Twelve Wednesday evenings, 18:00–21:00. Each week has one computing
tool and one ordinary psychology example. The University is closed
30 September. Reading Week includes 11 November. Weekly reports
(weeks 1–9) are due the following Tuesday at 17:00; drop the lowest.
Copy lab-notes/TEMPLATE.md.
50% weekly reports · 15% midterm check-in · 35% individual final
No graded group project. No graded peer review.
We catch everyone up on the GitHub form before the playground.
Create an account, start Education, paste the six-line form.
Do not buy Copilot. Complete / incomplete. Due tonight 21:00.
Customize the starter page in three ways. Then, in order: your own
repo with index.html, GitHub Pages on that repo, the
same file as the default index.html on a fork of the
entire class repository
(kylemath/Psych302-305-KyleMathewson), and a pull
request from that fork back to the class repo. The instructor will
merge the coolest one as the new default homepage.
18:00 GitHub account
Week 0 form on Canvas. Neighbours help. No credit card.
Three personal changes, then all four GitHub steps.
Results
Paste own-repo, Pages, fork, and PR URLs on the Week 1 Canvas assignment. List the three changes.
Modification
Arts: how it feels. Science: one change can be a number or a time. Disclose any AI use.
20:45 Discussion
Agentic coding: you stay the author; an assistant may propose an edit; you run it, read it, and keep or change it. Do not paste classmate data. Do not buy a plan. Full note on the Computing page. Week 4 is the first graded loop.
Preparation
Laptop. A GitHub login, or time in the first hour to make one.
Week 1 report: own-repo URL, Pages URL, fork URL, PR URL, three customizations, disclosure. Due Tuesday 8 September, 17:00.
Tool: p5.js pieces, then a timed response and a CSV
First hour: six short p5.js sketches (pixels, refresh rate, variables,
a stimulus, buttons, one timed click). Then the real instrument:
simple reaction time and optional choice RT. Anticipations under
150 ms and lapses over 2500 ms are excluded from the mean.
18:00 Introduction
A program is input, memory, and a display. We name the parts before we measure.
18:10 Studio
Walk p5.html in order. Remix a number or a string and Run.
19:15 Break
Write a predicted mean for the real block before the first trial.
19:25 Instrument
Instructor demo on rt.html, then a simple-RT block (~20 usable trials). Export the CSV.
20:05 Modification
Arts: the mark. Science: choice RT and the two means.
20:45 Discussion
Toy trial vs instrument: exclusions and a file. The number comes from the export, not a chatbot.
Reading
A short note on Donders (1868). Skim.
Report: prediction, mean RT, n, SD, one limitation (practice, anticipation, fatigue). Push the report and the CSV. The p5 sketches are the lesson, not a second product.
Example: a ten-item teaching inventory with two items in each of five
domains. Likert responses, reverse scoring, subscale means, CSV.
This is a classroom instrument, not a clinical scale.
18:00 Introduction
Self-report as data, not as a diagnosis. Write a predicted range for one subscale.
18:25 Demonstration
Complete the scale once as a class walk-through. Export.
18:40 Exercise
Students complete the inventory and save their table.
19:25 Results
Report one subscale mean and what it is (and is not) a measure of.
20:05 Modification
Arts: wording and layout of the items. Science: check a reverse-scored item by hand.
Reading
A short scale-construction note from the instructor.
Report: one subscale, item count, one limitation of self-report. CSV in data/.
Example: your Week 2 reaction times or Week 3 inventory scores.
Write the paper in report/REPORT.md, compute with
report/scripts/summarize.py, typeset with
report/main.tex. Run, read, change, run again.
Do not type the mean by hand.
18:00 Introduction
A report is a pipeline. The number in the paper must come from a file the script can read again.
18:25 Demonstration
Instructor runs build.sh on a sample CSV, reads the output, changes one line, runs it again.
18:40 Exercise
Copy the starter if needed. Point the script at your CSV. Write the prediction first.
19:25 Results
The generated block and one limitation. No draft exchange.
20:05 Modification
Arts: the sentences. Science: the script (exclusion or a second descriptive), then rebuild.
Example: one claim from your data, a figure, and a paragraph. The
graded work tonight is the individual
midterm check-in, not a critique.
18:00 Introduction
A result is a sentence plus a figure, not a gallery of charts.
18:25 Demonstration
A page that states a claim and one that does not.
18:40 Exercise
Build a short HTML results page from an existing CSV. Complete the check-in file.
19:25 Results
Instructor reads the check-in: question, tool, prediction, existing files.
20:05 Modification
Revise the check-in until the project is one comparison, not a programme of research.
Preparation
Bring a CSV and a one-sentence project idea.
Due in class:project/CHECKIN.md. Weekly report: path to the results page and the claim.
Week 9 · Wednesday 4 November 2026
last weekly report
Tool: a browser game that studies a question
Example: play or remix the go/no-go instrument on
game.html. Write the question first. Last
weekly report. Tonight’s n is still small; that is not the week’s lesson.
18:00 Introduction
A game is an instrument if it records a comparison you stated first.
18:25 Demonstration
Play eight practice trials. Write a predicted false-alarm rate.
18:40 Exercise
Run a scored block and export the CSV into data/.
19:25 Results
One sentence: condition, n, and what you will not claim.
20:05 Modification
Arts: feel and theme of the mark. Science: one extra logged variable.
Individual project. Use a tool already taught. No new libraries.
Classmate data are optional and must stay in the room unless you
wrote a one-screen consent the instructor approved.
18:00 Introduction
Common failures: no prediction, no file, appearance without a result.