This course was piloted in the spring of 2017. The second iteration of Distant Reading, which ran in the spring of 2018, was built around five projects:
Project 1: Google Ngram Viewer (Week One)
Project 2: Text Analysis of Student Writing – Level 1 (Week Two)
Project 3: Text Analysis of Student Writing – Level 2 (Week Three)
Project 4: Independent Project #1 (Weeks Four through Six)
Project 5: Independent Project #2 (Weeks Seven through Nine)
See below for short summaries of each assignment, and PDFs of our assignment sheets. The granular lesson plans involve formal modeling and scaffolding of skills and daily checking in with students during the projects, but this framework (above) and the assignment sheets (below) provide a sense of the broad structure of the course.
For those who saw previous versions of our curriculum, we made several notable changes to the curriculum and to our delivery of it. In year two:
- We left sentiment analysis entirely out of the course. It was too vague to provide analytical value, and the kids were far too attracted to its oversimplifications.
- We more more clearly identified the content, skills, and character objectives for the course (see above), and we referred to them daily among each other and with the students.
- We doubled down on the key skills — question formulation, problem decomposition, and argumentation — checking in daily on student progress on these fronts, which naturally also developed metacognitive awareness.
- We scaffolded coding more clearly over Projects 2 and 3, keeping the data sets relatively constant over the two assignments and focusing instead on deepening their coding capacity on a daily basis.
As licensed under a Creative Commons Attribution 4.0 International License, this curriculum is freely usable with attribution. Feedback also welcome.
Project 1: Google Ngram Viewer (Week One)
This three day project engages students in an introduction to thinking computationally about language. It prompts students to consider how words can function as data, and it guides them through a shared experience of asking questions about the relative value of looking at words this way.
Project 2: Text Mining Your Writing-Level 1 (Week 2)
Now we start working in Mathematica, and students begin text mining. This weeklong project challenges students to computationally analyze their own high school writing and to compare their writing to that of great writers. They learn to import text and to perform basic quantitative analyses, and they continue exploring the opportunities and limits of this kind of reading.
Project 3: Text Mining Your Writing-Level 2 (Week 3)
Here, students begin diving into more nuanced text mining approaches. Again, students analyze their own writing and set up comparisons with great writers. This time, the work deepens their familiarity with Wolfram Language, and they continue to explore the ways in which distant reading complements our understanding of a text.
Project 4: Independent Project #1 (Weeks 4-6)
After two weeks of daily practice with Wolfram Language, the students are ready to branch out on their own for the first time. We preface the independent project with two days of question formulation and problem decomposition, and then we let them loose gathering and cleaning data sets — and then trying out their new skills. At the beginning, we ask students to detail the steps they’ll need to take (from gathering and cleaning data all the way to revising their written work) and how long each step will likely take, and then throughout the project we start each class by checking in with each student to see if they are matching their predicted timeline. This first independent project gives them a chance to pursue areas of personal passion or interest.
Project 5: Independent Project #2 (Weeks 7-9)
Truly, the first independent project was a rough draft. When Project 4 finishes, students present their work to each other. They are enamored by each other’s approaches, and they share with each other the niche coding skills they have picked up while pursuing their questions. This inspires the second independent project, which is the final project of the course. In this, having seen what they and their classmates are truly capable of, they choose to either: deepen their own analysis from Project 4, begin a new topic of their own, or pick up where a classmate left off and deepen another student’s project. These final projects are the most polished. They have refined their data gathering and cleaning methods, they have developed better coding skills, and they are at their strongest at integrating code analysis with written analysis. Critically, they have developed a much keener sense of what kinds of questions they can ask, and they have developed a balanced sense of both the opportunities and limits of thinking computationally about text.