Working Memory Training and Online Multiplayer
Games: can a combination of the two be the future
in treating children with ADHD?
Joost Asselbergs, MSc.
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Abstract
Objective: This study examined the benefits of integrating game elements into standard working
memory (WM) training software. In particular it observed the effect game elements have on
motivation, WM performance and inattentive/hyperactive behaviour in children with ADHD. Method:
A total of 44 children with ADHD aged 7 to 15 years were randomly assigned to the control condition
(training without game elements), the medium condition (training with few game elements) or the
MORPG condition (training with a lot of game elements). The training software could be used at home
and the participants were asked to use the software for at least 2 hours a week over a period of 3
weeks. Results: Participants in the MORPG condition showed greater motivation compared to the
medium and control condition; scored significantly better on WM performance and displayed less
inattentive behaviour after the training. Participants in the medium and control condition didn’t show a
significant effect on WM performance and inattentive behaviour post-training. They did, however,
show a significant increase in hyperactive behaviour. Conclusions: This study shows that training in a
domestic setting can improve WM performance and decrease inattentive behaviour in children with
ADHD, but only when the training software is able to capture the interest and motivation of the
participants. Failing to meet these requirements will greatly diminish the potential of WM training and
may even lead to unwanted effects, such as increased hyperactive behaviour.
– Introduction –
No matter how hard he tried, little Shelley just couldn’t be still for long. Sometimes he would
get out of his seat and run around the classroom. Every morning he promised his mommy,
“I’ll be good today.” But every day something went wrong. ”Why do you keep doing things I
tell you not to do,” asked his daddy? ”By the time I think about what I am going to do, I’ve
already done it!” Shelley said sadly.
Quote from “Shelley, the hyperactive turtle” by Deborah M. Moss (1989)
Just like Shelley the hyperactive turtle described in the quote above do a lot of school-aged
children around the world display symptoms of hyperactivity and inattention severe enough to
meet the criteria for Attention –deficit/hyperactivity disorder (ADHD). ADHD is one of the
most common disorders diagnosed in childhood and affects 3-7% of school-aged children
around the world (American Psychiatric Association [APA], 1994; Biederman et al., 2000;
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Gallucci et al., 1993; Kadesjö & Gillberg, 1998). Children with ADHD experience serious
impairments in both social functioning and academic performance. Characteristic problems
are: problems with concentration and attention, controlling one’s impulses and excessive
movement (Barkley, 2004). Although some of these problems will diminish when the children
become older, most of them will still exist into adulthood (Rasmussen & Gillberg, 2000).
Because ADHD has become such a common diagnose in recent years, it should come as
no surprise that the subject of ADHD has gained a lot of attention from different fields of
study. Researchers who focus on school and learning for instance have actively studied the
effect of ADHD treatment on academic performance (Daley & Birchwood, 2010; Weyandt &
DuPaul, 2006). Other fields which have shown interest in the subject of ADHD, just to name
a few are: human resource management (De Graaf et al., 2008; Prevatt et al., 2010), clinical
research (Winstanley et al., 2006) and neuroscience (Bush et al., 2005; Krain & Castellanos,
2009). Although this vast amount of research on ADHD has provided scholars with a lot of
insight and understanding regarding ADHD and how it affects people, it is still not clear what
causes this disorder in the first place. Some researchers have identified risk factors like:
childhood exposure to environmental toxins (Curtis & Patel, 2008) or maternal smoking and
drug use (Chabrol & Peresson, 1997). Food additives seem to be another risk factor
contributing to hyperactive behaviour (Bellanti et al., 2005; Konofal et al., 2005; Pelsser &
Buitelaar, 2002) and Fliers et al. (2005) found that heredity plays a part as well, suggesting
that several genes may be associated with ADHD.
The last decade there is also a growing body of literature arguing that people with ADHD
have altered brain function and anatomy and especially executive dysfunction seems to be
getting a lot of attention (Ball et al., 2011; Powell & Voeller, 2004; Tripp & Wickens, 2009).
Although research into executive function (EF) has increased distinctly over recent years, the
theoretical framework in which it is situated is not entirely new. In the 1950s, the British
psychologist Donald Broadbent already made a distinction between "controlled" and
"automatic" mental processes. This distinction was later more fully characterized by the work
of Shiffrin and Schneider (1977), who introduced the concept of selective attention, to which
EF is closely linked. The concept of EF is broadly defined in the literature by using a wide
range of processes associated with EF. For instance Lezak (1983) uses Volition, purposive
action, planning and effective performance to describe EF. Others associate EF with a
supervisory attentional system (Norman & Shalice, 1986), a purposeful and coordinated
organization of behaviour and concept formation (Banich, 2004), reasoning and cognitive
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flexibility (Piguet et al., 2002). Despite the lack of clarity, there exists a general consensus
that EF refers to cognitive tasks originating from the dynamics of frontal cortical networks
and includes higher order functions such as cognitive flexibility, response-inhibition and WM
(Welsh, 2002; Wiebe et al., 2011). Cognitive flexibility is the ability to switch between
different strategies and behavioural responses in line with the context of the situation (Wiebe
et al., 2008). Response inhibition is the ability to filter out interfering information and
suppress inappropriate actions in a given situation (Anderson, 2002; Raaijmakers et al., 2008).
WM is the ability to actively hold information in the mind needed to do complex tasks such as
reasoning, comprehension and learning (Baddeley, 2007).
One of the components of EF that is often associated with ADHD is WM. This
relationship between WM and ADHD has been studied extensively and it has been repeatedly
demonstrated that there is a positive link between ADHD and WM deficits (Dowson et al.,
2004; Kofler et al., 2011; Mariani & Barkley, 1997; Westerberg et al., 2004; Willcut et al.,
2005). Deficits in WM for instance are associated with having problems maintaining attention
(Pickering, 2006) and are also linked with the ability to stay still for a longer period of time
(Klingberg et al., 2002).
Working memory
Working memory can be defined as the cognitive system responsible for the temporary
storage and manipulation of information which is essential for sustaining focused behaviour in
practical situations (Kane et al., 2007). The term "working memory" was first introduced by
Miller (1956). In his seminal work he argued that the memory span of young adults was
around seven elements, also called chunks. Atkinson and Shiffrin (1968) also applied the
term, "working memory" to describe their concept of short-term memory. Short-term memory
is the ability to remember information over a short period of time (spanning just a few
seconds). Most scholars nowadays use the concept of working memory to replace or include
the older concept of short-term memory. Extensive research has been done regarding the
theory of working memory and the literature identifies four main types of theoretical models.
Atkinson-Shriffin model. In order to describe the way memory works Atkinson and Shriffin
(1968) proposed a multi-store model consisting of three stages: sensory memory, short term
memory and long-term memory. Information is first detected and processed by one of the five
senses and enters the sensory memory. If attended to this information it enters the short-term
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memory. Information from the short-term memory is then transferred to the long-term
memory when the information is rehearsed. If rehearsal doesn’t occur the information will be
discarded from memory through the processes of displacement or decay.
Baddeley and Hitch. According to the influential model proposed by Baddeley and Hitch
(1974), WM consists of two storage systems and a central executive. The first storage system
is the phonological loop and is specialized in the storage and manipulation of verbal
information. The second storage system is the visuo-spatial sketchpad and is used for the
storage of visual and spatial information. The central executive is responsible for coordination
between the phonological loop and the visuo-spatial sketchpad and integrating the information
stored in both systems. In a more recent study Baddeley (2000) extended his model of
working memory by adding a fourth component, which he called the episodic buffer. The
episodic buffer acts as a third storage system, devoted to integrate verbal, spatial and visual
information in chronological order and combine this information into a unitary episodic
representation.
Cowan. An alternative view concerning working memory is provided by Cowan (1995; 2005),
who regards working memory not as a separate system, but as a part of long-term memory.
Working memory consists of long term memory representations that have been activated and
the ability of the individual to then focus attention to these activated representations. This
focus of attention is regarded as capacity limited and can hold up to four of the activated
representations. Oberauer (2002) extended this model by adding a third component, which
represents a more narrow focus of attention, limited to only one element. This third
component is embedded in the above mentioned focus of attention and is used for singleelement processing. For instance, you may hold four numbers at a time in your focus of
attention. Now imagine that you want to multiply each number by two. In this case
Oberauer’s additional component singles out a number for processing and after the number is
multiplied by two it shifts the focus to the next number, doing so until all the numbers have
been processed.
Ericsson and Kintsch. A more recent model is the long-term working memory model of
Ericsson and Kintsch (1995). They propose that we use skilled memory in everyday tasks. For
instance when we read a book, we are required to hold numerous concepts in our working
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memory; way more than the seven chunks proposed by Miller (1956). With a limited capacity
of only seven chunks our working memory would be full after just a few sentences and
wouldn’t we be able to comprehend the complex relations between the different thoughts
expressed in the text. Humans achieve this by storing most of the information in our long-term
memory and connecting them together with the aid of retrieval structures. We therefore only
need to hold a small amount of concepts in our working memory that then serve as directions
to retrieve any information related to the concepts found in our long term memory.
Working memory is generally considered to be a limited capacity structure. One of the first
researches to quantify this limit was Miller (1956). In his influential work he discovered that
the average memory span of young adults was around seven chunks, regardless whether the
chunks consisted of letters, numbers, words, or other units. More recent research (e.g. Hulme
et al., 1995) however showed that this capacity isn’t fixed and highly depends on the units
used. If the processing tasks are more demanding, then fewer attentional resources will be
available for processes related to storage and, in turn, lead to lower WM span outcomes.
Conversely, if the processing task is less challenging, than more attentional resources will be
available, leading to higher WM span scores. For instance the memory span is lower when
unknown words are used compared to known words. Cowan (2001) has also tried to measure
the capacity of our working memory and found that young adults were able to store four
chunks in there working memory. This number was lower for children and older adults. Other
theories suggest that working memory isn’t a limited capacity system by itself, but that there
is a constraint to the time a chunk can remain active in short-term memory without rehearsal
(Baddeley 1986; Richman et al.1995).
While it has long been assumed that working memory capacity has a strict limit (Cowan et
al., 2001, Miller, 1956), an increasing amount of evidence suggests that WM capacity can be
increased through intentional training (Klingberg et al., 2005; Morrison & Chein, 2010;
Verhaegen et al., 2004; Westerberg et al., 2007). This notion that targeted training can
improve WM capacity has generated a lot of attention and a brief overview regarding the
literature on WM training will be provided in the next section.
WM training
Research regarding WM training has gained a lot of attention in modern-day literature.
Especially the prospect of improving WM performance in children with ADHD, or other
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intellectual disabilities has caught the interest of many scholars (e.g. Klingberg et al., 2002;
Prins et al., 2011). Research however hasn’t been limited to this population. For instance other
studies have been conducted in patients with multiple sclerosis (e.g. Vogt et al., 2009), or
adults who have experienced a stroke (e.g. Westerberg et al., 2007). WM training comes in
different forms and can be broadly classified in two groups of training. One group consists of
strategy training, intended to promote the use of supplemental domain-specific strategies,
helping trainees to increase their WM capacity (MCNamara & Scott, 2001); whereas the other
group uses core training, which entails the continuous repetition of demanding WM tasks
designed to train WM performance (Klingberg et al., 2005; Vogt et al., 2009).
Strategy training. One approach to improving WM performance is through the use of strategy
training. Strategy training paradigms involve techniques intended to facilitate effective
processing, maintenance and retrieval from WM. Some of the most used techniques are:
grouping of items into chunks, rehearsing out loud, using mental imagery to make items more
meaningful and creating a story using the items to be memorized.
Research on strategy training supports the argument that WM performance can be
improved by teaching these various strategies. For instances a recent study by Turley-Ames
and Whitfield (2003) found that WM performance increased significantly in children as a
result of rehearsal strategy. In their study they used 124 undergraduate psychology students at
Washington State University. The participants were randomly assigned to either the control
condition (85 participants) or the rehearsal condition (66 participants). All participants were
presented a number of operation word sequences with set sizes ranging from 2 to 6 and with
three trials of each set size, hence a total of 15 trials. Each trial consisted of a simple math
problem and after providing the right answer to the math problem a to-be-remembered word
appeared. Each trial had a 7 second time restriction. After completing the operation span pretest, the rehearsal group received instructions and was asked to rehearse the to-be-remembered
words aloud as many times as possible, before going to the next trial. The children in the
control group only received the standard instructions. Afterwards 12 additional practice trials
were administrated so the rehearsal group could practice using rehearsal strategy, followed by
a post-operation span test. The results of this study showed a significant increase in WM span
scores for the rehearsal group compared to the participants in the control group. Other
strategies that have shown to be effective in training WM are, amongst others: the grouping of
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items into chunks (St Clair-Thompson et al., 2010) and telling a story using the to-beremembered items (McNamara & Scott, 2001).
Although these strategies can be effective in training WM and be useful in everyday
situations that require lists or groups of information to be memorized, like remembering
phone numbers, reliance on these tactics have typically been shown to improve performance
only in trained tasks or tasks that are closely related. A classic example of this phenomenon
can be found in a famous case study carried out by Chase and Ericsson (1982). A participant
and devoted cross-country runner was able to recall a digit span of 79 after hearing them only
once. He was able to achieve this amazing feat by chunking the digits into different running
times (e.g. 339 would be 3 minutes and 39 seconds, a near world record on the mile).
However when the subject was changed from digits to letters there was no transfer effect and
his memory span fell back to six elements. The results of strategy training tend to be only
applicable in trained context or similar tasks (near transfer) and do not generalize to a more
distant task context (far transfer) (Morrison & Chein, 2011).
Core training. Core training programs typically involve the repetition of WM tasks designed
to systematically target WM processes. Core training programs can consist of multiple tasks
focusing on different aspects of WM. One of such training program that has received
extensive research is Cogmed (Holmes et al, 2009; Klingberg et al., 2002; 2005) and covers a
large battery of various WM tasks, such as: backward digit span, location memory and a
version of the N-back task. Another training program that uses a battery of WM tasks is
COGITO (Schmiedek et al., 2010). By using a multifaceted training program there is a higher
chance that at least one of the tasks has a positive effect on WM performance. In addition
massive transfer effects can be gained potentially by focusing on different aspects of WM.
The downside however is the difficulty to pinpoint the exact components of the training
program that lie beneath the improved performance and determine which specific aspects of
WM are affected. In order to single out these effects research has also been carried out by
focusing on just one specific WM mechanism (e.g. Dahlin et al., 2008; Verhaeghen et al.,
2004).
Probably the most cited study that found a positive link between core training and WM
performance is the study by Klingberg et al. (2005). In their study 44 children with ADHD
(aged 7-12) were randomly assigned to either the treatment group or the control group. The
training program consisted of WM tasks implemented in a computer program that the children
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could use at home or at school. The program included verbal WM tasks (remembering digits
letters or phonemes) and WM visuospatial tasks (remembering the position of objects in a 4 x
4 grid). The duration of the training covered 5 weeks, with the children completing 90 trials
on each day of training. The level of difficulty was automatically adjusted on a trial-by-trial
basis, equalling the WM span of the child. The control group received the same treatment,
except that the difficulty of the WM trials remained on their initial level. After the training the
children in the treatment group showed a significant improvement on an untrained
visuospatial WM task (span-board task). In addition the children also performed better on:
response inhibition (Stroop task), verbal WM (digit-span task), complex reasoning (Raven
task) and showed a significant reduction on a number of parent-rated ADHD symptoms.
A more recent study by Prins et al. (2011) compared motivation and training performance
of a regular WM training program compared to a WM training program with game elements.
A total of 51 children with ADHD (aged 7-12) were randomly assigned to either the
experimental group or the control group. The children in the control group used a training
program similar to the one used by Klingberg et al. (2005). The children were presented with
a 4 x 4 grid and had to reproduce the sequence in which the squares were lit. After two
consecutive correct reproductions, the sequence increased by one and after two consecutive
wrong answers, the sequence decreased by one. Contrary to the training program used by
Klingberg et al. no additional animations or forms of feedback were included in the control
condition. The children in the experimental condition received the same WM task; however
game elements were added to the training program. These game elements included a storyline,
animations, a goal, identification with a game character, rewards and response cost (shots)
earned or lost throughout the game, competition, and control (the child chose when to do a
sequence). The training consisted of three sessions, with a training session held once a week.
After the training, the children who used the game version showed a significant increase in
memory span, whereas the children in the control group showed no significant gains. In
addition the children in the experimental group also showed greater motivation, and better
training performance (i.e. more sequences reproduced and fewer errors).
The literature on WM training acknowledges that WM performance can be improved by
targeted training. Both strategy training paradigms and core training programs have shown to
be effective in improving WM span. However the results of strategy training tend to be only
applicable in trained context or similar tasks and do not generalize to a more distant context.
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Research on Core training on the other hand showed many instances of positive transfer
effects, both near and distant (Jaeggi et al., 2008; Olesen et al., 2004; Westerberg et al., 2007).
Allthough core training yields the best results, doing the same WM tasks over and over
can be a very boring affair. This is especially true for children diagnosed with ADHD. When
tasks are very boring, repetitive, or without supervision the attention span of children with
ADHD will be very limited (Shanahan et al., 2008). Adding incentives to an otherwise boring
task may help children with ADHD enhance their motivation and boost their performance
(Sergeant et al., 1999). One feature that has shown to raise the motivation and interest of
children is the computerization of tasks (Pfiffner et al., 2006). Parents, teachers and clinicians
for instance have reported that children with ADHD concentrate for longer periods of time,
can sustain their attention and behave less impulsively when playing computer games
(Barkley, 2006).
The role of computer games in improving motivation.
There is a general consensus amongst academics that motivation plays a key role in effective
learning and that motivation influences how and why people learn as well as how they
perform (Pintrich & Schunk, 1996). According to the self-determination theory proposed by
Deci and Ryan (1985) can motivation be either extrinsic or intrinsic. Extrinsic motivation
refers to doing something in order to receive an external reward or avoid external punishment
such as: grades, money or detention. Intrinsic Motivation on the other hand refers to doing
something because it is inherently interesting or enjoyable (Ryan and Deci, 2000). According
to the basic needs theory three psychological needs are posited to underlie one’s intrinsic
motivation to initiate behaviour, namely: competence, autonomy and relatedness (Deci and
Ryan, 2002). Competence refers to the need to be effective in dealing with the environment in
which a person finds oneself; autonomy refers to the need to experience one’s behaviour as
emanating from or endorsed by the self, instead of being initiated by forces outside oneself;
relatedness refers to the need to interact with others and experience warm caring relationships.
Numerous studies on motivation and learning have shown that people perform better when
they are intrinsically motivated as compared to being extrinsically motivated. In a study by
Vansteenkiste et al. (2005) for instance, 95 female and 54 male obese Belgium children (age
11-12) were randomly divided over two experimental groups and one control group.
Participants of one experimental group were told that learning physical exercises are helpful
for attaining physical attractiveness (extrinsic goal). The other experimental group was told
9
that learning physical exercises are beneficial for attaining physical health (intrinsic goal). The
participants in the control group weren’t told anything about the relevance of the exercises.
Results showed that the intrinsic goal group had higher autonomous motivation, better test
performance and greater persistence both in short term and long term, compared to the control
group. The extrinsic goal group on the other hand showed less autonomous motivation, poorer
test performance and less long term persistence (although better short term persistence) then
the control group.
Many professionals in the field of education are aware that a key issue with learners of all
ages is motivation. Games, on the other hand, seem to be able to instinctively motivate players
to learn the complex rules and mechanics of a game in order to play the game effectively. This
difference in motivation can be attributed to an inherent difference in motivational approach.
Traditional teaching methodologies typically use extrinsic motivation, in the form of external
reward or punishment to motivate their students; while games appeal to the intrinsic
motivation of a player, by relying on his/her desire to take part in the activity for its own sake.
Integrating videogame elements into teaching programs could be a powerful way to facilitate
intrinsic motivation and increase performance (Habgood et al., 2005).
The popularity of computer games is still growing and recently they have also caught the
interest of educators and training professionals. The last decades there has been a major
change in teaching methodologies from a traditional, didactic model of instruction to a
learner-centered model, putting more emphasize on learning by doing, instead of learning by
listening. This shift away from traditional teaching paradigms has fostered the research how
various aspects of game design can be used to support intrinsic motivation and be integrated
into educational programs (Cordova & Lepper, 1996; Dickey, 2005, 2007; Gee, 2003; Ricci et
al., 1996). Malone (1980) and later Malone and Lepper (1984), proposed a set of game
characteristics that facilitate an intrinsically motivating experience, including: challenge,
fantasy, control, curiosity, competition, cooperation, and recognition.
Challenge. Challenge is an often tested characteristic that is closely related to the concept of
flow, a thin channel of difficulty where a player becomes completely absorbed in a game
experience, loses track of time and finds himself in a zone of optimal concentration
(Csikszentmihalyi, 1990; Habgood et al., 2005). If the game is too simple players will get
bored easily; too hard and they will become frustrated and lose their motivation. Hence
difficulty must be balanced perfectly in order to induce and maintain flow. A more recent
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study by (Piselli et al., 2009) showed that players enjoy games more when they are challenged
and can only beat the game by a thin margin. In addition they found that players find more
fulfilment in a narrow loss, than in an overwhelming win. This aspect of challenge is closely
linked to the player’s feeling of competence and self-efficacy. Players who conquer a
challenging part in the game experience a greater feeling of competence and self-worth,
encouraging them to advance even further in the game (Malone, 1980; Orvis et al., 2008;
Piselli et al., 2009).
Fantasy. Fantasy, emotion and motivation, are closely tied together. Creating a world in
which the player can escape their daily routine is an important aspect of game design. This
escapism provides the player with an opportunity to fulfil different emotional needs which
may not be met in real life (Freud, 1950). By telling an elaborative story and using
charismatic characters with whom the players can identify themselves, a genuine feeling of
really being part of the game materializes. When a player feels connected to the game in any
way, his motivation to follow the storyline will be enhanced and fuels the player’s desire to
continue playing (Malone and Lepper, 1984).
Control. Another import aspect that can facilitate intrinsic motivation is the matter of control.
By providing a game climate in which the players are in control of when to do certain tasks,
instead of prompting them to do the task right now, the feeling of autonomy and in effect
one’s intrinsic motivation will be greatly enhanced. Games typically make use of quests to
accomplish this. Quests are tasks given by in-game characters that a player must fulfil in order
to advance or grow stronger. By providing the players with the freedom to choose which
quests to accept or when to complete them, a feeling of control will be induced; which in turn
leads to enhanced motivation (Dickey, 2005).
Curiosity. Curiosity is an aspect that is closely linked with fantasy in a sense that it has a lot to
do with the storyline and character development. When a game has a very enticing storyline,
which keeps the gamers wondering how the story will continue, players will keep on playing
just to settle their curiosity (Pace, 2004). The same goes for character development. In many
games players can level up their characters, making them stronger and providing them with
new powers. Players tend to be curious as to what new powers they will get and how these
new found abilities will influence game dynamics (Qin et al., 2009).
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Cooperation, Competition & Recognition. Cooperation, competition & recognition are three
interpersonal motivation types and refer to the interaction players have with people whom the
player takes social cues from and evaluates himself against (Deen & Schouten, 2010). Players
for instance check their rankings to see how their abilities stack up to one another and become
motivated to outrank them (competition). Collaboration can be seen in games as forming
guilds or working together to defeat certain boss-enemies; while recognition refers to the
respect a player receives from others when he reaches a certain status, such as being the leader
of a guild or displaying a very high level of skill. Deen and Schouten (2010) argue that this
interaction with other players facilitates feelings of competence and autonomy, and in turn
increases motivation.
Computer games come in many different forms and can be categorized into a number of
genres, such as: puzzle, strategy, adventure, sport, shooters and massive multiplayer online
role playing games (MMORPG). Although each genre can be used for educational purpose,
the relative new game genre of MMORPG may exhibit the most potential when it comes to
capitalizing on the seven game characteristics proposed by Malone and Lepper (1984).
MMORPG is one of the most popular genres at the moment and provides a flexible
environment that fosters the intrinsic motivation of its players. A MMORPG can be
characterized by an interactive, narrative environment in which players are free to do what
they want. These enormous worlds and elaborate storylines are powerful tools to induce
fantasy and encourage curiosity. Another typical element of MMORPG’s are quests. Players
can receive quests from non player characters and have total control over which quests to
accept and when to complete these tasks. This feeling of control gets further enhanced by the
freedom the player has to explore the surroundings and decide upon what to do next.
MMORPG’s, as their name implies, have many players online at the same time interacting
with each other, offering ample opportunities for cooperation competition and recognition.
Players can team up in order to defeat stronger enemies, form guilds, or help others complete
certain quests. Players gain experience points for completing quests or defeating enemies,
making them stronger and evolve into more powerful creatures. This might include the gain of
new spell casting abilities or the ability to wear better armour or wield better weapons. By
putting emphasize on character development the players identify themselves with their
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character and see their avatar as an extension of oneself (Dickey, 2007). Leaderboards can be
used to fuel competition and recognition by letting players compete for top spot and gain the
respect of other players. Another way to enhance these feelings is to offer special weapons or
armour to the high level players and to put one’s combat level right next to their name for
everyone to see (Piselli et al., 2009).
– Theoretical framework –
Motivation: Motivation plays a key role in effective learning and influences how and why
people learn, as well as how they perform (Pintrich & Schunk, 1996). One precondition of
delivering sub-par performances is motivation. Students for example, who are motivated to
learn, perform better then unmotivated students. The same can be applied to the context of
WM training. Participants who are motivated to use the WM training program are likely to
reach better results than participants lacking this motivation.
One approach to increase participant’s motivation in training programs is the use of game
elements. The inclusion of game elements makes the, somewhat boring, training tasks more
enjoyable and motivates the participants to continue their training. One research that has
investigated this relationship is the study by Prins et al. (2011). They studied the effect of
game elements in WM training on motivation and training efficacy in children with ADHD.
They found that the use of game elements indeed had a positive effect on both training
efficacy and motivation, arguing that the motivation of the user can be improved by adding
game elements. Some limitation of their study however are the few game elements they added
and the relative short duration of the training, namely 3 sessions of 15 minutes (15 minutes
scheduled training and 15 minutes optional afterwards). The inclusion of such few game
elements may succeed in keeping the children motivated during this short time span, however
the chances are that they get bored very easily if the duration of the training would be much
longer. This thesis follows the line of reasoning that game elements improve motivation and
will even go one step further by comparing three different WM training conditions with each
other. A control condition that uses training software without game elements; a medium
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condition that uses training software with a few game elements and a MORPG condition that
uses WM training integrated in a MORPG, hence a lot of game elements. This study posits
that the participants in the MORPG condition will show the highest motivation, followed by
the participants in the medium conditions. The participants in the control condition will show
the least motivation.
WM performance: WM performance is known to play a role in academic outcomes, such as
math (Ashcraft and Krause, 2007; Miller and Bichsel, 2004) and reading comprehension
(Seigneuric et al., 2000). Furthermore studies have repeatedly demonstrated that there is a
positive link between ADHD and WM deficits (Dowson et al., 2004; Kofler et al., 2011;
Mariani & Barkley, 1997; Westerberg et al., 2004; Willcut et al., 2005). Recent studies have
found that WM performance can be increased by targeted training (e.g. Klingberg et al., 2005;
Prins et al., 2011). One underlying key aspect in training is motivation and one can argue that
higher motivation (in the form of more game elements) leads to better training results.
Alternatively one can also reason that game elements pose a distraction and interfere with
training efficacy, resulting in poorer results. For instance, showing a victory animation or
walking around in the game world takes away valuable time that otherwise could be spent
doing another WM task. Although this last argument certainly has its merits, this thesis
suggests that the motivational benefits gained by game elements outweigh the distraction they
may cause. In other words, this study hypothesizes that the MORPG condition will have the
largest positive effect on WM performance, followed by the medium condition. The control
condition will have the smallest positive effect on WM performance of the three training
conditions.
ADHD symptoms: The ultimate outcome of treating WM deficits in children with ADHD is
a decrease of the displayed symptoms. Research has shown that improved WM performance
may lead to significant reduction in symptoms of inattention and hyperactivity. For instance
Beck et al. (2010) found that parent ratings indicated significant improvement on inattention,
overall number of ADHD symptoms, planning/organization, and WM. These findings are in
line with the work of Klingberg et al. (2005), who found that improved WM performance in
children with ADHD resulted in a reduction of the parent-rated inattentive symptoms of
14
ADHD, both post-intervention and at a 3-month follow-up. In line with the literature this
study assumes that an increase in WM performance will lead to a decrease in inattentive and
hyperactive behaviour and proposes that the MORPG condition will have the largest negative
effect on ADHD symptoms, followed by the medium condition. The control condition will
have the smallest negative effect on ADHD symptoms of the three training conditions.
Corresponding to the aforementioned hypothesis the following conceptual model can be built
(Figure 1). The degree of game elements is believed to have a positive effect on motivation.
Motivation in turn should have a positive effect on time spent on WM tasks and WM
performance; and a negative effect on inattentive behaviour and hyperactive behaviour. Time
spent on WM tasks is believed to positively affect WM performance, whereas an increase in
WM performance should have a negative effect on both inattentive and hyperactive
behaviour.
+
+
+
+
-
-
-
Figure 1 Conceptual model
15
– Method –
Research design
The research strategy adopted in this study is a randomized controlled trial, consisting of two
experimental conditions and a control condition. Participants in the control condition received
training using basic WM training software without game elements. The medium condition
received training using WM training software with few game elements and the participants in
the MORPG condition received WM training using WM training software with a lot of game
elements.
Subjects
The research started out with 50 participants from the Netherlands who were gathered from
forums targeted at parents with children who have ADHD (e.g. www.Balans.nl and
www.adhd-xtra.nl) and different agencies that provide care and support to children with
psychological disorders like ADHD and autism spectrum disorders. Inclusion criteria were:
(a) diagnosed with ADHD or showing strong signs of ADHD symptoms (reported by their
parents); and (b) aged between 7 and 15 years. The participants were matched for age, gender
and ethnicity and evenly divided over the three conditions.
From the initial 50 children who agreed to participate in this experiment two dropped out
before the start of the training due to computer problems. During the course of the training
four children discontinued the training leaving a final sample of 44 children who completed
the whole experiment. The demographic information and baseline characteristics are shown in
Table 1. Noteworthy is that 91% (40/44) of the research sample was officially diagnosed with
ADHD and that the other 9% (4/44) showed strong signs of ADHD symptoms (as reported by
their parents) but were not officially diagnosed yet.
Materials
Besides the three different training programs described below, this study also used the
following questionnaires: SWAN Rating Scale for parents (Dutch version), the Child
Behaviour Checklist 6-18 years (Dutch version), a general checklist, and an exit
questionnaire.
16
SWAN Rating Scale for parents. This questionnaire is often used in research to get a good
representation of the ADHD symptoms (e.g. Hay et al., 2007; Polderman et al., 2007). The
Swan consists of 18 items, which are scored on a sevenpoint scale (-3 = far above average; -2
= above average; -1 = somewhat above average; 0 = average; 1 = somewhat below average; 2
= below average; 3 = far below average) (Swanson et al., 2005). Individual scores were
calculated for both the inattention and hyperactivity-impulsivity dimensions. A high score
indicates a higher level of ADHD symptoms, whilst a negative score indicates that the child
shows better than average attention/hyperactive behaviours.
Child Behaviour Checklist 6-18 years (CBCL). The CBCL is commonly used by scholars as a
screening instrument to portray the behavioural and emotional problems of a research sample
(e.g. Biederman et al., 2005; Karatekin et al., 2010). In this study only the second part of the
CBCL was used, which consisted of 113 items. The items are scored on a three point scale (0
= not at all, 1= sometimes, 2=often). The 113 items translate into the following subscales:
withdrawn, somatic complaints, anxious depressed, social problems, thought problems,
attention problems, rule-breaking behaviour and aggressive behaviour. (Achenbach &
Rescorla, 2001). Reliability analysis shows that the internal consistency of the CBCL was
high (Cronbach’s alpha = 0.82) and Internal consistency coefficients of the subscales ranged
from alpha = 0.48 to 0.85. The CBCL was administrated to verify if there were significant
differences in behavioural or emotional problems between the three training conditions.
General checklist. A general checklist was used to get relevant information regarding the
demographic characteristics of the subjects like: age, gender, prior computer knowledge and
average time spent playing videogames. These demographic characteristics were used to
verify if there were no significant differences amongst the three conditions.
Exit questionnaire. The exit questionnaire was used to measure motivation. The questionnaire
consisted of eight questions, which translate into the following four subscales: enjoyableness
of the program, enjoyableness of the WM tasks, voluntary engagement and continuation. The
total score for motivation is calculated by adding up all the item scores. A high score indicates
strong motivation to use the program, whilst a low score indicates that the child showed little
interest in the program, hence less motivation.
17
Table 1 Demographics and Baseline Characteristics.
Variables
Mean age (years)
Control (n=14)
Medium (n=15)
MORPG (n=15)
10,75 (2,24)
10.07 (2.46)
10.07 (2.58)
No. male (%)
12 (86%)
12 (80%)
12 (80%)
Ethnicity (Dutch)
13 (93%)
14 (93%)
14 (93%)
No. Diagnosed with ADHD (%)
13 (93%)
13 (87%)
14 (93%)
No. that use medication (%)
9 (70%)
9 (64%)
9 (64%)
No. With other disorders (%)
6 (43%)
8 (53%)
7 (47%)
Education Child
1.50 (1.22)
1.20 (0.68)
1.40 (0.99)
Education Mother
3.00 (0.88)
3.20 (1.15)
3.80 (0.77)
Education Father
2.93 (0.83)
3.13 (1.06)
3.67 (0.62)
14 (yes)
15 (yes)
15 (yes)
7.31 (3,31)
4.52 (2.55)
7.02 (3.32)
- Withdrawn
3.86 (2.88)
3.87 (2.97)
4.27 (3.10)
- Somatic complaints
1,93 (1,54)
2.57 (2.53)
1.20 (1.66)
- Anxious depressed
7.29 (3.95)
6.08 (4.41)
5.60 (3.66)
- Social problems
4.46 (2.85)
5.43 (3.86)
4.46 (2.85)
- Thought problems
1.79 (1.63)
1.36 (1.28)
1.79 (1.63)
- Attention problems
11.43 (3.65)
12.71 (3.91)
11.43 (3.65)
3.00 (2.26)
1.83 (2.33)
3.00 (2.26)
11.50 (5.85)
11.73 (6.11)
7.13 (4.45)
15.00 (3.80)
13.53 (4.87)
12.93 (4.54)
- Hyperactivity
9.79 (4.74)
12.40 (5.01)
7.73 (6.48)
Memory span
3.50 (0.76)
4.00 (0.85)
3.40 (0.83)
Computer game experience
Hours per week
CBCL 6-18
- Rule-breaking behaviour
- Aggressive behaviour
Swan (parents version)
- Inattentive
Note: Means; the SD is shown in parentheses
18
Training programs
All three training conditions, in essence, used the same training program in the sense that they
all made use of the same tasks to train one’s WM. The difference between the programs
however lies in the amount of game elements integrated in each program and the way
feedback is provided to the user. All three training programs have been created by the author
of this study, who has adequate knowledge and experience in computer programming and
game design. A small demo video of the three WM training programs can be found at
www.zorgbureauderugzak.nl/WM-training.php
MORPG training software. This training program consists of a large MORPG world, in which
multiple players can be online at the same time and interact with each other. The users are free
to walk around and do whatever they feel like doing. The user is represented by a character
with different attributes, like strength, magic and healthpoints. The main focus of the game is
to complete quests. These quests are given to the user by certain non player characters. When
a player accepts such a quest he gets into a battle screen in which he has to complete the
various WM tasks in order to defeat the opponent. When one succeeds in completing the task
he will receive gold and experience points. The gold can be used in shops to buy new armour,
weapons, clothing, healing potions or other items. The experience points are used to level up
your character, evolving the player into a stronger character every time he levels up. The
program is designed in such a way that the only way to become stronger and progress through
the game is by doing quests, ensuring that the user doesn’t become too distracted by all the
freedom. During the game the difficulty level of the WM tasks increases gradually. At the
start of the game the WM tasks will be rather easy, but as the player progresses through the
game the WM tasks will get harder and more challenging.
Medium training software. This training program is set in a more static environment and is
kind of similar to the experiment condition used in Prins et al. (2011). The story line is less
elaborating then that of the MORPG condition and the players are not part of a world in which
they can interact with each other or walk around freely. The program consists of a main menu
screen showing all the WM tasks. When a player clicks on a WM task he gets into a battle
screen in which he has to complete the different tasks in order to defeat the enemy and unfold
19
the story-line. Every time the user defeats an opponent three times the difficulty of the task is
enhanced and the user gains a level. Whenever the user loses three battles in a row, the
difficulty of the task is lowered.
Control training software. This is a training program that uses no game elements at all. The
user has no control in selecting which WM task he wants to do. The program just starts with
one task and waits until the user has done 20 trials before switching to another task. Every
time the user has two consecutive trials correct the difficulty gets increased, whenever the user
makes two errors in a row the difficulty of the task drops one level.
Description of the WM tasks
Simon task. This task is similar to the well know game Simon Says and comes in three
versions. In the first version a ring consisting of the colours green, red, yellow and blue is
depicted on the screen. The computer shows a sequence, for instance blue, red, yellow and the
user has to replicate the exact same sequence. In the second version the sequence starts with
one, and is extended by one, every time the user repeats it correctly. For instance: the first
sequence is yellow, the second could be: yellow, green, followed by the third: yellow, green,
green and the fourth: yellow, green, green, blue. This goes on until the user makes an error or
reaches a certain target number (Between 3 and 8, depending on which difficulty-level the
player is at the moment). The third version is the hardest one and is similar to the second
version, except instead of repeating the whole sequence, only the newly added colour will be
shown. To give an example: the first one is blue and the user presses the blue button. Then the
yellow button lights up, so the player now has to press the blue button followed by the yellow
button. This procedure continues until the user makes an error or reaches a target number
(again between 3 and 8, depending on which difficulty-level the player is at).
Memory task. This task is similar to the game memory. The user is shown a number of
pictures that come in pairs. After a set amount of seconds the pictures are flipped over and the
user has to reveal the pairs. The task ends if the player makes a mistake or when all the pairs
are revealed. The amount of pairs and seconds range from 4 to 9 pairs and 7 to 30 seconds,
depending on the level the player is at.
20
N-back task. The N-back task comes in two different versions. In the first task words
representing animals are shown one at a time. The user has to press a button when they think
that the word in front of them is exactly the same as the word shown N number of words ago.
The task ends after a number of items (20 to 28, depending on the difficulty-level). If the
player scores 60 % or more he was successful, if he scores less than 60 % he failed the task.
The score is determined by the following equation: (correct hits – wrong hits) / total possible
hits. The second task is a more visual task. In this task a random numbered sequence of
images representing fruit are shown one after another. Suddenly the sequence stops and the
users have to select the images of the fruit they saw N number of images ago. The task is
ended when the player makes a mistake. In both versions of the N-back task N can range from
2 to 5 (depending on the difficulty-level).
Number recall task. This task also comes in two different versions. In the first version the user
hears a sequence of digits which he has to replicate. For instance the digits 5, 6, 9 are called
out by the computer and the user has to repeat the digits in the exact same order, thus 5, 6, 9.
The second task works exactly the same however the user now has to repeat the sequence
backwards. For example if the sequence is again 5, 6, 9 the user has to type 9, 6, 5. The
amount of digits in both versions can range from 3 to 8 digits depending on the difficultylevel of the task.
Figure task. In this task a number of random coloured figures (blue, red, green, purple,
yellow, black, orange and white), (square, circle, triangle, cross, star, question mark and
exclamation mark) are depicted on the screen. The user has a few seconds to remember the
figures and is then asked what the colour of a certain figure was. The amount of figures and
the number of seconds that are shown to the user depend on the difficulty-level and ranges
from 3 to 7 figures and 7 to 24 seconds.
21
Outcome measures
Motivation. Motivation was measured by a post-test questionnaire asking the children how
much they liked using the WM training software. The questionnaire consisted of 8 questions
and a high score indicates strong motivation to use the training software, whereas a low score
indicates that the child showed little interest in the training software, hence being less
motivated.
WM performance. WM performance was measured using a computerized version of the spanboard task adapted from Klingberg et al. (2005). The user is shown a 4 x 4 grid consisting of
16 blue squares. The squares light up in a random order, one after the other. When the
sequence is finished the user has to replicate the exact same sequence. The first sequence
consists of three squares and after two consecutive completions the sequence is increased by
one square. If the user fails to replicate a sequence on two consecutive sequences the task is
ended and the number of squares of the last sequence minus one will be their memory span.
This task is also used by Prins et al. (2011) as the method of training WM and only differs
from the task used by Klingberg et al. (2005) in the way the grid is presented, using blue
squares instead of red circles. The span-board task will be used to measure the baseline score
of WM performance before the training and also to measure WM performance afterwards.
The span-board itself is not part of the training tasks and differs from the trained tasks in
respect to the stimuli that are used.
Inattentive and hyperactive behaviour. Inattentive and hyperactive behaviour was measured
using a parent rated questionnaire. Previous work on WM training (e.g. Klingberg et al.,
2005) has shown that ADHD symptoms significantly decreased after WM training based on
parent ratings. This research will follow the line of previous studies by measuring the ADHD
symptoms, using the SWAN Rating Scale for parents (Swanson et al., 1998). This
questionnaire was presented to the parents before the training and after the training to see if
there was a significant difference in inattentive and hyperactive behaviour at the end of the
training.
22
Procedure
Families expressing interest in participating in this research were sent detailed information
regarding the study, together with the following questionnaires: the SWAN Rating Scale for
parents, the CBCL, a general checklist and an informed consent form. Only after receiving
these forms did the parents receive the link where they could download there designated
training software. The training software can be used at home, using a standard PC or laptop
with Windows as operating system. When the program starts, it makes a connection to the
server and the training begins. The data was stored in a database when the user logged out.
The database is password-protected and is stored on a computer primarily used to act as a
server and a data storage system.
When the participants log in for the first time they had to complete the span-board task
before the actual training started. The duration of the training session was three weeks and the
training could be done at home in front of the computer. The parents of the children were told
that in order for the training to have an effect their child has to use the program for at least 2
hours each week, and the more they would use it the larger the improvement would be. A
previous study by Prins et al. (2011) found a significant increase in WM performance after
using a computerized WM training program during 3 sessions of 15 minutes (15 minutes
scheduled training and 15 minutes optional afterwards). Following the results of their study, a
minimum of 2 hours per week over a period of 3 weeks should be enough to see a significant
improvement in WM performance. If people showed inactivity for a week, they were sent an
e-mail asking them to please continue with the WM training. At the end of the training the
parents were asked to fill out the SWAN questionnaire for the second time, whilst the children
were prompted by the program to do the span-board task once more and fill out the exit
questionnaire.
23
- Results Pre-training group differences
Table 1 shows the demographic information and baseline characteristics of the participants.
Pre-training group differences were tested using analysis of variance and Chi-square tests
were used for categorical variables. The ANOVA’s and Chi-square tests showed that the three
training conditions didn’t differ significantly in terms of demographic variables and baseline
characteristics except for the CBCL 6-18 scale aggressive behaviour F(2,41) = 3.28, p < .05.
Although the post-hoc Tukey test didn’t show a significant difference between the three
groups (probably due to the relative small sample sizes), it hints that the participants in the
MORPG condition scored remarkably less on this scale compared to the other two groups.
Noteworthy is that all participants had at least some experience with videogames and that the
three conditions didn’t differ significantly on: average time spent playing videogames
(F(2,41) = 2.65, p = .083) and the baseline characteristics: memory span (F(2,41) = 2.33,
p = .110), inattentive behaviour (F(2,41) = 0.82, p = .446) and hyperactive behaviour
(F(2,41) = 2.73, p = .077).
Descriptive statistics and path analysis of the conceptual model
The descriptive statistics and the bivariate correlations of the variables used in this study are
shown in Table 2. It should be noted that these correlations are used for descriptive purpose
rather than for predictive value between the dependent and independent variables.
Table 2 shows that the participants on average spent close to 4 hours (3:59:38) on the WM
tasks; increased their memory span by 0.55; scored 0.73 points lower on the Swan inattentive
subscale and 0.50 points higher on the Swan hyperactive subscale. This latter finding seems
counterintuitive to the theory and our hypothesis that WM training has a negative effect on
hyperactive behaviour. Furthermore the correlation matrix shows a positive relation between
training condition and motivation (.509), suggesting that the degree of game elements has a
strong influence on motivation. Other noteworthy results are the relationship between
motivation and inattentive behaviour (-.132) and motivation and hyperactive behaviour
(-.500), suggesting that motivation has a larger impact on hyperactive behaviour, than on
inattentive behaviour.
24
Table 2 Means, Standard Deviations, and Zero-Order Correlations of Variables in the Path Analysis.
Variable
Mean
SD
(1)
(2)
(3)
(4)
(5)
(1) Condition
1.02
0.82
1
(2) Motivation
13.72
6.70
.509**
1
(3) TS WM tasks
3:59:38
1:40:23
.231
.758**
1
(4) WM performance
0.55
0.87
.403**
.490**
.446**
1
(5) Inattentive
-0.73
1.56
-.132
-.124
-.185
-.265
1
(6) Hyperactive
0.50
1.52
-.308*
-.500**
-.374*
-.333*
.167
(6)
1
Note: TS WM tasks = Time spent on WM tasks.
N = 44; * = correlation is significant at P<0.05; ** = correlation is significant at P<0.01
Path Analysis
In order to test the proposed model depicted in Figure 1, a path analysis was conducted. Path
analysis is a variant of structural equation modelling, which takes a hypothesis testing
approach to the multivariate analysis of a structural theory (Byrne, 1998). Using this method,
the presumed causal relations under examination are represented by a series of structural
equations, which can be portrayed in a path-diagram to allow for a clear conceptualization of
the theory under study.
This study used AMOS 5 (student version), which is a program designed by IBM to aid
SPSS in building structural equation models. AMOS 5 uses several fit indices to assess how
well the proposed model fits the sample data. First, the chi-square statistic can be used as a
measure of fit between the sample covariance and fitted covariance matrices (Byrne, 1998).
The higher the probability associated with chi-square, the closer the data fits the proposed
model compared to the full model. In addition, other indices that are used to assess the fit of
the proposed model to the sample data are: the Goodness of Fit Index (GFI), the Normed Fit
Index (NFI), and the Comparative Fit Index (CFI). Values of .900 or above for these indices
are indicative of good fit (e.g., Hu & Bentler, 1995; Schumaker & Lomax, 1996).
Furthermore, the Root Mean Square Error of Approximation (RMSEA) estimates lack of
fit compared to the full model by taking the error of approximation in the population into
25
account (Byrne, 1998). RMSEA values of .05 or less indicates a good fit, values .08 or less
indicate adequate fit, whilst values above .10 indicate poor fit (MacCullum et al., 1996). For
the proposed model tested, the fit indices showed that this model fitted the sample data
reasonably well. (χ2 = 8.955, df = 7, p = .256). The values for the GFI, NFI and CFI were all
above .900 (.941, .902, and .974, respectively). The RMSEA value however was .081, which
is borderline for adequate fit.
Table 3 Standardized regression weights path analysis.
Effect
On motivation
(Intercept)
Parameter
estimate
Standardized
estimate
t
p
.509
3.878
.000
.785
8.321
.000
(9.471)
of condition
4.151
On TS WM tasks
(364.151)
of motivation
706.724
On memory span
(.-442)
of motivation
0.048
.364
1.705
.088
of TS WM tasks
0.000
.160
0.751
.452
On inattention
(-,491)
of motivation
0.002
.008
0.046
.963
of memory span
-0.479
-.269
1.593
.111
On hyperactivity
(1.987)
of motivation
-0.100
-.443
-2.944
.003
of memory span
-0.201
-.116
-0.771
.441
Table 3 shows the decomposition of effects from the path analysis. Training condition (the
degree of game elements) had a significant effect on motivation (/3 = .509, t = 3.878,
p < .001). Motivation in turn had a positive significant effect on total time spent on the WM
tasks (/3 = .785, t = 8.321, p < .001); a negative significant effect on hyperactive symptoms
(/3 = - .443, t = -2.944, p < .01) and a positive, non significant, effect on WM performance
(/3 = .364, t = 1.705, p = .088). Contrary to hyperactive symptoms, motivation doesn’t appear
to have any direct effect on inattentive symptoms (/3 = .008, t = 0.046, p = .963)
26
In line with the proposed model time spent on WM tasks proved to have a positive effect
on WM performance, this effect however was non-significant (/3 = .160, t = 0.751, p = .452).
WM performance in turn, as expected, showed a negative effect on inattentive behaviour and
hyperactive behaviour. Again these results appeared to be non significant (/3 = -.269,
t = -1.593, p = .111 and /3 = -.116, t = -0.771, p = .441 respectively). Figure 2 illustrates the
path analysis of the proposed model and shows the residual path coefficients (R). These
coefficients represent the effect of variables not included in the model.
Figure 2 Model path analysis. Dotted lines indicate path coefficients for residuals (R).
Training outcomes
Motivation
Motivation was measured by an exit questionnaire asking the children eight questions
regarding how much they liked using their WM training program. A one-way ANOVA on the
four scales of the exit questionnaire revealed that there was a significant difference between
the groups on all scales of the questionnaire, except for the scale enjoyableness of the WM
tasks F(2,41) = 1.373, p = .265. Post hoc analyses with Bonferroni correction on the different
scales resulted in the following outcomes.
27
(enjoyableness of the program) Participants in the MORPG condition rated the game as
enjoyable significantly more (M = 14.07, SD = 3.67) compared to the participants in the
medium condition (M = 8.43, SD = 3.42), p < .001 and the control condition (M = 7.93,
SD = 3.43), p < .001. No significant difference was found for this scale between the medium
condition and the control condition.
(enjoyableness of the WM tasks) No group difference was found regarding the enjoyableness
of the WM tasks. This result was expected because the WM tasks were exactly the same in
each of the three conditions.
(voluntary engagement) Participants in the MORPG condition played the game significantly
more voluntarily (M = 2.40, SD = 1.68) compared to the participants in the medium condition
(M = 0.87, SD = 1.13), p < .01 and the control condition (M = 0.86, SD = 0.95),
p < .01. No significant difference was found for this scale between the medium condition and
the control condition.
(continuation) Participants in the MORPG condition were significantly more inclined to play
the game after the study (M = 1.47, SD = 0.83) compared to the participants in the control
condition (M = 0.57, SD = 0.76), p < .05. No significant differences were found for the
MORPG condition compared to the medium condition and the medium condition compared to
the control condition.
Following these results one can conclude that participants in the MORPG condition were
more motivated to use their training program compared to the participants in either the
medium condition or the control condition. The participants in the medium condition and the
control condition didn’t seem to differ significantly on motivation.
Time spent on WM tasks.
The total effective playtime of each participant was recorded automatically by the program.
To control for idle time (e.g. leaving the game running, whilst away from keyboard/mouse),
the effective playtime stopped recording after 3 minutes of no keyboard input or mouse
movement and resumed recording when the participant went active again. The parents were
told that the participants should use the training program for at least 2 hours each week in
28
order to see progress. The minimum, maximum, mean and standard deviation of the total
playtime per condition are presented in Table 4.The means clearly show that on average the
participants in the control and medium condition had difficulty in approaching the 2 hours per
week mark (6 hours in total) as compared to the MORPG condition (M = 3:38:19;
M = 4:05:37 and M = 6:52:18 respectively).
Table 4 Descriptives of Effective play time.
Conditions
N
Min
Max
Mean
SD
Control
14
1:20:58
6:14:43
3:38:19
1:26:14
Medium
15
2:02:31
6:36:52
4:05:37
1:20:59
MORPG
15
2:38:54
12:35:56
6:52:18
2:49:35
The Levene test of homogeneity of variance was significant p < .05 for total active
playtime; hence a Robust Tests of Equality of Means was conducted. The Welch statistic was
significant p < .01, which implies a significant difference between the three training groups.
Post hoc Tukey analyses showed that the average playtime of the MORPG group
(M = 6:52:18, SD = 2:49:35) was significant larger then both the medium group (M = 4:05:37,
SD = 1:20:59) p < .01 and the control group (M = 3:38:19, SD = 1:26:14), p < .001. No
significant difference on total active playtime was found between the medium group and the
control group.
Because the three WM training programs differ in the degree of game elements, total
effective playtime isn’t a true measure for time that is actually spent on the various WM tasks.
The MORPG condition for example allows players to roam around in a vast world and
interact with other players, which would qualify as game time, but not as time spent on WM
training. In order to get a true measure of time spent on training WM the actual time spent on
WM tasks was recorded as well for every participant (Table 5). From Tables 4 and 5 one can
calculate the percentage of time spent on WM tasks compared to the total playtime for each of
the three conditions (100% in the control condition; 92% in the medium condition and 67% in
the MORPG condition).
A one-way ANOVA was conducted to find out if there was a difference in time spent on
the WM tasks between the three training conditions. The ANOVA was non-significant,
29
indicating that the three training conditions didn’t differ significantly on the time participants
spent doing WM tasks F(2,41) = 1.405, p = .275.
Table 5 Descriptives of Time spent on WM tasks.
Conditions
N
Min
Max
Mean
SD
Control
14
01:20:58
6:14:43
3:38:19
1:26:14
Medium
15
01:54:58
06:09:20
03:44:46
01:15:00
MORPG
15
01:34:16
09:05:15
04:34:24
02:08:12
WM performance
WM performance was evaluated by looking at the participant’s baseline memory span and
their memory span after three weeks of training. A 3 x 2 (conditions x pre-test memory
span/post-test memory span) ANOVA with repeated measures on the last factor showed a
significant main effect for memory span F(1,41) = 19.599, p < .001, ε = .323 and a significant
interaction effect between memory span and condition F(2,41) = 4.840, p = .013, ε = .191
(Fig. 3A). To break down this interaction effect the ANOVA with repeated measures was
followed up by a post-hoc Tukey analysis. Participants in the MORPG condition showed
significant more improvement on WM performance (M = 1.07, SD = 0.88) compared to the
medium condition (M = 0.33, SD = 0.82) p < .05 and the control group (M = 0.21, SD = 0.18),
p < .001. No significant difference on WM performance was found between the medium
condition and the control condition.
To find out if participants in any of the three conditions showed a significant increase in
WM performance three separate paired-samples t-tests were conducted. On average,
participants in the MORPG condition experienced a significant increase between their
memory span pre-training (M = 3.40, SE = 0.21) and their memory span post-training
(M = 4.47, SE = 0.19), t(14) = -4.675, p < .001. Participants in the medium and control
condition on the other hand didn’t show a significant increase in memory span (t’s < 1.581,
p’s > .136).
30
ADHD symptoms
Inattention: Inattention symptoms were evaluated by looking at participant’s pre-training
score on the Swan subscale inattention (as rated by the parents) compared to their inattention
score after three weeks of training. A 3 x 2 (conditions x pre-test /post-test) ANOVA with
repeated measures on the last factor showed a significant main effect for inattention
F(1,41) = 9.141, p < .01, ε = .181. The interaction effect between inattention and condition
was non significant F(2,41) = 0.378, p = .688, ε = .018, hence no post-hoc analyses were
conducted for inattention (Fig. 3B). In addition three separate paired-samples t-tests were
conducted to see which conditions accounted for the significant main effect of inattention. On
average, participants in the MORPG condition experienced a significant decrease regarding
their inattention score pre-training (M = 12.93, SE = 1.17) and their inattention score posttraining (M = 11.93, SE = 1.08), t(14) = 2.185, p < .05. Participants in the medium and control
condition on the other hand didn’t show a significant difference between their inattentive
scores pre-training and post-training (t’s > -1.625, p’s > .126).
Hyperactive: Hyperactive symptoms were evaluated by looking at participant’s pre-training
score on the Swan subscale hyperactive behaviour (as rated by the parents) compared to their
hyperactive score after three weeks of training. A 3 x 2 (conditions x pre-test /post-test)
ANOVA with repeated measures on the last factor showed a significant main effect for
hyperactivity F(1,41) = 5.33, p < .05, ε .115. The interaction effect between hyperactivity and
condition was non significant F(2,41) = 2.634, p = .084, ε = .114, hence no post-hoc analyses
were conducted for hyperactivity (Fig. 3C). To test for significant effects on hyperactive
symptoms between pre-training and post-training three separate paired-samples t-tests were
conducted. On average, participants in the control condition experienced a significant increase
regarding their hyperactive score pre-training (M = 9.71, SE = 1.23) and their hyperactive
score post-training (M = 10.64, SE = 1.17), t(13) = -2.616, p < .05. Participants in the medium
condition also showed a significant increase in hyperactive score pre-training (M = 12.40,
SE = 1.29) and their hyperactive score post-training (M = 13.20, SE = 1.50), t(14) = -2.175,
p < .05. Participants in the MORPG condition didn’t show a significant difference between
their hyperactive scores pre-training and post-training (t(14)= -0.480, p = .638).
31
A
B
C
Fig 3 Interaction effects
32
- DiscussionThis thesis set out to study the effect game elements have on motivation, WM performance
and inattentive/hyperactive ADHD symptoms. In this study the treatment group that used the
MORPG training software performed considerably better than the participants in the medium
and control condition on the main outcome measures: motivation, WM performance and
inattentive/hyperactive ADHD symptoms. Participants in the MORPG condition showed the
most motivation, indicating that the degree of game elements definitely has a big impact on
the willingness and enthusiasm to spent time on the training. This finding is plausible and
concurs with the theory suggesting that integrating game elements into teaching programs
could be a powerful way to facilitate intrinsic motivation (Habgood et al., 2005).
Contrary to our hypothesis and the work of Prins et al. (2011) participants in the medium
condition didn’t appear to be more motivated than the participants in the control condition.
The medium condition and the control condition in the current study were very similar to the
game condition and control condition used by Prins et al., who did find a significant
difference between their game condition and control condition. One explanation for this
discrepancy between previous research and the findings from this study is that the training
format used in the Prins et al. study was different. In their study they used a special training
room to conduct the WM training. After 15 minutes of training the experimenter left the room
and the children were allowed to either keep on playing the game or read a magazine. The
present study however was situated in a home-based environment, which equals to much more
distraction. One can easily imagine that a child will be more motivated to continue the same
WM training program in a condition where he is restricted to either that or reading a
magazine, as compared to a condition in which the child has ample (and more fun)
alternatives, such as: playing real videogames on the pc or a gaming console, watching TV, or
spending time with friends. To compete with the aforementioned activities WM training
programs, that are designed for use at home, should integrate the WM training tasks into real
computer games. Merely adding a few game elements (represented by the medium condition
in this study) just won’t captivate the motivation of children with ADHD in a home-based
environment. Another point that clearly comes forward from the exit questionnaire is that the
WM tasks themselves need to be revamped. Out of the 44 participants, 32 found the WM
tasks uninteresting or boring and only one participant actually liked the WM exercises.
33
The participants in the MORPG condition experienced a significant increase in WM
performance after three weeks of training. This finding supports the theory that children with
ADHD can improve their WM performance by repeating certain WM tasks designed to
systematically target WM processes. The participants in the medium condition and control
condition on the other hand didn’t experience a significant impact on WM performance. The
inclusion of motivating game elements may affect WM performance in two ways: (a) it may
directly enhance the effect of training, or (b) it may increase the time spent using the training
program, and the enhanced training then improves the effect on WM performance. Since the
three conditions didn’t differ significantly on the actual time spent doing WM tasks it is viable
to attribute this to the first possibility, that training in a motivating game environment directly
enhances the effect of WM training. Path analysis also supports this notion, indicating that
motivation has a stronger effect on WM performance (/3 = .364, t = 1.705, p = .088), than
time spent on WM task (/3 = .160, t = 0.751, p = .452).
In line with the theory and with previous research (e.g. Beck et al., 2010) path analysis
showed a negative effect for WM performance on parent rated inattentive behaviour. This
effect however was non significant (/3 = -.269, t = -1.593, p = .111), but it hints that an
increase in WM performance may lead to a decline in inattentive behaviour. More support for
this claim can be found by taking a closer look at the different training conditions. Participants
in the MORPG condition experienced a significant decrease in parent rated inattentive
behaviour, whereas the participant’s inattentive scores in the medium and control condition
didn’t differ significantly after three weeks of training. These findings are plausible, given that
only the children in the MORPG condition experienced an increase in WM performance it
would be expected that only these children would display a significant decrease in inattentive
behaviour.
Contrary to what was expected did the children in both the medium and the control
condition display a significant increase in hyperactive behaviour after three weeks of training.
The children in the MORPG condition didn’t show any significant change in parent rated
hyperactive symptoms. These findings contradict the results found by a similar study
performed by Klingberg et al. (2005), who found a significant decrease in hyperactive
symptoms for children in their training condition. In their study the children were also
allowed to do the WM training in a home based environment and the training program they
used in their study resembles the control condition in this study. Although the actual WM
tasks that were trained may differ between the two studies, they both used a training program
34
without elaborating game elements, both included visuospatial and verbal WM tasks and both
had automatic adjusting of the difficulty levels to match the WM span of the child on each
task.
An explanation for these contradicting results is hard to find. It could be that the children
in the Klingberg et al. study may have been more motivated to participate in the training. This
however is pure speculation since they didn’t include a measurement for motivation. It is easy
to imagine that children (especially those with ADHD), who lack the motivation to carry out
the WM training, will be more rebellious and become defiant when their parents ask them to
persist with the training. The exit questionnaire clearly showed that most children in the
medium and control condition didn’t like the training and regarded the WM tasks as boring,
suggesting that the former explanation might indeed be the reason why hyperactive behaviour
increased in the medium and control condition. The children in the MORPG condition on the
other hand scored much better on the hyperactive scale and, although not significant, they
even showed a small decrease in hyperactive behaviour. The reason for not finding a
significant decrease in hyperactive behaviour for the MORPG condition may be explained by
the relative short time span of the training. In the Klingberg et al. study the participants
trained on average 40 minutes each day, for 25 days, whilst the participants in the MORPG
condition spent only an average of 4,5 hours (M = 04:34:24) doing the WM exercises. It is
possible that participants in the MORPG condition would have displayed a significant
decrease in hyperactive behaviour if the training was prolonged.
Research limitations
Despite the contribution of this thesis one has to realize that this study isn’t without
limitations. First, and perhaps the biggest limitation of this study is the relative small sample
size per condition. The total sample size in the present study was 44 (divided evenly over
three conditions), which is relative low compared to other similar studies like those of
Klingberg et al. (2005) and Prins et al. (2011) who used 44 subjects (divided over two
conditions) and 51 subject (divided over two conditions) respectively. A bigger sample size
would provide the ability to better detect significant differences between the three conditions.
Although a larger sample size would be desirable, it was very hard to find this many families
with children diagnosed with ADHD, who were willing to participate in this study during a
time span of three weeks.
35
A second limitation is the lack of a reliable assessment of the participant’s IQ. Since WM
is closely related to IQ it would have been good practice to conduct an IQ test, like the WISCIII, as a baseline measurement for IQ. Due to time constraints and practical issues it was opted
to use the education level of the participant and that of both parents as an alternative measure
for IQ. A third limitation of this study is the use of several WM tasks that haven’t been tested
in previous research, such as the various simon tests and the figure recall test. Although the
extent to which these tasks measure WM performance hasn’t been tested yet, it is fair to say
that the nature of these tasks trigger certain WM processes. For example in the simon task the
participants have to store a sequence of colours in their WM for a short period of time and are
then asked to reproduce that exact same sequence.
A fourth limitation of this study is that it doesn’t become clear which of the various game
elements in particular contribute to superior training outcomes. To determine the impact of
these elements, further research needs to examine them in a systematic way. A fifth limitation
of this study is the lack of follow up testing. As a consequence no information is available
regarding the durability of the training effects.
Future research
Future studies can broaden our understanding on WM training by extending the conceptual
framework adopted in this thesis and by using alternative methodologies. One direction future
research can take is extending the conceptual framework to other conditions in which WM
deficits are prominent, such as individuals who suffered traumatic brain injury or a stroke
affecting the frontal lobe. A second path researches can take is examining the effect WM
training and increased WM performance have on other areas closely related to WM processes,
like math and reading comprehension. It is possible that WM training can have a positive
effect on those skills as well through enhanced WM performance. Third, one of the limitations
of this study was the lack of follow up testing. Future research should include a follow up
measurement several months after the training to asses the stability of the training effects. A
fourth course future research can embark on is having a critical look at the various WM tasks
and try to come up with designs that are more fun and enticing for the participants. Most
children in this study found the WM tasks boring and tedious. In my opinion a lot of ground
can be gained by designing WM tasks that are more appealing to children and at the same
time still train certain aspects of WM.
36
Scientific contributions
This study has extended recent research on the field of WM training by examining the effect
game elements have on motivation, WM performance and behaviour in children with ADHD.
Instead of using the rather simplistic WM training designs used in previous studies (Klingberg
et al., 2005; Prins et al., 2011), this study took WM training research to a whole new level by
comparing three different types of computerized WM training programs: one with no game
elements, one with a few game elements and one that was converted into a real MORPG. Also
this study presents valuable information regarding WM training in a home-based environment. Most research on WM training has been done in typical research settings, whilst little
research has been conducted in a domestic setting. This thesis contributes to the literature by
painting a true picture of the caveats that go together with WM training in a domestic setting.
Furthermore this study came up with a conceptual model explaining the important role of
game elements and motivation on WM performance and ADHD behaviour.
Clinical implications
This study shows that WM performance can be enhanced by training, but only when this
training is able to capture the motivation of the learner. When it comes to children with
ADHD, traditional WM training programs without game elements won’t work in a domestic
setting. Not only do they not work, they also seem to have a counterproductive effect on
hyperactive behaviour because those WM programs are too boring and unimaginative for
these children. Simply adding a few game elements to the training program won’t cut it either.
Only when WM training programs become as fun and enticing as real videogames, will they
show their true potential. Designers of WM training programs should therefore try to find
ways to integrate WM exercises into real games and on top of that try to come up with new
designs that make the WM tasks in particular more appealing.
This study also showed that, in addition to enhanced WM performance, proper training
may also result in a decrease of parent rated inattentive symptoms. These findings may have
far reaching implications concerning individuals for whom executive deficits and inattention
problems pose a struggle in everyday functioning or academic performance. Well designed
WM training programs, which capture the motivation and interest of the learners, my very
well be the future in treating ADHD or other conditions in which WM deficits are prominent.
Even though the training program used in the MORPG condition needs further
development, the results are promising with regard to the use of well designed training
37
software as treatment of ADHD or WM deficiencies. Hopefully this study encourages other
researchers and game developers to do further research on this topic and provide Shelley the
hyperactive turtle and all children with ADHD with a fun and effective alternative to
mainstream treatments, such as medication or behavioural therapy.
38
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