Going for the simple, essential ideas and the classic CS models! What should be the patterns and the examples that students walk away with from the computer science course? Or, what are the patterns and the standard problems the students should be exposed to and practice how to solve?
Example problem: build a flow chart with the steps required to draw a square or a triangle.
Sample problems that can be expressed as a computational algorithm and formulate them in natural language, plus build diagrams (flow charts). Example of problems: draw a square, check that a password meets the strong password conditions. Additional classic problems: algorithm for getting ready for school or making a peanut butter and jelly sandwich.
Examples from Microsoft Interview list:
Questions to consider: Are there any patterns in your problem/activity? Are there actions that you can name and use as abstractions in your algorithm? Draw a flow chart with the elements you identified (main abstraction that would turn into custom blocks or functions).
Making pizza example, creating an algorithm and putting the steps together with focus on decomposition (functions as "code recipes"). Variation or guide about the functions (or custom blocks): make them flow chart blocks for a given recipe. That can be used later for introducing functions as abstractions. You may review the rubric criteria here.
For pattern focused problem: binary search. Find a number in a sorted list in an efficient way (related to guess the number game) with a version to find a name in a phone book, or a word in the dictionary.
Do we see a pattern? When we do something in a similar way multiple times, that is a pattern that involves repeating a certain structure (the loop in the flow chart) ! For drawing a square can we have a function that defines the structure that gets repeated?
Functions are reusable blocks of code that perform specific tasks
Functions make your code more organized
Information to complete the task is passed as arguments
PRISM (5 elements instead of 4)
Patterns, Reasoning, Ideas, Situation, Methodsbut with Abstraction and Generalization the Computational Thinking also has 5 elements.Patterns: big ideas, how do the patterns work in different placesReasoning: how do the pieces "connect"
this is the reverse of decompositionIdeas - viewpoints: map to abstractionsSituation = the bigger picture (generalization)Methods - checking the answer, testing (similar to algorithm but focussed on checking)Computational thinking elements
Patterns, Decomposition, Algorithm, Abstraction and Generalization
Aside on subjects that are familiar to me: for college algebra, for example, it might be guiding the students to recognize the patterns to work with perfect squares for modeling quadratic functions, and the difference of squares for factoring (this would require encouraging them to go beyond always computing the square of a binomial with the long algorithm that they have learned first - they need to learn to appreciate the abstraction that helps them do less work and see the answer faster - problems with simple numbers rather than large numbers or decimals would help here). For precalculus it is the points on the unit circle and their coordinates, with arcs in radians - everything should fall into place from the definition of the trigonometric functions (ah, functions ... another abstraction from college algebra).
Redefine computational thinking and its elements in a setting focused on learning to code and solve problems with computers.
The main ideas transcend the programming languages (need to make sure we are not too caught in the details of how Scratch, JavaScript or Python could be used to solve a particular problem, and keep things general where we can).
General and transferable! This would be aligned with Calvin's vision - programming language not being relevant in the course (his UTeach version of the course already used python and not JavaScript/p5.js for the second half). And we have these big ideas from the AP Principles but we have to support them with more concrete examples. Not the "create something out of nothing!"
Or throwing in a lot of material borrowed from different places for a claim of originality and high-standards, instead of going for the simple, essential ideas and the classic CS models.
Aside: "How would you test a pen?" from the Microsoft Interview list. Here is an article discussing the intention of the question, and how one should start with asking questions about the users and the purpose of the tool to design a comprehensive test.
A first college course in Computer Science.
The computational thinking practices are central to the study and practice of computer science. Students should practice and develop these skills on a regular basis over the span of the course.
New for the following school year: Just a bit about AI biases and using AI to generate code. Ending with merge sort on data collections in Unit 4.
Focus on procedural abstraction and data abstraction!
Unit 1: high level abstractions - using objects and methods
Unit 2: Algorithms with Selection and Repetition
learning outcome: Represent patterns and algorithms that involve selection and repetition found in everyday life using written language or diagrams.
3.1.A.7 Prior to implementing a class, it is helpful to take time to design each class including its attributes and behaviors. This design can be represented using natural language or diagrams.
Unit 3: Abstraction and Program Design
Abstraction is the process of reducing complexity by focusing on the main idea. By hiding details irrelevant to the question at hand and bringing together related and useful details, abstraction reduces complexity and allows one to focus on the idea.
Data abstraction
Data abstraction provides a separation between the abstract properties of a data type and the concrete details of its representation. Data abstraction manages complexity by giving data a name without referencing the specific details of the representation. Data can take the form of a single variable or a collection of data, such as in a class or a set of data.
An attribute is a type of data abstraction that is defined in a class outside any method or constructor. An instance variable is an attribute whose value is unique to each instance of the class. A class variable is an attribute shared by all instances of the class.
Procedural abstraction provides a name for a process and allows a method to be used only knowing what it does, not how it does it. Through method decomposition, a programmer breaks down larger behaviors of the class into smaller behaviors by creating methods to represent each individual smaller behavior.
A procedural abstraction may extract shared features to generalize functionality instead of duplicating code.
This allows for code reuse, which helps manage complexity.
A file is storage for data that persists when the program is not running. The data in a file can be retrieved during program execution.
Sorting algorithms:
Selection sort repeatedly selects the smallest (or largest) element from the unsorted portion of the list and swaps it into its correct (and final) position in the sorted portion of the list.
4.15.A.3 Insertion sort inserts an element from the unsorted portion of a list into its correct (but not necessarily final) position in the sorted portion of the list by shifting elements of the sorted portion to make room for the new element.
Unit 4: data collections
4.4.A.1 Traversing an array is when repetition statements are used to access all or an ordered sequence of elements in an array.
4.5.A.1 There are standard algorithms
• determine a minimum or maximum value • compute a sum or average • determine if at least one element has a particular property • determine if all elements have a particular property • determine the number of elements having a particular property • access all consecutive pairs of elements • determine the presence or absence of duplicate elements • shift or rotate elements left or right • reverse the order of the elements