Show HN: Prepform – AI and spaced-repetition to optimize learning

Hey I've been playing with your app, I'm very excited about it! But, it seems like the site is pretty buggy, having just tried it on a few different browsers. Specifically, the test review doesn't show relevant information that was available while taking the exam, (i.e. the constants are missing, not even whitespace where they should be). I could share screenshots of what I'm seeing with you if you like. Also, it keeps saying I have 20 tasks for today- I click and it has me take another exam, and afterward, I still have 20 tasks to do.

That said I'm getting a lot out of taking these exams and I'm very excited about this project!

https://news.ycombinator.com/item?id=28309645 https://westurner.github.io/hnlog/#comment-28309645 :

Re: Phonemic awareness and Phonological awareness,

> What are some of the more evidence-based (?) (early literacy,) reading curricula? OTOH: LETRS, Heggerty, PAL:

> Which traversals of a curriculum graph are optimal or sufficient?

> You can add https://schema.org/about and https://schema.org/educationalAlignment Linked Data to your [#OER] curriculum resources to increase discoverability, reusability.

https://news.ycombinator.com/item?id=24527589 https://westurner.github.io/hnlog/#comment-24527589 :

>> A bottom-up (topologically sorted) computer science curriculum (a depth-first traversal of a Thing graph) ontology would be a great teaching resource.

>> One could start with e.g. "Outline of Computer Science", add concept dependency edges, and then topologically (and alphabetically or chronologically) sort.

>> https://en.wikipedia.org/wiki/Outline_of_computer_science

>> There are many potential starting points and traversals toward specialization for such a curriculum graph of schema:Things/skos:Concepts with URIs.

> https://westurner.github.io/hnlog/ ... Ctrl-F "interview", "curriculum"

OpenBadges as Blockcerts for Q12 competencies

I love spaced repetition apps (I’ve used Anki and Zorbi in the past). This looks pretty cool!

What’s the purpose of AI here?

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.