But these come and go. SuperMemo 1.0 for Mac developed in 1993-1995 was terminated due to being unprofitable. See: Why does not SuperMemo World develop SuperMemo for Mac. In 2018, SuperMemo 17 seems to work in Parallels. Until now we had no complaints about major incompatibilities. See: SuperMemo in Parallels Desktop for Mac.

Mnemosyne (named for the Greek goddess of memory, Mnemosyne) is a line of spaced repetition software developed from 2003 until the present. Spaced repetition is an evidence-based learning technique that has been shown to increase the rate of memorization.^[2]

Features[edit]

• Spacing algorithm based on an early version of the SuperMemo algorithm, SM-2,^[3] with some modifications that deal with early and late repetitions.^[4]
• Supports pictures, sound, video, HTML, Flash and LaTeX
• Portable (can be installed on a USB stick)
• Categorization of cards
• Learning progress statistics
• Stores learning data (represented as decks of cards that each have a question and an answer side) in '.mem' database files, which are interoperable with a number of other spaced repetition applications
• Plugins and JavaScript support
• Review cards on Android devices.
• Synchronization between other machines

Overview[edit]

Each day, the software displays each card that is scheduled for repetition. The user then grades their recollection of the card's answer on a scale of 0–5. The software then schedules the next repetition of the card in accordance with the user's rating of that particular card and the database of cards as a whole. This produces an active, rather than passive, review process. The rationale behind this approach is that (because of the spacing effect), over time, the number of repetitions done per day is reduced, increasing the rate of recall (when compared to passive learning techniques), with minimal time spent learning.

Software[edit]

Mnemosyne is written in Python, which allows for its use on Microsoft Windows, Linux, and Mac OS X. A client program for review on Android devices is also available but needs to be synchronized by the desktop program. Users of the software usually make their own database of cards, although pre-made Mnemosyne databases are available, and it is possible to import SuperMemo collections and text files. SQLite is used by the program to store files. Imports of flashcard databases from Anki, as well as databases from older versions of Mnemosyne are possible.

Research[edit]

Mnemosyne collects data from volunteering users, and is a research project^{[clarification needed]} on long-term memory.^[5]

An August 2009 version of the dataset was made available via BitTorrent;^[6] a January 2014 version is available for download.^[7] Otherwise, the latest version is available from the author, Peter Bienstman, upon request.^[8]

See also[edit]

References[edit]

1. ^'Files'. Sourceforge. Retrieved 11 July 2020.
2. ^Smolen, Paul; Zhang, Yili; Byrne, John H. (25 January 2016). 'The right time to learn: mechanisms and optimization of spaced learning'. Nature Reviews Neuroscience. 17 (2): 77–88. arXiv:1606.08370. Bibcode:2016arXiv160608370S. doi:10.1038/nrn.2015.18. PMC5126970. PMID26806627.
3. ^SM-2 Optimization of learning, Master's Thesis, University of Technology in Poznan, 1990 and adapted for publishing as an independent article on the web. (P.A.Wozniak, May 10, 1998)
4. ^'Principles', The Mnemosyne Project, retrieved June 3rd, 2008
5. ^http://www.mnemosyne-proj.org/principles.php
6. ^Announcement; torrent index
7. ^https://groups.google.com/d/msg/mnemosyne-proj-users/tPHlkTFVX_4/oF61BF44iQkJ
8. ^http://groups.google.com/group/mnemosyne-proj-users/browse_thread/thread/e00801ebb3bbfa72

External links[edit]

• Mnemosyne project website
• Review of Mnemosyne and comparison with Anki and SuperMemo (Q1-Q2 2008)
• Review of Mnemosyne at foolsworkshop.com
• Review of Mnemosyne and comparison with Anki (Q1 2009)
• David Harding (2009). 'Mnemosyne and Anki'. Ubuntu User magazine article.

SuperMemo (from 'Super Memory') is a learning method and software package developed by SuperMemo World and SuperMemo R&D with Piotr Woźniak in Poland from 1985 to the present.^[1] It is based on research into long-term memory, and is a practical application of the spaced repetition learning method that has been proposed for efficient instruction by a number of psychologists as early as in the 1930s.^[2]

The method is available as a computer program for Windows, Windows CE, Windows Mobile, (Pocket PC), Palm OS (PalmPilot), etc. Course software by the same company (SuperMemo World) can also be used in a web browser or even without a computer.^[3]

The desktop version of SuperMemo (since v. 2002) supports incremental reading, as well as traditional creation of question and answer flashcards^[4].

Software implementation[edit]

The SuperMemo program stores a database of questions and answers constructed by the user. When reviewing information saved in the database, the program uses the SuperMemo algorithm to decide what questions to show the user. The user then answers the question and rates their relative ease of recall - with grades of 1 to 5 (1 is the hardest, 5 is the easiest) - and their rating is used to calculate how soon they should be shown the question again. While the exact algorithm varies with the version of SuperMemo, in general, items that are harder to remember show up more frequently.^[5]

Besides simple text questions and answers, the latest version of SuperMemo supports images, video, and HTML questions and answers.^[6]

Since 2002, SuperMemo has had a unique set of features that distinguish it from other spaced repetition programs, called incremental reading. Whereas earlier versions were built around users entering information they wanted to use, using IR, users can import text that they want to learn from. The user reads the text inside of SuperMemo, and tools are provided to bookmark one's location in the text and automatically schedule it to be revisited later, extract valuable information, and turn extracts into questions for the user to learn. By automating the entire process of reading and extracting knowledge to be remembered all in the same program, time is saved from having to manually prepare information, and insights into the nature of learning can be used to make the entire process more natural for the user. Furthermore, since the process of extracting knowledge can often lead to the extraction of more information than can actually be feasibly remembered, a priority system is implemented that allows the user to ensure that the most important information is remembered when they can't review all information in the system.^[7]

Algorithms[edit]

The specific algorithms SuperMemo uses have been published, and re-implemented in other programs.

Different algorithms have been used; SM–0 refers to the original (non-computer-based) algorithm, while SM-2 refers to the original computer-based algorithm released in the 1987 (used in SuperMemo versions 1.0 through 3.0, referred to as SM-2 because SuperMemo version 2 was the most popular of these).^[8] Subsequent versions of the software have further optimized the algorithm.

Piotr A. Wozniak, the developer of SuperMemo algorithms, released the description for SM-5 in a paper titled Optimization of repetition spacing in the practice of learning. Little detail is specified in the algorithms released later than that.

In 1995, SM-8, which capitalized on data collected by users of SuperMemo 6 and SuperMemo 7 and added a number of improvements that strengthened the theoretical validity of the function of optimum intervals and made it possible to accelerate its adaptation, was introduced in SuperMemo 8.^[9]

In 2002, SM-11, the first SuperMemo algorithm that was resistant to interference from the delay or advancement of repetitions was introduced in SuperMemo 11 (aka SuperMemo 2002). In 2005, SM-11 was tweaked to introduce boundaries on A and B parameters computed from the Grade vs. Forgetting Index data.^[10]

In 2011, SM-15, which notably eliminated two weaknesses of SM-11 that would show up in heavily overloaded collections with very large item delays, was introduced in Supermemo 15.^[11]

In 2016, SM-17, the first version of the algorithm to incorporate the two component model of memory, was introduced in SuperMemo 17.^[12]

The latest version of the SuperMemo algorithm is SM-18, released in 2019.^[13]

Description of SM-2 algorithm[edit]

The first computer-based SuperMemo algorithm has 3 inputs, being the repetition number, easiness factor, and inter-repetition interval. The repetition number is fairly self-explanatory, the easiness factor is a measure of how easy recall of an answer was, and inter-repetition interval describes the time (in days) between repetitions.

A grade is given by the pupil, and with that, the variables are modified in the following way:

If the grade given is greater than equal to 3, indicating a correct answer:

• If repetitions = 0
  • interval = 1
• If repetitions = 1
  • interval = 6
• If repetitions > 1
  • interval = interval * easiness
• repetitions = repetitions + 1
• easiness = easiness + (0.1 - (5 - grade) * (0.08 + (5 - grade) * 0.02)

If the grade given is less than equal to 3, indicating an incorrect answer:

• repetitions = 0
• interval = 1

If easiness goes below 1.3, raise it to 1.3

Criticism of SM3+[edit]

The SM-2 algorithm uses the performance on a card to schedule only that card, while SM-3 and newer algorithms use card performance to schedule that card and similar cards. The additional optimizations sometimes yield perverse results – answering 'hard' on a card may yield an interval longer than answering 'easy' on a card – and are criticized as reducing the robustness of the algorithm, making it more sensitive to variations – non-uniform difficulty of cards (a problem in versions 4 to 6, according to Woźniak), inconsistencies in studying, and so forth.^[14]

Woźniak disagreed with the criticism,^[15] but in practice the other factors affecting study make differences less important.

Non-SuperMemo implementations[edit]

Some of the algorithms have been reimplemented in other, often free programs such as Anki, Mnemosyne, and Emacs Org-mode's Org-drill. See full list of flashcard software.

Supermemo Mac

The SM-2 algorithm has proven most popular in other applications, and is used (in modified form) in Anki and Mnemosyne, among others. Org-drill implements SM-5 by default, and optionally other algorithms such as SM-2.

References[edit]

1. ^Wolf, Gary (2008), 'Want to Remember Everything You'll Ever Learn? Surrender to This Algorithm', Wired Magazine
2. ^Spitzer, Herbert F. (December 1939). 'Studies in Retention'(PDF). Journal of Educational Psychology. 30 (9): 641–656. doi:10.1037/h0063404. ISSN0022-0663.
3. ^Biedalak K., Murakowski J., Woźniak P.: Using SuperMemo without a computer – Paper and pencil method
4. ^Purdy, Kevin (2010), 'Use Incremental Reading to Memorize Large Batches of Data', Lifehacker
5. ^Wolf, Gary (April 21, 2008). 'Want to Remember Everything You'll Ever Learn? Surrender to This Algorithm'. Wired. ISSN1059-1028. Retrieved January 30, 2019.
6. ^'SuperMemo: What's new in SuperMemo 17?'. super-memory.com. Retrieved January 30, 2019.
7. ^'SuperMemo Guru: Minimum Definition of Incremental Reading'. supermemo.guru. Retrieved December 3, 2019.
8. ^3. Account of research leading to the SuperMemo method, 3.1. The approximate function of optimal intervals and 3.2. Application of a computer to improve the results obtained in working with the SuperMemo method, P. A. Woźniak, Optimization of learning, Master's Thesis, University of Technology in Poznan, 1990.
9. ^'SuperMemo Algorithm - SuperMemo Help'. help.supermemo.org. Retrieved May 1, 2019.
10. ^'SuperMemo Algorithm - SuperMemo Help'. help.supermemo.org. Retrieved May 1, 2019.
11. ^'SuperMemo Algorithm - SuperMemo Help'. help.supermemo.org. Retrieved May 1, 2019.
12. ^'Algorithm SM-17'. supermemo.guru. Retrieved May 1, 2019.
13. ^'Algorithm SM-18'. supermemo.guru. Retrieved May 9, 2020.
14. ^What spaced repetition algorithm does Anki use?, 'If you are very consistent in your studies and all cards are of a very similar difficulty, this approach can work quite well. However, once inconsistencies are introduced into the equation (cards of varying difficulty, not studying at the same time every day), SM3+ is more prone to incorrect guesses at the next interval – resulting in cards being scheduled too often or too far in the future. 'Furthermore, as SM3+ dynamically adjusts the 'optimum factors' table, a situation can often arise where answering 'hard' on a card can result in a longer interval than answering 'easy' would give. The next times are hidden from you in SuperMemo so the user is never aware of this.'
15. ^'History of spaced repetition'. www.supermemo.com. Retrieved May 7, 2019.

External links[edit]

Articles

• Tomasz P. Szynalski: Use spaced-repetition software (SRS) – An introduction to spaced-repetition and SuperMemo
• Pawel Kowalczyk: Learn English with SuperMemo – How SuperMemo can help learn English
• Patrick Kenny: Memory Software: SuperMemo – A guide to using SuperMemo to study Japanese

Supermemo App On Mac Os X