LearnSphere

This is a final project for CS5060 at Utah State University.

Bayesian Methods

Handle dependencies by running the following commands:

python3 -m venv .lsvenv
source .lsvenv/bin/activate
pip3 install numpy matplotlib scipy

Run the algorithms with the following commands:

• Content type selection: python3 bayesian/content.py
• Learning pathway selection: python3 bayesian/pathways.py
• Quiz customization: python3 bayesian/quizzes.py
  • See old versions of quiz customization with lower scores at bayesian/quizzes/quizzes#.py

See the pre-generated (on our machines) results at:

• Content type selection: bayesian/content/results.txt and the plots contained in that folder
• Learning pathway selection: bayesian/pathways/results.txt and the plots contained in that folder
• Quiz customization: bayesian/quizzes/results.txt
  • See old versions of quiz customization with lower scores at bayesian/quizzes/results#.txt

Explore / Exploit

Run the algorithm and view a plot with the following command:

python3 exploreExploit/finalProject.py

Further documentation is found at https://github.com/mossbiscuits/learnSphere/blob/main/exploreExploit/README.md

Scheduling

Run the simulation of the algorithm with

python3 scheduling/scheduling.py

A report on the success of the simulation will be printed.

You can change the number of students and number of tutors in scheduling/scheduling.py

Insurance / Quality Guarantee

Run the algorithm and view plots with the following command:

python3 insurance/guarantee.py

The ouptut of this script is a calculation of the student's price, assuming a $1000 base operating cost, a 15% profit, and a money-back guarantee for students who fail.