Essential Computing Concepts — Learning Goals, Concepts, & Examples (Draft)

This document proposes a set of essential computing concepts and associated learning outcomes for a computing major as an alternative to CS2023 that leaves greater space for interpretation and institution-specific customization. See here for a more complete description of the motivation, context, and contributors to this project. We welcome feedback (e.g., alycebradykzooedu).

	Essential Concepts⇓	Illustrative Examples⇓
1. Read and understand code using multiple paradigms (including object-oriented and non-OO) ⇓ a. Identify functionality of basic constructs; Describe or predict program behavior; Interpret or explain purpose; b. Test functionality c. Independently learn new constructs/library/functionality???? (In N instead?) Read and understand code written in an unfamiliar language? d. Read code in a language you're not familiar with	Basic syntatic and semantic meaning of programming entities Understanding order of execution Aggregate Data Types Functions - modularization of executing entities Communication between executing entities Relationships between data entities	Arrays/Lists Classes Inheritance, Composition, Delegation References/pointers IS-A and HAS-A Relationships High-order functions Expressions, Operators Variables, Data Types, Assignment Conditionals Iteration Recursion
2. Analyze and predict complexity of code ⇓ a. Recognize different basic levels of time complexity in code; b. Analyze or evaluate factors such as readability, efficiency, ease of use	Different complexity classes Different problems have different levels of space/time complexity Different solutions to a problem may have different levels of space/time complexity Some problems aren't solvable Code analysis (formal and/or informal) Empirical timing/simulation Complexity analysis and notation Infeasible/intractable problems	Chess, Traveling Salesperson Best/average/worst-case analysis Constant, logarithmic, linear, polynomial, exponential, factorial time complexity Understanding NP-hard and NP-complete problems
3. Computers as Models: Understand how computers represent data and execute programs ⇓ a. Understand the concept of stored programs and the fetch/execute cycle b. Understand the difference between machine language and high-level languages and that machine langs are architecture-dependent c. Explain the impact of word size on data representations; d. Apply access control concepts to shared resources in some artifact; Implement an artifact with interacting parts (Note: these are mostly lower level of Bloom's taxonomy but maybe that's appropriate? This isn't Comp. Engineering)	Von Neumann model Memory hierarchy File hierarchy Binary representation of all types of data Issues related to floating point representation Uncomputable problems Concurrency issues Parallelism Cloud computing Shared resources, access control Authentication, authorization Machine Learning models	Processor/ALU/Registers architecture Any specific machine language (e.g., MIPS) Pipelining Superscalar computation Virtual machines Turing machines Signed and unsigned integers Floating point representation UTF GIF, JPG, PNG, etc (Is "data types" from A also here?) Lambda calculus Shared data, "Simultaneous" access Locks Publish/subscribe Message queues Undecidability proofs Assemblers/Disassemblers Compilers/Interpreters

	Essential Concepts ⇓	Illustrative Examples ⇓
1. Design and create computing artifacts that solve problems using multiple paradigms ⇓ 1.1 Reproduce basic constructs; Modify existing code; Implement program units; Test or validate individual modules; 1.2 Choose appropriate algorithmic approaches; Choose appropriate data structures; 1.3 Integrate interacting modules; Test/validate integrated modules; Refactor code; 1.4 Design multi-modular, extendable artifacts; Design and create artifacts using diverse problem-solving approaches 1.5 Build artifacts using interacting data entities	Essential Concepts from (A) Abstraction, modularization Data structure design Abstract Data Types / Encapsulation Algorithmic approaches to problem solving Reuse and extension of existing software Control order of execution Top-down/Bottom-up design and implementation OOD Testing (and Verification?) Documentation	Encapsulation Polymorphism Design patterns Refactoring High-order functions Lazy evaluation Immutable data Currying Functional composition Linear and non-linear data structures Databases Classic algorithmic approaches Randomized algorithms Optimization Machine Learning Agile, waterfall, ... Libraries/APIs Version control Inter-process communication Shared memory Message passing Locks/Semaphmores Multi-layer architectures (e.g. robotics/agents) Event-based programming Cloud computing
2. Describe or design a solution to a problem using appropriate models ⇓ Need progression of Bloom's taxonomy learning goals	Theoretical models of computation Imperative model (Memory + State Changes) Actual models of computation Parallel computing Algorithmic Design (here? In B1?) Social and societal contexts Legal and ethical frameworks	streaming models of computing quantum computing distributed computing GPU programming logic models of computing functional computing Sub-symbolic computing (e.g. ANNs) Instruction sets State machines / Automata? Virtual machines
3. Design computing artifacts for human stakeholders and the benefit of society ⇓ Identify all relevant stakeholders to a problem or system Apply a principled approach to collecting and documenting requirements information from a diverse set of stakeholders Analyze or evaluate factors such as ease of use, accessibility, user impact, ethical impact (usability studies) Analyze for privacy and security concerns Understand the different roles of data collection from users Evaluate the impact of computing products on social and community infrastructure	UI Design User requirements and specifications Testing and evaluation Privacy, security, usability Ideation Involving stakeholders in the process User documentation	Human-computer interfaces Human-robot interfaces User models User impact studies Usability studies Societal impact studies

C. Use Math, Data, and Analytics for Computer Science (expand all)		Essential Concepts ⇓	Illustrative Examples ⇓
	1. Data Management and Analysis / Storing, Managing, and Analyzing Data ⇓ Understand how to store, clean, maintain, analyze, and learn from data sets of various sizes Need progression of Bloom's taxonomy learning goals Possibly mostly lower levels of Bloom's taxonomy; this isn't Data Science	Data types (from a data science perspective) Statistics Visualization Data cleansing Data management	databases data mining/exploration noise (large data tools or methodologies that are different, e.g. mapreduce or others) linear regression clustering classic ML methods: decision trees, nearest neghbor, Naive Bayes machine learning Findable, Accesible, Interoperable, Reproducible Data (FAIR Data) CRUD (Create/Read/Update/Delete)
	2. Understand mathematics enough to solve problems and learn new concepts ⇓ Translate informal assertions or conditions to formal logical statements Evaluate given Boolean expressions to determine their truth values Manipulate Boolean expressions to obtain simplified expressions or to determine their negative form. Explain and modify forms of functions, including formal and actual parameters, and returned value(s) Explain the properties/characteristics of common functions, and their growth rates Analyze logical arguments Evaluate basic elements of counting Modify and explain performing modular arithmetic with integers Determine logical validity of proof Identify counter examples	Boolean logic, boolean algebra Basic probability Basic statistics Basic graph theory Induction Recursion Combinations, permutations, inclusion/exclusion Pidgeon-hole principle	Arithmetic average, median, standard deviation Calculus (as relevant) Linear algebra (as relevant) Set theory

	Essential Concepts ⇓	Illustrative Examples ⇓
1. Produce effective texts about computing systems and techniques for a variety of audiences and purposes ⇓ Identify the various audiences for a piece of text and their narrative needs Adopt common narrative structures for text based on its use context Write about technical topics using terminology and specificity appropriate for the audience Revise and edit one's own text or the text of others	Audience awareness Following models or templates for text Writing about technical concepts with precision Identifying and avoiding unhelpful use of jargon Written communication Verbal communication Code/comments	User documentation Code documentation, with different structures and level of formalism Creating social media content Writing research posters and/or research papers Writing design documents, scope documents, other project management texts
2. Collaborate effectively ⇓ Participate in group problem solving, ensuring that the perspectives and expertise of all team members are heard and given consideration Apply principled techniques for task decomposition and assignment between team members and tracking project progress Exhibit respectful and productive communication within a team, both verbal and in various written modalities Provide professional peer review of teammates’ work and their participation within the collaboration	Version control Teamwork	Pair Programming Team Roles Literate Programming Cultural competence Coordination Planning Time Management
3. Understand and apply socially-responsible design and analysis of computing systems ⇓ Identify the possible social impacts of a technical system and contrast the possible social impacts of alternative system designs Recognize the bias inherent in a data collection process and its impact on outcomes and product impacts Critique algorithm choices in problem solutions for fairness, bias, and unequal impact More needed…..	Debating ethical issues Awareness of prior ethical failings Design decisions with ethics in mind Designation of and impacts on stakeholders Security and privacy fundamentals	Machine learning ethics
4. Learn to learn computing-related approaches and tools ⇓ Learn to choose correct language/paradigm for a problem Analyze a new problem to identify the connections to established areas within CS and known problems Analyze a new problem to identify tools or technologies that may be required Identify credible sources of information or instruction about a technique, tool, or language Construct and execute a learning plan for gaining proficiency in a new paradigm, technique, language, etc. Self-assess one's level of knowledge and identify priorities for additional practice or education	Find, read, and apply ideas from technical resources Assess the credibility of instructional or guidance documents Identify existing tools to solve a problem and be able to select between options Draw connections between new problems and areas of the CS curriculum that will inform its solution	Incorporating new constructs into a program based on API documentation Using message board or comment thread content in an informed and critical manner Using AI appropriately for specific contexts Identifying toy problems, mini projects to help practice a new skill

Essential Computing Concepts — Learning Goals, Concepts, & Examples (Draft)

A. Read, Understand, and Analyze Models (expand all)

Essential Concepts⇓

Illustrative Examples⇓

1. Read and understand code using multiple paradigms (including object-oriented and non-OO) ⇓

2. Analyze and predict complexity of code ⇓

3. Computers as Models: Understand how computers represent data and execute programs ⇓

B. Design and Create Using Models (expand all)

Essential Concepts ⇓

Illustrative Examples ⇓

1. Design and create computing artifacts that solve problems using multiple paradigms ⇓

2. Describe or design a solution to a problem using appropriate models ⇓

3. Design computing artifacts for human stakeholders and the benefit of society ⇓

C. Use Math, Data, and Analytics for Computer Science (expand all)

Essential Concepts ⇓

Illustrative Examples ⇓

1. Data Management and Analysis / Storing, Managing, and Analyzing Data ⇓

2. Understand mathematics enough to solve problems and learn new concepts ⇓

D. Be a Responsible Computing Practitioner (expand all)

Essential Concepts ⇓

Illustrative Examples ⇓

1. Produce effective texts about computing systems and techniques for a variety of audiences and purposes ⇓

2. Collaborate effectively ⇓

3. Understand and apply socially-responsible design and analysis of computing systems ⇓

4. Learn to learn computing-related approaches and tools ⇓