Human-Centric-Machine-Learning/memorize
Code and real data for "Enhancing Human Learning via Spaced Repetition Optimization", PNAS 2019
This project helps educators and learning platform designers optimize how and when to present learning material to students. It takes historical learning data, specifically records of what a student studied and whether they remembered it, and outputs a refined schedule for when to review that material. Anyone involved in designing learning experiences, such as course creators, educational app developers, or trainers, can use this to improve retention.
189 stars. No commits in the last 6 months.
Use this if you want to apply a research-backed algorithm to spaced repetition scheduling using existing learning data to maximize student retention.
Not ideal if you're looking for a complete, production-ready spaced repetition system, as this provides the core algorithm for research replication.
Stars
189
Forks
27
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 10, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Human-Centric-Machine-Learning/memorize"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
SomeB1oody/RustyML
A high-performance machine learning library in pure Rust, offering statistical utilities, ML...
smartcorelib/smartcore
A comprehensive library for machine learning and numerical computing. Apply Machine Learning...
open-spaced-repetition/fsrs-rs
FSRS for Rust, including Optimizer and Scheduler
open-spaced-repetition/fsrs-optimizer
FSRS Optimizer Package
rust-ml/linfa
A Rust machine learning framework.