Frequently Asked Questions
How do I use the optimal learning methods?
The are four ways the work on this website might be applied.
- Individual - You can use the link above to work with the current MoFaCTS online, with a variety of content.
- Courseware - Feel free to contact email@example.com to request a teacher account to upload content. Unfortunately we do not have an authoring tool, however we can share example files, which may be adequate for users familiar with basic XML.
- Sharing - The code is shared on bitbucket.org at this link.
How does the method determine what is optimal?
The algorithm for scheduling practice uses a mathematical model of learning to predict when new practice should occur for recall to be optimal later. This model accounts for:
- When prior practice occurred
- How many prior practices occurred
- Spacing between prior practice was
- Whether prior practice occurred as testing or passive study
- Duration of prior practices
- An individuals history of success or failure with tests
- What type of practice occurs
- Semantic relationships between different items
How does the method determine when to introduce new items?
New items are introduced only when previously introduced items are above a critereon level of performance. Since the system checks all previously introduced items after each practice, it is able to detect whether a previously introduced item has fallen (through forgetting) below the critereon, or whether it is time to introduce a previously unseen item, since the previously introduced items are still all above the critereon.