Difference between revisions of "Subset learning"
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''Subset review'' is a review of a portion of the learning material (e.g. before an exam, in problem solving, in creative writing, etc.). The portion can be determined with [[Find elements|search]], by [[Glossary:Branch|branch]] selection in '''[[Contents window|Contents]]''', by [[Concepts|concept]], and other means that select a subset of [[Glossary:Element|elements]]. The reviewed subset material may be sorted by its position in the [[Glossary:Knowledge_tree|knowledge tree]] ('''[[Contents window|Contents]]'''), [[Glossary:Priority|priority]], [[Glossary:Difficulty|difficulty]], [[Glossary:interval|interval]], recency, text size, etc. The review may also be semantic or neural where connections between elements determine the sequence of review.
''Subset review'' is a review of a portion of the learning material (e.g. before an exam, in problem solving, in creative writing, etc.). The portion can be determined with [[Find elements|search]], by [[Glossary:Branch|branch]] selection in '''[[Contents window|Contents]]''', by [[Concepts|concept]], and other means that select a subset of [[Glossary:Element|elements]]. The reviewed subset material may be sorted by its position in the [[Glossary:Knowledge_tree|knowledge tree]] ('''[[Contents window|Contents]]'''), [[Glossary:Priority|priority]], [[Glossary:Difficulty|difficulty]], [[Glossary:interval|interval]], recency, text size, etc. The review may also be semanticor neuralwhere connections between elementsdetermine the sequence of review.
== Review types ==
== Review types ==
Revision as of 09:12, 27 February 2016
Subset review is a review of a portion of the learning material (e.g. before an exam, in problem solving, in creative writing, etc.). The portion can be determined with search, by branch selection in Contents, by concept, and other means that select a subset of elements. The reviewed subset material may be sorted by its position in the knowledge tree (Contents), priority, difficulty, interval, recency, text size, etc. The review may also be semantic or neural where connections between elements determine the sequence of review.
Search and review
Search and review in SuperMemo is a review of a subset of elements that contain a given search phrase. For example, before an exam in microbiology, a student may wish to review all his knowledge of viruses using the following method:
- search for all elements containing the phrase virus (e.g. with Ctrl+F)
- review all those elements (e.g. with Ctrl+Shift+L)
The review may include all subset elements (e.g. Learning : Review all in the browser with Ctrl+Shift+L), or only the elements that are outstanding for review on that particular day (e.g. Learning : Learn in the browser with Ctrl+L). Before you execute the review, you can randomize the review material (Ctrl+Shift+F11), sort it by priority, by recency, by interval, by size, by age (in the learning process), etc. You can also apply your default sorting criteria with Ctrl+S in the browser. All forms of review run on all elements except for (1) dismissed elements and (2) those elements that have already been processed on this particular day. The latter condition makes sure that you can do a comprehensive review in various subsets without duplicating your work on a given day. You can overcome the block of double review on a given day by using Add to outstanding (see below).
If you build an extensive collection of things worth learning, subset review may help you learn about Subject A, and do a value-rich review of your material across many others domains at the same time! You kill many birds with one stone.
Search : Find elements (from the main menu) makes it possible to define OR-searches and to save search definitions. This way you can, for example, choose a set of terms that define your "diabetes" subset and use them each time you want to review your "diabetes" material.
The parameter Subset in the Statistics window indicates the progress of repetitions in subset learning. This field displays the number of items, the number of topics, and the number of pending elements in subset learning. The name in the parentheses describes the currently processed subset.
In the simplest case of branch review, the button Learn at the bottom of the contents window can be used to execute outstanding repetitions on a selected branch of the knowledge tree. For example, to make repetitions in the Medical Sciences branch, click that branch and then click Learn. Using Learn in the contents window is like using Learn in the element window, except only elements belonging to the selected branch will be considered in making repetitions.
- Select the branch in Contents
- Choose Learning : Review all on the Process branch> menu (Ctrl+Shift+L)
- open the branch (View : Branch from the contents menu), or open a subset (View : Subset from the main menu) in the browser, or search for a set of elements (e.g. with Ctrl+F)
- randomize the content of the browser (e.g. Ctrl+Shift+F11)
- execute a review (e.g. Learning : Review all on the browser processing menu)
If you want to semantically connect a group of elements related to a single subject in incremental reading, you can use subset review based on the elements' tree structure created in the incremental learning process. This way you can quickly review all elements related to a topic whose "big picture" became hazy.
- go to any element that makes a part of the knowledge structure related to given problem
- use the Reference link button () on the navigation bar to get to the original article
- use Contents to find a relevant subbranch (or stay at the root of the article to review it all)
- browse the selected branch (e.g. with Ctrl+Space)
- choose Learning : Review all (Shift+Ctrl+L) to review all elements in the logical/semantic sequence (the sequence of branches reflects the order of processing of individual paragraphs in the article)
You can also choose Learning : Learn branch on the element menu to begin branch learning for one of the ancestors of the currently displayed element. You can use this method if you encounter interesting material that you would like to refresh more thoroughly before you proceed with your standard learning process.
Neural review is a subset review based on the concept of spreading activation known from neural networks. Like impulses in the brain, spreading activation in a network of elements and concepts feeds new elements into your incremental review. This way, if you choose Go Neural in SuperMemo (e.g. Learn : Go neural from the main menu on the current element, or Go neural on a selected registry member (e.g. picture) from the registry menu), it will serve you with associated ideas for the purpose of semantic review, big picture review, creativity, problem solving, etc.
- review only the outstanding material: Learn (Ctrl+L) will execute only the outstanding repetitions, i.e. the elements that have been scheduled for review today or before today
- review all the material: Review all (Shift+Ctrl+L) will execute all outstanding repetitions as well as force mid-interval repetitions on all elements in a subset (except those elements that have already been reviewed today). Review of non-outstanding elements is equivalent to Learning : Execute repetition (Shift+Ctrl+R) available from the element menu
- review all topics: Review topics works like Review all but it does not include items, i.e. it forces a review of all topics in a subset (except topics that have already been reviewed today). Each time you do subset review on a set of items, you add some extra time to the total cost of learning of that portion of the material. This is why you may wish to exclude items from a review that occurs often
All the above options are available on Process collection> : Learning submenu of the File main menu item, Process branch> : Learning submenu of the contents menu, or Process browser> : Learning submenu of the browser menu.
Adding elements to the learning queue
Instead of spending your time on a thorough review of a branch or subset, you may prefer to intersperse the review material in your standard learning process. You can do it with Learning : Add to outstanding.
Add to outstanding is a rationalization upon 2 extremes:
- well-timed incremental learning (or classical spaced repetition)
- subset review (e.g. before an exam)
On one hand you can proceed with your outstanding queue, on the other, you can smuggle some subset review in between. For example, you might learn about the superiority of "intermittent fasting" over "fasting". You will want to investigate the subject to perhaps employ it in your lifestyle. However, you do not want the subject to be buried in thousands of articles you keep reading. Nor do you want it to monopolize your learning time on a given day. You can import several articles on intermittent fasting and spread them sparsely in your outstanding queue with Add to outstanding. By the end of the day, you will have a peek at all those articles, have them all well prioritized, and integrated with the learning process (in proportion to the value of the newly discovered content).
- you will not get the instant gratification from the instant review of a hot topic
- you risk that new imports will displace the articles of interest before you manage to give them a preview
In other words, Add to outstanding is a more extreme version of Priority : Spread, but not as radical as Learning : Spread in the browser (irreversible rescheduling), or subset review (reversible review).
Repeating items before topics
If you ever neglect learning, you may wish to unload your item backlog ahead of your topic backlog. You can optimally do it with a change to your sorting criteria. However, you can also start your day from 100% item repetitions:
- Choose View : Outstanding from the main menu
- In the element browser, sort repetitions by type (items first)(e.g. by clicking the header of the Type column), or choose Child : Items
- Choose Learn on the browser menu to make repetitions (or Tools : Save repetitions on the same menu to make the sorting permanent)
Ideally, in incremental reading, you should have items and topics mixed up. This will help you achieve balance between retention of the old material and the inflow of the new material. By working with items first, you risk slowing down learning by working on high retention. That's a step back to classical SuperMemo.
Advance is not a form of review. However, it makes it possible to shorten the intervals and speed up the review. For example, if your exam comes in 100 days, you can shorten all intervals in a subset to less than 100 days with Advance.
- those whose interval is shorter than the advance interval
- those who have been repeated today (use Learning : Add to outstanding if you need to go around this limitation)
If you would like to review the material related to Auguste Comte (1798-1857) do as follows:
- Press Ctrl+F and paste Auguste Comte in the search box
- Press Enter or click Find (this will search your collection and open a browser with the results)
- Choose one of the subset learning options:
- to prevent forgetting: press Ctrl+L or choose Process browser> : Learning : Learn to review only the outstanding material. This will help you review only the items that are most likely to be forgotten and a portion of topics that have been scheduled for review for today
- to learn new things: choose Process browser> : Learning : Review topics to review all topics related to Auguste Comte
- to maximize the review (e.g. before an important deadline): press Shift+Ctrl+L or choose Process browser> : Learning : Review all to review all topics and to force a repetition on all items related to Auguste Comte. Remember that premature review of items may paradoxically slow down your long-term learning
If your history exam is approaching and you cannot cope with all repetitions in the collection, make sure that at least your daily portion of history is thoroughly reviewed:
Repeat the procedure daily. However, in the last 3-5 days, you could follow that yet with Process branch> : Learning : Review all to protectively refresh the material that would optimally be scheduled after the exam. This strategy is not recommended for long-term learning! It departs from the optimum timing for review to consolidate memories. It should only be reserved for a situation when burning school situation forces you to neglect your long-term planning.