MC 501 Assignments

MC 501 Assignments

Every assignment in this course, grouped by phase, with what it asks for and when it is due.

Each assignment shows a short description. Open Full requirements for the detail: what to include, how long, and what earns full credit.

NoteHow to turn work in

Submit everything in Blackboard, under the assignment for that week. Keep your work in your course workspace and commit it to GitHub as usual for the backup and the history, but committing is not submitting – grading happens from the Blackboard submission. Two assignments also ask you to paste a GitHub link into the submission; the link goes in the submission, it does not replace it.

Everything is due at 11:59 PM on the Monday listed. You are welcome to submit any assignment early. Attempts are unlimited, so an early submission can still be revised right up to the deadline.

Submissions are unlimited. If you upload the wrong file, or want to revise before the deadline, submit again.

Every assignment is a part of the paper

Almost nothing here is submitted and then finished. Each assignment builds a component the next one consumes, and the White Paper is those components assembled and argued. That is why the workspace has the folders it has:

Folder What accumulates there
02_Literature/ one file per source, named author-year-slug.md
03_Project/01_Prospectus/ the question, the design, the justification
03_Project/02_Codebook/ definitions, then the instrument itself
03_Project/03_Data/ the sampling plan, the pilot, the coded data, the scripts
03_Project/04_Drafts/ figures, drafts, and the White Paper

Each assignment below says where its artifact lives. Keeping it there is not bookkeeping: it is what makes the methods section writable in December, because by then the trail is already on disk.

Everything at a glance

Due Assignment Phase Points
Monday, August 31 Syllabus Contract Phase II 25
Monday, September 7 GitHub Profile Phase II 25
Monday, September 21 Librarian Visit Report Phase II 50
Monday, September 28 CITI Ethics Certification Phase III 25
Monday, September 28 Annotated Manuscripts, three articles Phase III 25
Monday, October 5 Topic Selection and Research Questions Phase III 25
Monday, October 12 Research Proposal, sentence outline Phase III 75
Monday, October 19 Definitions Practice Phase III 25
Monday, October 26 Extended Codebook and Reliability Protocol Phase III 75
Monday, November 2 Sampling Plan and Pilot, 20 to 30 items Phase III 75
Monday, November 9 Data Wrangling in R Phase IV 50
Monday, November 16 Describing Data in R Phase IV 100
Monday, November 23 Inferencing Data in R Phase IV 100
Friday, December 18 White Paper and conference poster Phase V 200
Every week Weekly reading journal, 14 entries Phase I 140
End of term Journal consistency Phase I 10

Total: 1025 points.

Phase I: The Journalist

Foundation. The habit that runs the whole term. Phase page

Weekly reading journal

10 points each, 14 entries, plus 10 points for consistency. 150 points total. Due every Monday at 11:59 PM.

Each week, write a reflection on the assigned reading. Choose one of three thinking paths:

Full requirements, including length and the fourteen due dates
  1. The Connector: link the reading to your project, other concepts, or real-world examples
  2. The Troubleshooter: document a challenge you encountered and how you worked through it
  3. The Critic: ask critical questions, challenge assumptions, or explore implications

MC 501 requirements

Length: 450 to 500 words per entry.

You read the textbook with the Graduate edition toggle on, which reveals a required graduate extension in every chapter. Each week also carries an assigned peer-reviewed article, listed in the MC 501 schedule.

Your entry must explicitly engage that week’s assigned graduate reading, not only the chapter. An entry that addresses chapter content alone receives a maximum of 7 of 10 points.

Submission

Write your journal entry in your course workspace (01_Journal/) by copying the journal template (_templates/journal-entry.md) into that folder, then submit via Blackboard.

What Makes a Strong Entry

  • Depth over breadth: focus on one or two ideas rather than summarizing everything
  • Specificity: reference specific concepts, examples, or passages
  • Connection: link to your own project or to prior readings
  • Honesty: it is fine to be confused or skeptical, so document your thinking

If you used AI

If AI shaped any work you submit that week, note in the entry what you used it for and how you checked its output. This is required by the syllabus.

The fourteen entries

There is no Week 14 – that is Thanksgiving.

Week Due
Week 1 Monday, August 31
Week 2 Monday, September 7
Week 3 Monday, September 14
Week 4 Monday, September 21
Week 5 Monday, September 28
Week 6 Monday, October 5
Week 7 Monday, October 12
Week 8 Monday, October 19
Week 9 Monday, October 26
Week 10 Monday, November 2
Week 11 Monday, November 9
Week 12 Monday, November 16
Week 13 Monday, November 23
Week 15 Monday, December 7

Phase II: The Architect

Planning. Get the tools working and the project pointed at something answerable. Phase page

Syllabus Contract

25 points. Due Monday, August 31, 11:59 PM. (Week 1)

Open the syllabus contract template in VS Code, fill in the acknowledgment fields, and render it to PDF. This confirms your software (R, VS Code, Quarto) is installed and working.

GitHub Profile

25 points. Due Monday, September 7, 11:59 PM. (Week 2)

Create a professional GitHub account with:

Full requirements
  • A professional username and profile photo
  • A profile README introducing yourself and your research interests

GitHub Setup Guide

Librarian Visit Report

50 points. Due Monday, September 21, 11:59 PM. (Week 4)

Meet with a SIUE librarian to identify and evaluate a data archive for your research project. You arrange this meeting yourself; no librarian visits MC 501. You meet ours in MC 500. If you need more than that, book a subject librarian at Lovejoy Library. Write a report documenting:

Full requirements
  1. The archive: name, type, scope
  2. What you learned: date range, searchability, export options
  3. Feasibility: can this source answer your research questions?
  4. Librarian consultation: what did the librarian recommend?

Submit as PDF: Lastname_ArchivistReport.pdf

Phase III: The Builder

Operationalization. Turn concepts into an instrument a stranger could apply. Phase page

CITI Ethics Certification

25 points. Due Monday, September 28, 11:59 PM. (Week 5)

Complete the CITI Program’s Social and Behavioral Research training, and draft the determination you would actually file.

What you submit: two files. The CITI completion certificate as a PDF, and your non-human-subjects determination draft.

Where it lives: 04_Resources/ for the certificate, 03_Project/01_Prospectus/ for the determination. The determination is the first draft of the ethics paragraph in your methods section.

Requirements

  1. The course completed is Social and Behavioral Research, the student version, not Responsible Conduct of Research.
  2. The institution reads Southern Illinois University Edwardsville, your name matches your enrolled name, and the certificate carries its verification ID.
  3. The determination draft, 300 to 400 words, stating: what your data is, where it came from, whether the people in it are identifiable, why the project does or does not meet the federal definition of human-subjects research, and which exemption or determination category you would claim.
  4. The draft names one fact that would change the answer. A public corpus that became identifiable through a join is the obvious case, and it is the one your own project runs closest to.

How it is graded. Certificate 10, determination 10, the changed-answer paragraph 5.

Where it comes from. Week 3 and Chapter 3. What it feeds: the methods section of your White Paper, which has to state the determination rather than assume it.

How this loses points. Concluding “not human subjects” without naming the definition you applied. The determination is an argument from a rule, not a verdict, and a reviewer will ask which rule.

Annotated Manuscripts, three articles

25 points. Due Monday, September 28, 11:59 PM. (Week 5)

Annotate three peer-reviewed articles, then write the paragraph that makes them one body of work rather than three summaries.

What you submit: the three annotated PDFs, plus one document holding the per-article notes and the synthesis.

Where it lives: 02_Literature/, one file per source named author-year-slug.md, with the APA references added to a running 02_Literature/references.md. The synthesis paragraph is the first draft of a paragraph in your Research Proposal.

Requirements

  1. All three are peer reviewed and report original research.
  2. For each article: the gap the authors claim, the theoretical framework or its absence, the method with unit and sample, the finding in one sentence with its effect size and n where reported, and the limitation the authors admit.
  3. A synthesis paragraph, 200 to 300 words, that names what the three converge on, where they disagree or qualify each other, and the limitation they share.
  4. The synthesis leads with the claim and carries its citations at the end, in the form (Lastname, YEAR). A chain of author-led sentences is an annotated bibliography, not a synthesis.
  5. One sentence naming the gap the three leave open, and what your study would do about it.
  6. APA 7 references for all three.

How it is graded. Per-article notes 12, four points each. Synthesis 8. The gap sentence 3. References and the peer-reviewed requirement 2.

Where it comes from. Week 4 and Chapter 4. What it feeds: the Research Proposal two weeks later, whose introduction is this synthesis with a study attached.

How this loses points. Three good summaries and a weak synthesis is the failure this assignment is designed to expose, and it is worth 8 of 25. Picking three articles that agree with each other is the other: convergence with nothing to qualify it gives you nothing to argue with.

Topic Selection and Research Questions

25 points. Due Monday, October 5, 11:59 PM. (Week 6)

Commit to a topic and write the questions your study will answer.

What you submit: one document, roughly one page.

Where it lives: 03_Project/01_Prospectus/. These are the questions the paper answers, edited in place rather than rewritten.

Requirements

  1. A topic statement of two to three sentences naming the phenomenon, the population and the context.
  2. One to three research questions, numbered, each written as a question.
  3. Hypotheses where you have a directional prediction, labeled H1, H2, each a stated relationship between two variables, each with one sentence of theoretical justification for the direction. A prediction with no reason behind it is a guess.
  4. A variable preview: for each question, the construct, what you would measure, the unit of analysis and the expected level of measurement.
  5. A justification, 100 to 150 words, of why the answer matters, written for a reader in your field rather than for me.
  6. The five-criteria check, applied in writing to each question.

How it is graded. Topic 3, questions 5, hypotheses with justification 5, variable preview 5, justification 4, five-criteria check 3.

The five criteria, in full

Run every question past these before you submit it:

  1. Specific. Name the platform, specify the outcome, bound the population. “How does social media influence politics?” leaves all three undefined.
  2. Measurable. Some concepts resist operationalization. “Do authentic streamers build better communities?” cannot be studied until authentic and better become something you can record.
  3. Answerable within your constraints. Decades of data or hundreds of interviews is not a semester project.
  4. Not already answered. Your literature review tells you whether the question is genuinely open.
  5. It matters. A question can be answerable and still be trivia.

Two of these you can check yourself. Not already answered and it matters are judgments you make from your reading, and the check asks you to state the judgment and the evidence behind it.

Where it comes from. Weeks 5 and 6, Chapters 5 and 6. What it feeds: the Research Proposal, which is these questions with a method and a sampling plan attached.

How this loses points. A directional hypothesis with no theory behind it costs the justification points even when the direction turns out right. Submitting a subject rather than a question is the other common one.

Full requirements

Run every question past the five criteria before you submit it:

  1. Specific. Name the platform, specify the outcome, bound the population. “How does social media influence politics?” leaves all three undefined.
  2. Measurable. Some concepts resist operationalization. “Do authentic streamers build better communities?” cannot be studied until authentic and better become something you can record.
  3. Answerable within your constraints. Decades of data or hundreds of interviews is not a semester project.
  4. Not already answered. Your literature review tells you whether the question is genuinely open.
  5. It matters. A question can be answerable and still be trivia.

Research Proposal, sentence outline

75 points. Due Monday, October 12, 11:59 PM. (Week 7)

A structured sentence outline that could serve as the front half of a conference paper.

What you submit: one document, 1,200 to 1,800 words, in outline form. Every point is a full sentence carrying a claim, never a topic label. “Parasocial interaction and chat” is a label; “Viewers of smaller channels report stronger parasocial bonds, which should leave a visible trace in how they address the streamer” is a point.

Where it lives: 03_Project/01_Prospectus/. This outline is the front half of the White Paper in skeleton form. Every point becomes a sentence or a paragraph in December.

Requirements

  1. Framing, three to five points: the phenomenon, why it matters now, and what is at stake in getting the answer wrong.
  2. Theoretical positioning, four to six points naming the lens, what it predicts, and what a competing lens would predict instead. The competing lens is not optional; a framework with no rival is a decoration.
  3. The literature, six to ten points, organized by theme rather than by author, carrying the convergence, the nuance and the shared limitation. This is your synthesis paragraph expanded.
  4. The gap, two to three points, resting on that shared limitation.
  5. Research questions or hypotheses, stated formally, each traceable to a point in the theory section.
  6. The proposed design, eight to twelve points: unit of analysis, population and frame, sampling method, measurement, the reliability protocol you intend, and the analysis you plan before you see the data.
  7. Contribution, two to three points, written for your field rather than for this course.
  8. References in APA 7 for everything cited.

How it is graded. Framing 8, theory 15, literature 15, gap 8, questions 10, design 15, contribution 4.

Where it comes from. Weeks 5 and 6, Chapters 5 and 6. What it feeds: the front half of the White Paper, and the analysis plan you commit to before running anything.

How this loses points. Topic labels instead of sentences is the failure this assignment is named for. The second is a design section that names no analysis, which quietly defers every hard decision until after you have seen the data, which is exactly what this term has been arguing against.

Definitions Practice

25 points. Due Monday, October 19, 11:59 PM. (Week 8)

What you submit: one document, one entry per variable, at least four variables.

Where it lives: 03_Project/02_Codebook/. Written in the form the Extended Codebook needs next week.

Requirements. Each variable carries all six:

  1. A conceptual definition, with a citation to where the construct comes from. At this level a construct you invented needs more defense than one you borrowed.
  2. An operational definition: exactly what a coder does to assign a value.
  3. The level of measurement, named, with one sentence on what that permits and forbids in analysis.
  4. The coded values, in full, with what each means.
  5. At least two edge-case decision rules, written as rules.
  6. One sentence on validity: what would make you doubt this measures the construct you named in point 1.

How it is graded. Five per variable across the six elements, with the operational definition, the edge cases and the validity sentence carrying the weight.

Where it comes from. Week 7, Chapter 8. What it feeds: the Extended Codebook a week later.

How this loses points. Restating the conceptual definition in the operational slot. If your operational definition contains no verb a coder performs, it is not one yet.

Define at least four variables with conceptual definitions, operational definitions, levels of measurement, coded values, and edge-case decision rules.

Full requirements

Each variable needs both kinds of definition, written side by side.

A conceptual definition says what the variable means in the abstract: what is this meant to capture? An operational definition is the recipe – exactly what a coder does to assign a value, detailed enough that a stranger could follow it and measure the same thing you measured.

Conceptual tells you what the variable is for. Operational tells you what counts as evidence. A conceptual definition with no operational definition is an idea you cannot measure; an operational definition with no conceptual definition is a procedure that has lost track of why it exists. That is how a study ends up precisely measuring something nobody wanted to know.

Extended Codebook and Reliability Protocol

75 points. Due Monday, October 26, 11:59 PM. (Week 9)

What you submit: the codebook and the protocol, as one document with two headed parts.

Where it lives: 03_Project/02_Codebook/, starting from _templates/codebook.md. The protocol becomes the intercoder reliability report that the paper’s methods section is graded on.

The codebook

  1. Study overview, the question, the corpus, the period.
  2. The unit of analysis, stated exactly.
  3. Every variable with the six elements from Definitions Practice.
  4. Decision rules for cases that do not sort themselves.
  5. Two or three prototypical examples per category, drawn from your corpus.

The reliability protocol

  1. The statistic you will report, named, with one sentence on why it rather than another. Percent agreement alone is not acceptable and the protocol should say why.
  2. The threshold you will treat as adequate, with the source of that threshold.
  3. The training procedure: how the second coder learns the instrument.
  4. A fresh reliability sample, separate from the training set, with its size and how it is drawn.
  5. What happens when it fails: the revision rule, and how the codebook changes without quietly fitting itself to one coder’s habits.
  6. What you will report even if the number is disappointing.

How it is graded. Codebook 40, protocol 35, weighted toward points 6, 9 and 10.

Where it comes from. Week 8, Chapter 8 and the Lombard reading. What it feeds: the pilot, and the transparency markers the White Paper methods section is graded on.

How this loses points. A protocol that reports agreement from the training set. The training set is contaminated by the conversation that reconciled it, and reporting from it is a common and serious error rather than a technicality.

Submit your complete codebook (study overview, unit of analysis, all variables with decision rules) plus a qualitative memo describing the patterns you observed during immersion.

Full requirements

Your codebook adds a formal reliability protocol: the planned sample size for the reliability subset, the target alpha threshold, the coder training procedure, and the revision triggers that say in advance what you will do if the threshold is missed.

Sampling Plan and Pilot, 20 to 30 items

75 points. Due Monday, November 2, 11:59 PM. (Week 10)

What you submit: the plan and the pilot as one document, plus your coded pilot data and the code that computed the reliability figure.

Where it lives: 03_Project/03_Data/. The plan becomes the methods section in past tense, and the reliability computation becomes one of the three transparency markers.

Requirements

  1. Population, frame, and the gap between them, stated rather than assumed.
  2. The sampling method, named and justified, including what it makes estimable and what it does not.
  3. The size, with its justification. Either an effect-size estimate borrowed from a named study, a smallest effect of interest argued from consequence, or a resource constraint stated as a constraint and paired with a sensitivity analysis. Name which of the three you are using.
  4. Twenty to thirty items coded, submitted as data.
  5. A reliability figure computed on those items, with the code that produced it.
  6. Every edge case, with the decision made and the rule added.
  7. The revisions the pilot forced, before and after.
  8. One paragraph on what the pilot tells you about the full run: how long it will take, and what you will change.

How it is graded. Plan 25, size justification 15, pilot data and reliability computation 20, edge cases and revisions 15.

Where it comes from. Week 10, Chapter 10 and the Hayes and Krippendorff reading. What it feeds: the full coding run and the methods section.

How this loses points. A sample size justified by convention rather than by argument. “Eighty percent power” is a convention, and Lakens spends a paper on why that is not a justification by itself.

Design your sampling strategy (search terms, date range, method) and pilot test your codebook on 20 to 30 items, a large enough subset to compute a meaningful reliability statistic. Document edge cases, coding decisions, the alpha you obtained, and any revisions the pilot forced.

Phase IV: The Analyst

Execution. Draw the sample, wrangle the data, and describe what is there. Phase page

Data Wrangling in R

50 points. Due Monday, November 9, 11:59 PM. (Week 11)

What you submit: your script, and twitch_analysis.RDS.

Where it lives: 03_Project/03_Data/. The three reviewer-facing comments are the raw material of the provenance statement the paper has to carry.

Requirements

  1. Hand-code one variable for a 30-message sample drawn with sample_messages().
  2. Load the chat and stream tables.
  3. Convert the timestamps from epoch milliseconds, dividing by 1,000 first.
  4. Derive message length.
  5. Join the stream-level information on, naming the key in a comment.
  6. Handle missing values explicitly, reporting the count and the decision.
  7. Export as twitch_analysis.RDS.
  8. Three comments naming decisions a reviewer could challenge, each stating the rule and its justification. This is the graduate half of the assignment: the script is a methods section in executable form.
  9. The script runs start to finish in a clean session.

How it is graded. Steps 1 to 7 are four points each. The three reviewer-facing comments are 15. A clean-session run is 7.

Where it comes from. Week 11, Chapter 11 and the Wickham reading. What it feeds: both remaining R assignments.

How this loses points. A script that runs only because of something already in your environment. Restart and run it top to bottom before you submit.

Using the Twitch data from the v2v package:

Full requirements
  1. Hand-code a variable for a 30-message sample drawn with sample_messages()
  2. Load and clean the chat and stream tables (timestamps, message length, factors, missing values)
  3. Join the stream-level information back onto the chat messages
  4. Export a clean dataset as twitch_analysis.RDS

This assignment teaches the full data pipeline, from raw input to analysis-ready output.

Describing Data in R

100 points. Due Monday, November 16, 11:59 PM. (Week 12)

What you submit: your Quarto document and its rendered output.

Where it lives: 03_Project/03_Data/ for the script, and figures are written to 03_Project/04_Drafts/figures/ by the code that makes them, because the paper renders them rather than re-creating them.

Requirements

  1. Frequency tables for the key categorical variables.
  2. Mean, median and standard deviation for every continuous variable you report.
  3. At least two ggplot2 figures, labeled, titled and captioned.
  4. A cross-tabulation of two variables.
  5. Alt text on every figure.
  6. Distributional diagnostics behind each figure: the shape of the variable, whether the assumptions of the test you are heading toward look tenable, and where the outliers are. This is where the extra 25 points over the undergraduate version sit.
  7. Two to three sentences interpreting each output.
  8. Every display choice disclosed: bin width, axis caps, collapsed categories.

How it is graded. Tables and the three numbers 20, figures 20, cross-tab 10, alt text 5, diagnostics 25, interpretation 15, disclosures 5.

Where it comes from. Week 12, Chapter 12 and the Lakens reading. What it feeds: the Results section and next week’s test.

How this loses points. Producing the figure and skipping the diagnostic. The figure is the claim; the diagnostic is whether you are allowed to make it.

Using twitch_analysis.RDS:

Full requirements
  1. Create frequency tables for key categorical variables
  2. Build ggplot2 visualizations (bar charts, trend lines) with professional formatting
  3. Create a cross-tabulation showing the relationship between two variables
  4. Write two to three sentences of narrative interpreting each output

MC 501: this assignment is worth 100 points rather than 75, and the extra weight is in the diagnostics. Report the distributional checks behind each figure, not only the figure: what the shape of the variable is, whether the assumptions of the test you are heading toward look tenable, and what the outliers are.

Inferencing Data in R

100 points. Due Monday, November 23, 11:59 PM. (Week 13)

What you submit: your Quarto document and its rendered output.

Where it lives: 03_Project/03_Data/. The paper calls this script for its numbers.

Requirements

  1. State the hypothesis in plain English before any code.
  2. Choose the test from your variable types and justify the choice against one alternative you rejected.
  3. Check and report the assumptions, with the diagnostics shown rather than asserted.
  4. Run the test.
  5. Report it in APA format with statistic, degrees of freedom, p and n.
  6. Report the effect size, expressed in the units of your data as well as by its conventional label.
  7. Report the power statistics behind the design: what effect this sample could detect, and what that means for a null result.
  8. Distinguish statistical significance from practical size explicitly, in a paragraph that would survive a reviewer.
  9. One paragraph on what the result does not license.

How it is graded. Hypothesis 8, selection and rejected alternative 15, assumptions and diagnostics 20, APA reporting 15, effect size 15, power 15, significance against size 12.

Where it comes from. Week 13, Chapter 13 and the Lakens et al. reading. What it feeds: Results and Discussion.

How this loses points. Going wide instead of deep. Three tests reported shallowly score below one test reported with its assumptions, its diagnostics, its effect size and its power.

Using twitch_analysis.RDS:

Full requirements
  1. State your hypothesis in plain English before running any code
  2. Select the appropriate test based on your variable types:
    • Two categorical variables: chi-square test of independence
    • One categorical (two groups) by one continuous: independent t-test
    • Two continuous variables: Pearson correlation
  3. Run the test in R and report it in APA format
  4. Interpret the result in plain English
  5. Calculate and report the effect size

MC 501: go deeper on the same test rather than adding more tests. Report and check the assumptions, show the diagnostics, and give the power statistics behind the design. Distinguish statistical significance from practical size explicitly.

Phase V: The Publisher

Inference and Publication. Test the relationship and publish the result. Phase page

White Paper and conference poster

200 points. Due Friday, December 18, 11:59 PM.

What you submit: three things. The paper as Lastname_WhitePaper.pdf, the poster as a PDF, and the live URL. All three are required for the submission to be complete.

Where it lives: 03_Project/04_Drafts/. Every section has a source file above it by now; the poster is built from the same figures rather than from new ones.

The paper carries the three transparency markers, and they are graded as such:

  1. A data provenance statement: where the corpus came from, what was excluded and why.
  2. An intercoder reliability report giving the statistic, its value, the training protocol and the threshold applied.
  3. The pre-registration disclosure with its OSF URL, and an honest account of anything that departed from the plan.

The rest of the paper

  1. Introduction and framing, situating the question in the theoretical literature.
  2. Methods, repeatable in detail.
  3. Results, without interpretation, carrying effect sizes and the power statistics.
  4. Discussion, interpreting, distinguishing significance from size, and stating the limits.
  5. Abstract summarizing all four sections, and complete APA 7 references.
  6. It renders from source with no pasted numbers, and is live at a public address.
  7. Reflection, one specific paragraph naming what proved harder than expected, what the data could not answer, and what a second attempt would change.

The poster carries the study at a glance: question, method, one figure that does real work, the finding with its effect size, and the limitation. It is read standing up from three feet away, so it is not the paper shrunk.

How it is graded. The component weights in the syllabus govern: framing 25, methods 55, results 40, discussion 40, abstract and references 15, reproducibility and publication 15, reflection 10.

How this loses points. Methods carries more weight than any other section, and a methods section that omits one of the three transparency markers cannot reach full credit no matter how good the writing is.

The final deliverable: your study written up in IMRaD, rendered to PDF and published to the web. Graduate students also submit a conference poster.

Full requirements and what to submit

Submission (MC 501)

Submit to Blackboard by Friday, December 18, 2026 at 11:59 PM:

  1. PDF file, renamed to Lastname_WhitePaper.pdf
  2. Live URL, your GitHub Pages link
  3. Poster PDF, renamed to Lastname_Poster.pdf

Important: All three must be present for the submission to be considered complete.

The full rubric, the project structure, and the recommended writing order are on the Phase V page.

Anything here that disagrees with the syllabus is a mistake. Tell me and I will fix it: the syllabus is the contract of record.