Designing Internet Research (Winter 2020)

From CommunityData
Designing Internet Research
COM528 - Department of Communication, University of Washington
Instructor: Benjamin Mako Hill (University of Washington)
Course Websites:
Course Catalog Description:
Focuses on designing Internet research, assessing the adaptation of proven methods to Internet tools and environments, and developing new methods in view of particular capacities and characteristics of Internet applications. Legal and ethical aspects of Internet research receive ongoing consideration.

Overview and Learning Objectives

What new lines of inquiry and approaches to social research are made possible and necessary by the Internet? In what ways have established research methods been affected by the Internet? How does the Internet challenge established methods of social research? How are researchers responding to these challenges?

These are some of the key questions we will explore in this course. The course will focus on assessing the incorporation of Internet tools in established and emergent methods of social research, the adaptation of social research methods to study online phenomena, and the development of new methods and tools that correspond with the particular capacities and characteristics of the Internet. The readings will include both descriptions of Internet-related research methods with an eye to introducing skills and examples of studies that use them. The legal and ethical aspects of Internet research will receive ongoing consideration throughout the course. The purpose of this course is to help prepare students to design high quality research projects that use the Internet to study online communicative, social, cultural, and political phenomena.

I will consider the course a complete success if every student is able to do all of these things at the end of the quarter:

  • Discuss and compare distinct types of Internet research including: web archiving; textual analysis; ethnography; interviews; network analyses of social and hyperlink networks; analysis of digital trace data, traditional, natural, and field experiments; design research; interviewing; survey research; design research; and narrative and visual analyses.
  • Describe particular challenges and threats to research validity associated with each method.
  • For at least one method, be able to provide a detailed description of a research project and feel comfortable embarking on a formative study using this methodology.
  • Given a manuscript (e.g., in the context of a request for peer review), be able to evaluate a Internet-based study in terms of its use its methodological choices.
  • Use a modern programming language (like Python) to collect a dataset from a web API like those published by Twitter, Reddit, or Wikipedia.

Note About This Syllabus

You should expect this syllabus to be a dynamic document. Although the core expectations for this class are fixed, the details of readings and assignments will shift based on how the class goes, guest speakers that I arrange, my own readings in this area, etc. As a result, there are three important things to keep in mind:

  • Although details on this syllabus will change, I will try to ensure that I never change readings more than six days before they are due. I will send an announcement no later than before each Wednesday evening that fixes the schedule for the next week. This means that if I don't fill in a reading marked "[To Be Decided]" six days before it's due, it is dropped. If we don't change something marked "[Tentative]" before the deadline, then it is assigned. This means that if you plan to read more than six days ahead, contact the teaching team first.
  • Because this syllabus a wiki, you will be able to track every change by clicking the history button on this page when I make changes. I will summarize these changes in the weekly an announcement on Canvas sent that will be emailed to everybody in the class. Closely monitor your email or the announcements section on the course website on Canvas to make sure you don't miss these!
  • I will ask the class for voluntary anonymous feedback frequently — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments to courses that I teach while the quarter progressed based on this feedback.

Books

This class has no textbook and I am not requiring you to buy any books for this class. That said, several required readings and many suggested readings, will come from several excellent books which you might want consider adding to your library:

These books include:

  • Burgess, Jean, Alice Marwick, and Thomas Poell, eds. 2018. The SAGE Handbook of Social Media. London, UK: SAGE. [Available through UW libraries]
  • Foucault Welles, Brooke, and Sandra González-Bailón, eds. 2018. The Oxford Handbook of Networked Communication. London, UK: Oxford University Press. [Available through UW libraries]
  • Hargittai, Eszter, and Christian Sandvig, eds. 2015. Digital Research Confidential: The Secrets of Studying Behavior Online. MIT Press. [Available through UW libraries
  • Hesse-Biber, Sharlene Nagy, ed. 2011. The Handbook of Emergent Technologies in Social Research. Oxford, UK: Oxford University Press.
  • Hewson, Claire, Carl Vogel, and Dianna Laurent. 2016. Internet Research Methods. London, UK: SAGE. https://doi.org/10.4135/9781473920804. [Available through UW libraries]
  • Snee, Helene, Christine Hine, Yvette Morey, Steven Roberts, and Hayley Watson, eds. 2016. Digital Methods for Social Science: An Interdisciplinary Guide to Research Innovation. New York, NY: Palgrave-Macmillan. [Available through UW libraries]

Technical Skills

Nearly all of our structured in-person meetings and all of our readings will focus on teaching conceptual skills related to Internet research. These skills involve the "softer" skills of understanding, designing, and critiquing research plans. These are harder to teach, evaluate, and learn but are ultimately what will make a research project interesting, useful, or valid. When the course has been taught in the past by other faculty, it has been entirely focused on these types of conceptual skills.

That said, I also believe that any skilled Internet researcher must be comfortable writing code to collect a dataset from the web or, at the very least, should have enough experience doing so that they know what is involved and what is possible and impossible. This is essential even if your only goals is to manage somebody else writing code and gathering data or work productively with a collaborator who is doing so. As a result, being successful in this class will also require technical skills.

Because students are going to come to the class with different technical skillsets, we well be devoting a relatively small chunk of class time to developing technical skills. Instead, I'm requiring that students build these skills outside of our meetings together if they do not have them already.

In particular, I want every student to have the following three things:

  1. Basic skills in a general purpose high-level programming language used for Internet-based data collection and analysis. I strongly recommend the Python programming language although other programming languages like Ruby and Perl are also good choices. Generally speaking, statistical programming languages like R, Stata, Matlab are not well suited for this.
  2. Familiarity with the technologies of web APIs. In particular, students should understand what APIs are, how they work, and should be able to read, interpret, and process data in JSON.
  3. Knowledge of how to process and and move data from a website or API into a format that they will be able to use for analysis. The final format will depend on the nature of the result but this might be a statistical programming environment like R, Stata, SAS, SPSS, etc or a qualitative data analysis tools like ATLAS.ti, NVivo or Dedoose.

If you are already comfortable doing these things, great.

If you are not yet comfortable, I am going to be organizing three free workshops called the Community Data Science Workshops on Saturdays in April and May and I extremely strongly recommend that you attend them. The workshops will teach exactly the skills I'm expecting you to have and attending the full series of workshops will be enough to fulfill this requirement.

The workshops will meet four times so please block these out on your calendar now:

  1. Friday 4/8 6-9pm
  2. Saturday 4/9 9:45am-4pm
  3. Saturday 4/23 9:45am-4pm
  4. Saturday 5/7 9:45am-4pm

I've offered this workshops four times previously and they have always been oversubscribed. As a result, you should register for these workshops immediately. You can find the registration link on this page. Please mention that you are in this class when you register so that we make sure that you accept your application.

I have taught these workshops four times before in 2014 and 2015. If you have taken them in the past, you do not need to take them again. If you took them before but are feeling unsure about your skills, you will be welcome to come back to review and brush up on the material.

If you do not have the technical skills required above and you will not attend the workshops, you're going to be responsible for learning this material on your own. Although this is totally fine, I suspect it present a major challenge to success in this class. If you will be in this situation, contact me before the quarter starts.

Assignments

The assignments in this class are designed to give you an opportunity to try your hand at using the conceptual material taught in the class. There will be no exams or quizzes. Unless otherwise noted, all assignments are due at the end of the day (i.e., 11:59pm on the day they are due).

Weekly Reflections

Deliverables
(1) Post a message in the appropriate course discussion board; (2) Respond to at least one of your classmates before class.
Due Date
(1) Every Monday (on a week with reading); (2) Every Tuesday at 1:30 (on a week with reading)
Maximum length
1,000 words

For every week that we have readings (i.e., every week except for the consulting weeks and and the final presentation weeks), I'm asking everybody to reflect on the readings by the day before class and to share their reflections with everybody else. Because we're skipping the first week, that works out to a total of six reflections.

Reflections should be no more than 1000 words (about one single-spaced page). So everyone will have a chance to read the reflections before class, response papers should be posted to our course website the day before (i.e., before midnight each Monday) so that we can all read and construct responses. Please also pose one or two open-ended discussion questions that may serve as jumping off points for our in-class conversation. Don't bother with summarizing (we've all done the reading after all) and focus on engaging with ideas.

In terms of content, response papers offer you an opportunity to engage the readings by identifying common or conflicting premises, thinking through potential implications, offering political or cultural examples, posing well-supported objections, or outlining critical extensions. In my experience, the most thought provoking reflections go beyond pointing out things that one wonders about or finds interesting and explain why you find it interesting.

Turn in your response paper to Canvas by posting a new message in the appropriate day in the course the discussion board.

I'd also like everybody read over everybody else's responses and respond to at least on person—evening things out so that not everybody response to one person would be nice, but use your judgement.

Research Project

As a demonstration of your learning in this course, you will design a plan for an internet research project and will, if possible, also collect (at least) an initial sample of a dataset that you will use to complete the project.

The genre of the paper you can produce can one of the following three things:

  1. A draft of a manuscript for submission to a conference or journal.
  2. A proposal for funding (e.g., for submission for the NSF for a graduate student fellowship).
  3. A draft of the methods chapter of your dissertation.

In any the three paths, I expect you take this opportunity to produce a document that will further your to academic career outside of the class.

Project Identification

Due Date
January 24
Maximum paper length
800 words (~3 pages)
Deliverables
Turn in [in the appropriate Canvas dropbox]

Early on, I want you to identify your final project. Your proposal should be short and can be either paragraphs or bullets. It should include the following things:

  • The genre of the project and a short description of how it fits into your career trajectory.
  • A one paragraph abstract of the proposed study and research question, theory, community, and/or groups you plan to study.
  • A short description of the type of data you plan to collect as part of your final project.

Final Project

Outline Due Date
February 23
Maximum outline length
2 pages
Presentation Date
March 10
Paper Due Date
March 20
Maximum outline length
8000 words (~27 pages)
All Deliverables
Turn in in the appropriate Canvas dropboxes

Because the emphasis in this class is on methods and because I'm not an expert in each of your areas or fields, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is so important. Instead of providing all of this details, instead feel free to start with a brief summary of the purpose and importance of this research, and an introduction of your research questions or hypotheses. If your provide more detail, that's fine, but I won't give you detailed feedback on this parts.

The final paper should include:

  • a statement of the purpose, central focus, relevance and significance of this research;
  • a description of the specific Internet application(s) and/or environment(s) and/or objects to be studied and employed in the research;
  • key research questions or hypotheses;
  • operationalization of key concepts;
  • a description and rationale of the specific method(s), (if more than one method will be used, explain how the methods will produce complementary findings);
  • a description of the step-by-step plan for data collection;
  • description and rationale of the level(s), unit(s) and process of analysis (if more than one kind of data are generated, explain how each kind will be analyzed individually and/or comparatively);
  • an explanation of how these analyses will enable you to answer the RQs
  • a sample instrument (as appropriate);
  • a sample dataset and description of a formative analysis you have completed;
  • a description of actual or anticipated results and any potential problems with their interpretation;
  • a plan for publishing/disseminating the findings from this research
  • a summary of technical, ethical, human subjects and legal issues that may be encountered in this research, and how you will address them;
  • a schedule (using specific dates) and proposed budget.

I also expect each student to begin data for your project (i.e., using the technical skills you learn in the class) and describe your progress in this regard this in your paper. If collecting data for a proposed project is impractical (e.g., because of IRB applications, funding, etc) I would love for you to engage in the collection of public dataset as part of a pilot or formative study. If this is not feasible or useful, we can discuss other options.

I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class.

Participation

The course relies heavily on participation and discussion. It is important to realize that we will not summarize reading in class and I will not cover it in lecture. I expect you all to have read it and we will jump in and start discussing it. The "Participation Rubric" section of my detailed page on assessment gives the rubric I will use in evaluating participation.

Assessment

I have put together a very detailed page that describes the way I approach assessment and grading—both in general and in this course. Please read it carefully I will assign grades for each of following items on the UW 4.0 grade scale according to the weights below:

  • Participation: 30%
  • Weekly Reflection: 15%
  • Proposal identification: 5%
  • Final paper outline: 5%
  • Final Presentation: 10%
  • Final Paper: 35%

Schedule

Week 1: Tuesday January 7: (I) Introduction and (II) Ethics

Part I: Introduction and Framing

Required Readings:

  • Agre, Phil. 2004. “Internet Research: For and Against.” In Internet Research Annual: Selected Papers from the Association of Internet Researchers Conferences 2000-2002, edited by Mia Consalvo, Nancy Baym, Jeremy Hunsinger, Klaus Bruhn Jensen, John Logie, Monica Muerero, and Leslie Regan Shade. Vol. 1. New York: Peter Lang. http://polaris.gseis.ucla.edu/pagre/research.html. [Available free online]
  • Lazer, David, Alex Pentland, Lada Adamic, Sinan Aral, Albert-Laszlo Barabasi, Devon Brewer, Nicholas Christakis, et al. 2009. “Computational Social Science.” Science 323 (5915): 721–23. https://doi.org/10.1126/science.1167742. [Available through UW libraries]

Optional Reading:

Part II: Ethics

Required Readings:

  • franzke, aline shakti, Anja Bechmann, Michael Zimmer, and Charles M. Ess. 2020. “Internet Research: Ethical Guidelines 3.0.” Association of Internet Researchers. https://aoir.org/reports/ethics3.pdf. [Available free online]

To frame a conversation around research ethics, lets read this piece:

  • Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” Proceedings of the National Academy of Sciences 111 (24): 8788–90. https://doi.org/10.1073/pnas.1320040111. [Available through UW libraries]

And these pieces that are all vaguely in response to it:

Optional Readings:

  • Department of Health, Education, and Welfare, and National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. 2014. “The Belmont Report. Ethical Principles and Guidelines for the Protection of Human Subjects of Research.” http://www.hhs.gov/ohrp/policy/belmont.html. [Available free online]
  • Frankel, Mark S., and Sanyin Siang. 1999. “Ethical and Legal Aspects of Human Subject Research on the Internet.” Workshop Report. Washington, DC: American Association for the Advancement of Science. Available free online]

Week 2: Tuesday January 14: (I) Web Archiving and (II) Textual Analysis

Part I: Web Archiving

Required Readings:

Optional Readings:

Optional readings related to the ethics of automated data collection:

Part II: Textual Analyses

Required Readings:

Optional Readings:

I'm assuming you have at least a rough familiarity with content analysis as a methodology. If your not as comfortable with this, check out the Wikipedia article to start. These help provide more of a background into content analysis (in general, and online):

  • Van Selm, Martine & Jankowski, Nick, (2005) "Content Analysis of Internet-Based Documents." Unpublished Manuscript. [Available in Canvas]
  • Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, Calif.: Sage Publications. [Available from Instructor]
  • Krippendorff, K. (2005). Content analysis: an introduction to its methodology. Thousand Oaks; London; New Delhi: Sage. [Available from Instructor]

Examples of more traditional content analysis using online content:

Another example of topic modeling, but from political science:

Week 2: Friday January 17: CDSW Session 0

As description in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Winter 2020) which I am running concurrently with this class.

This session will run from 6-9pm and is the only session which can probably be missed. Please do contact me, however, if you will not be able to attend it.

Week 2: Saturday January 18: CDSW Session 1

As description in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Spring 2020) which I am running concurrently with this class.

This session will run from 9am-3pm. Details on the CDSW Spring 2020 page.

Week 3: Tuesday January 21: (I) Ethnography and (II) Interviews

Part I: Digital & Trace Ethnography

Required Readings:

Optional Readings:

Note: You may also be interest in reading the essay by Hine that boyd is responding to. [Available in Canvas]

This is the canonical book-length account and the main citation in this space:

  • Hine, C. (2000). Virtual ethnography. London, UK: SAGE Publications. [Available from Instructor]

These are all other interesting and/or frequently cited examples of Internet-based ethnographies:

Note: Dodgeball is a mobile social network system (MSNS) that allows groups of friends to connect and meet up via mobile phone. The author employed participant observation in order to understand norms of interaction in the MSNS "space".
Note: To conduct the study reported in this paper the authors created a used a fake profile in order to observe the psychological support offered to participants.
Note: Fantastic more general introduction but takeaways that are more specifically targetted toward people studying virtual reality type environments with virtual physicality.

Apropos of class discussion:

Part II: Online Interviewing

Required Readings:

Note: Short article you can basically skim. Read it quickly so you can cite it later.

Optional Readings:

Note: Strongly focused on enthnographic interviews with tons of very specific details. Fantastic article on interviewing, although perhaps a bit weak on Internet specific advice.
Note: One of the earliest books on online life and one of the earliest attempts to do online interviewing. This is dated, but highlight some important challenge.
Note: Start reading on page 8 on "The Internet and the Interview". The beginning is a theoretical argument that's not really relevant to this class.* Chou, C. (2001). Internet heavy use and addiction among Taiwanese college students: an online interview study. CyberPsychology & Behavior, 4(5), 573-585. [Available through UW Libraries]

Alternate Accounts:

These texts are largely redundant to the required texts above but do provide a different perspective and examples:

  • Salmons, J. (2014). Qualitative Online Interviews: Strategies, Design, and Skills. SAGE Publications.
This is a book that lays out what claims to be a comprehensive account to online interviewing. Take a quick through the preface and table of contents and read Chapter 1. [Both Available in Canvas.]
I have the book and am happy to loan my copy to anybody in the class that thinks this will be a core part of their research.
  • Morgan, David L. and Bojana Lobe, "Online focus groups," Ch. 9 in HET. [Available in Canvas]
  • Gaiser, T. J. (2008). Online Focus Groups. In N. G. Fielding, R. M. Lee, & G. Blank (Eds.), The SAGE Handbook of Online Research Methods (pp. 290–307). London, UK: SAGE Publications, Ltd. [Available through UW Libraries]

Optional readings related to the ethics of identify subjects:

Week 4: Tuesday January 28: (I) Network Analysis and (II) Experiments

Part I: Network Analysis

Required Readings:

Part II: Experiments

Required Readings:

Optional Readings:

This is really just a more in-depth version of the experiments in the Restivo and van de Rijt article described above.
Note: This piece is, more or less, a continuation of the Restivo and van de Rijt piece included above but it is longer and goes into much more depth on at least one of the important theoretical issues.
Note: We've already read but I'd like to discuss it again.

Week 4: Saturday February 1: CDSW Session 2

As description in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Spring 2020) which I am running concurrently with this class.

This session will run from 10am-4pm. Details on the CDSW Spring 2020 page.

Week 5: Tuesday February 4: (I) Surveys and (II) Humanistic Analysis

Part I: Surveys

Required Readings:

Note: This journalistic account of the research may also be useful.

Optional Readings:

If you don't have a background in survey design, these two have been recommended by our guest speaker as good basic things to read:

  • Krosnick, J. A. (1999). Survey Research. Annual Review of Psychology, 50(1), 537–567. [Available through UW Libraries]
  • Krosnick, J. A. (1999). Maximizing measurement quality: Principles of good questionnaire design. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of Political Attitudes. New York: Academic Press.

These are other texts on the subject that you might find useful:

Part II: Narrative, Discourse and Visual Analysis

Required Readings:

Narrative Analysis:

Visual Analysis:

Optional Readings:

Narrative Analysis:

Visual Analysis:

Note: Although I'm not a fan of infograpraphics as a genre, I suppose it makes sense that visual communication people would put together a pretty good one! If you're already familiar with visual analysis from the rhetorical tradition, there's not going to be a lot new here. If this is new for you, this will help you frame and understand the other readings.
Note: This is a two part (each part is one hour) lecture and tutorial by a expert in computer vision. I strongly recommend watching Part I. I think this gives you a good sense of the nature of the kinds of challenges that were (and still are) facing the field of computer vision and anybody trying to have their computer look at images.

These five paper are all technical approaches to doing image classification using datasets from Internet-based datasets of images like Flickr, Google Image Search, Google Street View, or Instagram. Each of these describes interesting and challenges technical issues. If you're interested, it would be a great idea to read these to get a sense for the state of the art and what is and isn't possible:

Discourse Analysis:

Note: Combines quantitative and qualitative computer-mediated discourse analysis methods.*

Week 6: Tuesday February 11: Crowdsourced Data Analysis and Experimentation

Assignment:

  • Find and complete at least 2 "hits" as a worker on Amazon Mechnical Turk. Note that to do this you will need to create a worker account on Mturk.
    • Record (write down) details and notes about your tasks: What did you do? Who was the requester? What could you was the purpose of the task (as best you could tell)? What was the experience like? What research applications can you (not) imagine for this kind of system?
  • Design and deploy a small-scale research task on Mturk. Note that to do this, you will need to create a requester account on Mturk. Be sure to allow some time to get the task design the way you want it! Some ideas for study designs you might do:
    • A small survey.
    • Classification of texts or images (e.g., label tweets, pictures, or comments from a discussion thread).
    • A small experiment (e.g., you can do a survey where you insert different images and ask the same set of questions. Check out the Mturk requester getting started guide
  • Prepare to share details of your small-scale research task in class, including results (they will come fast).

Note: In terms of running your task, it will cost real money and you have to put money on your Amazon account yourself. You've each got a $3 budget. Please use your credit card to put $3 on your account right away. I will pay each of you $3 in cash next week to reimburse you for the cost of running the experiment.

Required Readings:

Optional Readings:

Resources:

Week 6: Saturday February 15: CDSW Session 3

As description in the section on technical skills above, I expect everybody who is not comfortable with at least basic programming and data collection to attend the Community Data Science Workshops (Spring 2020) which I am running concurrently with this class.

This session will run from 9am-3pm. Details on the CDSW Spring 2020 page.

Week 7: Tuesday February 18: Consulting Week (i.e., no group meeting)

During this week, we not meet together. Instead, I will schedule one-on-one in person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collection, etc.

Week 8: Tuesday February 25: Consulting Week (i.e., no group meeting)

During this week, we not meet together. Instead, I will schedule one-on-one in person meetings of an hour with each student individually to catch up with you about your project and to work directly with you to resolve any technical issues you have run into with data collect

Week 9: Tuesday March 3: (I) Design Research and (II) Digital Trace and Sensor Data

Part I: Design Research

Today we'll have a guest visitor — Andrés Monroy-Hernández who is director of HCI research at SNAP and formerly of from Microsoft Resarch's FUSE labs. Andrés is affiliate faculty in the Department of Communication and Department of Human-Centered Design and Engineering at UW. Monroy-Hernández research involves studying people by designing and building systems. He's built a number of very large and successful socio-technical systems as part of his research. In his graduate work, he build the Scratch Online Community which is now used by more than 10 million people.

I've asked him to come and talk to us about design research as a process. As a result, it will be helpful to read about two projects he has worked on recently that he will talked to us about. Those projects are called NewsPad and Eventful.

Required Readings:

Part II: Digital Trace and Sensor Data

Required Readings:

Read any 2 of these 4 chapters from the Handbook of Emerging Technology in Social Research:

Week 10: Tuesday March 10: Final Presentations

Administrative Notes

Your Presence in Class

As detailed in the section on participation and in my page on assessment, class participation is a critical way that I will assess learning in the class. Obviously, you must be in class in order to participate. If you need to miss class for any reason, please contact me ahead of time (email is best). In the event of an absence, you are responsible for obtaining class notes, handouts, assignments, etc.

Office Hours

I will hold office Hours on Thursdays 1-2pm in Communications (CMU) 333. In addition to my scheduled office hours, I am generally in my lab in CMU 306. Feel free to stop by at any time or to contact me to arrange a time to meet.

Religious Accommodations

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available at Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

Student Conduct

The University of Washington Student Conduct Code (WAC 478-121) defines prohibited academic and behavioral conduct and describes how the University holds students accountable as they pursue their academic goals. Allegations of misconduct by students may be referred to the appropriate campus office for investigation and resolution. More information can be found online at https://www.washington.edu/studentconduct/ Safety

Call SafeCampus at 206-685-7233 anytime–no matter where you work or study–to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus’s team of caring professionals will provide individualized support, while discussing short- and long-term solutions and connecting you with additional resources when requested.

Academic Dishonesty

This includes: cheating on assignments, plagiarizing (misrepresenting work by another author as your own, paraphrasing or quoting sources without acknowledging the original author, or using information from the internet without proper citation), and submitting the same or similar paper to meet the requirements of more than one course without instructor approval. Academic dishonesty in any part of this course is grounds for failure and further disciplinary action. The first incident of plagiarism will result in the student’s receiving a zero on the plagiarized assignment. The second incident of plagiarism will result in the student’s receiving a zero in the class.

Disability Resources

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to uw at your earliest convenience so we can discuss your needs in this course.

If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DRS at 206-543-8924 or uwdrs@uw.edu or disability.uw.edu. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS. It is the policy and practice of the University of Washington to create inclusive and accessible learning environments consistent with federal and state law.

Other Student Support

Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live, and believes this may affect their performance in the course, is urged to contact the graduate program advisor for support. Furthermore, please notify the professors if you are comfortable in doing so. This will enable us to provide any resources that we may possess (adapted from Sara Goldrick-Rab). Please also note the student food pantry, Any Hungry Husky at the ECC.

Credit and Notes

This will be third time I have taught this course at UW in its current form. This syllabuses draws heavily from previous versions. Syllabuses from earlier classes can be found online at:

This syllabus was inspired by, and borrows with permission from, a syallbus from an earlier version of this class taught by Kirsten Foot. Professor Foot last taught the course in Spring 2014.

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