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Grading Scale
Grading Scale
93-100% A
90-92.9% A-
88-89.9% B+
83-87.9% B
80-82.9% B-
78-79.9% C+
73-77.9% C
70-72.9% C-
68-69.9% D+
63-67.9% D
60-62.9 % D-
<60% NC
Medium-Sized Exercises (20%)
Medium-Sized Exercises (20%)
We will be completing two medium-sized assignments during the semester to gain more in-depth experience with concepts from the class. Each is worth 50% of this part of your grade.
Friday, September 29th: Collection Dive Assignment Folder and Instructions
Friday, November 10th: Personal Data Postcard
Reflection Posts (35%)
Reflection Posts (35%)
For each post (5 total, so each is worth 20% of total Reflection Post grade), please write:
approximately 250 words (about 3 paragraphs)
Each of these posts will take ~45-60 minutes between prep work and writing, so if you're spending a lot more or less time on these exercises, feel free to talk to me about how you might approach them differently.
I will offer activities for these posts- tools to experiment with, questions to ponder, data to explore, etc. These activities are designed to provide opportunities for you to apply key concepts, learn about new projects or organizations, and do some hands-on exploration.
Your post will be graded on a simple three tier scale:
Full credit: The post incorporates the elements outlined in the assignment prompt. The writing is focused and thoughtfully and coherently responds to the prompt. It demonstrates that you have reflected on the process and engaged with the relevant materials.
Half credit: The post is mostly description or summary, without consideration of alternative perspectives, and few connections are made between ideas. The entry reflects a passing engagement with the topic.
No credit: The post is missing or late, or consists of one or two disconnected sentences.
Topics
Week 3- Friday, Sept. 22nd: Project Sleuthing Exercise
Week 7- Friday, October 20th: Data Cleaning Lab Reflection
Week 9- Monday, November 27th: Personal Geographies
Week 11- Friday, November 19th: Network or Text Analysis Lab Reflection
Week 14- Friday, December 8th: Data Autoethnography
Final Project (30%)
Final Project (30%)
We will be working in groups to analyze a data set, create visualizations about that data set, and present findings to the class.
Final Project Datasets:
Week 3- Wednesday, Sept. 20th: Group Project Interest Survey- submit individually (5%)
Week 7- Wednesday, Oct. 25th: Group Project Charter and 3 Research Questions (15%)
Week 9- Wednesday, Nov. 1st: Group Project Data Critique (15%)
Week 12- Wednesday, Nov. 29th: Group Project 3 Draft Visualizations (20%)
Week 14- Wednesday, Dec. 6th: Group Project Presentation Tool and Peer Check-in Day (5%)
Week 15- Friday, Dec. 13th: Group Project and Presentation (35%)
Week 16 (Exam Days)- Friday, Dec. 15th: Self-Reflection and Group Evaluation Form - submit individually (5%)
Final Project Elements
Final Project Elements
a brief statement articulating the significance and purpose of the project (~100 words)
a brief statement articulating the audience for the project (~50 words)
a description of your methodology (in other words, what did you do? Did you clean the data first? What next? Did you isolate certain columns? How did you decide what visualizations to create, and how did you create them?) (~250 words)
the issue that the project addresses (your research question and findings) (~250 words)
a brief review of the literature/evidence you examined (~100 words)
concluding thoughts/future paths of exploration? (~50 words)
at least 3 visualizations (geographical, timeline, text analysis, drawing, data visualization, and/or video) presenting evidence and claims
image, name, and brief bio for each of the project's contributors (~50 words or ~2 sentences per person)
Examples:
Sample Project Template in Google Sites (with Lorem Ipsum text)
These projects are from Miriam Posner’s students at UCLA. This was a graduate level class, so I will not be expecting this level of engagement. But it’s helpful to get a sense of what’s going on.
The visualizations used in this example are totally appropriate but the students weren’t able to do as much primary and secondary research into their dataset/topics because of the duration of the module. I would expect more research than this for this semester’s final project.
Copyright/Image Resource:
Attendance & Participation (15%)
Attendance & Participation (15%)
Discussion Questions
Over the course of the term: Discussion Questions:- sign up to offer two discussion questions/prompts for one day of class. We will sign up for these during the second week of class.
Attend class regularly:
you are allowed 2 unexcused absences. Please email or speak to me if you need to miss a class beyond these two unexcused absences.
Prepare for class:
come to class ready to engage with your classmates and me and/or the guest speaker
complete any activities assigned for the class session (providing an example of a successful and unsuccessful data visualization, for instance)
this doesn't mean I expect each of you to speak in every class session, but you'll get more out of the conversation if you know the context of our discussion (provided through the assigned readings, viewings, data sets, projects, lectures, or tools)
this is an inclusive classroom built on mutual respect, civility, and the ability to listen to others.
Use technology intentionally:
there will be times in class when I will actively encourage you to use your computer or tablet to further build your "Google Fu" (we'll talk more about this in class).
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