It’s hard for students to know what and for how long to study, and when they’ve mastered something. By analyzing a student’s performance, we estimate their current knowledge, and use it to determine factors such as the best repeat frequency, the type of question, and the difficulty of the question.

For example, studies show that students stop studying when they don’t fully know the material, and they don’t study material they think they’ve already learned. By guiding them through a study plan and using spaced-repetition, we can improve the speed of learning and reduce the effects of forgetting.

Our model accounts for forgetting, guessing, the order of answers, and a student’s baseline knowledge to predict their current knowledge. While we are currently training the model for the NY Regents exam, we have tested it on EdNet, the largest publicly-available education dataset available, and it has the highest predictive accuracy among competing models, with an AUC of 0.7892.

The purpose of AI here is to create a student model to personalize learning, increase students' motivation, and increase their speed of learning.

It’s hard for students to know what and for how long to study, and when they’ve mastered something. By analyzing a student’s performance, we estimate their current knowledge, and use it to determine factors such as the best repeat frequency, the type of question, and the difficulty of the question.

For example, studies show that students stop studying when they don’t fully know the material, and they don’t study material they think they’ve already learned. By guiding them through a study plan and using spaced-repetition, we can improve the speed of learning and reduce the effects of forgetting.

Our model accounts for forgetting, guessing, the order of answers, and a student’s baseline knowledge to predict their current knowledge. While we are currently training the model for the NY Regents exam, we have tested it on EdNet, the largest publicly-available education dataset available, and it has the highest predictive accuracy among competing models, with an AUC of 0.7892.

I appreciate the feedback!

This "mistake log" will use spaced-repetition to show their mistake just as they are about to forget it. I think this is a huge missed opportunity for companies like Duolingo and Khan Academy. My goal is to guide students through a study plan, so they know what to study and for how long.

I appreciate the feedback!

A high-quality education helped me pursue my interests and achieve my goals. I started Prepform so students of all backgrounds have access to the same kind of education.

I grew up in Southern California, surrounded by dozens of SAT prep programs, and I swear I must have gone to all of them. Different programs followed different styles and techniques, but the strategy they shared was to create a study plan and review mistakes.

A study plan is taking a diagnostic test,

setting a target score,

creating a study schedule,

identifying mistakes, and finally

reviewing those mistakes.

I wanted to take this structure and optimize it with machine learning, while accounting for elements of human learning and memory. I'm a big fan of SuperMemo, a memorization technique developed by Piotr Wozniak, where you review material just as you're about to forget it. Cognitive psychology tells us human forgetting follows a pattern, but Piotr quantified this behavior to identify the precise moment forgetting happens.

The goal was to build on his research with AI and tailor it to not only test prep but to the individual student, and make it the engine of the study plan.

The result is Blended Prep, which guides students to internalize knowledge rather than memorize material, and gives them the best chance to ace their next exam.

I'm so excited to share this with the HN community, and would love to know what you think. You can try it out at https://prepform.com. Thanks for reading.

Comments URL: https://news.ycombinator.com/item?id=30631991

Points: 50

# Comments: 18

I wanted to take this structure and personalize it for each student using machine learning, spaced repetition, and data visualization.

Students first take practice tests, then analyze their mistakes using the data visualization tools. Then they log their mistakes, where they answer “What did I do wrong?”, “What should I have done instead?”, and “What is the clue in this question that I missed?”. This is the key here, since reviewing mistakes is where the bulk of learning happens.

Lastly, the mistakes will be shown again at appropriate intervals, and included in future practice tests.

The app is currently training the AI model, which will be used to predict the odds of a student answering a question correctly.

Comments URL: https://news.ycombinator.com/item?id=30592139

Points: 1

# Comments: 1