Instructor preparation and assessment
45–60 minute pre-session preparation, 90-minute teaching method, 10-point formative rubric, exam preparation and session report template.
Teacher’s Kit · PALS Weeks 1–6
Every executable R line explained, with expected results, misconceptions and 90-minute teaching plans. Open the six-week Instructor Kit →
Use this without internet: Download the offline HTML website ZIP, extract it, and open index.html to browse the public instructor guides and R/Statistics lessons.
One public starting point
This is the open version of the instructor workspace. Planning notes, model answers and exercises are available without signing in to the private knowledge system. Use the course source materials only where you have appropriate access.
Preparation guidePublic answer keyFour-week open practice companion
Original student R exercises, worked solutions and fictional data for familiarisation, wrangling, AQ scoring logic and visualisations. Open the pack · Download ZIP.
All teaching materials
First 90-minute lesson flow
A time-boxed teaching screen with diagnostic questions, R examples, independent practice and follow-up.
Programming Fundamentals — Lesson 0
Original beginner introduction: algorithms, variables, R versus RStudio, missing data and a synthetic psychology example.
Lesson 0 public answer key
Fully worked base R code, expected numbers, misconceptions and diagnostic explanations. Answers are intentionally public.
UCL PALS practicals — Weeks 1–4
Pedagogical alignment: familiarisation, data wrangling, AQ-10 summaries and ggplot2 visualisation.
Weeks 1–4 programming syntax guide
Detailed first four weeks' R code and explanations, with bilingual navigation.
Statistics Lab
Independent statistics pathway with conceptual modules, optional R labs, worksheets and mathematics.
Lesson 1 student practice
Independent learner workbook and diagnostics; public instructor answers are clearly marked elsewhere.
Course map
| Stage | Core concepts | Where to go |
|---|---|---|
| 0 — Foundations | Problem → algorithm → code → check; R/RStudio, vectors, NA | Learn · Solutions |
| 1 — Familiarisation | Objects, vectors, functions, data frames | Lesson 1 |
| 2 — Wrangling | CSV, join keys, select/filter/mutate/group_by | Lesson 2 · Guide |
| 3 — Summaries | AQ-10, pivot_longer, mean/SD, validity limits | Lesson 3 |
| 4 — Plots | ggplot2, histogram, scatter, box/violin, error bars | Lesson 4 |
How to teach a 90-minute session
| Minutes | Activity |
|---|---|
| 0–8 | Prior knowledge and diagnostic questions |
| 8–20 | Define the problem, algorithm, input and output |
| 20–37 | Predict outputs, then live-code together |
| 37–55 | Student completes guided R tasks |
| 55–60 | Short break |
| 60–77 | Independent transfer problem |
| 77–86 | Debugging and statistical interpretation |
| 86–90 | Exit ticket and short assignment |
See full preparation and grading guide · Open the live lesson flow
Public publishing boundaries
The original UCL lecture PDFs, worksheets and student-supplied ZIPs are not republished because public redistribution rights are not established. Student records, real participant data, private links and access credentials are never posted. Our own curriculum explanations, synthetic data, answer keys and teacher methodology are public here.
These solutions are intended for instructor reference and self-study, not presented as official university answers.
Teaching and publication status
The files and links are committed to the existing GitHub publication branch. A live HTTP/browser check of the custom domain is separate from source verification. This teaching hub itself is a static public page and does not synchronise private Divan records.