The Learning-Focused Approach to Syllabus Construction

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Issues with Data Collection

I experienced several setbacks in obtaining permission to use Department syllabi. In an effort to be as open and ethical about my research as possible, I made inquiries at the IRB office at Virginia Tech to ask whether my project needed approval or not seeing that I was not interacting with humans in such a way that my data was dependent on interviews or one-on-one meetings. While my project does not involve the direct study of human participants or the transcription of interview recordings, syllabi can be considered intellectual property and therefore would require special approval before I could begin my analysis. I applied for Institutional Review Board (IRB) through the use of a Human Research Determination Form and was granted exemption, “Not Human Subject Research,” with the recommendation of acquiring departmental approval (IRB # 19-119).
At the request of the IRB, I emailed the acting department chair as well as met with the director of composition to gain permission to use Department syllabi housed in a Google drive folder accessible to all in the English Department. The results of both of these meetings concluded that I would need to contact each First-Year Writing instructor individually, for permission to use their syllabi.
I was able to work around contacting each instructor individually, by first compiling a list of possible sample owners from the drive folder described above. I crafted a single email describing the nature of the study, how I was collecting samples, as well as how I would be de-identifying the data. I also included the IRB case number I had been given to show that I had followed ethical procedures before arriving at this step. I used Gmail’s “Label” feature to create an email list that I then copy-and-pasted in the “blind carbon copy” bar in my email header.

Sample Collection and Coding Strategies

My First-Year Writing syllabi samples all came from a Google drive folder that instructors within the Department of English at Virginia Tech are required to share their syllabi in. In an effort to make my emailing process as efficient as possible, I first compiled a list of all First-Year Writing instructors who had uploaded their syllabi to the shared folder. To do this, I simply read through the titles of all the syllabi collected within the folder, listing those who had uploaded First-Year Writing syllabi. In the end, my email list comprised of 41 individual First-Year Writing instructors. I crafted the permission email described above and received a total of 27 responses: 25 granting permission and only 2 responses not granting permission. My response rate of approximately 65.8% (including negative responses) may be attributed to my own familiarity with those teaching First-Year Writing in the English Department at Virginia Tech, however in several of the positive responses I received, instructors mentioned the need for research in the rhetorical nature of syllabi and were more than willing to grant me access to their syllabi.
In an effort to maintain anonymity and reduce the opportunity for biases to become in my coding, I de-identified all syllabi samples by first removing all names and office information. Office information included office number and building location as well as information regarding office hour availability. I also removed all information regarding the course being taught—ENGL 1105 or ENGL 1106—as well as any and all contact information belonging to the instructor; office and/or personal telephone numbers as well as email addresses. To remove this information in Word documents I simply manually deleted the above information by highlighting and entering “delete” on my keyboard. For PDF documents, I anchored solid white text boxes over identifying information. Only de-identified samples were saved to my Google drive. No changes were made to the original syllabi housed in the Department shared folder.
To help facilitate the segmentation and coding of my data, I turned to Cheryl Geisler’s Analyzing Streams of Language: Twelve Steps to the Systematic Coding of Text, Talk, and Other Verbal Data for help in segmenting my samples into manageable portions of data.
“The analysis of verbal data begins when we segment the data into some unit for analysis” (Geisler 29).Using the information gathered from Geisler’s chapters, I separated my data into three large units of analysis and then further divided these units into smaller sub-segments of data: words, conversations, and textual elements.


This unit of analysis focused on aspects of language within the documents themselves. I read each document and separated the words and phrases on the pages into smaller sub-segments devoted to how the language functioned rhetorically on the pages: positive and negative emotive language, indexicals, personal pronouns, as well as total number of words in the document.
Focusing on positive and negative emotive language helped determine the overall tone of document. Indexical language, or ways conversations and interactions were evident verbally in the text, was used to show how interactions between student, instructors, and administration were evident, verbally, in the text. I kept track of the use of personal pronouns throughout my reading so that I may determine who the key informants of the document may be. To determine the total number words I simply used the word counting feature in Google docs and input the total words count found.


This section allowed for space to go into greater detail in the indexical conversations/interactions uncovered through indexical coding described above. I further divided this section into two smaller sub-segments: conversations directly addressed to students and conversations addressed to the speaker. This section was harder to code for as most, if not all conversations found in the document seemed to be addressed to students with a very minimal interaction between instructors themselves, nor the university. Little to no conversations seemed to be addressed to the university.

Textual Elements

Utilizing a coding section that dealt with textual elements of a text allowed for a greater understanding of the how the genre functions at a basic level as well as rhetorically. I centered this segment around discourse principles found within the document, however I also had a category devoted to describing how the individual documents were organized. In addition to organization, my other categories were: genre components-boilerplate, genre components-creative, and metadiscourse (conversations found within the document about the document). I label “genre components-boilerplate” and “genre components-creative” to distinguish where university mandated language was used within the document and where instructors exercised creative license either in changing the language of the university mandated discourse, or simply added new discourses through the creation of policies or conversations individual to the instructor.

Disclosure of Limitations and Conflict of Interest

Limitations exist within my data sample. First, my syllabi samples were all gathered from one university and therefore cannot be considered a representative sample. Furthermore, my total sample size of 25 also compounds the issue with sample representativeness; my goal in this study was to amass a sample of at least 50 syllabi from universities and colleges across the United States, however after experiencing timing setbacks within my own department, I was forced to use only what I had immediately available.
There could also be a potential conflict of interest as my sample came from an English Department in which I am actively teaching First-Year Writing. My close proximity to other First-Year writing instructors and GTAs may have contributed to the higher than average response rate I received from my emails asking for permission. However, as was described in detail earlier in this chapter, all syllabi were de-identified before being collated in a central folder. My de-identifying of syllabi samples helped in reducing any explicit bias in my coding.

Beginnings of a Plan of Study
Outline of Work
Defining Syllabi
The Syllabus as a Contract
The Learning-Focused Approach to Syllabus Construction
Realizing the Greater Need
Issues with Data Collection
Sample Collection and Coding Strategies
Textual Elements
Audience Invoked
Audience Engaged
Rules and Conventions
Organization as a Genre Convention
Networks and Contact Zones
Theoretical Frameworks: Building a Foundation through Rhetorical Genre Theories
Genres in Composition Studies
Utilizing Frameworks: Contact Zones and Actor-Network Theory
Contact Zones and Performance
Navigation and Actor-Network Theory
Making an Argument for Syllabi
Communicating Performance: First-Year Writing Syllabi as Rhetorical Contact Zones

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