FullRecall is an application that can help you memorize the knowledge for lifelong periods with the minimum time investment. Underlying artificial neural network gradually grasps your forgetting curve to schedule optimal days for the reviews–i.e., days when you will be close to forgetting an information.
The problem of forgetting
We do forget. Nothing stays in our brain forever without reviews. If something is important to you, you'll think about it from time to time—these repetitions, conscious or not, will reinforce your memory of this fact. We often come across new information and we want to keep it in memory even though we may not need it for the next weeks, months or even years. In order to not forget we have to do repetitions. However, how much time spent on repetitions is optimal? What is the optimal time of a review?
FullRecall is the solution here. Ensures that you remember new things, but don't forget what you have already memorized. Reviews are scheduled on days when you're close to forgetting, so sometimes you may actually forget an information, but FullRecall learns from these mistakes, self-corrects scheduling, to minimize chances of such lapses in the future.
In practice: how does work with FullRecall look like?
The software is similar to common flashcard programs: knowledge is stored in question-answer pairs. You add the question-answer pairs yourself (coming up with a concise, clear-cut question-answer pair, for every information, is a learning experience by itself), or use ready-made question-answer collections. In review mode you'll have presented the questions, one by one. To every question you'll think about an answer, and after a while you'll be confronted with the correct answer. After seeing the correct answer, you'll be asked for a grade that estimates how well you remembered the correct answer. The grade gives FullRecall a feedback. The program stores also other data, given current grade is able to schedule next optimal review time (and later learn itself if there was a mistake: if scheduled interval was too long or too short—i.e., if your grade on the next review is below or above "good"). With FullRecall you can learn new things fast, without worrying about repetitions of what you remember—the software assures that even if you forget something that is in your FullRecall learning database, you'll be soon reminded about it.
What's New in This Release:
· Many underlying changes in the network code, focused on eliminating any possibility of data loss and making code robust against exceptional situations, as in current PC version (1.4.15.1).
What's New in 1.3.36:
· Bugfix in network sync: a very small percentage of syncs could fail to sync (with report of the problem to user on "import", or without reporting any problem whatsoever on "export").
What's New in 1.3.35:
· Safer saving of all important learning database files (esp. against problems with lack of free disk space).
· Updated core code to be in sync with PC version
What's New in 1.3.34:
· Optimization resulting in faster saving of database and faster network sync/import/export in big databases.
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Supported operating systems:
Windows Mobile 2003, Windows Mobile 5.0, Windows Mobile 6 Classic, Windows Mobile 6 Professional, Windows Mobile 6.1 Classic, Windows Mobile 6.1 Professional, Windows Mobile 6.5 Professional
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FullRecall (Maemo/MeeGo) FullRecall (Maemo/MeeGo) is an application that can help you memorize the knowledge for lifelong periods with the minimum time investment. Underlying artificial neural network gradually grasps your forgetting curve to schedule optimal days for the reviews-i.e., days when you will be close to forgetting an information.
The problem of forgetting
We do forget
FullRecall (Maemo) FullRecall is an application that can help you memorize the knowledge for lifelong periods with the minimum time investment. Underlying artificial neural network gradually grasps your forgetting curve to schedule optimal days for the reviews–i.e., days when you will be close to forgetting an information.
The problem of forgetting
We do forget