)* to safeguard sensitive information and ensure its trustworthiness and accessibility. |
| DLCS25.HS.28 | Investigate the motivations behind hacking and examine the associated ethical considerations. |
| DLCS25.HS.29 | Appraise the trustworthiness of new or unfamiliar resources in order to make safe choices when downloading, installing, and using software. |
| DLCS25.HS.30 | Compare alternative computing architectures, including cluster and quantum computing, to classical computing systems. |
| DLCS25.HS.31 | Explain the interactions between application software, operating systems, drivers, and hardware. |
| DLCS25.HS.32 | Compare and contrast the common metadata elements of various file types. |
| DLCS25.HS.33 | Develop and implement troubleshooting strategies to identify and correct problems with computing devices. |
| DLCS25.HS.34 | Research and explain the impact of computing technology on career pathways across different industries and career fields. |
| DLCS25.HS.35 | Research and share information regarding current AI applications in various career fields. [AI] |
| DLCS25.HS.36 | Analyze the implications of data privacy and consent for making informed decisions about personal data security. |
| DLCS25.HS.37 | Identify and evaluate the consequences of technology-related laws and policies, including those addressing privacy, accessibility, and intellectual property. |
| DLCS25.HS.38 | Analyze the ethical issues related to AI technologies and evaluate their societal and ecological impacts. [AI] |
| DLCS25.HS.39 | Predict the transformative effects of hypothetical future technologies. [AI] |
| DLCS25.HS.40 | Follow Americans with Disabilities Act (ADA) standards to design digital artifacts that reduce barriers caused by the digital divide, disability, or bias. |
| DLCS25.HS.41 | Research and report potential dangers and unintended consequences of over-reliance on AI tools. [AI] |
| DLCS25.HS.42 | Explain how systems learn user preferences and behaviors to deliver personalized content and targeted advertisements. [AI] |
| DLCS25.HS.43 | Investigate the mental health risks associated with excessive technology use, including social isolation, anxiety, and depression, and develop strategies to mitigate them. |
| DLCS25.HS.44 | Evaluate the usability of software applications for broad audiences by considering feedback from real-world users. |
| DLCS25.HS.45 | Identify a problem best solved through human-machine collaboration, decomposing it into tasks suited for each. |
