README for Group Assignment 1.3 Warmup Activity

CEGM1000 MUDE: Week 1.3, Friday, Sep 20, 2024.

This Warmup Activity is provided to help you come prepared to the Friday in-class session. It is meant primarily to: a) give you a preview for the specific grading criteria for the GA (since this is our first graded assignment!), and b) help those new to programming focus on a few key things to enable them to contribute to their own group more effectively. We recognize that you may not have much spare time to spend on this; if this is the case, we recommend you focus your time on reviewing the textbook and the WS from this week.

Files provided in the warmup activity are:

• this README, Warmup_README.md
• a notebook, Warmup.ipynb
• auxiliary files warmup.py and 2 *.csvfiles in subdirectory data_warmup/

For convenience the files have been packaged as a warmup_1_3.zip file which you can download, unzip and open as a working directory in VS Code.

BELOW THIS LINE TEXT FROM THE ACTUAL README FROM GA 1.3 IS PROVIDED.

A link to access this GA will be provided in class on Friday morning. It will work the same way as PA 1.3 (on GitHub).

There are several files to be aware of:

1. README.md: this file, which contains and introduction and instructions.
2. Analysis.ipynb: a Jupyter notebook which contains the primary analysis to complete during the in-class session.
3. Report.md: a Markdown file containing questions about the findings of Analysis, as well additional questions to check your understanding of the topic for this week.
4. functions.py: a Python file that defines plotting functions for the last Task of the notebook
5. Auxiliary files: a data subdirectory that contains csv files.

The grade for this assignment will be determined as follows (described below):

• The notebook Analysis.ipynb is worth 20%
• Each question in the Report.md is worth 20%

Submission

This assignment is to be turned in by uploading files to GitHub (as done with PA 1.3, but for the GA 1.3 repository). You only need to upload modified files (e.g., Analysis.ipynb and Report.md). Do not change the file names when uploading.

The deadline to upload the assignment to GitHub is 12:30. There is a one-hour grace period where you can upload the files without receiving a penalty; however, you can not use this grace period to improve your answers or analysis; it is only for fixing problems with uploading files. You should stay in the classroom to complete this task so that teachers can help you with any problems you may have. The 13:30 deadline is strict; no assignment will be accepted after this time without a grading penalty, and you must notify us via email that your assignment is late.

Working Method

You are expected to read and work through README, Analysis and Report sequentially. Most of the questions require you to finish Analysis.ipynb first, but depending on how you allocate tasks between group members, it is possible to work on this in parallel. Make sure you save time for peer reviewing each others work before completing the assignment!

We highly recommend you use the VS Code Live Share feature to work on the assignment, especially for the Report.md. It may be useful to identify one group member at the beginning of the session to download the files and set up the Live Share session.

Grading

Your GA will be graded on a 10 point scale and consists of notebooks (e.g., Analysis.ipynb) and a number of questions in the Report.md. Individual notebooks and questions will be graded on a 10 point scale, and the total grade for the GA is calculated as a weighted average of each. Each question will be graded on the relevance and correctness of your answers (not the length!). Unless otherwise stated, the notebook and other programming-related content is graded primarily for completeness.

Individual Questions

Here is an example for how an individual question may be graded:

• 0-5 points: excessive use of AI in an unedited form. Answers are general and not relevant. No quantitative information included. Significant parts of the questions not answered.
• 6-8 points: good answers, captures the main points. Perhaps too long or misses important insights into the problem, or contains limited quantitative information. If analysis was not completed, or concept not well understood, the answers contain useful discussion, information or insight into the issue faced by the group.
• 9-10 points: exceptional answers that are quantitative, concise and insightful.

Note that you can still pass the questions/assignments even if you do not complete the analysis! You are always welcome to report issues in the last question of the Report.md.

Notebooks and Programming

Individual tasks in the notebook are not graded. If the notebook is completed successfully you will generally get full credit, unless it is clear that something was implemented incorrectly, or if the notebook was submitted in an unreadable state (i.e., without code cell outputs visible).

Here is an example for how your notebook may be graded:

• 0-5 points: incomplete without explanation. Unreadable results in GitHub.
• 6-9 points: incomplete but contains explanation justifying reason or evidence that work was done.
• 10 points: notebook completed as requested.

Tips for Writing the Report

Keep these key points in mind:

1. Don't regurgitate information from the assignment or book---we are not interested in your ability to copy/paste our own material.
2. Shorter is better!
3. Use quantitative information: for example, instead of "data set 1 is bigger than data set 2" try "data set 1 has 446 values with a mean of 5.6 m and std dev of 1.2 m. Data set 2 has 25 values with a mean of 4.9 m and std dev of 0.1 m.
4. You are welcome to use AI tools, but please state whether or not you did so (e.g., "ChatGPT gave the bullet list of pros/cons, which we then modified.").
5. If you use AI, do not copy/paste questions and answers blindly! It is best to ask small questions and modify the answers using your knowledge of the topic and our activity. Answers that we suspect of containing a significant AI component will not recieve any points.
6. Group members should peer review answers before submission (especially if you are using AI tools).

Assignment Context

An introduction to the actual assignment will be provided here on Friday.

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