techmowgli

Check out Prepform! We're an AI-based Edtech company expanding access to education for students of all backgrounds: https://prepform.com. We're not currently hiring, but we're looking to hire soon! If you're interested, feel free to send me a message.

Actually, no need for the screenshots. I found the bugs. Thanks again!

The role 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.

Thanks!

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.

Hey I'm really glad to hear that! I'll look in to why the tasks aren't updating, but I'm having trouble recreating the missing constants in test review. I'd really appreciate it if you can send me a screenshot of what you're seeing! Again, thanks so much for checking it out.

Thanks! That's a great point. I hadn't thought about how guiding students through a curriculum could also be a way to gamify the experience. I agree that a more structured path, at least through the first pass, would be more helpful for students.

I appreciate the feedback!

I'll make it clearer that it's free! The business model is B2B, and the B2C part is to show off the tech.

Agreed. It's nice to have fresh eyes looking at the site. Thanks!

Thanks for taking the time for the screenshot and highlighting! I'll look into the bug.

Thanks! My learning philosophy is that the bulk of learning happens when you review material. I want students to see the topics they missed, and answer 3 questions about their mistakes: "What did I do wrong?", "What should I have done instead?", and "What is the clue in this question that I missed?".

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!

Prepform is built on the idea of a study plan, where you: 1. Take a diagnostic test 2. Set a target score 3. Create a study schedule 4. Identify mistakes, and finally, 5. Review mistakes.

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.