Below you will find a general outline of the sixth major formulation
of the repetition spacing algorithm used in SuperMemo. It is referred to as Algorithm SM8
since it was first implemented in SuperMemo 8.
Although the increase in complexity of Algorithm SM8 as compared with its predecessor, Algorithm SM6, is incomparably greater than the expected benefit
for the user, there is a substantial theoretical and practical evidence that the increase
in the speed of learning resulting from the upgrade may fall into the range from 30 to
50%. Note that a newer version of the algorithm exists: Algorithm
SM11.
Historic note: earlier releases of the algorithm Although the presented algorithm may seem complex, you should find it easier
and more natural once you understand the evolution of individual concepts such as
EFactor, matrix of optimum intervals, optimum factors, and forgetting curves.
 1985  Paperandpencil version of SuperMemo is
formulated (Algorithm SM0). Repetitions of whole
pages of material proceed along a fixed table of intervals. See also: Using SuperMemo without a computer
 1987  First computer implementation makes it
possible to divide material into individual items. Items are classified into difficulty
categories by means of EFactor. Each difficulty
category has its own optimum spacing of repetitions (Algorithm SM2)
 1989  SuperMemo 4 was
able to modify the function of optimum intervals depending on the student's performance (Algorithm SM4)
 1989  SuperMemo 5
replaced the matrix of optimum intervals with the matrix of optimal factors (an optimum
factor is the ratio between successive intervals). This approach accelerated the
modification of the function of optimum intervals (Algorithm SM5)
 1991  SuperMemo 6
derived optimal factors from forgetting curves plotted for each entry of the matrix of
optimum factors. This could dramatically speed up the convergence of the function of
optimum intervals to its ultimate value (Algorithm SM6)
 1995  SuperMemo 8 PreRelease 1
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 possibility to accelerate its modification, esp. in early stages of
learning (Algorithm SM8). New concepts include:

Algorithm SM8
SuperMemo computes optimum interrepetition intervals from the
grades scored by individual items in learning. This record is used to estimate the
current strength of a given memory and the difficulty of the underlying item.
This difficulty expresses complexity of memories and the effort needed to
produce unambiguous and stable memory traces. SuperMemo takes the requested
recall rate as the optimization criterion (e.g. 95%), and computes the
intervals that satisfy this criterion. The function of optimum intervals is
represented in a matrix form (OF matrix) and is subject to modification based on
the results of the learning process.
This is a more detailed description of the Algorithm SM8:
 Interrepetition intervals are computed using the following formula:
I(1)=OF[1,L+1]
I(n)=I(n1)*OF[n,AF]
where:
 OF  matrix of optimal factors, which is modified in the course of
repetitions
 OF[1,L+1]  value of the OF matrix entry taken from the first row and
the L+1 column
 OF[n,AF]  value of the OF matrix entry that corresponds with the
nth repetition, and with item difficulty AF
 L  number of times a given item has been forgotten (from
"memory Lapses")
 AF  number that reflects absolute difficulty of a given item (from
"Absolute difficulty Factor")
 I(n)  nth interrepetition interval for a givent item
 The matrix of optimal factors OF used in Point 1 has been derived
from the mathematical model of forgetting and from similar matrices built on data
collected in years of repetitions in collections created by a number of users. Its initial
setting corresponds with values found for a lessthanaverage student. During repetitions,
upon collecting more and more data about the student’s memory, the matrix is
gradually modified to make it approach closely the actual student’s memory
properties. After years of repetitions, new data can be fed back to generate more accurate
initial matrix OF. In SuperMemo 2000, this matrix can be viewed in 3D with Tools
: Statistics : Analysis : 3D Graphs :
OFactor Matrix
 The absolute item difficulty factor (AFactor),
denoted AF in Point 1, expresses the difficulty of an item (the higher it is,
the easier the item). It is worth noting that AF=OF[2,AF]. In other words, AF denotes the
optimum interval increase factor after the second repetition. This is also equivalent
with the highest interval increase factor for a given item. Unlike EFactors in Algorithm SM6 employed in SuperMemo 6 and SuperMemo 7, AFactors
express absolute item difficulty and do not depend on the difficulty of other items in the
same collection of study material
 Optimum values of the entries of the OF matrix are derived through a
sequence of approximation procedures from the RF matrix which is defined in the same way
as the OF matrix (see Point 1), with the exception that its values are taken from the real
learning process of the actual student. Initially, matrices OF and RF are identical;
however, entries of the RF matrix are modified with each repetition, and a new value of
the OF matrix is computed from the RF matrix by using approximation procedures. This
effectively produces the OF matrix as a smoothed up form of the RF matrix. In simple
terms, the RF matrix at any given moment corresponds to its bestfit value derived from
the learning process; however, each entry is considered a bestfit entry on it’s own,
i.e. in abstraction from the values of other RF entries. At the same time, the OF matrix
is considered a bestfit as a whole. In other words, the RF matrix is computed entry by
entry during repetitions, while the OF matrix is a smoothed copy of the RF matrix
 Individual entries of the RF matrix are computed from forgetting
curves approximated for each entry individually. Each forgetting curve corresponds with a
different value of the repetition number and a different value of AFactor (or memory
lapses in the case of the first repetition). The value of the RF matrix entry corresponds
to the moment in time where the forgetting curve passes the knowledge retention point
derived from the requested forgetting index. For example, for
the first repetition of a new item, if the forgetting index equals 10%, and after four
days the knowledge retention indicated by the forgetting curve drops below 90% value, the
value of RF[1,1] is taken as four. This means that all items entering the learning process
will be repeated after four days (assuming that the matrices OF and RF do not differ at
the first row of the first column). This satisfies the main premise of SuperMemo, that the
repetition should take place at the moment when the forgetting probability equals 100%
minus the forgetting index stated as percentage. In SuperMemo 2000,
forgetting curves can be viewed with Tools
: Statistics : Analysis : Curves:
 The OF matrix is derived from the RF matrix by: (1) fixedpoint power
approximation of the RFactor decline along the RF matrix columns (the fixed point
corresponds to second repetition at which the approximation curve passes through the
AFactor value), (2) for all columns, computing DFactor which expresses the decay
constant of the power approximation, (3) linear regression of DFactor change across the
RF matrix columns and (4) deriving the entire OF matrix from the slope and intercept of
the straight line that makes up the best fit in the DFactor graph. The exact formulas
used in this final step go beyond the scope of this illustration.
Note that the first row of the OF matrix is computed in a different way. It corresponds to
the bestfit exponential curve obtained from the first row of the RF matrix.
All the above steps are passed after each repetition. In other words, the theoretically
optimum value of the OF matrix is updated as soon as new forgetting curve data is
collected, i.e. at the moment, during the repetition, when the student, by providing a
grade, states the correct recall or wrong recall (i.e. forgetting) (in Algorithm SM6, a separate procedure Approximate
had to be used to find the bestfit OF matrix, and the OF matrix used at repetitions might
differ substantially from its bestfit value)
 The initial value of AFactor is derived from the first grade
obtained by the item, and the correlation graph of the first grade and AFactor (GAF graph). This graph is updated after each repetition in which a
new AFactor value is estimated and correlated with the item’s first grade.
Subsequent approximations of the real AFactor value are done after each repetition by
using grades, OF matrix, and a correlation graph that shows the correspondence of the
grade with the expected forgetting index (FIG graph). The grade
used to compute the initial AFactor is normalized, i.e. adjusted for the difference
between the actually used interval and the optimum interval for the forgetting index equal
10%
 The FIG graph is updated after each
repetition by using the expected forgetting index and grade values. The expected
forgetting index can easily be derived from the interval used between repetitions and the
optimum interval computed from the OF matrix. The higher the value of the expected
forgetting index, the lower the grade. From the grade and the FIG graph (see FIG graph in Analysis),
we can compute the estimated forgetting index which corresponds to the postrepetition
estimation of the forgetting probability of the justrepeated item at the hypothetical
prerepetition stage. Because of the stochastic nature of forgetting and recall, the same
item might or might not be recalled depending on the current overall cognitive status of
the brain; even if the strength and retrievability of memories of all contributing
synapses is/was identical! This way we can speak about the prerepetition recall
probability of an item that has just been recalled (or not). This probability is expressed
by the estimated forgetting index
 From (1) the estimated forgetting index, (2) length of the interval
and (3) the OF matrix, we can easily compute the most accurate value of AFactor. Note
that AFactor serves as an index to the OF matrix, while the estimated forgetting index
allows one to find the column of the OF matrix for which the optimum interval corresponds
with the actually used interval corrected for the deviation of the estimated forgetting
index from the requested forgetting index
To sum it up. Repetitions result in computing a set of parameters
characterizing the memory of the student: RF matrix, GAF graph and FIG
graph. They are also used to compute AFactors of individual items that characterize the
difficulty of the learned material. The RF matrix is smoothed up to produce the OF matrix,
which in turn is used in computing the optimum interrepetition interval for items of
different difficulty (AFactor) and different number of repetitions (or memory lapses in
the case of the first repetition). Initially, all student’s memory parameters are
taken as for a lessthanaverage student, while all AFactors are assumed to be equal
Optimization solutions used in Algorithm SM8 have been perfected
over 10 years of using the SuperMemo method with computerbased algorithms (first
implementation: December 1987). This makes sure that the
convergence of the starting memory parameters with the actual parameters of the student
proceeds in a very short time. Similarly, the introduction of AFactors and the use of the
GAF graph greatly enhanced the speed of estimating individual item difficulty. The
adopted solutions are the result of constant research into new algorithmic variants. The
postulated employment of neural networks in repetition spacing is
not likely to compete with the presented algebraic solution. Although it has been claimed
that Algorithm SM6 is not likely to ever be substantially
improved (because of the substantial interference of daily casual involuntary repetitions
with the highly tuned repetition spacing), the initial results obtained with Algorithm
SM8 are very encouraging and indicate that there is a detectable gain at the moment of
introducing new material to memory, i.e. at the moment of the highest workload. After
that, the performance of Algorithms SM6 and SM8 is comparable. The gain comes from
faster convergence of memory parameters used by the program with actual memory parameters
of the student. The increase in the speed of the convergence was achieved by employing
actual approximation data obtained from students who used SuperMemo
6 and/or SuperMemo 7
Algorithm SM8 is constantly being perfected in successive releases
of SuperMemo, esp. to account for newly collected repetition data, convergence data, input
parameters, etc.
If you would like your own software to use the Algorithm SM8, read
about SM8OPT.DLL
If you would like to use SuperMemo, but you would like to use a
different repetition spacing algorithm, you might want to find out about repetition scheduling plugin options
Frequently Asked Questions
(Zoran
Maximovic, Serbia,
Sep 25, 2000)
Question:
In approximation graphs in Tools : Statistics : Analysis, some of
the curves "jump out" of the graph area. What is wrong?
Answer:
This was a harmless bug in the algorithm in
SuperMemo 98/99. The assumption is that intervals cannot grow beyond the value
of AFactor. For that reason, the maximum RFactor should equal the relevant AFactor.
However, in plotting the forgetting curves, higher values of UFactors are used
as repetitions may be delayed (e.g. with Mercy, user procrastination,
etc.). The algorithm puts a cap on the maximum RFactor value (along the
theoretical assumption that RFactors cannot be greater than corresponding AFactors).
However, the implementation used the maximum UFactor value as the cap (the one
used in plotting the forgetting curve). Consequently, RFactors could grow
larger than AFactors and the curve would "jump out" of the graph,
which displays the correct cap.
This bug should have little effect on the learning process. The higher cap does
not invalidate the correctness of RFactors. It just does not prevent very long
intervals in case of very good repetition results.
This bug has been fixed in SuperMemo 2000