Week 7: Discovery and Data Analysis
7.0 Discovery and Data Analysis
Hours: 19-21
Orientation
Data is fetishized. Its purported objectivity is problematic in that it obscures the subjective forces involved in its gathering, testing, authorizing, interpreting, circulating, and framing. For the purpose of our curriculum, we regard data as both qualitative and/or quantitative; we approach both valences in terms of their rhetorical function as part of a larger narrative framework. In other words, we examine the role data plays in helping writers/scientists/engineers tell stories. This week’s theme is discovery (i.e., what does our data show?) and its tactic, data analytics, examines the cultural formation of data into knowledge; that is, we position data and data collection as cultural practices that emerge from a specific time and place, under particular conditions, regulated and adjudicated by disciplinary norms, beliefs, assumptions, and commitments the impact of which differs by stakeholder. We approach data-analysis-as-cultural-formation through the following questions: whom (or what) does the data represent? What are the cultural practices, procedures, and processes that legitimize data as data? When does it become excluded? -delegitimized? -by whom? What story does data tell? How will the data help my project group tell stories?
Activities
Premise
While data may be objective, the cultural practices that uphold, affirm, and deploy its status are subjective. While numbers may not lie, numbers can be used to mislead. Critical attention must engage both levels of data, its purported objectivity and its cultural malleability. As an aspect of culture, attention must be given to the ways in which people have used data to justify and/or take power.
Purpose
The purpose of this exercise is to examine the cultural frameworks upholding specific data gathering practices employed and recognized by scientific and mathematical communities. A humanities equivalent would be to examine the cultural construction of close-reading practices. (STEM-oriented)
Preparation
Students review recent data collecting and data representing methodologies featured in lab reports, science journals, and datasets relevant to course theme and creative discovery projects.
Protocol
In writing groups, students attend to paratextual information surrounding data: identifying board members, their publishing histories and prior experiences, publishing heuristics, peer review standards, data analysis protocol, etc. Once collected, writing groups share findings with the whole class and discuss patterns, trends, and/or themes made visible by the exercise.
Premise
Findings are situational; they are not absolute. Narratives entail resolutions, and with the added cultural weight of “data,” those resolutions are imbued with finality, authority, and conclusion.
Purpose
The purpose of this exercise is to foreground the situational aspects of research findings by asking project groups to identify trends, patterns, and themes across datasets aggregated by themselves.
Preparation
Preparation will depend on course-specific research requirements, but in general, writing groups will need to have fulfilled the research component by week 7 in order to complete this activity. It should be noted—and perhaps directly stated to students—that students have been informally collecting data since week 1 (see activities, “rumors and hearsay,” “show-and-tell,” “compelling stories,” “literature review,” “annotated bibliography,” “I-We-You,” and “Reverse Engineering.”
Protocol
Students reflect on their found datasets through three waves: trends, discovery, sequencing, and impact by responding to a series of questions. Students may choose how to record their responses to the conversation, but it must be shared in their group’s writing folders.
- Trends
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- What trends, patterns, or themes do you observe in your data?
- What gaps or incongruities? How will you make sense of these?
- Findings
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- What does your data/research reveal? What did you find out?
- What might your data/research obscure or hide?
- Organization/Sequencing/Storytelling
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- How will you organize the data?
- What constraints does this genre impose?
- What story do you want to tell when talking about this data? How will you tell it?
- Impact
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- What impact do you want to have on your readers/audience? [Aristotle’s three appeals as a baseline] What is the desired impact of your story?