Part III Module Advisor

A local web app that helps Computer Science students choose their optional Part III modules. Answer a short questionnaire about your interests, preferred assessment style, workload and career goals, and the advisor ranks the Semester 1 and Semester 2 modules, flags prerequisite problems, and suggests a balanced 5-module plan (3 modules in Semester 1, 2 in Semester 2).

Everything runs on your own machine: no accounts, API keys, or database. Every run is saved to a CSV file so you can compare plans later.

Contents

• What it does
• Screenshots
• Setup
• Using the advisor
• Saved runs (CSV)
• Troubleshooting
• Running tests
• How the advisor works

What It Does

• collects your preferences through a guided questionnaire
• ranks modules for Semester 1 and Semester 2, with a score and the reasons behind it
• checks known prerequisite constraints, including modules you took in earlier years
• builds a balanced 5-module plan with 3 options in Semester 1 and 2 in Semester 2
• links to a detail page for every module (overview, syllabus, assessment, study time)
• saves every run as a row in a local CSV file (data/runs.csv)

Screenshots

1. Fill in the questionnaire. Pick your interests, career goal, assessment and workload preferences, and any earlier modules you have already taken.

2. Review the ranked modules. Each recommendation shows its score, assessment breakdown, workload profile, and why it matched your answers.

3. Check the balanced plan and warnings. The advisor picks 3 + 2 modules that satisfy known prerequisites and lists anything you should double-check.

4. Open a module for details. Every module has its own page built from the source markdown.

Setup

Prerequisites

Tool	Version	Check with
Node.js (includes npm)	20.19 or newer, or 22.12 or newer (the current LTS is recommended)	node -v
Git	any recent version	git --version

The steps below work the same in a macOS/Linux terminal and in Windows PowerShell or Command Prompt.

Steps

1. Clone the repository and move into it:

   git clone https://github.com/dragonfisher29/Part-III-Module-Advisor.git
   cd Part-III-Module-Advisor
   

2. Install dependencies (only needed the first time, and after pulling updates):

   npm install
   

   This installs the exact versions pinned in package-lock.json. It takes under a minute and needs an internet connection.

3. Start the app:

   npm run dev
   

   Wait until the terminal shows something like:

   ▲ Next.js 15.5.x
   - Local:        http://localhost:3000
   ✓ Ready in 1572ms
   

4. Open http://localhost:3000 in your browser. Keep the terminal open while you use the app; press Ctrl + C in the terminal to stop it.

Updating to the latest version

git pull
npm install
npm run dev

Using the Advisor

1. Fill in your name, email, and degree route, then choose a career goal.
2. Tick at least one interest, plus any COMP1xxx / COMP2xxx modules you have already completed so their prerequisites count as satisfied.
3. Choose your preferred assessment style, workload, and theory/practical balance, and optionally add notes.
4. Click Get Recommendations. The results appear on the right (below the form on narrow screens), and the run is saved to data/runs.csv.
5. Click a module name to open its detail page, then Back to advisor to return. Change any answer and submit again to compare plans.

Saved Runs (CSV)

Each submission appends one row to data/runs.csv in the project folder. The file and folder are created on the first run, and the first line holds the column names. Open the file in Excel, Numbers, Google Sheets, or any text editor.

Column	Example
saved_at	2026-10-02T10:11:37.016Z (UTC)
name, email, degree_route	Ada Lovelace, [email protected], Computer Science Part III
interests	ai-ml; data; web-cloud
assessment_preference, workload_preference, career_goal	coursework, balanced, ai-data
broadening_interest, ai_ml_interest	false, true
prior_modules	COMP1202; COMP2208 (empty if none)
theory_practice_balance, notes	balanced, Prefer hands-on coursework over exams.
plan_semester_1	COMP3227 Web Architecture and Hypertext Technologies; COMP3222 Machine Learning Technologies; COMP3204 Computer Vision
plan_semester_2	COMP3211 Advanced Databases; COMP3208 Social Computing Techniques
top_semester_1, top_semester_2	the top 5 ranked modules for each semester
warnings	COMP3224 expects COMP3223, but you have not marked it as already taken.; ...

List values are separated by ; . Fields containing commas, quotes, or line breaks are quoted, so spreadsheet apps read them correctly.

data/ is git-ignored, so your saved runs (which include names and emails) are never committed. To start fresh, close the file in any spreadsheet app and delete data/runs.csv.

This is the beginning of the README. Read the rest at the original. Read the full README ↗