Course Recommender System Documentation
1Introduction
The Course Recommender System is designed to assist students in selecting courses by leveraging the following factor:
- People Score (PS) – Measures how relevant a course is based on enrollment patterns of students who took similar courses.
People Score is used to calculate the Final Similarity Score (FS). The CPI Booster Score (CBS) indicates historical grading trends.
2Dataset Description
| Field | Description |
|---|---|
| Course Code | Unique course identifier |
| Course Name | Official course title |
| Course Description | Detailed content description |
3Model Workflow
Step 1: User Input
- Course History: List of previous courses taken by the user.
Step 2: Score Computation
People Score (PS)
Measures how often students who took a course in the user’s history also took another course.
PS(h, c) is defined as:
$$ PS(h, c) = \frac{|Students(h) \cap Students(c)|}{|Students(h) \cup Students(c)|} $$Students(h) = Set of students who took course h.
Students(c) = Set of students who took course c.
Step 3: Final Score
Final Score (FS) is given by:
$$ FS = \sum \frac{PS(h_i, c)}{1 + (i - 1)} $$
PS(c) = Net People Score of course c.
4Recommendations
Course Details
Code, Name, Description
Final Score
Combined FS value
CBS Score
CPI Booster Rating
5CPI Booster Score
- CBS helps students gauge how a course may impact CPI. It is based on historical grade distributions.
- A higher CBS score means students have historically scored well in that course.
Detailed mathematics: CBS Documentation
6Interface
Input
Search & History
→
Process
Compute Scores
→
Output
Ranked Courses
7Summary
- System recommends courses based on textual similarity and past enrollment trends.
- The Final Score (FS) is used to rank and recommend courses.
- The CBS Score helps students choose courses that can boost their CPI.
- Higher CBS Score = Easier grading trends.