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Simulating Context in Mobile Learning Games for Testing and Debugging

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Especially software running on mobile devices does increasingly rely on contextual information such as time and location. And whenever a software product is affected by context, this context has to be replicated for testing and debugging. This paper introduces an external context manipulation interface for a previously developed learning item scheduler. The scheduler determines when to present a learning item in a learning game based on previous interaction in order to maximize learning efficiency and is based on psychological models. As inter-presentation-intervals can be in the range of days to months, system testing cannot be conducted in a conventional manner. Hence, virtual time hops can be used to fast forward to any specific point in virtual time which would make the software act like it was system time. The approach has shown to be a valuable debugging and testing aid and can be extended for other contextual information sources.
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Simulating Context in Mobile Learning Games for Testing and Debugging
Florian Schimanke
Dept. of Computer Science
HSW University of Applied Sciences
Hameln, Germany
schimanke@hsw-hameln.de
Robert Mertens
Dept. of Computer Science
HSW University of Applied Sciences
Hameln, Germany
mertens@hsw-hameln.de
Leonard Hill
Dept. of Computer Science
HSW University of Applied Sciences
Hameln, Germany
Leonard.hill@hsw-hameln.de
Oliver Vornberger
Dept. of Mathematics & Computer Science
University of Osnabrueck
Osnabrueck, Germany
oliver@uos.de
Abstract Especially software running on mobile
devices does increasingly rely on contextual information
such as time and location. And whenever a software
product is affected by context, this context has to be
replicated for testing and debugging. This paper
introduces an external context manipulation interface
for a previously developed learning item scheduler. The
scheduler determines when to present a learning item in
a learning game based on previous interaction in order
to maximize learning efficiency and is based on
psychological models. As inter-presentation-intervals
can be in the range of days to months, system testing
cannot be conducted in a conventional manner. Hence,
virtual time hops can be used to fast forward to any
specific point in virtual time which would make the
software act like it was system time. The approach has
shown to be a valuable debugging and testing aid and
can be extended for other contextual information
sources.
Keywords: game based learning, mobile learning, spaced
repetition, SuperMemo, learning game, debugging
I. INTRODUCTION
When it comes to organizing one’s own learning, there
are different approaches that can be followed. Each of those
strategies has its own pros and cons and it is not always
possible to determine if one strategy is really better than the
others. One perspective to judge the different approaches is
their impact on short term or long term memory. Usually,
when students organize their learning, they often tend to
techniques like “massing” or “cramming” which refer to
learning a special topic in a very short amount of time,
usually short before an exam or a test. The reason is that
these techniques appear to be the most promising
approaches because they seem to make studying easy and
fast due to their short term performance. Therefore, massing
and cramming are seen as learning techniques that mainly
strengthen the short term memory. On the other hand,
educators are aiming on a more sustainable knowledge,
which is stored in the students’ long term memory. One
promising approach to reach this goal is to use less massing
and cramming and more a technique which is known as
“spacing”. This approach may appear much slower since it
is carried out over a much longer time but is proven to have
a better effect on long term learning. The learning content is
not learned in a squeezed way and in a short amount of time
but is repeated after a certain amount of time over and over
again, which helps flattening the so called forgetting curve
[1] and therefore improves the retention of this content with
each repetition. In order to achieve the best learning results,
the intervals between repetitions of the same content should
also increase the better the learner remembers the correct
answer [2]. On the other hand, the intervals should also be
shortened if the content is remembered less well. There are
already some sophisticated algorithms available, which
determine those intervals by judging the learner’s
performance in past repetitions and combining this with
other factors like the number of already done repetitions.
One of the most widely spread algorithms is called “SM2”,
which was originally developed for learning with learning
cards. In our research we want to combine spacing with the
motivating and immersing effect of learning games. In
earlier research [3] we have already shown that both
approaches play well together if there are made some
adjustments. For example, we have already found out that
using the strictly time-based SM2 algorithm alone may lead
to corrupted calculation values if the learners decide to play
the game several rounds in a row [4]. We have therefore
integrated our round-based FS algorithm to solve this
problem.
In order to keep the original idea of the spacing
approach we have to make sure that the correct algorithm is
in charge at a given time and that the calculations, which are
made by the respective algorithm are correct. Since spacing
follows a time-based approach it is impractical to always
wait until the calculated interval has passed before we can
check if the algorithms work correctly, especially since the
first repetition is always scheduled on the next day and the
second repetition six days after the first repetition, as
defined by the SM2 algorithm. Further repetitions may even
be months in the future. We have therefore developed a
method to simulate the spaced repetition concept in a mobile
learning game through virtual time hops.
The remainder of the paper is organized as follows. In
the related work section we present the status quo of related
fields of research with a focus on the spacing approach and
the used SM2 algorithm before we describe why and how
we added the FS auxiliary algorithm when using SM2 in a
mobile learning game. After that we present some
considerations about how we can simulate the work of the
two algorithms in a self-developed prototype learning game
in order to ensure that the original idea of the spacing
approach stays intact. Finally, we draw a conclusion of our
findings and considerations and give a forecast for future
work on this topic.
II. RELATED WORK
As already mentioned, there are several studies that have
proven spacing as the better way to contribute to long term
retention, beginning with Ebbinghaus [1]. Kornell [5] adds
that spacing is also more effective than cramming and van
Note [6] found out that there is no difference in terms of test
results when interrupting the time between cramming and a
test or not. While there is only little consensus about which
learning technique has a better effect on long term retention,
most studies found out that cramming has indeed a positive
effect on short term memorization and is therefore popular
among students when preparing for an exam. However,
looking at the forgetting curve by Ebbinghaus [1] clearly
shows that learning and remembering is basically a matter
of time and retention. He created a formula showing the
degradation of memories: R = e(t/S) where R is memory
retention, S is the relative strength of memory, and t is time.
The solid line in figure 1 shows an example of this formula.
As can be seen, with each repetition the forgetting curve
starts anew and thus gets flatter over time, which is
represented by the dotted lines after each repetition. This
effect shows how important it is to repeat learning the same
subject multiple times over a long period with different
intervals. Therefore, the more an item is reviewed and
remembered correctly, the longer intervals between the
repetitions may be scheduled. Since the forgetting curve
gets flatter over time it can clearly be stated that spacing has
a positive effect on long term memorization.
Figure 1: Alteration of the forgetting curve through repetition
according to Ebbinghaus [1] and estimations from Paul [7]
Since spacing is a very promising way to achieve a long
term learning effect, we want to combine this approach with
the motivating effect of learning games. In this scenario an
algorithm would determine which topic or task in the game
should be presented at a given time and at which frequency.
There are already several algorithms for scheduling
repetitions in computer-based flashcard implementations.
One of the most widely spread algorithms is delivered by
SuperMemo1. It is called “SM” plus an extension indicating
the version. SM2 is the algorithm which is today used the
most and which we will therefore also use for our research.
SM2 uses a scale between 0 and 5 for values that are
referred to as “quality of the response”. After each card
users have to judge how well they remembered the
corresponding information. A card is rated 0 or 1 if the
learner does not know the answer or has completely
forgotten it. 1 means the card is already getting more
familiar than a card with grade 0 and will therefore be
repeated a little less often. The algorithm will then keep on
repeating the card until the learners grade it with a 2 or
higher, which means that they think that they will be able to
remember it for at least one or two days. This point signals
the transition from short to long term memory.
SM2 will compute repetition dates for cards rated with
grade 2 for a repetition so that the learner might still be able
to remember it with some effort. If that date is too soon, the
learner might rate it with a grade of 3 or higher, which will
push the next repetition farther into the future. On the other
hand, if the interval was too long and the user has already
forgotten the card, he or she can rate it 1 or 0 again so that
the algorithm will start to repeat it sooner and more
frequently. If the learner keeps on rating a card 4 or 5, SM2
will keep increasing the interval between two repetitions. By
1 http://www.supermemo.com/
lowering the grade the learners can make the algorithm
repeat a card more frequently again, should they feel that
remembering the correct answers is too hard. If they feel
that SM2 keeps choosing the correct frequency they should
keep rating the card with 4.
As it was shown by the forgetting curve, the better
remembered materials may have a longer time between
repetitions than the ones that are not remembered well. Due
to the fact that the learner is able to re-rate the items every
time they are presented, it is also possible to give an item a
lower ranking and therefore repeat it more often and in a
shorter period of time. By rating the cards learners can
therefore influence the frequency at which they are
presented based on their learning progress. This decreases
the dependency between type of information and the
number of learning sessions needed to remember a certain
card [5].
Once the information has been transferred into long term
memory, a continued rehearsal assists in strengthening the
memory trace for that information and thus ensures that it is
not forgotten over time. The intervals between these
rehearsals have to be short enough to ensure that the
information is still available from long term memory. In
terms of spaced repetitions according to Wozniak [8]
“Intervals should be as long as possible to obtain the
minimum frequency of repetitions […]” but “[…] short
enough to ensure that the knowledge is still remembered”.
According to Bahrick and Phelps [9] the optimal intervals
are the longest possible intervals that do not lead to
forgetting.
From these statements it can be seen that the intervals
between the repetitions of the same content play a key role
in the spaced repetition approach. However, when using
spacing in a mobile learning game, the motivating effect of
this game may backfire when it drives the learner to playing
the game over and over again simply out of enjoyment.
Without any adjustments this could lead to early repetitions
of the same learning item, especially if there is only limited
content. We have therefore developed the FS algorithm (FS
= Follow-Up Sequence) as a helper algorithm for SM2,
which takes over the content selection from SM2 if the
learner decides to play the game more than once at a
scheduled repetition. Furthermore, the FS algorithm
introduces a flag, which locks the last played item in order
to avoid back-to-back presentations of the same learning
content, which could make the game boring to the learners
over time. Besides that, the FS algorithm mimics the idea
behind the SM2 algorithm. It uses different values to
calculate a ranking based on the learner’s performance on a
certain learning item and presents well remembered items
less frequently than those that the learner has some
difficulties to remember.
III. T
HE
B
LACK
-B
OX
P
ROBLEM
The SM2 and the FS algorithm are provided to a
learning game as a joint framework. This involves the need
for certain interfaces, which are used for data exchange
between the game and the framework. Depending on how
much is known about what happens behind an interface, this
implementation is either seen as a black-box or as a white-
box. In an ideal black-box implementation, completely
nothing is known about what happens inside the framework.
On the other hand, in a white-box implementation, the
operations inside the framework can be observed [10].
The work of the two algorithms in our implementation
happens completely in the background and thus transparent
to the using learning game and to the user. While this is
intended, it also makes it difficult for researchers and
educators to get an insight on whether and how the
implementation performs in a real world scenario. Having
this insight is very important to ensure that the algorithms
are implemented correctly and that the correct algorithm is
in charge at the right time. Implementing the framework as a
white-box is not an option in this case because of the time-
based nature of the approach.
Figure 2: Black-Box concept
An abstract example about how a black-box works can
be seen in figure 2. Our framework consists of the SM2
algorithm as well as the FS algorithm and the corresponding
data stores. The learning game on the other hand is
responsible for providing the content and the user interface,
as can be seen in figure 3. In this architecture our
algorithmic framework is represented by the black-box. The
communication between the framework and the learning
game is realized through defined interfaces which correlate
to the input and the output of the black-box concept shown
in figure 2. In this case, the black-box accepts data from the
learning game in terms of the learner’s performance on an
individual task (Input). This data is then processed inside
the box which either leads to a next repetition date
scheduled by the SM2 algorithm according to the spaced
repetition approach or to a content selection based on the FS
algorithm if it was an early repetition. This information is
then given back to the learning game which presents the
respective content accordingly. However, while everything
that happens inside the black-box should be implemented as
intended, there is no way of simulating and visualizing this,
despite waiting for the interval to elapse and see what
happens.
Figure 3: Framework architecture
Both algorithms are well documented, which helps
substantially when implementing them into our approach.
However, there can always be bugs or inaccuracies when
developing software. This may happen even more
frequently when different software components have to play
together. In this case, having a black-box in which the
whole logic takes place can be cumbersome. Especially with
our approach, in which the time component plays an
important role this would also mean that we would have to
wait until a calculated interval is over in order to see
whether the calculation was correct or if it worked at all. We
have already implemented a logging functionality, which
keeps track of all values and their manipulation over time
but this is only visible inside our development environment.
The black box behavior makes it also hard to see whether
the correct algorithm was in charge in a certain situation.
While all this should be transparent to the learner, there
should be some kind of simulation for the developers or
educators in order to illustrate what is happing inside the
black box.
IV. S
IMULATING
V
IRTUAL
T
IME
H
OPS AND
I
LLUSTRATING THE
S
PACED
R
EPETITION
A
PPROACH
To mitigate the aforementioned black-box problem, we
have developed a web app which can be connected to the
learning game and serve as a trigger for virtual time hops as
well as an interface, which can visualize the activities that
are taking place inside the black-box, i.e. the algorithmic
framework. Due to the main purpose of this web app we
have called it “Time Machine”.
The Time Machine consists of two components: the
TimeMachineServer and the TimeMachineClient which
visualizes the algorithm's data. The server application
receives and emits the socket.IO-messages and distributes
messages to all connected clients. No further logic is
implemented in this component. The TimeMachineClient
picks up the server's messages and analyzes and converts
them into graphical representations. Additionally, it offers
the opportunity for interaction with the iOS-App. For
example, it is possible to simulate the algorithm's work by
changing the reference time and calculate a new next
repetition date.
We ultimately decided to use web sockets for the
connection between the Time Machine and the learning
game due to their characteristic that they interact in almost
real-time. The client is developed with different
frameworks:
Used framework Purpose
socket.IO Communicate with the serve
r
an
d
to implemen
t
web sockets
Angular.js Use
d
to realize data-
b
inding and
use
r
-interaction
Bootstrap Use
d
to
uil
a fas
t
an
d
responsive
use
r
interface
Table 1: Used frameworks for the Time Machine
Since spaced repetition is a time-based approach, we
needed a way to simulate elapsed repetition intervals in
order to demonstrate that the SM2 algorithm works
according to the spaced repetition approach and that the FS
algorithm takes over in case of an early repetition.
Therefore, the Time Machine needs to work in a bi-
directional way as can be seen in figure 4. On one hand
there has to be a way to trigger the time hops through the
Time Machine and push this data to the learning game, on
the other hand there has to be a way to push some values
back to the Time Machine from the learning game to
illustrate the correct functionality. To accomplish this data
exchange between the learning game, which contains the
algorithmic framework and the Time Machine, there has to
be some kind of interface in place.
Figure 4: Time Machine architecture
As mentioned above, the connection between the web
app and the learning game is realized through a web socket,
which also serves as the interface for the data exchange. We
chose a web app over a native application because of the
platform independency and the lightweight implementation.
The communication between the web app and the learning
game is also realized in a lightweight way through JSON.
The data exchange between the two components can be
triggered bi-directional, depending on the two following use
cases.
Creating virtual time hops
Through a simple interface, which can be seen in figure
5, a certain date can be set in the Time Machine. As soon as
this happens, this date gets pushed to the connected learning
game, which causes the algorithmic framework to
recalculate the next scheduled repetition. The idea behind
this is to simulate how the algorithms will react at a certain
date in the future. Therefore, when this date is set for
example three days in the future, this will cause the game to
simulate what would happen if the learner plays the game
on that day. Since it is not possible to externally alter the
date of an iPad, on which our prototype learning game runs,
the virtual time hop is realized based on the time difference
between the current date and the date set by the Time
Machine. Adding to the aforementioned example, setting the
date three days in the future will deduct this time span from
the originally calculated next repetition date. This will affect
the display notifications as well as the selection of the
correct algorithm and the content selection. If the next
scheduled repetition is still in the future, the FS algorithm
will be in charge to select the appropriate content.
Figure 5: Time Machine date setting
If the date for the next scheduled repetition is reached or
already passed, this will cause the SM2 algorithm to present
that scheduled content and calculate the next repetition date
based on the learner’s performance on it. Setting a new date
in the future through the Time Machine can be done any
number of times and the learning app will always react
based on the manipulated values. When the connection to
the Time Machine is lost or when manually triggered, a roll-
back will be initiated and all original data will be restored.
Illustrating the algorithms’ work
In order to illustrate the activities inside the black-box,
i.e. the algorithmic framework, a subset of the data that is
used by the two algorithms is sent back to the Time
Machine where it gets visualized in an intuitive to use
interface. This data includes which algorithm was in charge,
the number of times a certain content was played, the next
repetition date, the date of the last presentation of that
content, the quality of the response, the easiness factor and
the last result (right or wrong). These values are sent to the
Time Machine from the learning game as a JSON string in
real-time and the interface of the Time Machine gets
updated with the new values immediately.
Since the data that is used to illustrate the algorithms’
work is the same data that is also used by the algorithms to
schedule repetitions and make content selection decisions,
this approach visualizes directly the work of the
implementation. This is also supported by different graphs,
which show the different repetitions, the respective
performance and the impact on the algorithms’ work. All
visualizations are either based on the current date or on the
date set by the Time Machine if applicable. Therefore, both
components of the Time Machine are working together in
order to test, verify and illustrate the correct work of the
algorithmic framework, i.e. the black-box.
As a side-effect, using a tool like the Time Machine not
only shows the internal states of the implementation and
visualizes the work inside of the black-box but also helps in
finding and fixing bugs. All these functionalities make the
Time Machine a valuable and important tool for developing
and testing the implementation of the spaced repetition
approach in a mobile learning game.
V. F
URTHER
U
SE
C
ASES
While the presented approach is especially useful for our
scenario, in which we needed to simulate time hops in order
to visualize and verify the work of our spaced repetition and
content selection algorithms, it also opens the door for other
approaches related to mobile learning games. One example
are location-based learning games. In case those games rely
on a specific location to present their context-related
content, it would be hard to test their functionality without
moving to those locations. Using a similar approach like we
did with the Time Machine in order to simulate time hops, it
would be possible to fake a location the same way we were
able to fake a different date in order to make the algorithms
and the game react to it. As an example, this is already
being done by some players of Pokémon Go who use a
“location faker” in order to suggest to the game that they
were at a different location. Just like the Time Machine, this
would also be helpful to test and debug the learning game.
VI. CONCLUSION AND FUTURE WORK
In this paper we discussed a way to simulate the spaced
repetition concept in a mobile learning game by creating
virtual time hops through an external web-app. Spacing is
generally seen as a promising way to improve a long term
retention of a learned content and there are already
sophisticated algorithms available, which can be
implemented into software in order to make use of this
approach for example in mobile learning games. While the
determination and the calculation of the optimal intervals
between the learning sessions should be completely
transparent for the learner, it is important for researchers
and developers to get an insight on how the algorithms
work. Not only is this important to ensure that the
calculations are in line with the idea behind spaced
repetitions but also to make sure that the correct algorithm is
in charge at the right time. The latter is necessary due to the
fact that there is a need for a second auxiliary algorithm in
addition to the spaced repetition algorithm which takes over
the content selection if the learner should decide to play
several rounds of the learning game in a row or outside the
calculated intervals, leading to early repetitions.
In order to illustrate the work of the two used algorithms
(SM2 and FS) we have connected our prototype learning
game to a web-app we have called “Time Machine”.
Through this web-app we were able to create virtual time
hops by setting a date in the future and handing this date
over to the game, which then adjusts its values and
notifications accordingly. On the other hand, the game
pushes its values about the learning progress and the
scheduled repetitions to the Time Machine where they are
illustrated for each task in a sophisticated user interface.
This approach not only helps to get an insight on how the
two algorithms work, how they interact and how the values
used for calculating the intervals get manipulated according
to the learner’s performance. It also helps with finding and
fixing bugs and with further improving the whole approach.
By using the Time Machine we were able to demonstrate
that our architecture of two interacting algorithms for
repetition scheduling and content selection works correctly
according to the spaced repetition concept. We were also
able to make some adjustments and improvements along the
way and to fix some minor bugs.
While the Time Machine helped us in different ways to
get an insight in our architecture and in making
improvements, it also revealed some approaches for future
work. One aspect is that currently, the prototype game alters
the score for each content which is used by the algorithms as
“quality of response” only by right or wrong answers and
increments or decrements the value accordingly. At a future
stage this altering should be done in a more sophisticated
way in order to better reflect the actual learning
performance of the users. One approach for this could be to
analyze the time between the presentation of the content and
the learners’ answer and then draw conclusions from that.
Another field for future work is a better dealing with early
or delayed repetitions. While the current prototype app is
based on the SM2 algorithm, the next logical step would be
to substitute this with the more advanced SM11 algorithm
for repetition scheduling. This would also open the door for
comparing the performance results of learners having used
the SM2 version with those of learners having used the
SM11 version to see if there is indeed a change in both
using the game and its effect performance progress.
Future work should focus on further improving the
auxiliary algorithm (FS algorithm) based on the presented
findings about short term memory and its impact on the
retention if the learners play several rounds of the game in a
row. This should then be taken into consideration in order to
achieve an even better calculation of the next scheduled
repetition as well as on the content selection.
In order to realize a wider test of our a approach we
are also planning to release the framework which contains
our algorithms for repetition scheduling and content
selection and the needed interfaces to interested game
developers, who can then integrate it into their learning
games as described in [4] and send anonymous data about
the learning progress and the app usage back to us for
analysis. By this, we will be able to make a more
statistically significant evaluation to get a deeper insight in
how learners would use our mobile learning game and to
further improve our concept.
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... This happens for example when there are early repetitions, which are not in line with the spacing approach. During our research we have also developed a tool, which helps to simulate the work of the algorithms in the framework [6]. This is necessary for two reasons. ...
... Another problem is that the correctness of the calculations is impractical to check since spaced repetition is a time-based approach. This led us to develop a simulation tool, which lets us create virtual time hops in order to verify the correct work of the framework at some date in the future [6]. With the so-called "Time Machine", it is possible to select a date in the future and see how the framework would react to a user interaction on that date through an easy to operate web interface. ...
... Based on these simple values we are able to see whether the results of the algorithmic framework inside of the black box are as intended according to the spaced repetition approach. Our unit-tests will be concluded using the already developed Time Machine web-app [6], which we have extended in its functionality to also support unit testing. The Time Machine is therefore the main interface for the inputs and outputs of our tests. ...
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Repetition fosters learning. And games take the dullness from repetition. Hence, learning games promise to be a valuable addition to any learning media portfolio. But how do learners know which content they should learn at a given time in order to get the best learning results? This paper introduces an approach for mobile learning games that eases this problem in order to maximize learning outcomes based on training intervals and the learner’s performance. The approach is illustrated by a prototype implementation which uses an example from language learning in order to focus not on the learning topic but on the implemented concepts. Content selection is based on the SM2 algorithm for spaced repetition learning, an established standard in calculating item presentation intervals for optimal learning performance. The paper also analyses usage behavior for a number of test users and draws conclusions for future modifications of the content selection scheme.
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Thirty-five individuals who had learned and relearned 50 English-Spanish word pairs were tested for recall and recognition after an interval of 8 years. Two variables, the spacing between successive relearning sessions and the number of presentations required to encode individual word pairs, are excellent predictors of the likelihood of achieving permastore retention. Optimum recall occurs for words encoded in 1–2 presentations and accessed at intervals of 30 days. Both variables yield monotonic retention functions that account for a range of variation from 0% to 23% recall. These variables also have very significant effects on the recognition of unrecalled words. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
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Thirty-five individuals who had learned and relearned 50 English-Spanish word pairs were tested for recall and recognition after an interval of 8 years. Two variables, the spacing between successive relearning sessions and the number of presentations required to encode individual word pairs, are excellent predictors of the likelihood of achieving permastore retention. Optimum recall occurs for words encoded in 1-2 presentations and accessed at intervals of 30 days. Both variables yield monotonic retention functions that account for a range of variation from 0% to 23% recall. These variables also have very significant effects on the recognition of unrecalled words.
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Cramming refers to the practice of intense study in close temporal proximity to an impending exam, and is an often-utilized study method in today's fast-paced world. In the present study, researchers investigated the efficacy of cramming. In a quasi-experiment, one group of students crammed by studying immediately before a test of symbol recall, while another group of students performed a cognitive task between studying and their test. Analysis of test scores showed that there was no significant difference between cramming and non-cramming test-preparation techniques. This research might be useful to students attempting to justify cramming, or to teachers attempting to find new methods of test preparation. Pages: 11-13 The lives of Americans are amongst the most hectic in the world (Levine & Norenzayan, 1999). Therefore, any time saved can potentially help relieve the stress of a busy lifestyle. One might wonder, however, whether efforts to save time impact the performance of one's daily tasks and charges. One time-saving practice is "cramming." Cramming refers to the practice where a student studies the material of an impending examination starting at some period preceding the exam, and stops studying at a time very close to the beginning of the exam, in many cases as the test materials are handed out. One of the few surveys done on the subject indicates that many students view cramming favorably (Sommer, 1968). Additionally, there are self-report data that indicate that students who engage in cramming often have high grade point averages, and believe that they perform as well or better than their non-cramming counterparts (Vascha & McBride, 1993). Creating further support for cramming is an experiment conducted by Barrouilet, Bernardin, and Camos (2004) investigating memory span. The researchers concluded that short-term memory decays as a function of time if rehearsal is not permitted. Based on this work, one might expect that if students are relying on short-term memory for the test, less time allowed to lapse between the last look at study 1 Kent Van Note (vann0114@umn.edu) is a senior in the College of Liberal Arts at the University of Minnesota. He will receive his BA in Psychology in the Fall of 2009. His interests include biological and social psychology. After graduation, Kent plans to attend graduate school and pursue a Master's Degree in Social Work.
Book
Component Software: Beyond Object-Oriented Programming explains the technical foundations of this evolving technology and its importance in the software market place. It provides in-depth discussion of both the technical and the business issues to be considered, then moves on to suggest approaches for implementing component-oriented software production and the organizational requirements for success. The author draws on his own experience to offer tried-and-tested solutions to common problems and novel approaches to potential pitfalls. Anyone responsible for developing software strategy, evaluating new technologies, buying or building software will find Clemens Szyperski's objective and market-aware perspective of this new area invaluable.