Communication and Social Networks (Fall 2022)

= Course Information =
 * COM 411: Communication and Social Networks
 * Location: BRNG B274
 * Class Hours: Tuesdays and Thursdays; 10:30–11:45 AM

Instructor

 * Instructor: Jeremy Foote
 * Email: jdfoote@purdue.edu
 * Office Hours: Tuesdays; 2:00–4:00pm and by appointment

= Course Overview and Learning Objectives =

Communication is inherently a social process. This class focuses on understanding how the structure of relationships between people influence communication patterns and behavior. This perspective can help us to understand a broad set of phenomena, from online communities to friendships to businesses. The course will also introduce students to using network visualizations to gain and share insights about network phenomena.

Students who complete this course will be able to:
 * 1) Understand the foundations of social network theory and analysis.
 * 2) Critically read and comprehend concepts, results, and implications presented in studies of social networks.
 * 3) Learn how networks are related to social phenomena in their personal and professional worlds.
 * 4) Gain a basic understanding of gathering network data and analyzing them using the programming language R.

= Required resources and texts =

Laptop
One of the goals of this class is a basic understanding of analyzing and visualizing network data in R. The labs on campus have R on them, and we are meeting in a computer lab so that those who need to can use the lab computers, but I recommend that you put R on your computer and do the assignments on your computer. In order to do this, you will need a machine with at least 2GB of memory. Windows, Mac OS, and Linux are all fine but an iPad or Android tablet won't work.

Talk to me ASAP if you don't have a laptop that will work or if your laptop dies. There are a few options that can work out - either through on-campus lab computers or using a virtual machine.

Readings

 * Required texts:
 * Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets. Cambridge University Press. [web edition (free)] [pre-print pdf (free)] [(print edition (Amazon))]
 * Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside [web edition(free)]


 * Other readings: Other readings will be made available on Brightspace.

Reading Academic Articles
Many of the readings will be academic articles. I do not expect you to read every word of these articles. Rather, you should practice intentional directed skimming. This article gives a nice overview. The TL;DR is that you should carefully read the abstract, introduction, and conclusion. For the rest of the article, focus on section headings and topic sentences to extract the main ideas.

Other suggested books

 * Barabasi, A-L. (2002). Linked: The new science of networks. Cambridge, MA: Perseus.
 * Scott, J. (2000). Social network analysis: A handbook (2nd edition). London: Sage Publications.
 * Watts, D. J. (2004). Six degrees: The science of a connected age. WW Norton & Company.
 * Christakis, N. and Fowler, J. (2009). Connected : the surprising power of our social networks and how they shape our lives

= Course logistics =

Note About This Syllabus
Although the core expectations for this class are fixed, the details of readings and assignments may shift based on how the class goes. As a result, there are three important things to keep in mind:


 * 1) Although details on this syllabus will change, I will not change readings or assignments less than one week before they are due. If I don't fill in a "To Be Determined" one week before it's due, it is dropped. If you plan to read more than one week ahead, contact me first.
 * 2) Closely monitor the class Discord. Because this a wiki, you will be able to track every change by clicking the history button on this page. I will also summarize these changes in an announcement on Discord that should be emailed to everybody in the class if you have notifications turned on.
 * 3) I will ask the class for voluntary anonymous feedback frequently. Please let me know what is working and what can be improved.

Class Sessions
This course will follow "flipped" classroom model. I expect you to learn most of the content of the course asynchronously. The goal of our time together is not to tell you new things, but to consolidate knowledge and to clear up misconceptions.

The Tuesday meeting will be a collaborative, discussion-centric session. Typically, about two-thirds of each session will be devoted to a discussion of the readings and videos from that week. The remaining third will be used to review the assignments.

The Thursday meetings will be more like a lab. Some of these sessions will include synchronous activities; often they will be a time for me to introduce and help with R assignments. Sometimes they will be more of a co-working time, where you can work on assignments and I can be available to answer questions.

Getting Help
Your first place to look for help should be each other. By asking and answering questions on Discord, you will not only help to build a repository of shared information, but to reinforce our learning community.

I will also hold office hours after our class on Thursdays, from 2-4 (sign up here). If you come with a programming question, I will expect that you have already tried to solve it yourself in multiple ways and that you have discussed it with a classmate. This policy lets me have time to help more students, but it's also a useful strategy. Often just trying to explain your code can help you to recognize where you've gone wrong.

I will also check Discord at least once a day. I encourage you to post questions there, and to use it as a space where we can help and instruct each other. In general, you should contact me there. I am also available by email. You can reach me at [mailto:jdfoote@purdue.edu jdfoote@purdue.edu]. I try hard to maintain a boundary between work and home and I typically respond only on weekdays during business hours.

Online Resources
Programming can be difficult and frustrating and confusing, but you will get it! I have put together a few resources to help you with the programming portion of the course.


 * Finding and fixing bugs in your code [Video] [R Markdown file] [HTML file]
 * Intro to ggraph and tidygraph [R Markdown file] [HTML file]

= Assignments =

There will be multiple types of assignments, designed to encourage learning in different ways.

Participation
I expect you to be an active member of our class. This includes paying attention in class, participating in activities, and being actively engaged in learning, thinking about, and trying to understand the material.

To make sure that everyone has an opportunity to participate and to encourage you to do the assignments, I will randomly select students to discuss readings or to explain portions of homework assignments and labs.

Discussion Questions
In order to make sure that we are prepared to have a productive discussion, you are required to submit one or two discussion questions that you think would be interesting to discuss on Monday by noon. Post your questions on the shared Google Doc at https://docs.google.com/document/d/1AK4MhWLVwDuxqvTwLomN7hu4aLpo59n5qXgEap8SBNc/edit?usp=sharing; try to group similar questions together.

Questions should engage with the readings and either connect to other concepts or to the "real world". Here are some good example questions:


 * The readings this week talked a lot about how network ties get created. I made a list of my closest friends and I realized that most of them only became friends after we happened to be in the same groups over and over again. What role does repetition have in forming ties?
 * I was confused by the reading on social capital. What's the difference between social capital and power? And if they are the same, then why not just call it "network power"?
 * Imagine you were asked to analyze the network of a big company to help them to identify people who deserve a raise. What measures would you use to identify them? What would you not use?

Some weeks will also include more practical homework (mostly data manipulation and visualization in R). On those weeks, portions of our discussions will center around going over homework questions and identifying places where folks are still confused.

Homework/Labs
There will be a number of homework assignments. At the beginning of the class, these will be designed to help you to grasp foundational network concepts. As the class progresses, more and more of them will be analyzing and visualizing networks in R.

Exams
There will be one take home exam. It will assess your understanding of core communication and social networks concepts.

Final Project
Students will work on a Final Project that explains how network analysis and a network approach can benefit an organization.

A number of intermediate assignments through the semester will help you to gain the skills and data necessary to be successful.

= Grades =

This course will follow a "self-assessment" philosophy. I am more interested in helping you to learn things that will be useful to you than in assigning grades. In general, I think that my time is much better spent in providing better feedback and in being available to work through problems together.

The university still requires grades, so you will be leading the evaluation of your work. This will be completed with me in three stages, at the end of weeks 5, 10, and 16. In each stage, you will use this form to reflect on what you have accomplished thus far, how it has met, not met, or exceeded expectations, based both on rubrics and personal goals and objectives. At each of these stages you will receive feedback on your assessments. By the end of the semester, you should have a clear vision of your accomplishments and growth, which you will turn into a grade. As the instructor-of-record, I maintain the right to disagree with your assessment and alter grades as I see fit, but any time that I do this it will be accompanied by an explanation and discussion. These personal assessments, reflecting both honest and meaningful reflection of your work will be the most important factor in final grades.

We will use the following rubric in our assessment:


 * 20%: class participation, including attendance and participation in discussions and group work
 * 20%: Labs and homework assignments
 * 25%: Exam
 * 35%: Final Project

The exam will be graded like a normal exam and the score will make up 25% of your grade. For the rest of the assignments (and the other 75% of your grade), I will provide feedback which will inform an ongoing conversation about your work.

My interpretation of grade levels (A, B, C, D/F) is the following:

A: Reflects work the exceeds expectations on multiple fronts and to a great degree. Students reaching this level of achievement will:
 * Do what it takes to learn the principles and techniques of social networks, including looking to outside sources if necessary.
 * Engage thoughtfully with an ambitious final project.
 * Take intellectual risks, offering interpretations based on synthesizing material and asking for feedback from peers.
 * Share work early allowing extra time for engagement with others.
 * Write reflections that grapple meaningfully with lessons learned as well as challenges.
 * Complete all or nearly all homework assignments at a high level.

B: Reflects strong work. Work at this level will be of consistently high quality. Students reaching this level of achievement will:
 * Be more safe or consistent than the work described above.
 * Ask meaningful questions of peers and engage them in fruitful discussion.
 * Exceed requirements, but in fairly straightforward ways - e.g., an additional post in discussion every week.
 * Compose complete and sufficiently detailed reflections.
 * Complete many of the homework assignments.

C: This reflects meeting the minimum expectations of the course. Students reaching this level of achievement will:
 * Turn in and complete the final project on time.
 * Be collegial and continue discussion, through asking simple or limited questions.
 * Compose reflections with straightforward and easily manageable goals and/or avoid discussions of challenges.
 * Not complete homework assignments or turn some in in a hasty or incomplete manner.

D/F: These are reserved for cases in which students do not complete work or participate. Students may also be impeding the ability of others to learn.

Extra Credit for Participating in Research Studies
If you feel like you need to earn extra credit in order to earn the grade that you would like, the course is signed up for extra credit through the Brian Lamb School of Communication Research Participation System.


 * Please review the instructions before you sign up for studies; to view the instructions go to https://www.cla.purdue.edu/communication/research/participation/students.html
 * You can sign up to participate in studies by logging into http://purdue-comm.sona-systems.com/.

TikTok Extra Credit Assignment
After our discussion on March 22, some folks expressed interest in a TikTok challenge to see who could get the most followers on TikTok. If you are interested in participating, then see the explanation below:


 * Explanation of TikTok Assignment

= Schedule =

NOTE This section may be modified throughout the course to meet the class's needs. Check back in weekly.

Week 1: Introductions and the network perspective
August 23

Assignment Due:
 * Sign up for Discord and introduce yourself
 * Take this very brief survey

Required Readings:
 * None

Concepts:
 * Class overview and expectations — We'll walk through this syllabus.
 * What are social networks?
 * Why study networks?

August 25

Assignment Due:
 * Read the entire syllabus (this document)

Readings:

Class Schedule:
 * Network simulation activity (bring a computer)
 * Start work on Homework 1

Week 2: Network representations
August 30

Assignment Due (on Monday):
 * Install R and RStudio on your computer. This tutorial should help you to succeed.
 * Homework 1
 * Discussion questions (Due Monday at noon!)

Lecture Video (before class):
 * Network Data and Network Types Lecture [19:18]

Readings (before class):
 * James M. Cook, What is a Social Network?
 * James M. Cook, Individuals versus Networks
 * (Optional/skim) Freeman, L. C. (2000). Visualizing social networks. Journal of social structure, 1(1), 4.

Concepts:
 * Complex systems and networks
 * Individual and collective behavior

September 1

Class Schedule:
 * Go through Parable of the Polygons by Nicky Case
 * Start work on R Lab 1

Week 3: How are communication networks formed?
September 6

Assignment Due (on Monday):
 * R Lab 1
 * Discussion Questions

Lecture Video:
 * Tie Formation [12:43] [Slides]

Readings:
 * Feld, S. L. (1981). The focused organization of social ties. American Journal of Sociology, 86(5), 1015–1035.
 * McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27, 415–444.
 * [Optional] Monge, P. R., & Contractor, N. S. (2003). Theories of communication networks. Oxford, UK: Oxford University Press. (pp. 298--314) - On Brightspace under Content > Readings

Note: This week involves reading two academic articles. Read this to understand my expectations and some tips for reading and understanding these articles.

Concepts:
 * Exposure, formation, maintenance, decay
 * Homophily
 * Reciprocity
 * Triadic closure


 * Class Slides

September 8

Supplementary R lectures (watch before class):
 * Why R + Programming principles lecture [12:53]

Class Schedule:
 * R Lab 2 - Creating Networks

Week 4: Small group networks
September 13

Assignment Due:
 * Discussion questions
 * R Lab 2 - Creating Networks (right-click, save to your computer, and open in RStudio)
 * Homework explanation video

Lecture video:
 * Networks in small groups [14:43] [Slides]

Readings:
 * Krackhardt, D., & Hanson, J. R. (1993). Informal networks: The company behind the chart. Harvard business review, 71(4), 104-111.
 * Katz, N., Lazer, D., Arrow, H., & Contractor, N. (2004). Network theory and small groups. Small Group Research, 35(3), 307–332.

Concepts:
 * Informal networks
 * Networks and group outcomes

August 15


 * Work on R Lab 3 - Mutating and filtering

Week 5: Ego networks and network perception
September 20

Assignment Due:
 * Discussion questions
 * R Lab 3 - Mutating and Filtering
 * Turn in your Self Assessment Reflection on Brightspace

Lecture:
 * Ego networks and network perceptions lecture [17:14] [Slides]

Readings:
 * Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. University of California. (Chapter 9) - Just read the first 2 sections - Introduction and Ego Network Data
 * Marsden, P. V. (1987). Core Discussion Networks of Americans. American Sociological Review, 52(1), 122–131.
 * Pentland, A.S. (2016). Research: You Have Fewer Friends than You Think. (2016, May 12). Harvard Business Review.
 * Smith, E. B., Menon, T., & Thompson, L. (2012). Status Differences in the Cognitive Activation of Social Networks. Organization Science, 23(1), 67–82.

September 22

Class Schedule:
 * Ego Network Activity
 * R Lab 4 - R Intro to ggraph

Week 6: Power, centrality, and hierarchy
September 27

Assignment Due:
 * R Lab 4 - R Intro to ggraph
 * Discussion questions

Video lecture:
 * Power, centrality, and hierarchy [18:44] [Slides]

Readings:
 * Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Chapter 10: Centrality and Power
 * Healy, K. (2013). Using Metadata to find Paul Revere.
 * Centrality measures. Matthew Jackson. From Social and Economic Networks course
 * Centrality Eigenvector Measures. Matthew Jackson
 * (Optional) Holliday, Audrey, Campbell, & Moore, (2016). Identifying well-connected opinion leaders for informal health promotion

Class Schedule:

September 29

Class Schedule:
 * R Lab 5 - Aesthetics in ggraph

Week 7: Social Capital, structural holes, and weak ties
October 4

Assignment Due:
 * R Lab 5 - Aesthetics in ggraph
 * Discussion questions

Lecture Video:
 * Capital and Social Capital [16:02] [Slides]

Readings:
 * Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.1086/225469
 * Kadushin, C. (2012). Networks as Social Capital, in Kadushin, C. (2012). Understanding Social Networks. Theories, Concepts and Findings. Oxford: Oxford University Press.
 * Putnam, R.D. (1995). Bowling Alone: America's Declining Social Capital. Journal of Democracy 6(1), 65-78.
 * (Optional) Bourdieu, P. (1986). The forms of capital. In J. Richardson (Ed.) Handbook of Theory and Research for the Sociology of Education (New York, Greenwood), 241-258.
 * (Optional) Rainie, L. and Perrin, A. (2019). Key findings about Americans’ declining trust in government and each other. Pew Research Center.
 * (Optional) Burt, R. S. (2000). The network structure of social capital. Research in Organizational Behavior, 22, 345–423.

October 6

Class Schedule:
 * R Lab 6 - Visualizing Power

Week 8: Small worlds
October 11

OCTOBER BREAK

October 13

Assignment Due:
 * R Lab 6 - Visualizing Power
 * Discussion questions - Just one question this week

Lecture Video:
 * Small worlds video [18:45] [Slides]

Readings:
 * The Science of Six Degrees of Separation[video][9:22]
 * Travers, J. and Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 32(4):425-443
 * (Optional but short) Dodds, P. S., Muhamad, R., & Watts, D. J. (2003). An Experimental Study of Search in Global Social Networks. Science, 301(5634), 827.

Week 9: Scale-free networks and the friendship paradox
October 18

Assignment Due:
 * Social Search Assignment
 * Discussion questions

Lecture Video:
 * Scale-free networks and the Friendship Paradox[18:21] [Slides]

Readings:
 * Feld, Scott L. (1991), Why your friends have more friends than you do. American Journal of Sociology, 96 (6): 1464–1477. https://doi.org/10.1086%2F229693
 * Early Detection of an Outbreak using the Friendship Paradox
 * Networks are everywhere with Albert-László Barabási

(Optional)
 * Christakis, N. A., & Fowler, J. H. (2010). Social Network Sensors for Early Detection of Contagious Outbreaks. PLOS ONE, 5(9), e12948. https://doi.org/10.1371/journal.pone.0012948

October 20

Class Schedule:
 * Six Degrees of Wikipedia Activity

Week 10: Social influence and diffusion
October 25

Weekly lecture:
 * Social Influence and Contagion[22:12] [Slides]

Assignment Due: Challenge]]
 * Turn in your Self Assessment Reflection on Brightspace
 * [[Communication and Social Networks (Fall 2022)/Dutch School Data Visualization challenge|Dutch School Data Visualization
 * Discussion questions

Readings:
 * Chapter 4, "Special People", in Watts, D. J. (2011). Everything is Obvious: Once you know the answer. New York, NY: Crown Business.
 * Duncan Watts on Common Sense
 * [Optional] Centola, D., & Macy, M. (2007). Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 113(3), 702–734.
 * [Optional] Christakis, N. A., & Fowler, J. H. (2012). Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine, 32, 556–577.

October 27


 * Troubled Lands

Week 11: Communities and Core-periphery
November 1

Assignment Due:
 * One discussion question
 * Submit two exam questions on Brightspace

Video Lecture:
 * Communities and Core-periphery [23:15] [Slides]

Readings:
 * Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences.
 * Barberá, P., Wang, N., Bonneau, R., Jost, J. T., Nagler, J., Tucker, J., & González-Bailón, S. (2015). The critical periphery in the growth of social protests. PLoS ONE.
 * (Optional) Hanneman, R. A., & Riddle, M. (2005). Cliques and sub-groups. In Introduction to social network methods. University of California.

November 3

Class Schedule:
 * R Lab 7 - Finding and visualizing groups in networks (Right-click, save, and open in RStudio).

Week 12: Technology and networks
November 8

Assignment Due:
 * R Lab 7 - Finding and visualizing groups in networks
 * Discussion questions

Lecture Video:
 * Technology and networks [19:38]

Readings:
 * Pariser, E. Beware Online Filter Bubbles TED talk
 * Fletcher, R. The truth behind filter bubbles: Bursting some myths.
 * Bail, C. Should we break our echo chambers?
 * Cohen, M. Context Collapse

(Optional)
 * Kleinberg, J. (2012). The Convergence of Social and Technological Networks. In M. Agrawal, S. B. Cooper, & A. Li (Eds.), Theory and Applications of Models of Computation.
 * Chris Bail, et al. (2018). Exposure to opposing views on social media can increase political polarization. PNAS.

November 10


 * Exam review

Week 13: Collective behavior
November 15

Assignment Due:
 * One discussion question
 * Keep working on the final project

Readings:
 * Becker, J., Brackbill, D., & Centola, D. (2017). Network dynamics of social influence in the wisdom of crowds. Proceedings of the National Academy of Sciences, 201615978.
 * Video discussion with Dr. Becker (watch after reading paper)

November 17


 * Take-home exam is due
 * The exam is open book and open note
 * You may want to work on this Practice R Exercise
 * Do The Wisdom or Madness of Crowds Simulation
 * Play network game?

Week 14: Networks and collaboration
November 22

Assignment Due:
 * 1 Discussion Question

Lecture video:
 * Networks and Collaboration[17:19]

Readings:
 * Read the Wikipedia Article about The Wealth of Networks
 * Skim section two of Benkler, Y. (2002). Coase’s Penguin, or, Linux and "The Nature of the Firm." The Yale Law Journal, 112(3), 369.

November 24

THANKSGIVING BREAK

Week 15: Networked racism
November 29

Assignment Due:
 * R Lab 8 - Calculating network statistics
 * Rough draft of Final Project on Brightspace and sent to your "peers"

Readings:
 * Fernandez, R. M., & Fernandez-Mateo, I. (2006). Networks, Race, and Hiring. American Sociological Review, 71(1), 42–71. Read the introduction (pp. 42–47) and the Summary and Conclusion (pp. 65–67)
 * Sunstein, C. R. (1991). Why markets don’t stop discrimination. Social Philosophy and Policy, 8(02), 22–37. https://doi.org/10.1017/S0265052500001114

December 1

No class - work on Final Project
 * (Optional) Advanced network visualizations in R

Week 16: Network Visualization Principles
December 6

Assignment Due:
 * Peer feedback on final project

Class Schedule:
 * Review principles of good network visualizations
 * Put examples at https://padlet.com/jdfoote1/networks (I will explain in class)
 * Work on final projects

December 8

No class - work on Final Project

Finals week
Assignment Due:
 * Final Project - Due Wednesday, May 4
 * Turn in your Final self reflection on Brightspace

= Policies =

Attendance
I try very hard to make our meeting times valuable to you and I expect that you attend. That being said, we are in the midst of a pandemic, so if you feel sick or think that you might have COVID, please do not come to class. If you need to miss class, then it is your responsibility to seek out support from classmates for notes, handouts, and other information.

Only I can excuse a student from a course requirement or responsibility. When conflicts can be anticipated, such as for many University-sponsored activities and religious observations, please inform me of the situation as far in advance as possible. For unanticipated or emergency conflicts, when advance notification is not possible, contact me as soon as possible on Discord or by email. In cases of bereavement, quarantine, or isolation, the student or the student’s representative should contact the Office of the Dean of Students via email or phone at 765-494-1747. Our course on Brightspace includes a link to the Dean of Students under 'Campus Resources.'

Classroom Discussions and Peer Feedback
Throughout the course, you may receive, read, collaborate, and/or comment on classmates’ work. These assignments are for class use only. You may not share them with anybody outside of class without explicit written permission from the document’s author and pertaining to the specific piece.

It is essential to the success of this class that all participants feel comfortable discussing questions, thoughts, ideas, fears, reservations, apprehensions and confusion. Therefore, you may not create any audio or video recordings during class time nor share verbatim comments with those not in class linked to people’s identities unless you get clear and explicit permission. If you want to share general impressions or specifics of in-class discussions with those not in class, please do so without disclosing personal identities or details.

Academic Integrity
While I encourage collaboration, I expect that any work that you submit is your own. Basic guidelines for Purdue students are outlined here but I expect you to be exemplary members of the academic community. Please get in touch if you have any questions or concerns.

Nondiscrimination
I strongly support Purdue's policy of nondiscrimination (below). If you feel like any member of our classroom--including me--is not living up to these principles, then please come and talk to me about it.

Purdue University is committed to maintaining a community which recognizes and values the inherent worth and dignity of every person; fosters tolerance, sensitivity, understanding, and mutual respect among its members; and encourages each individual to strive to reach his or her own potential. In pursuit of its goal of academic excellence, the University seeks to develop and nurture diversity. The University believes that diversity among its many members strengthens the institution, stimulates creativity, promotes the exchange of ideas, and enriches campus life.

Accessibility
Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let me know so that we can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247.

Emergency Preparation
In the event of a major campus emergency, I will update the requirements and deadlines as needed.

Mental Health
If you or someone you know is feeling overwhelmed, depressed, and/or in need of mental health support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at 765-494-6995 during and after hours, on weekends and holidays, or by going to the CAPS office of the second floor of the Purdue University Student Health Center (PUSH) during business hours.

Incompletes
A grade of incomplete (I) will be given only in unusual circumstances. The request must describe the circumstances, along with a proposed timeline for completing the course work. Submitting a request does not ensure that an incomplete grade will be granted. If granted, you will be required to fill out and sign an “Incomplete Contract” form that will be turned in with the course grades. Any requests made after the course is completed will not be considered for an incomplete grade.

Additional Policies
Links to additional Purdue policies are on our Brightspace page. If you have questions about policies please get in touch.