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Learning Objectives

A proactive programmer studying discrete structures should demonstrate the mastery of these learning objectives in the categories of mathematical communication, rigorous programming, and effective communication. According to Robert Talbert, a learning objective must be "a clear, measurable action that a student should be able to do once the lesson is over".1 For the purposes of this site, these learning objectives are not for a specific lesson but rather an entire course for either an Allegheny College student or, for the duration of self-paced learning, all other emerging proactive programmers.

Mathematical Communication

A person learning discrete structures with the mindset of a proactive programmer will be able to demonstrate mastery of the following mathematical communication skills:

Discrete Structures

  • Given the opportunity to input, store, and output a type of data, pick the correct discrete structure (e.g., a number, set, sequence, stream, dictionary, or tree) and then:
    • Import it and then integrate it into a correct, well-tested, and well-documented Python program consisting of between one hundred and five hundred lines of code.
    • Use formal and precise mathematical language to correctly describe the representation, behavior, and limitations of the type of data.
    • Justify the decision to use a specific discrete structure to solve a problem.
  • Given the formal definition of a discrete structure that is described with mathematical notation, correctly translate it into a Python program, bearing in mind the ways in which the working system may diverge from the formal definition due to, for instance, implementation concerns related to computational costs and storage overheads.
  • Given the formal definition of a discrete structure and its implementation as a Python program, correctly describe its properties, demonstrate and verify those properties through testing and/or proof, describe how these properties are similar to and/or different from other discrete structures, and explain when it is appropriate to use the structure.
  • Complete Python programming tasks with a discrete structure. For instance:

    • Using the formal definition of Boolean logic operations like and, or, and implies, translate a truth table discrete structure into an equivalent implementation in a Python program that uses conditional logic, function definitions, and function calls.
    • Use the formal definition of a monoid to describe and predict the output of operations (e.g., concatenation) on textual strings in the Python programming language.
    • Use the formal definition of a monoid to describe and predict the output of operations (e.g., concatenation) on finite and infinite streams in a Python program.
    • Use the formal definition of a set and set-theoretic operations like union, intersection, and containment check to implement a Python program that stores data in a set and correctly manipulates the data using the aforementioned operations.
    • Given the formal definition of the domain and range of a mathematical function, implement that function as either a traditional Python function or a dictionary.

Mathematical Functions

  • Leveraging an understanding of higher-order functions, lambda functions, and generating functions, use a problem statement involving one or more discrete structures to:
    • Translate the formal notation and equations used in the problem statement to an algorithm that describes, in a step-by-step fashion, how to solve the problem.
    • Pick the most suitable type of function(s) for the problem, implement it in an industry-standard manner using the Python programming language, and then use testing to demonstrate its correctness and experimentation to evaluate its performance.
    • After implementing a function in Python using, for instance, higher-order functions like map, reduce, and filter, translate that to an equivalent implementation using, for instance, lambda functions and/or traditional Python functions.
    • After using Python type annotations to precisely document the type and structure of the input and output of a Python function, use a type checker to confirm that all of the functions in a program communicate with each other in a type-safe fashion.
    • Use formal statements to defend the validity of the design, implementation, and testing decisions made when solving a problem with the chosen function(s).

Data Analysis

  • Given one or more sets of textual, numerical, categorical, binary, or combined data (that may have missing and/or corrupted data values), implement, test, use, and evaluate a Python function that supports data analysis through the completion of these steps:
    • Input, process, check, and clean the data sets to confirm their correctness.
    • Run an appropriate statistical technique (e.g., the calculation of a mean, median, or standard deviation) to summarize and analyze the data.
    • Use an appropriate data visualization technique that can create graphs and diagrams that highlight the salient properties of the data set (e.g., show the general trends in the data set while drawing attention to the dispersion of values).
    • Use statistical analysis and data visualization techniques to identify hidden trends in a data set, supporting the prediction of new data values and properties.
    • Implement and use an appropriate data processing method that transforms the data into any format required by any external functions (e.g., convert from JSON to CSV).

Rigorous Programming

A person learning discrete structures with the mindset of a proactive programmer will be able to demonstrate mastery of the following programming skills:

Python Programming

  • Write short Python functions of ten to twenty lines that have the following characteristics:
    • Has function and variable names that adhere to an industry-standard coding style.
    • Has descriptive comments for module definition and both the function's declaration and the function's code that adhere to an industry-standard coding style.
    • Features a source code format that adheres to an industry-standard coding style.
    • Passes an automated test suite, written with an industry-standard framework such as Pytest, showing that it correctly implements the specification for the function.
    • Performs the specified operation in an efficient fashion, as determined through experiments that evaluate the function's performance in seconds or milliseconds.
    • Correctly uses assignment statements, iteration constructs, conditional logic, function invocation, and function recursion in a way that passes the function's test suite, works efficiently, and conveys the intended function's purpose in a Pythonic fashion.
    • Correctly uses discrete structure variables such as numbers, tuples, sets, streams, and sequences in a way that passes the test suite, works efficiently, and conveys the intended purpose of the function in a Pythonic fashion.
    • Correctly performs file and console input and output, ensuring that all input and output is displayed and stored correctly, is not corrupted, and is processed efficiently.
    • Correctly performs calculations for statistical properties of a data set (e.g., the mean, median, and standard deviation), while also clearly displaying the data, its relevant summarization values, and the interpretation of all statistical properties.
  • Write correct Python programs consisting of between one and five hundred lines of source code that correctly solve problems using the aforementioned discrete structures (e.g., number, set, or sequence) and function type (e.g., procedural, functional, and lambda).
  • Uses the features of the Python programming language to create classes and objects that solve problems through the use of the object-oriented programming paradigm.
  • Implement and run Python programs with the following environments and tools:
    • Python programs with dependencies, packaging, and tasks, managed by Poetry.
    • Python scripts without external dependencies that are run with the interpreter.
    • Jupyter notebooks run through either Jupyter Lite or Google Colab.
    • Python code segments run in a read-eval-print loop (REPL) on Jupyter Lite.

Programming Tools

  • Use a Python programming environment to complete these tasks while implementing a Python program consisting of between one and five hundred lines of code:
    • Install, upgrade, and use Python 3.8, 3.9, or 3.10 programming environment to create, run, and debug a Python program through a terminal window and/or a text editor.
    • Use Poetry to install a Python program's dependencies, create a virtual environment, and run it without error in an isolated and self-contained configuration.
    • Use and create a test suite implemented with Pytest to detect a failure in a Python program and then effectively use tools like a text editor and a terminal window to find and fix the fault, ultimately confirming that the Python program no longer contains the fault and that the fix did not compromise other functions in the program.
    • Use a text editor like VS Code to implement, test, debug, document, and manage the source code of a Python program, leveraging plugins for workflow enhancement.

Version Control

  • Use the GitHub platform and the Git version control system in the following fashion:
    • Clone a GitHub repository without error using either a command in a terminal window or an extension for Git integration in a text editor like VS Code.
    • Write short and descriptive commit messages that explain the specific way in which a commit changes the source code and documentation in the GitHub repository.
    • Navigate reports produced by GitHub Actions so as to determine which aspects of a GitHub repository do and do not adhere to a project's specification.
    • Use the GitHub Flow model to implement specific features in a branch of a GitHub repository and then merge that branch to the main one only after all the checks run by GitHub Actions pass as required and code reviews confirm the code's correctness.
    • Create and discuss programming and technical writing issues through the use of the GitHub Issue Tracker and the GitHub Discussion Forum, furnishing descriptive titles and problem descriptions that adhere to industry best practices and project templates.
    • Submit completed projects that pass all of the instructor-provided and industry-standard checks, as evidenced through the report of a passing build by GitHub Actions.

Technical Communication

A person learning discrete structures with the mindset of a proactive programmer will be able to demonstrate mastery of the following technology-mediated communication skills:

  • Follow a systematic process when using resources to implement Python programs:

    • After detecting an error in a Python program through the use of tools like GatorGrader or Pytest, develop a satisfactory understanding of the problem's possible root cause and then search sites like Stack Overflow for potential solutions.
    • After evaluating suggested solutions to a programming problem that originate from sites like Stack Overflow, develop a way to fix an error in a Python program.
    • Independently develop solutions to Python programming problems even if the specific technical challenge that you face was not previously covered in a course session or as part of your prior review of technical content.
  • Use Discord to effectively communicate about technology in the following fashion:

    • In the correct public channel, post a question that includes source code segments, screenshots, and a description of the steps taken in an attempt to solve a problem.
    • Answer questions that were posted in a public channel, responding with source code segments, screenshots, and links to external references, pointing the proactive programmer who asked the question in the right direction without solving the problem for them and limiting their opportunity to learn more about the topic in question.
    • Proactively offer to help others who may need to learn scientific, mathematical, or technical knowledge and skills that a learner has already mastered by sharing the acquired knowledge or skill, how you can be contacted, and how you can best assist.

Improving Objectives

As Robert Talbert explains, a learning objective is clear when it is "clear from the students' perspective" and measurable when there is "some way to know whether the objective has been met" or "how far away the learner is from meeting it". Do you see a way in which we can improve the learning objectives for discrete structures? If you do, then please participate in the proactive community by sharing your ideas for improving them!

  1. See Robert Talbert's article entitled How to Write Learning Objectives for more details about how to design learning objectives for an academic course. From your perspective what does it mean to write learning objectives that are both clear and measurable? 

Updated: 2021-08-29   Created: 2021-08-12
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