See the issue list for a list of all issues. Please check this list before submitting a new issue. There are filters you can use in order to sort by issues or contributions.
- In the title, be short and concise that identifies the issue. Include the specific lesson or relevant instructional material. For example, "L1 Slides incorrect definition"
- In the main body, 1) describe the error as you see it and 2) the suggested solution. If the issue is about any of the jupyter notebooks, datasets, or python scripts, please include any system information that may be helpful for debugging like:
- Where you were running the script (terminal, anaconda, jupyterlab, Google Colab, etc.)
- Any error messages that came up when running the code
- How we could reproduce your error (sample code, required packages/datasets, etc.)
- Any workarounds you've discovered
- Include any relevant screenshots or images.
- Appropriately tag your post with a relevant label. You can use more than one, but try not to use more than 2.
- In the title, choose a short but descriptive title for your suggestion. For example, "Scaffold teaching python"
- In the main body, go into more detail about what your contribution is. Be sure to include
- What issue prompts the need for your contribution? How does it add to student learning or instructional practices?
- Which lesson(s) is it most relevant to, and, if it's more lessons, where would it be placed in the sequence?
- If it's a contribution, are there any copywrite notices to be aware of
- If it's a request, what are the goals of your request, and outline how those could be achieved.
- Appropriately tag your post with the relevant label. Try not to use more than 2 labels. If you are contributing a resource, please make sure that your resource is not violating copywrite or privacy rules, that your resource is at least viewable to the public, and that you use the
contributionlabel.
If you've implemented the curriculum, we'd like to hear your overall impressions of the curriculum. Please make sure to tag these posts with a overall feedback label. And please answer the following questions in your description.
- Your context (age group, distance teaching/in person/hybrid
- The degree to which you followed the curriculum. And if you did or didn't follow it, why?
- Do you think students met the learning outcomes:
- The standards identified in the Unit Plan
- Ability to problem solve using deep learning techniques
- Confidence and excitement for deep learning techniques
- Understanding challenges and limitations of deep learning (e.g. bias in datasets, robustness of models, etc.)
- Were there any particular lessons that went really well? Or any that went really poorly? What made them succeed, or what could have aided the lessons that went poorly?
- Did the timing for each lesson work out for your context?
- Any other general pieces of feedback on the curriculum or recommendations for other instructors who implement this curriculum in the future?