Communication Studies, Carleton University
Class Schedule: Thursdays, 18:00-21:00
Location: Azrieli Pavilion (AP) 132
Instructor: Dr. Tracey P. Lauriault
E-Mail: Tracey.Lauriault@carleton.ca (Please use cuLearn)
Office: 4110 River Building
Office Hours: Tuesday and Thursday 3:30-5:30 or by appointment
| Course Description and Objectives |
|---|
| Assignments |
| Readings and Schedule |
The collection of data about us generally starts when we are born, (sometimes earlier – consider the example of ultrasound), and then continues throughout the course of our lives. Data about us are collected when we go to school and then enter the labour force. We are also tracked throughout our lives via the unique identifier of a Social Insurance Number. Data are also produced as we go about our ordinary daily activities – when we shop, bank, make phone calls, order a taxi, use social media, navigate the city, vote, exercise, and file taxes. Data are also collected as we do exceptional things such as cross international borders, check into hospital, when we commit infractions, at death and even afterward depending on what happens to our bodies, how our families and friends commemorate and remember us and if our actions are recorded and archived. We are datafied, living a dataless life is nearly impossible as is ‘living below the radar’, we each have a data double that leaves behind a data shadow.
In this course we will examine what the data generated by and about us do, how they are used, who owns them, and if and when linked, filtered through algorithms, search engines or visualized into maps, how they determine actions and material outcomes.
The course covers a wide range of topics, themes and concepts such as data infrastructures, smart cities, the internet of things (IoT), surveillance and sousveillance, social physics, algorithmic governance, predictive policing,the quantified self, location based services, data collection devices and geodemographics among many others.
The objectives of this course are to learn to:
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distinguish big data from small data, and know about types of data;
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conceptualize data as part of socio-technological and political processes, as a form of discourse and as media;
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recognize the interconnections between data, their infrastructures, collection and dissemination technologies, software, platforms and forms and how these influence how data are produced and are used;
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identify data politics and critically read data polities; and
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think about data-based knowledge, the construction of facts and the framing of the truth.
| Assignment | Weight |
|---|---|
| Assignments (5 @ 10% each) | 50% |
| Essay Proposal | 5% |
| Final Essay | 20% |
| Final Exam | 25% |
| Total | 100% |
(5 X 10%=50%), due by noon on the Thursday before class as per the schedule below:
Pick 5 out of the 10–ONE to THREE page assignments each are worth 10 marks. Include the following in the document header:
Big Data and Society, COMM 3409A, Submitted to: Dr. Tracey P. Lauriault, Assignment #, dd/mm/yyyy, First and Last Name, Student ID
Assignments involve a data based exercise related to a given week’s topics and readings. These provide you with hands on experience: in searching for data; discovering data sources; reading metadata; documenting processes; with methods to critically think about issues and begin to uncover the techno-politics hidden in technological documents, algorithms, data collection technologies, infrastructures and platforms. Classroom discussions will be informed by discoveries and issues encountered when doing the assignments. Assignments will also provide you with the necessary tools and skills to write the paper.
Assignments will only be considered submitted for grading once uploaded to cuLearn and the submission statement is accepted. Under ordinary circumstances, assignments will not be accepted in hard copy or by email. See the Schedule below.
There are two parts to the paper.
- Final Essay (20% due Monday Dec. 7 just before exams): It is a short - 16 page MAX essay, including references and the cover page. The essay will be a critical reading of one or more datasets or databases of your choice. Along with a description of the dataset/database including things such as its intended use, description, institutional owners and etc., the essay should include information and a discussion if warranted about collection technologies, methodologies, dissemination platforms, portals, infrastructures, sources and so on. Paper topics, issues, or concepts wherever possible should stem from course readings and/or classroom lectures and datasets should be selected keeping that in mind. The assignments, lectures, readings and classroom discussions will provide you with the material required to write the essay.
Include the following on the title page:
Big Data and Society COMM 3409A Final Paper Title Author: First and Last Name Student ID Submitted to: Dr. Tracey P. Lauriault Date: dd/mm/yyyy
The paper will only be considered submitted for grading once uploaded to cuLearn and the submission statement is accepted and after the proposal has been submitted and evaluated. Under ordinary circumstances, essays will not be accepted in hard copy or by email.
- Final Essay Proposal (5% due Week 7: Oct.15): This is compulsory, and will be marked with comments and suggestions by Week 8 just before study break. You can of course do this earlier! This should include a tentative title, the topics you wish to examine, why this important or significant and is of interest to you. Ensure to have the name and the source of the dataset/database (s) you would like to study, point out how it relates to what you wish to discuss, include a list of related topics/issues/concepts you might discuss and/or use to support your argument, and a preliminary bibliography. You can do this as a summary, as an abstract, or a table of contents with bullet point notes. This should be no longer than 2 pages.
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Upload your assignments & paper in a Microsoft Word .doc or .docx file (you can export to these formats even if you do not own a copy of Microsoft Word)
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Use 12 pt, double-spaced type - essay and single spaced type - assignments in an appropriate font (e.g., Times New Roman, Garamond), flush left, with 1-inch margins and indented paragraphs.
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Number the pages of your paper.
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Pick a citation style and be consistent with it.
- Under ordinary circumstances, assignments will not be accepted more than 1 week after the due date. Extensions must be requested in advance, in person, and will typically require documentation of an extended illness or other significant disruption to your ability to complete required academic work.
The final exam will include a combination of multiple choice questions, short answer questions about concepts, topics, and issues, and one long answer essay question. The university sets the time and location. Please do not make end-of-term travel plans until these exam schedules have been posted. If you miss the exam, you will have to apply for a formal deferral from the Registrar’s Office – there is no guarantee this application will be approved.
| Week | Theme |
|---|---|
| Week 1 (Sept.3) | Introduction - Counting |
| Week 2 (Sept.10) | Big Data |
| Week 3 (Sept.17) | Small Data – Census, Administrative data and Mapping |
| Week 4 (Sept.24) | Sorting – Forms, loyalty cards, classification and geodemographics |
| Week 5 (Oct.1) | Prediction - Credit, Policing, Algorithmic Governance |
| Week 6 (Oct.8) | Sensors – Wearables, LBS, IoT/UbiComp/Smart City |
| Week 7 (Oct.15) | Earth Observation – Satellites, Radar, GNSS – GPS/Galileo and UAVs/Drones |
| Week 8 (Oct.22) | User Generated Content - Citizen Science, Crowdsourcing & Volunteered Geographic Information |
| Fall Break (Oct 26 – 30) | No classes |
| Week 9 (Nov.5) | Data-Based Activism |
| Week 10 (Nov.12) | Command and Control - Indicators, Benchmarks, Dashboards and Control Rooms |
| Week 11 (Nov.19) | Data as Cultural Artifacts and Remembering - Portals, Library Databases and Archives |
| Week 12 (Nov.26) | Critical Data Studies |
| Week 13 (Dec.3) | Making Sense of it all and Review |
Desrosieres, Alain (1998) Introduction: Arguing from Social Facts in The Politics of Large Numbers: A History of Statistical Reasoning. Cambridge: Harvard University Press. pp.1-16.
Porter, Theodore M. (1995) Cultures of Objectivity Introduction in Trust in Numbers: The Pursuit of Objectivity in Science and Public Life, Princeton University Press. pp. 3-11.
We will discuss the following datasets, databases and analytical methods in class. This will get us warmed up for the term and lay the ground work for how you can approach the 1 page assignments and your essay.
Look for a dataset that interests you and attempt to download the data. In no more than one page, explain the download process (the steps you took and whether you had difficulty downloading them), where you found these data (e.g. a portal, news blog, data library, etc.), and describe the dataset in such a way that 10 years from now you or a stranger could decipher what they are and what they could be used for.
Explain your interest in this dataset, what you might use the data for and explain what led you to trust these data. Be sure to provide a full citation of the dataset in the data citation format of your choice. Here are some useful guides:
The following readings and lecture address how big data are generated, the big data hype, Big Data industry clusters, national drivers and the rise in power of the quants.
Bell, Jennifer (2015) The Secret Life of Big Data, in Boellstorff, Tom and Maurer, Bill, (Eds.) Data, Now Bigger and Better! Chicago: Prickly Paradigm Press, pp.7-26.
Crawford, K., 2014, The Anxieties of Big Data, The New Inquiry, April.
Kitchin, Rob. (2014), Chapter 1, Conceptualizing Data pp.1-26 and Chapter 4, Big Data, pp.67-79, The Data Revolution. London: Sage
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Kitchin, Rob. (2014), Chapter 5,Enablers and Sources of Big Data, pp. 80-89. The Data Revolution. London: Sage.
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The MASSTech Big Data Report
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The MASS TLC Big Data Report
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Government of Ireland: Assessing the Demand for Big Data and Analytics Skills, 2013 – 2020 Report
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McKinsey Global Institute: Big data: The next frontier for innovation, competition, and productivity
Referring to the characteristics of big data provided by Kitchin, conduct an inventory of the big data you produce and generate a list. Select 3 to 5 from this list and describe their characteristics, explain how you generate these, what devices you use to do so, who owns them, how you access them, how these get used and what these might say about you.
In this lecture we will examine the social shaping qualities of data and maps, how they are part of our social and geographical imaginaries, and how these are the tools for nation building and state management. We will also explore why we believe maps, how to interrogate them and the rise of social media, geotagging and how these data tell stories.
Anderson, Benedict. (1991). Chapter 10, Census, Map, Museum in Imagined Communities: Reflections on the Origin and Spread of Nationalism. Revised Edition, Verso, New York. Pp.163-186.
Alonso, William and Starr, Paul (1989) Introduction (Eds) The Politics of Numbers, New York: Russel Sage Foundation, pp. 1-6.
Starr, Paul and Corson, Ross (1989) Who will have the Numbers? The Rise of the Statistical Services Industry and the Politics of Public Data, Chapter 14 in Alonso, William and Starr, Paul (Eds) The Politics of Numbers, New York: Russel Sage Foundation, pp. 415-447.
Caquard, Sebastien. (2014). Cartography II: Collective Cartographies in the Social Media Era. Progress in Human Geography, 38: 141. Pp.141-150.
Harley, J. B. (1989). Deconstructing the Map. Cartographica, 26 (2), pp.1-20.
Wilson, Matthew W. (2012) Location-Based Services, Conspicuous Mobility, and the Location-Aware Future. Geoforum (43), pp.1266-1275.
Government of Canada Report: The Canadian Geospatial Data Infrastructure vision, mission and roadmap - The way forward.Alternative link here. Natural Resources Canada, Information Product 28e, 2012; 20 pages.
Video: Hans Rosling, The River of Myths, BBC4
Video: Hans Rosling, New insights on poverty, Ted Talk
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List of Indicators in Gapminder
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Federation of Canadian Municipalities (FCM) Municipal Data Collection Tool (MDCT) Follow the sign in instructions
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Maps, Data and Government Information Centre (MADGIC), MacOdrum Library,
Read the Principles listed in the CGDI Vision, Mission and Road Map, and write a 1 page opinion piece on the policy orientation of these principles for Canada. Keep in mind some of the concepts in Anderson, Alonso, Starr, and Corson.
In this lecture we get 'sorted out' and learn about how things get sorted
Bowker, Geoffrey C. and Susan Leigh Star. (2000), Chapter 1, Introduction: To Classify is Human, in Sorting Things Out: Classification and Its Consequences, MIT Press.
Turow, Joseph. (2006). Chapter 6, The Customized Store in Niche Envy: Marketing Discrimination in the Digital Age. MIT Press. pp. 125 - 147
I will also be referring to the following sources, which you should treat as ‘recommended for review’.
Bivens, Rena, (Forthcoming 2015) The Gender Binary Will Not be Deprogrammed: Ten Years of Coding Gender on Facebook, 2014 SSRN Working Paper.
Policy Report: On the Data Trail: How detailed information about you gets into the hands of organizations with whom you have no relationship. A Report on the Canadian Data Brokerage Industry. Canadian Internet Public Policy Clinic, Ottawa, April 2006.
Corporate blog post: How Big Data Is Remaking Customer Loyalty Programs, by Woodie, Alex in Datanami, December 8, 2014.
Podcast: Turow, Joseph. (2012) How Companies Are 'Defining Your Worth' Online. Fresh Air, National Public Radio, February 22. Also see the Excerpt of the book The Daily You at the bottom of the page.
Video: UK's first interactive virtual grocery store, Tesco
Go to the Environics Analytics website and lookup your geodemographic profile in the PRIZM5 data segmentation product. Copy and paste the description, explain how the tool works, and in a paragraph or two critically discuss whether or not you fit the profile. Also explain how your profile was created, provide the names and sources of 2 or 3 of the datasets used to create it. Be sure to include all of the steps you took to discover this information, such as urls and sources.
This class has some heavy reading – apologies -- it will take time, but I promise these readings will be worth it. We have discussed many issues to far, and here we get into the meat/tofu and potatoes of big data. You will learn how data are plugged into algorithms that predict outcomes that in turn get acted upon.
Williamson, Ben (2014) Governing Software: Networks, Databases and Algorithmic Power in the Digital Governance of Public Education, Learning, Media and Technology, 40:1, 83-105
Guzic, Keith, (2009) Discrimination by Design, Predictive Data Mining as Security Practice in the United States ‘War on Terrorism’ ,in Surveillance Systems, 7(1) pp. 1-20.
Marron, Donncha (2007) Lending by Numbers’: Credit Scoring and the Constitution of Risk within American Consumer Credit, Economy and Society, 36(1) pp.103-133.
I will also refer to the following resources in class.
Magnet, Shohana, (2009) Bio-Benefits: Technologies of Criminalization, Biometrics, and the Welfare System. Chapter 10 in Hier, Sean P. and Greenberg, Josh, Surveillance: Power, Problems and Politics, Toronto, UBC Press. pp. 169-183.
Report: Summary and Chapter 1 of Perry, Walter L.; McInnis, Brian; Price, Carter C.; Smith, Susan C.; and Hollywood, John S. (2013) Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations; Washington D.C.: The RAND Corporation, pp. xxiii-xxiv and 1-15.
Magazine Article: Moser, Whet (2013) The Small Social Networks at the Heart of Chicago Violence, Chicago Mag, Politics and City Life, Dec. 9.
Trailer:
In this assignment you are asked to consider the benefits and risks related to predictive analytics as discussed in the readings and discuss how these could affect you or people in your network and how much you might be willing to give up for safety and protection.
In this lecture we learn about data collected by the sensors we wear and those embedded into our cities.
Batty, M., Axhausen, K.W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G. and Portugali, Y. (2012) Section 2 State of the Art in Smart cities of the future. European Physical Journal Special Topics 214. Pp. 6-26.
Mann, Steve; Nolan; Jason and Wellman, Barry (2003) Sousveillance: Inventing and Using Wearable Computing Devices for Data Collection in Surveillance Environments, Surveillance & Society 1(3) pp. 331-355.
The following are resources I will refer to in the lecture, especially Crang and Graham.
Crang, Mike and Graham, Stephen (2007) Sentient Cities: Ambient Intelligence and the Politics of Urban Space, Information, Communication & Society, 10(6). pp. 789-817.
Magazine Article: Jordan, M. and Pfarr. Forget the Quantified Self. We Need to Build the Quantified Us, Wired, April 2014.
Blog Post: A Taxonomy of Sensors, Puneet Kishoor in blog Punkish.org, April 2, 2014.
Industry Blog Post: The Beginner’s Guide to Quantified Self (Plus, A list of the best of the best personal data tools out there). Mark Moschel from technori.com writing for Under Armour.
Industry Blog Post: “Soft” Sensors Are Breaking Into Four Major Industries, Posted by Todd Gidsby, Techcrunch, August 1, 2015.
Useful Reference: Gurrin, Cathal; Smeaton, Alan F. and Doherty, Aiden R. (2014), LifeLogging: Personal Big Data, Foundations and Trends in Information Retrieval: Vol. 8: No. 1, pp 1-125.PrePrint Version found here
Select one of the apps listed in The Beginner’s Guide to Quantified Self or one of the related apps you use. Read the Terms of Use / Licence and describe who has rights over the data collected.
In addition, explain the way data are delivered to you, what kind of trend data & tools & visualizations are provided, and how do you get access to these? What kind of data are you providing them? Finally, provide your thoughts about data access and ownership. Be sure to provide URLs, the name of the app, the company that produces it, if there was a cost associated, and if you cannot download the ToU or Licence, copy paste the text and add it as an appendices.
This is a continuation of last week’s lecture on sensors, only this week we are looking at sensors in the sky.
Byers, Michael (2008) For Sale: Arctic Sovereignty? How losing a Canadian satellite to the US would be like losing our eyes on the North.The Walrus. June.
Tully, Mike. (2013) The Rise Of The [Geospatial] Machines Part 1: The Future With Unmanned Aerial Systems (UAS), , The Rise Of The [Geospatial] Machines Part 2: Business And Privacy, and The Rise Of The [Geospatial] Machines Part 3: New Opportunities In The Coming Unmanned Aerial System (UAS) Age. Sensors and Systems.
Warf, B. (2007), Geopolitics of the Satellite Industry. Tijdschrift voor economische en sociale geografie, 98 (3) pp. 385–397.
Below are some useful EO data and information resources for you.
Lewis, James Andrew (2004) Galileo and GPS: From Competition to Cooperation, Center for Strategic and International Studies June 2004.
Lauriault, Tracey P. and Wood, Jeremy, (2009) GPS Tracings – Personal Cartographies. The Cartographic Journal 46 (4) Art & Cartography Special Issue. pp. 360–365. Alternate link here
Educational Resources:
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Canadian Space Agency (CSA) information on Satellites
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See the Geodetic Reference Systems and Satellite Imagery and Air Photos at the Geomatics Canada, Canada Centre for Mapping and Earth Observation (CCMEO)
Go to the American Association for the Advancement of Science (AAAS) website and select one of any of the EO human rights case studies or one of their human rights reports and documents. Explain how EO technologies were used to document a crisis or a human rights issue, include how the issues was reported, something about the organizations involved, how the data were accessed, and your reflections on this type of analysis and reporting and why you think the organizations involved chose this route.
Week 8 (Oct. 22) – User Generated Content - Citizen Science, Crowdsourcing & Volunteered Geographic Information
In this lecture we look into data being produced by ‘amateurs’, collected by the ‘crowd’, crowdsourced labour practices and disrupting formal data producing institutions.
Bonney, R., Cooper, C.B., Dickinson, J., Kelling, T.P., Rosenberg, k. V. and Shirk, J. (2009) ‘Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy.’ BioScience 59 (11), pp. 977–84.
Haklay, M. (2013) 'Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation.’ In: Crowdsourcing Geographic Knowledge, Sui, D., Elwood, S. and Goodchild, M. Eds, Springer Netherlands.
Video: Buscher, Monika (2014) Digital urbanism in crises: A hopeful monster? At the Software and the City Workshop, Programmable City Project, National University of Ireland Maynooth.
Below are some of the additional resources I will refer to in class.
Kingsley; Sara C., Gray, Mary L. and Suri; Siddharth (2014) Monopsony and the Crowd: Labor for Lemons? Internet Politics and Policy Conference 2014 Proceedings, pp. 1-44
Keynote Lecture Videos:
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Gray, Mary L., Crowdsourcing and Labour (2014).
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Lintott, Chris, Citizen Science and Zooniverse(2014).
Discuss whether or not government could benefit from user generated data, and if so how could government and other institutions such as aid agencies authoritatively stand behind these? The Government of Canada did recently use some crowdsourced data, and some concerns were noted, find examples of this at any level of government and discuss any of the concerns listed.
Crowdsourcing we examine ways that individuals and groups get things counted or counting what has been collected, making data accessible and open and the politics thereof.
Campbell, Rebecca; Shaw, Jessica and Fehler–Cabral, Giannina (2015)Shelving Justice: The Discovery of Thousands of Untested Rape Kits in Detroit, City & Community, 14 (2) 2, pp.151–166.
Comas-d’Argemir, Dolors, (2015) News of Partner Femicides: The Shift from Private Issue to Public Problem.European Journal of Communication, 30(2), pp. 121-137.
Zuiderwijk, A. and Janssen, M. 2014, Open data policies, their implementation and impact: A framework for comparison, Government Information Quarterly (31) 17–29.
Below are resources related to the topics in the readings.
TV Show: Law & Order Special Victims Unit, Season 12 Episode 3, Behave.
Website: Joyful Heart Foundation
NFB Documentary Film: Who's Counting? Marilyn Waring on Sex, Lies and Global Economics, NFB Documentary, Produced by Terry Nash (1995).
Newspaper Article: Arthur, Charles and Cross, Mitchel, (2006)Give us back our crown jewels, The Guardian, March 9.
Industry Reports:
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McKinsey, Open data: Unlocking innovation and performance with liquid information.
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Deloitte, Driving economic and social growth Designing an open data strategy for Public Sector organizations.
Compare and contrast the issues related to counting and data collection with open data as forms of data based activism. When doing so consider the policies being addressed, the data being accessed, the actors involved and evidence based decision-making.
Ranking and comparing are what this lecture is about, and data are almost always aggregated into, in order to distil meaning, here we discuss the move toward measuring, the power of dashboards and monitoring places from the control room.
Matten, Shannon (2015) Mission Control: A History of the Urban Dashboard, Places. Moonen, T. and Clark, G. (2013) Sections 1 & 2, The Business of Cities 2013: What do 150 city indexes and benchmarking studies tell us about the urban world in 2013? Jones Lang LaSalle.
Ribes, D. and Jackson, S.J. (2013) Data bite man: The work of sustaining long-term study. In Gitelman, L. (Ed) “Raw Data” is an Oxymoron. MIT Press, Cambridge, pp 147-166.
Below are additional resources that I will refer to in class.
Hoyman, Michele and Faricy, Christopher (2008) It Takes a Village A Test of the Creative Class, Social Capital, and Human Capital Theories, Urban Affairs Review. DOI:10.1177/1078087408321496, Alternate link here.
Huggins, R. (2009) Regional Competitive Intelligence: Benchmarking and Policy-Making, Regional Studies, 44(5): 639-658.
Kitchin, Rob, Lauriault, Tracey P. and McArdle, Gavin (2015) Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards, Regional Studies, Regional Science, 2 (1) pp. 6-28, Alternate link here.
Blog Post: Florida, Richard, (2012) What Critics Get Wrong About the Creative Class and Economic Development, Atlantic City Lab, July 3rd.
Seminar Video: Tkacz, Nathaniel (2014) Dashboards and Data Signals, Programmable City Project Seminar Series.
Select an indicator system of your choice, choose one or two of the indicators or indices in that system, explain how it is constructed, including data sources, how it is represented, the policy it aims to inform and assess if it meets intended objectives, and if not why.
Week 11 (Nov. 19) – Data as Cultural Artifacts and Remembering - Portals, Library Databases and Archives
Lauriault, T. P. and D. R. F. Taylor, 2012, The Map as a Fundamental Source in the Memory of the World, UNESCO Memory of the World in the Digital Age: Digitization and Preservation, Vancouver, BC.
McKemmish, Sue (1996) Evidence of me, The Australian Library Journal, 45(3) pp.174-187. Alternate link here.
Ruus, Laine G. M. (1982) The University of British Columbia Data Library, Library Trends 30(3) pp.397-406.
I know, this looks like a dry one, it is not, Mckemmish’s paper is a classic and having said that it is a necessary evil. On the positive site there is no assignment this week! Below are some institutional resources.
Lauriault, T. P., B. Craig, P. L. Pulsifer, and D. R. F. Taylor, (2008), Today's Data are Part of Tomorrow's Research: Archival Issues in the Sciences. Archivaria #64, pp. 123-179.
Report: O’Carroll, Aileen and Webb, Sharon (2012) Digital Archiving in Ireland, National Survey of the Humanities and Social Sciences. Dublin: The Digital Repository of Ireland.
Resources:
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MADGIC (Maps, Data and Government Information Centre)
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CODATA International Council for Science: Committee on Data for Science and Technology
Kitchin, R. and Lauriault, T. P. (2016 Forthcoming) Towards Critical Data Studies: Charting and Unpacking Data Assemblages and Their Work. In Eckert, J. et al (Eds) Geoweb and Big Data, Lincoln, University of Nebraska Press.
Dalton, Craig and Thatcher, Jim, What does a critical data studies look like, and why do we care? Seven points for a critical approach to ‘big data.’
Kitchin, R., 2014, Chapter 11: [Making Sense of the Data Revolution](, http://catalogue.library.carleton.ca/record=b3683612) in The Data Revolution. London: Sage.
Being here is crucial! We will review the 12 weeks of lessons, go over the exam and provide the exam essay questions, and will respond to any querries.
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This course uses cuLearn, Carleton’s learning management system. Updates to course information and readings will be posted on cuLearn.
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Assignments are to be submitted on cuLearn by noon on Thursday (see Schedule below).
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To access your courses on cuLearn, go to carleton.ca/culearn. For help and support, go to carleton.ca/culearnsupport/students. Any unresolved questions can be directed to Computing and Communication Services by phone at (613) 520-3700 or via email at ccs_service_desk@carleton.ca.
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Please note that cuLearn is tied to your Carleton email address. Any notifications related to the class will go to that email. Check it regularly.
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If you are sending me email, please ensure that COMM 3409A is added in the subject line with the topic of discussion
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Outside of class time, you can make use of various digital tools to engage with the class. For example, use the discussion forum on cuLearn to ask questions of peers, TAs, and the professor or to talk with your classmates about course material and assignments.
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I will respond to student emails within 24hours between Monday-Friday, 10-5PM, be sure to have COMM 3409A is added in the subject line. Please note that I do not respond to email during the weekend.
COMM3409 Syllabus by Tracey P. Lauriault is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://github.com/TraceyLauriault/COMM3409_Fall2015.