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 ppavlik@memphis.edu 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.
  • Research - Using the shared code it is fairly straightforward for a javascript programmer to make modifications for specific research. Collaobrations with the Optimal Learning Lab are also possible if we have shared goals.

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.