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Service Learning |
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- Lectures: Thursdays, August 26 - December 9th, 2021.
- Room: Stanley Thomas Hall (Building 10)
- Online: TBD -- some lectures will be on Zoom and we will not meet every week, see Tulane Canvas for details.
- Time: 18:00 - 19:00
The Partners for this semester are:
- Partner: Code For New Orleans
- Primary Contact: Ryan Harvey, [email protected]
- Partner: The Data Center
- Primary Contact: Lamar Gardere, [email protected]
This is an optional Service Learning component to this course that satisfies a Tier I or Tier II Service Learning Requirement. This is a small section with and participation is hard capped at 10, if there is no room on the Registrar's Website, there is no room.
The aim of the service learning component of this course is twofold:
- To encourage students to apply their data science skills to local issues and
- To deeply think and engage with where data comes from, why it is collected, and who data centric systems may affect.
Thus through service learning and civic engagement students will be able to apply the skills they learn in this class to important problems in our local community.
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Code for New Orleans is a group of local volunteers focused on making city services accessible and easy to use and improving the lives of residents. Code for NOLA is a brigade of Code for America, a national non-profit focused on civic activism with technology in local contexts across our nation.
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The Data Center is a fully independent nonprofit, expert at bringing data together from multiple sources. In doing so, we are uniquely able to step beyond the limits of analyzing data from just one perspective and take a 360–degree look at issues that matter most to our region from the government, business, nonprofit, and community perspective. In these ways The Data Center realizes its mission to build prosperous, inclusive, and sustainable communities through neutral and objective data and analysis for informed decisionmaking.
Note: All meetings this semester will be over Zoom, all recordings of every lecture is available in Canvas
| Week | Class Day | Activity | Notes / Links |
|---|---|---|---|
| 1 | 8/26 | Overview, Introduction to CPS | Slides |
| 2 | 9/2 | No Class | |
| 3 | 9/9 | Partner Introduction | Slides |
| 4 | 9/16 | No Class | |
| 5 | 9/23 | Meeting to Discuss Projects | |
| 6 | 9/30 | Entering Communities Training | |
| 7 | 10/1 | No Class | |
| 8 | 10/8 | No Class | |
| 9 | 10/15 | Milestone 1 Review with Ryan and Lamar | |
| 10 | 10/22 | No Class | |
| 11 | 10/29 | Checkin Updates! | |
| 12 | 11/5 | No Class | |
| 13 | 11/12 | No Class | |
| 14 | 11/19 | Final Presentations |
Students will have a 20 hour service learning obligation which will be fulfilled by attending the required meetings of the extra course with community partners, working on their final project which will focus on applications to the community and students will be required to complete four written reflections both on their attendance at meetings with the community partners and on required readings.
The final project that students complete in this course will be focused on using data from either the city of New Orleans or another local/regional data source identified in conjunction with the instructor and community partners.
Note: Attendance and interaction with the community is a requirement for the optional Service Learning portions of the course. If you do not attend/complete all the meetings and reflections you will not pass this course.
Specifically the service learning component of the course will be graded as follows.
- 25% - Attendance at all required meetings and participation in class / discussions.
- 25% - Reflection Papers.
- 50% - Evaluation of Final Tutorial as made by the instructor and community partners in conjunction with the rubric for the Final Tutorial.
If you enroll in this service learning there are some requirements over and above the Intro to Data Science course, specifically.
- You must complete your Final Project in a team consisting of students in the service learning component only.
- The grade for this component will be the grade for your final notebook. This makes the whole semester worth a significant proportion of your final grade.
- (text from Jimmy Huck) Even though Service Learning does not comprise the totality of your Data Science grade, you cannot pass the class if you do not pass the service learning component of the course. Conversely, if you fail the course, you cannot pass the Service Learning portion of the course no matter how well you do. This is a serious commitment, treat it with respect.
Kickoff Meeting: The volunteers at Code for NOLA will work with students in an initial kickoff meeting to outline the services and goals of the organization; show students examples of past work, and engage in a dialog on possible data science projects for the semester.
Mid-Semester Review: About half way though the semester (between Milestone 1 and 2) students will meet with a chosen community partner, go over their notebooks with their initial data analysis, and discuss feedback and options for additional analysis.
Final Presentation: In addition to their final pitch in class, students in the service learning component will have an extra presentation to the partners to receive feedback and discussion on their project. Feedback from the partners will factor into the total grade students receive in the evaluation of their Final Tutorial.
In addition to the focus of their main final project, students will be expected to complete four required reflections as part of the service learning component of the course. Grades for these reflections (12.5% each) will be a crucial component of their service learning component grade. The reflections will be graded on the clarity of presentation, grammatical and structural coherence, and the critical thoughtfulness of the reflection essay's response to the prompts outlined below. These prompts will help you think about some of the themes and ideas we will be learning about in class and lectures especially as it comes to the application of Data Science to communities and problems in those communities. Additional resources and readings for this section come from Berkeley's Human Context and Ethics of Data Science Course.
Reflections will be equally spaced throughout the semester including after the first and last meeting with the community partners.
Prompt 1: Reflect on our kickoff meeting with the community partners. Briefly describe each partner and their overall mission. Discuss your initial thoughts and impressions, consider answering the following questions.
- What types of data were you most surprised to see available? What about data that isn't available?
- What does this say about our community and the city?
- What topic(s) mentioned interest you? Which one(s) do you think you can provide help with over the semester?
- Any other thoughts about the topics discussed at the kickoff meeting.
- Read Ethics of Big Data Research. https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005399
Prompt 2: For our Second Reflection we will think about both our meeting with our partners, our entering communities training, and the Data Ethics discussions that have happened in class. Please make sure to read the following articles:
- Opengovdata.io: Civic Hacking. https://opengovdata.io/2014/civic-hacking/
- White Privilege: Unpacking the Invisible Knapsack: https://www.racialequitytools.org/resourcefiles/mcintosh.pdf
For this reflection think about some of the following issues given your readings and the meetings with our partners. Feel free to address other issues as well, I encourage you to bring outside thoughts and information to the reflections as well.
- Thinking about the start of the project to now, can you identify any assumptions you made / thought / felt that have changed regarding the use of data in our communities? If what, explain and why.
- Think about who could be impacted by your analysis, either positively or negatively, list out these stakeholders and identify some risks and rewards for each.
- Did the partners raise any questions that you hadn't thought of before?
- Have any of the readings brought specific assumptions or aspects of your tutorial into focus in a different light?
- What changes could you / are you planning for your project in light of this?
Prompt 3: Read S. Barocas and d. boyd (2017) "Engaging the Ethics of Data Science in Practice," Communications of the ACM, Vol. 60 No. 11, Pages 23-25. This paper deals directly with ethics and community impact in the area of data science. Think about your project, where the data comes from, who it impacts, and what you hope to achieve. Discuss the ethical implications of your work in this context.
Prompt 4: Reflect on your final presentation at Code for NOLA. Were you happy with how it went? What questions did you get that you were not ready for? Was there something that you didn't think of that was raised during the presentation or review?
Reflection Grading Rubric: Each reflection will be worth 50 points. I expect your reflections to engage with the course themes and concepts. Each essay will be graded on the clarity of expression, grammatical and structural coherence, and the critical thoughtfulness of the reflections response to the prompts that I have provided. Each reflection should be at least two type written, size 12 font, 1-inch margin, double spaced documents to be turned in via Canvas. They may be longer if you wish but this is a minimum.
- (10 Points) Professionalism: You have written a coherent, grammatically correct, essay that is coherent, meets formatting requirements.
- (10 Points) Completeness: Your responses addresses all of the questions asked the prompt.
- (20 Points) Thoughtfulness/Depth: You have explored ideas beyond the surface and discussed both your perspective and the partner's perspective on the problems at hand. You have considered more than one view and discussed these views.
- (10 Points) Personal: You discussed in your reflection how your perspective informs or complicates the prompt and questions. You have reflected on your own experience in the context of the new information.
| Full | 80% | 60% | 30% | 0% | | : ---- :| : ---- :| : ---- :| : ---- :| : ---- :| |You completely and fully met the criteria mentioned in depth. | You have met most of the criteria but missed a key issue. | You have missed major components of the required criteria or they are incorrect / inappropriate. | You have missed most components of the required criteria and/or may of them are incorrect. | You have failed to answer or provided little to no evidence of work.|
All,
One of the aspects of class that we've had to skip out on a bit is ideas around data privacy and the societal implications there of. So, to remedy that please read the following four articles for your hurricane makeup.
- Engaging the Ethics of Data Science In Practice, Communications of the ACM, 2017. https://cacm.acm.org/magazines/2017/11/222176-engaging-the-ethics-of-data-science-in-practice/
- Palantir Has Secretly Been Using New Orleans to Test It's Predictive Policing Technology, The Verge, 2018. https://www.theverge.com/2018/2/27/17054740/palantir-predictive-policing-tool-new-orleans-nopd
- Welcome to the Age of Privacy Nihilism, The Atlantic, 2018. https://www.theatlantic.com/technology/archive/2018/08/the-age-of-privacy-nihilism-is-here/568198/
- What Does GDPR Mean for Me? An Explainer. The Conversation, 2018. https://theconversation.com/what-does-gdpr-mean-for-me-an-explainer-96630
These are four somewhat different articles. The first deals with some issues in DS education and things to think about as you go forth and work with data. The second is an example (and a local one) of some of the unchecked issues surrounding data. The last two deal with privacy in the modern age, the implications of joining data together (what we just learned to do!) and some new regulations in the EU about data privacy.
We'll try to take some time in class next week to discuss these but after you have read the articles you should do the following to get the conversation going here.
- For ONE of the articles, write a post about what you felt was the main take away, and how it relates to an issue in your planned professional life or even your personal/student life now. What are the data or ethical issues at play? Is there anything that you can identify to change?
- Followup with TWO other people's posts -- is there something that was overlooked from the articles, is there another resource or perspective to their post you can bring? You should try to comment on someone writing about an article other than the one you did.
Complete all of these tasks by class time on Thursday October 15th!
Before COVID the plan was to have students attend the regular Code For NOLA Meetings. This is NOT REQUIRED FOR FALL 2020.
Students will be required to attend the Code for NOLA meetings to engage with the community of developers working to use code and data to improve access too and understanding of city data in New Orleans.
Regular Code for NOLA Meetings: In order to engage the students in the community of developers working to improve the city using data and technology, students will be required to attend the regular meetings of Code for NOLA during the semester.
During the Fall 2020 semester the meetings will take place at LaunchPad a startup incubator downtown at 400 Poydras Street, Suite 900, New Orleans, LA, 70130. The location can be easily accessed by a short walk from the 1500 Poydras Dropoff of the Green Shuttle Line or by the RTA St. Charles Streetcar. Specifically, students will attend on:
- Sept 10, 2020.
- Oct 8, 2020.
- Nov 12, 2020.
These meetings typically go from 7p to 9:30p (students can depart earlier). Most of the meeting is time to work in small groups on projects so students are encouraged to use this time to engage with other developers to improve their projects.