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    memory-scheduler

    1.0.1 • Public • Published

    memory-scheduler

    This is a tool made to help people schedule their learning in a more efficient way. It is very simple to use.

    For more detail on why I make this lib, check this post.

    Installation

    • npm : $ npm install memory-scheduler
    • yarn: $ yarn add memory-scheduler

    Guide

    To learn with this algorithm, two arguments have to be fed to it:

    • intervals([int]): intervals between each study session.
    • scroreToProgressChange([int]): how to update the progress based on the score the user gives when reviewing items

    For one item we want to learn, store two data:

    • dueDate(int): the next day scheduled to review this item.
    • progress(int): How many times continuously the user has correctly answered this item.

    When reviewing an item, send these data to the calculate function and get the updated record of that item:

    • score(int): how confident the user is with this item.
    • prevRecord(object): the previous record of this item
    • now(int): the date of today

    The answer is deemed as correct only when the score is equal to the length of scroreToProgressChange, and in this circumstance the nextDute is intervals[progress] days after today.

    Otherwise, the answer is deemed as incorrect and the next review is scheduled at tomorrow.

    In both cases, progress should be updated in this way: progress+=scroreToProgressChange[score].

    Example

    import  MS from 'memory-scheduler';
     
    const DAY_IN_MINISECONDS = 24 * 60 * 60 * 1000;
     
    const today = Math.round(new Date().getTime() / DAY_IN_MINISECONDS);
     
    const yesterday = today-1;
     
    const ms = new MS([1, 2, 3, 8, 17], [-3, -1, 1]);
     
    const record = ms.getInitialRecord(yesterday);
    const updatedRecord = ms.calculate(1, record, today);
     

    Keywords

    none

    Install

    npm i memory-scheduler

    DownloadsWeekly Downloads

    1

    Version

    1.0.1

    License

    MIT

    Unpacked Size

    101 kB

    Total Files

    7

    Last publish

    Collaborators

    • lotp