a python homework assignments

a python homework assignments

Effective Strategies for Creating Engaging Python Homework Assignments

1. Introduction to Python Homework Assignments

Why create homework assignments in Python? Because many fields benefit from the power and flexibility that Python provides as a result of its extensible structure. Scientists and engineers are able to perform complex numerical calculations. Programmers can write programs with a few lines of code and its speed. Thus, any professional who works with large amounts of data and is beginning their career in programming, Python is the best programming language to learn. Since Python has an easily understandable syntax, it ends up being easy to read, write, and debug. By studying Python, students can develop data manipulation, web development, systems management, and data analysis; i.e., Python may be more important than students realize in regards to their immediate future and careers.

A good educational strategy is to combine traditional classes with the use of homework assignments that provide application opportunities to the software used in class. Furthermore, homework assignments can provide statements that allow students to develop software and improve their skills from Python. However, the success of homework assignments during a teaching semester depends on the development of stimulating and interesting themes. So, to encourage student participation, the strategies and techniques used in creating homework assignments can range from the choice of theme proposed to the use of other Python resources. The goal of this chapter is to give teachers some strategies to more broadly implement topics of current relevance in Python and increase the attractiveness of homework. The objective is to stimulate students by using the proposed themes which are generally related to new technologies within the community and the profession in which the students are being trained.

2. Key Elements of Engaging Python Homework Assignments

Homework is a central activity and assessment tool in introductory programming courses. Design principles for good homework suggest that assignments should be meaningful, promote learning, provide appropriate challenge, and be engaging. With experience, this is relatively easy to achieve in a paper-based environment but challenging in an electronic environment, especially for programming. Little work has been reported in the literature on the types of assignments that work well, how to construct good assignments, and the features of interactive automated assessment tools that can encourage the production of engaging assignments. This paper describes our use of an XML-based protocol for homework submission and grading that allows a range of formative and summative questions to be asked and is suitable for electronic submission of Python programs in beginning programming courses. It reports our experiences of creating a variety of homework assignments, ranging in both topic and difficulty.

This paper has two goals. The first is to discuss six key elements to consider when creating an effective homework assignment, with a focus on Python programming assignments for introductory computer science (CS) students. Once the key elements are established, the next step is to consider effective strategies for implementing those ideas in practice. The paper’s second goal is to focus on implementation and learning from experience by sharing the concepts of engaging homework assignments and how to create them. Specific strategies for creating engaging and effective programming assignments are presented and discussed. Many homework assignments contain similar problems that, if changed slightly, would promote more learning and better practice. Consider a strategy to create more engaging homework assignments that will attract less frustration and improve learning.

3. Assessment Methods and Rubrics for Python Homework

The activities that can be assessed in a Python assignment can take many forms. More involved ones would include activities that require students to write code for some purpose, such as data wrangling, feature selection, normalizing or transformation, or the creation of new columns. An assignment on building a predictive model to find the use of so-called oversampling techniques using a ‘sklearn’ Python package for such a task would also be an appropriate high-level learning activity – here both the task and the technique can be framed in natural language without exposing the student to offensive terms while giving them the requisite training, experience, and knowledge to get their first data science job. The assignment should also ensure that students make the right choices using Python code. For this, your testing and your rubric should evaluate the important branching points from the problem. These deal with a set of pre-processing decisions: selecting and excluding training columns, splitting the dataset, removing columns with high levels of missingness or correlated columns, and imputing remaining missing data.

4. Incorporating Real-World Applications and Projects

Codify the students’ ability to apply the code to a practical system. If students can solve a relevant and relatable problem, they’ll understand how to use the code in more practical situations. To incorporate projects, I’d suggest starting with small two-week projects introducing the project in the first week, having the students present their problem and projected code in the middle of the week to get feedback and update the plan. At the end of the two weeks, have the students present their final projects to the class. As the projects get more complicated, these projects can either become bigger or become part of the focus of the class, with individual students working on different aspects of the different problems. A final project is suggested. Start during midterm time and report the projects at the final exam time.

Feedback is the most important part of the project process. Give the students feedback on the proposed project and on the students’ ability to communicate about their proposals. After the first project outline, the feedback can be used. After the second project, feedback and possibly early grades can encourage the students to deliver a project they’re proud of. If the students feel like they’re already ‘almost there,’ giving them feedback that they’re almost there will promote improvement. Do the final feedback at the end.

5. Best Practices for Providing Feedback and Support

One rule of thumb when providing feedback to students is to be kind and constructive. Students appreciate encouraging messages when they ‘get things right’, and respond well to constructive feedback that helps them to understand what they ‘got wrong’ and why their solution didn’t meet the assignment requirements.

Provide good documentation and examples: Students will look for documentation and help before asking for help from you.

Set expectations: Provide a grading rubric or guideline. The rubric should include an assessment of code style, design patterns used, etc.

Provide demonstration data: To help students verify that their code works on at least some sample cases before they submit it to Canvas, provide a small amount of sample data so they can test it themselves.

Scripted unit tests: Provide students with a set of automated test case they can run against their code. These can serve as both a check of their code correctness and as a check on the expected interface of their code.

Common reasons why their code didn’t pass the autograder: When students don’t pass the auto tests, they will want to understand why their test failed.

Issue updates: If multiple students miss something in the assignment, they are going to expect that the points be given back and/or a detailed discussion of the points if they are not given back.

Encourage peer interactions: Encourage students to ask questions and share solutions on the discussion board to foster peer interactions.

Re-use the previous semester’s questions: This is probably a bit more advanced, but try to keep a bank of quizzes and test questions so that you can reuse good questions in the future (groupId of the question on canvas is the same, students can still see the question on the Discussion Board).