| Incremental reading |
Contents:
Incremental reading is a learning technology that makes it possible to read thousands of articles at the same time without getting lost. It begins with importing articles from electronic sources, e.g. the Internet. The student then extracts the most important fragments of individual articles for further review. Extracted fragments are then converted into questions and answers. These in turn become subject to systematic repetition that maximizes the long-term recall of processed texts. The review process is handled by the proven repetition spacing algorithm known as the SuperMemo method.
| Incremental reading may seem complex at first. However, once you master it, you will begin a learning process that will surpass your expectations as to what is possible in the area of knowledge acquisition! |
Advantages of incremental reading:
| Only SuperMemo makes it possible to implement incremental reading. Incremental reading requires continual retention of knowledge. Depending on the volume of knowledge flow in the program, the interval between reading individual portions of the same article may extend from days to months. Repetition spacing is the foundation of incremental reading which is based on stable memory traces in-between reading bursts |
See also: incremental reading from user's perspective by Len Budney
Five basic skills of incremental reading
Incremental reading requires a collection of skills that you will perfect only with passing time and growing experience. This overview will help you handle the most basic skills and help you make a start with incremental reading.
Skill 1: Importing articles
To import an important article to SuperMemo, follow these steps:
Tips:
Skill 2: Reading articles
You could precede reading articles with conveniently locating the Read toolbar on your screen. Choose Window : Toolbars : Read, place the toolbar in a convenient place on the screen and press Ctrl+Shift+F5 (to save the chosen layout as your default layout). If you do not see the Windows menu read about levels.

This is a simplified algorithm for reading articles:
Skill 3: Extracting fragments, questions and answers
In the course of traditional reading, we often mark important paragraphs with a marker. In SuperMemo, those paragraphs should be extracted as separate elements that will later be used to refresh your memory. Each extracted paragraph or section becomes a mini-article that will be subject to the same reading algorithm as discussed above. Extract your fragments and single sentences with Remember extract (Alt+X). Remember to add necessary context clues to make sure the extracted fragment does not become meaningless with time. If you cannot recall the necessary context, use the reference source link button on the element toolbar to jump to the article from which the extract had been produced.

SuperMemo will demonstrate to you that extracting important fragments and reviewing them at later time will have an excellent impact on your ability to benefit from the reading material at later times. However, it will also show that once the review intervals grow beyond 200-300 days, passive review will often become insufficient. At that time you will need to use Remember cloze (blue Z icon on the Read toolbar or Alt+Z). This option will convert single sentences into question-and-answer items.

For example, if you have extracted the following fragment from your reading about the history of the Internet:
The Internet was started in 1969 under a contract let by the Advanced Research Projects Agency (ARPA) which connected four major computers at universities in the southwestern US (UCLA, Stanford Research Institute, UCSB, and the University of Utah)
you may discover than when review intervals become long enough, you may not actually be able to recall the name of the ARPA agency or even forget the year in which the Internet started. You can then select an important keyword, e.g. 1969, and use Remember cloze to produce the following item:
Question: The Internet was started in [...]
under a contract let by the Advanced Research Projects Agency (ARPA) which
connected four major computers at universities in the southwestern US (UCLA,
Stanford Research Institute, UCSB, and the University of Utah)
Answer: 1969
In the course of learning, you will yet need to polish the above item by manual editing it to a more compact and understandable form:
Question: The Internet was started in [...](year)
under a contract let by the ARPA agency
Answer: 1969
The editing added the following benefits to the above item:
Important! Your work on extracting fragments, producing cloze deletions and editing them should also be incremental. In each review, do only as much work on the learning material as is necessary! Extracting and editing in intervals adds additional benefit to learning and is more time-efficient. Each time you rethink structure and formulation, you hone the representation and "connectivity" of a given piece of knowledge. In addition, your priorities change as you proceed with learning. At times it may result in over-investing your time in a piece of knowledge that is no longer relevant. The incremental approach should not only refer to the reading process but also to the follow-up processing and formulation
Skill 4: Repetitions and review
SuperMemo is based on repetition. You make repetitions of the learned material in order to ensure that your knowledge retention reaches the desired level (usu. 95-98%).
In SuperMemo, your incrementally processed articles will also be subject to repetitions. We will often use the more intuitive term review in reference to incrementally processed material; after all, when you resume reading an article after a certain interval of time, you are not actually repeating anything. You are simply delving into new sections of the same material and extracting newly acquired wisdom into separate elements (Remember extract).
The algorithms used to make (1) standard repetitions of question-and-answer material and (2) reviewing reading material are similar. Most importantly, all repetitions and review are made in increasing intervals. In incremental reading, you will constantly see inflow of new material to your collection. Unprocessed material will need to compete with the newly imported material. Increasing review intervals makes sure that your old material fades into lower priority if it is not processed quickly. Naturally, the speed of processing will depend on the availability of your time and the value of the material itself. Articles that are boring, badly written, less important for your work or growth, will receive smaller portions of your attention and may go into long review intervals before you even manage to pass a fraction of the text. That is an inevitable side effect of a voluminous flow of new information into your collection (and your brain). However, intervals and priorities can easily be adjusted. If the priorities change, you can modify the way you process important articles. Upon next review you can read the whole article or revert it to a short-interval review. You can even use search tools (Ctrl+F) to locate more articles on the subject you feel you have neglected and reprioritize these as well.
The algorithm for repeating questions-and-answer (e.g. cloze deletions) is quite complex and you do not have much influence on the timing of repetitions (see: SuperMemo Algorithm). This stems from the need to keep a high level of knowledge retention, which can be compromised by manual intervention.
However, the algorithm for determining inter-review intervals in incremental reading is much simpler and is entirely under your control. Each article receives a number called A-Factor that determines how much intervals increase between subsequent reviews (the name A-Factor is used here for orthogonality; however, A-Factors here correspond to the extinct concept of E-Factor known from earlier versions of SuperMemo and should not be confused with A-Factors used by items). For example, if A-Factor=2, review intervals will increase twice after each review. A-Factors are determined heuristically on the basis of the length of the text and other factors. Long texts will receive low A-Factors (e.g. 1.1), while short extracts will receive higher A-Factors (e.g. 1.8). You can change the value of A-Factor associated with a given article by choosing Ctrl+Shift+P. A-Factors associated with items cannot be changed by the user.
You can also control the review timing by manually adjusting inter-review intervals. Use Ctrl+J (Reschedule) or Ctrl+Shift+R (Execute repetition) to determine the date of the next review. Ctrl+J will increment the current interval, while Ctrl+Shift+R will first execute a repetition and then set the new interval to the selected value. In other words, if you current interval is 100 and you specify the value of 3 in Reschedule, your new interval will be 103 and the last repetition date will not change. If you do the same with Execute repetition, you new interval will be 3 and the last repetition date will be set to today.
Skill 5: Handling large volumes of knowledge
With incremental reading, your work with SuperMemo will freely combine and mix reading with standard repetitions of knowledge. Randomizing the sequence of repetitions should be encouraged. Only random coverage of the material will provide you with a true sense of your progress. You can randomize your daily portion of repetitions and review with Learn : Random : Randomize repetitions (Ctrl+Shift+F11).
By using Randomize repetitions, your repetitions will not favor more accurate processing of material based on the length of the interval, element type (e.g. articles, extracts, question-and-answer items, etc.), contents (i.e. branch of the knowledge tree) or degree of processing. Random repetitions will help you better understand possible negative trends such as excessive inflow of new material, lower retention (mostly as a result of frequent rescheduling), poor formulation of newly created cloze deletions, low quality or applicability of the acquired knowledge, excessive emphasis on certain subject at the cost of other subjects, etc.
Your hunger for new knowledge may quickly result in substantial overflow of new material at the cost of the quality of knowledge and retention. For this reasons you may, but do not have to, decide to execute your repetitions in the following stages:
Postpone makes it possible to reschedule only a subset of repetitions. For example you can opt to delay repetitions in these subsets:
Postpone uses a number called a delay factor that is used to increase intervals of outstanding repetitions. Intervals are simply multiplied by the postpone factor. For example, if you choose to Postpone with the delay factor of 1.1, all intervals will be multiplied by 1.1 and will increase by 10%. Postpone will always increase intervals by no less than one day from the present day. This way, all items on which Postpone is executed fall out of the outstanding subset.
You will mainly execute Postpone with Ctrl+Alt+P in three ways:
in the browser: if you want to postpone any subset of items that you can generate with browser operations (see: Using subsets)
in the element window: if you want to postpone any ancestor branch to which the currently repeated element belongs
Here are some typical ways in which you will execute Postpone:
If you would like to make your repetitions using the above suggestions, you should add all your new material to a selected branch, and transfer individual items to target categories such as sociology, psychology, history, etc., only then when you are sure that these items have received their final wording and meet your stringent quality criteria. As a rule, you should not ever use Postpone on your mission critical knowledge. Postpone will reduce your knowledge retention. It should only be reserved for dislodging the excess of newly processed articles, extracts and cloze deletions. It can also be used sparingly on branches with lower priority (e.g. Private, Hobbies, etc.).
To efficiently work with categories you should be familiar with the following subjects:
History of incremental reading
Incremental reading might be as important for SuperMemo as the original repetition spacing algorithm. It eliminates a number of bottlenecks to learning at the acquisition stage.
Older SuperMemos: In the years 1987-1998, users of SuperMemo had only two alternatives in the area of collecting learning material for learning with SuperMemo: (1) type it in and formulate it manually or (2) obtain ready-made learning material from colleagues, SuperMemo Library, etc.. The only support for learning from electronically available material was via Copy and Paste.
SuperMemo 99 made the first step towards efficient reading of electronic articles by introducing reading lists and the first primitive reading tools: extracts and clozes. Reading lists are prioritized lists of articles to read. Extracts make it possible to split larger articles into smaller portions. Clozes makes it possible to convert short sentences into question-answer format by means of cloze deletions
SuperMemo 2000 greatly increased the efficiency of reading by introducing the concept of incremental reading. Incremental reading makes it possible to simultaneously read dozens of articles. Each article is read in small increments fully controlled and prioritized by the user and/or the default learning process. Components of incremental reading introduced in SuperMemo 2000: new A-Factor-based topic repetition scheme (i.e. learning algorithm), read points, formatting extracts and clozes (SuperMemo 99 would ignore formatting), text highlight and ignore, source article link, reading toolbar, branch and browser learning, branch and browser postpone, and support for longer articles (SuperMemo 99 imposed 64K limit on articles).
SuperMemo 2002 brings incremental reading to a new level. For SuperMemo 2002, incremental reading becomes the primary learning mode for middle-level and advanced students. SuperMemo 2002 introduces HTML-based incremental reading. For the first time, the user will see little difference between the material in his web browser and in SuperMemo. Other new features introduced by SuperMemo 2002: wholesale learning material import from Internet Explorer, mid-interval repetitions that make it possible to review portions of material without damage to the learning process (Algorithm SM-11), search-based learning (i.e. subset learning in which the subset is defined by advanced search tools), dynamically modified A-Factors that fine-tune the priorities without user intervention, postpone wizard that obviates reading lists, separate topic/item statistics and new incremental reading progress statistics, reference labeling, and more.
see Incremental reading from user's perspective by Len Budney