How to Use Game Elements to Enhance Learning: Applications of the Theory of Gamified Learning

Abstract

The theory of gamified learning (Landers RN, Simul Games 45(6):752–768. doi:10.1177/1046878114563660, 2014) presents a theoretical model in which game elements, drawn from the serious games literature, are used in isolation or in limited combination to gamify existing instructional processes in order to improve learning. Critically, individual game elements must be linked to specific behavioral, motivational, or attitudinal outcomes, which in turn must be linked to learning outcomes, in order for gamification to be effective. Without establishing such links, gamification may appear to be unsuccessful when implementations have in fact succeeded. In this chapter, we expand upon the theory of gamified learning by providing applied examples of each of the nine major categories of game elements and linking those elements theoretically to the behavioral and attitudinal constructs they are best predicted to affect. In short, we explain how to gamify learning in a scientifically supported fashion. We conclude with recommendations for both research and practice of gamification in learning.

Full text

Landers, R. N., Armstrong, M. B., & Collmus, A. B. (2017). How to use
game elements to enhance learning: Applications of the theory of gam-
ified learning. In M. Ma, A. Oikonomou, & L. C. Jain (Eds.), Serious
Games and Edutainment Applications (Vol. 2; pp. 457-483). Surrey,
UK: Springer.
Chapter 18
How to Use Game Elements to Enhance
Learning: Applications of the Theory of
Gamified Learning
Richard N. Landers, Michael B. Armstrong and Andrew B. Collmus
Abstract The Theory of Gamified Learning (Landers, 2014) presents a theoreti-
cal model in which game elements, drawn from the serious games literature, are
used in isolation or in limited combination to gamify existing instructional pro-
cesses in order to improve learning. Critically, individual game elements must be
linked to specific behavioral, motivational, or attitudinal outcomes, which in turn
must be linked to learning outcomes, in order for gamification to be effective.
Without establishing such links, gamification may appear to be unsuccessful when
implementations have in fact succeeded. In this chapter, we expand upon the
Theory of Gamified Learning by providing applied examples of each of the nine
major categories of game elements and linking those elements theoretically to the
behavioral and attitudinal constructs they are best predicted to affect. In short, we
explain how to gamify learning in a scientifically supported fashion. We conclude
with recommendations for both research and practice of gamification in learning.
18.1 Introduction
Gamification, which involves the implementation of game elements in non-game
contexts, has become a popular method by which to improve classroom instruction
at relatively low cost. Consideration of gamification often comes when learning
designers hear about the potential of game-thinking to improve education or em-
ployee training but realize that the creation or adoption of a standalone digital
game is typically quite expensive, at least when it is “done right” by carefully
matching game design to pre-specified learning objectives. Gamification, in con-
trast, can generally be implemented in pre-existing instruction to improve learning
outcomes without great expense. In fact, the primary advantages to gamification
in comparison to the development of serious games are cost and convenience. The
key, however, is doing so in such a way that learning gains are realized.
In order to provide some guidance on how to actually obtain increase learning
from gamification, Landers (2014) introduced the theory of gamified learning.

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This theory has two major components: a framework describing categories of
game elements that are most likely to improve learning and a theoretical model
linking gamification efforts and learning. The framework can be used to identify
specific game elements that can be applied in learning contexts, such as game fic-
tion, control, or immersion. The model can be used to support theoretical linkages
between such elements and learning via attitudinal and behavioral change. For
example, elements might be used to increase learning by first increasing the likeli-
hood that learners will enter a state of flow (Shernoff, Csikszentmihalyi, Schnei-
der, & Shernoff, 2003). Landers and Landers (2014) provided the first test of this
theory, linking leaderboards to learning performance through increased time-on-
task. In short, the use of leaderboards designed to increase time-on-task can in-
crease the total amount of time that learners spend on a learning project, in turn
increasing their performance on the project. Such linkages are the key to realizing
the benefits of gamification and to avoid becoming one of the vast number of pre-
dicted “gamification failures” that have been warned (Pettey & van der Meulen,
2012).
Unfortunately, research in this domain is still quite nascent, so relatively few
such links have been established and even fewer empirically supported. Given
this, the purpose of this chapter is to provide theoretical support and practical ex-
amples for each of the game element categories described in the theory of gami-
fied learning. We will do this by first describing the theory of gamified learning,
its game element category framework, and its theoretical process model. Next, we
split our presentation by six major psychological theories that lend support to the
application of these categories to learning. Within each of these six sections, we
first present a summary of the theory alongside a description of current empirical
support for it and second present each game element that could take advantage of
this theory to realize learning gains, describing empirical research where available.
These suggested relationships are summarized in Table 18.1, alongside the theory
of gamified learning.
Importantly, the approaches listed here are very likely not the only ways to re-
alize learning gains through gamification. In our review of the literature, we
simply found them to be most promising given current research on both gamifica-
tion and psychology. Thus we present them in the hopes that they will provide
guidance to both researchers looking for theories to support specific gamification
interventions and also to practitioners looking for those gamification interventions
that are most likely to produce gains for their learners. Given this, we conclude
this chapter with recommendations for how to best utilize this information in both
research and practice, including specific guidance on methodology and statistical
testing in both contexts.

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Table 18.1 Game element categories from the theory of gamified learning and theories identi-
fied to take advantage of those element categories
Attribute
Theory
Definition
Action language
Presence Theory
The method and interface by which com-
munication occurs between a player and the
game itself
Assessment
The Testing Effect
The method by which accomplishment and
game progress are tracked
Conflict/challenge
Goal-Setting Theory
The problems faced by players, including
both the nature and difficulty of those prob-
lems
Control
Self-Determination Theory
The degree to which players are able to alter
the game, and the degree to which the game
alters itself in response
Environment
Presence Theory
The representation of the physical surround-
ings of the player
Game fiction
The Narrative Hypothesis
The fictional game world and story
Human interaction
Social Constructivism
The degree to which players interact with
other players in both space and time
Immersion
Presence Theory
The affective and perceptual experience of a
game
Rules/goals
Goal-Setting Theory
Clearly defined rules, goals, and infor-
mation on progress toward those goals, pro-
vided to the player
Note. Categories and definitions are taken verbatim from Landers (2014).
18.2 The Theory of Gamified Learning
Broadly, the theory of gamified learning 1) defines gamification in terms of learn-
ing, linking the research on game elements from both the serious games and gami-
fication literatures, 2) presents a theoretical framework of game elements likely to
improve learning outcomes and 3) presents a general model of the psychological
processes by which gamification is likely to improve learning outcomes. We de-
scribe each of these aspects of the theory in turn.
Landers (2014) defines gamification of learning as “the use of game elements,
including action language, assessment, conflict/challenge, control, environment,
game fiction, human interaction, immersion, and rules/goals, to facilitate learning
and related outcomes” (p. 757). This list of nine game element categories, which
also appears in Table 18.1, is not exhaustive or comprehensive; instead, it was de-
veloped as a list of those elements most likely to be usable to improve learning.
This was based upon prior research conducted by Bedwell, Pavlas, Heyne, Lazza-
ra and Salas (2012) in turn based upon work by Wilson et al (2009). Specifically,
across these two studies, these researchers developed a list of ways that seri-

4
ous/learning games are commonly manipulated in order to increase learning ac-
cording to both game players and developers, then refining this list to a more par-
simonious set of categorical labels, in order to minimize non-meaningful overlap
between them. If any game elements can be manipulated to improve learning,
these are likely to be those elements. Given this, Landers described how these el-
ements could be applied in isolation, outside the context of a game, to gamify
learning. In this sense, serious games and gamified learning both utilize the same
game element toolkit; they simply differ in how those elements are applied.
The importance of parsimony in this domain cannot be overstated. The serious
games literature has long suffered from a high degree of construct overlap, with
multiple studies examining the same underlying game element but giving it differ-
ent labels. Instead of continuing to waste research effort by maintaining inde-
pendent research streams, Bedwell and colleagues (2012) suggested that there are
underlying attribute categories of games that vary systematically, and that it is
these categories that are driving learning differences rather than the superficial dif-
ferences sometimes suggested in the literature. Attempting to theoretically define
gamification makes these divisions more obvious; although Wilson and colleagues
(2009) “fantasy” and “mystery” game elements certainly are operationalized dif-
ferently, they are unlikely to lead to dramatically different outcomes. Instead,
these dimensions were empirically collapsed to a single “game fiction” dimension
that unites the literatures on various types of fiction. Such models are ultimately
more useful when developing and identifying empirical support in this domain.
Landers (2014) was the first to provide a list of examples of gamification based
upon Bedwell et al.’s (2012) parsimonious list of game attribute categories, and
these categories and their definitions appear in Table 18.1. Importantly, game el-
ements within each of these categories can be applied in isolation, which is what
distinguishes gamification from the development of a serious game. For example,
assessment can be implemented independently as a gamified learning design,
without any other game elements. Importantly, each game element can be sup-
ported with different psychological theories or aspects of those theories. For ex-
ample, as will be described later, rules/goals is most likely to change learner be-
havior given the tenets of goal-setting theory (Locke & Latham, 2013a) whereas
game fiction is most likely to change learner behavior given the ideas supported
by the narrative hypothesis (Graesser, Hauft-Smith, Cohen & Pyles, 1980).
The particular way to go about this is also described by the theory of gamified
learning (Landers, 2014). Specifically, gamification is hypothesized to influence
learning through one of two (or both) theoretical paths. In both cases, gamifica-
tion influences an intermediary learner behavior or attitude but after that, the ef-
fect diverges. Some types of gamification influence learning outcomes because
this targeted behavior/attitude itself influences learning. In these cases, the target-
ed behavior/attitude is called a mediator. This relationship is depicted in Figure
18.1. Other types influence learning outcomes because this behavior/attitude
changes how effective the existing instructional content is. In these cases, the tar-
geted behavior/attitude is called a moderator. This relationship is depicted in Fig-

5
ure 18.2. Critically, the specific path to learning from gamification must be mod-
eled accurately, as a moderator, mediator, or both, for researchers to truly under-
stand what a particular gamification intervention does, and how.
Fig. 18.1 Causal path from a game element to learning outcomes through mediational learner at-
titudes/behaviors, as proposed by the theory of gamified learning (adapted from Landers, 2014).
Fig. 18.2 Causal path from a game element to learning outcomes through moderating learner at-
titudes/behaviors, as proposed by the theory of gamified learning (adapted from Landers, 2014).
In the first explicit empirical test of this model, Landers and Landers (2014)
provide an example of the first type. Specifically, the use of leaderboards (them-
selves a combination of rules/goals, conflict/challenge, and assessment) influenced
the amount of time students put into a course project. This variable, called time-
on-task, was thus a target learner behavior. Prior research also suggests that time-
on-task is itself critical to learning (Brown, 2001). Thus, time-on-task was hy-
pothesized and tested as a mediator in the relationship between leaderboards and
learning, in the context of a learning activity.
Many researchers leave the specific nature of this relationship empirically un-
tested, which we believe is harmful to the development of this research literature.
For example, Nicholson (2015) proposes a concept called meaningful gamifica-
tion, which suggests that for gamification to be impactful, it should contain six
components: play, exposition, choice, information, engagement, and reflection.
Thus, Nicholson is implicitly suggesting two mediators in series between gamifi-
cation and learning. First, gamification must facilitate the freedom to explore (i.e.,
play), create stories for learners (i.e., exposition), etc., according to this six-
dimension model. Second, once these learning states have been created, the learn-
er must experience from them some kind of meaning, presumably a psychological
state. Third, once the learner has experienced meaning, this experience of mean-
ing should lead to learning outcomes. A model summarizing this relationship for
play is shown in Figure 18.3. These are all testable ideas, yet empirical support

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for them is not available. The theory of gamified learning provides a model under
which to provide such support.
Fig. 18.3 Nicholson’s (2015) meaningful gamification via play expressed in terms of the theory
of gamified learning.
In summary, the theory of gamified learning provides a framework of game el-
ement categories that researchers and practitioners can focus upon with the goal of
improving learning through gamification. These categories are not exhaustive
lists of game elements, but rather describe groups of game elements that are likely
to produce similar effects for similar reasons. The theory also provides a process
model describing the linkages between such game elements and learning outcomes
through intermediary psychological processes. In the sections that follow, we will
describe several psychological theories that we find provide the most promising
intermediary processes to fulfill this role.
18.3 Goal-Setting Theory and Self-Regulation
Game elements can influence learning through the mediational effects of self-
regulation, which refers to the continual process of comparing one’s goals to one’s
own performance and adjusting their behaviors as necessary to minimize the gap
(Locke, 1968). One prominent application area of self-regulation is described by
goal-setting theory (GST), a motivational theory explaining how goals can be cre-
ated in order to maximize performance (Locke & Latham, 2013a). GST provides
many specific mechanisms for this; for example, the motivational affordances of
goal-setting are maximized when there is feedback on one’s progress toward a
goal (Ambrose & Kulik, 1999). Setting goals and striving for their achievement
forces one to engage in self-regulatory processes. However, not all goals are
equally effective. A common mnemonic summarizing the recommendations from
goal-setting research is that to maximize outcomes, goals should be SMART: spe-
cific, measurable, attainable, realistic, and time-bound (Doran, 1981). SMART
goals have been applied to increase learning quite effectively, and numerous
guides are available explaining how to do so (e.g., O’Neill & Conzemius, 2006).
The use of GST to increase motivation and performance in a wide variety of
contexts is very well supported by decades of empirical evidence. There is even
an abundance of meta-analytic evidence, the most comprehensive sort of scientific
evaluation currently available (for a review of this, see Locke & Latham, 2002).

7
As a result, there is currently a scientific consensus that goal-setting interventions
generally influence the behaviors they target.
In the learning context, there is one subtle effect not proposed in core GST
worth discussing in greater detail: performance goals commonly decrease learning
(Seijts & Latham, 2005). The most common reason provided for this is that goal-
setting interventions encourage the “wrong” behaviors. Trying to get a high score
on a test, for example, encourages the learner to focus on and plan for “getting a
high score” instead of learning. In such a situation, spending time planning how
to cheat on the test might be seen as a valid way to “get a high score” but is in fact
counterproductive to learning. Furthermore, to the extent that the learner does not
know how to get a high score (i.e., by learning), time will be wasted, and the goal
will not result in the desired outcome. To avoid this problem, learning goals are
best set when increased learning is the desired outcome (e.g., setting a goal of “I
will learn three different strategies to solve this problem” instead of a performance
goal of “I will solve this problem in the next hour”; Winters & Latham, 1996).
While researching performance versus learning goals, it was observed that indi-
viduals who set learning goals tended to report higher levels of satisfaction. Spe-
cifically, Latham and Brown (2006) found that, among MBA students, learning
goals lead to the highest satisfaction ratings among various goal conditions.
18.3.1 Applying Goal-Setting Theory via Rules/Goals Game
Elements
As previously mentioned, the motivational affordances of goal-setting theory are
maximized when there is feedback on progress toward the goal. This naturally co-
incides with the rules/goals game element and is potentially one of the easier ele-
ments to capitalize on in gamified learning. When learners receive feedback on
their progress toward any goal, they are able to better understand the problem that
goal represents and refocus their learning efforts as needed. Similarly, rules can
help illuminate a learning concept or theoretical boundary condition.
Garris, Ahlers, and Driskell (2002) specify that rules and goals should be clear
in order to facilitate learning. This is likely necessary but not sufficient for learn-
ing. Clearly stated rules and goals can be followed and acted upon, but if they are
boring, learners will not be motivated to engage with those rules or learning goals.
Garris and colleagues further note that goals must also be flexible enough to allow
for a range of actions. It is in this flexibility that players’ styles and strategies
come through. If rules are too constrictive, then the following of rules is no longer
fun because it does not allow for the expression of style and strategy.
In the context of the theory of gamified learning, the importance of rules/goals
is that set rules/goals successfully influence a particular targeted attitude or behav-
ior. Rules/goals game elements are generally broad enough that this target could
be any learning-relevant attitude or behavior. For example, consider an instructor

8
that has noticed students are no longer attending a lecture-based class. A goal
could be set for the class to attain at least a 90% attendance rate every day, with a
small bonus point award at the end for the entire class for attaining this goal. The
specific nature and value of the bonus point award is irrelevant; extant research on
GST suggests that the precise value of rewards is less critical than their mere ex-
istence. Their primary role is instead to signify accomplishment of the goal, as a
point of pride. In this case, the target behavior is in fact learning whereas the in-
termediary learner behavior targeted by gamification is attendance, an example of
the mediational path in the theory of gamified learning. Thus, in this example,
rules/goals are hypothesized to influence attendance, which is hypothesized to in-
fluence learning.
Importantly, such rules/goals should not be targeted at learning itself. As noted
earlier, performance goals often interfere with true learning goals. If the instructor
described above had instead set a goal for the class to “get a high score on the next
test,” such a directive is likely to interfere with learning. Gamification must feel
optional such that learners perceive that they are choosing to engage with the
rules/goals willingly. This was a key aspect of Landers and Landers’ (2014) lead-
erboard intervention; the leaderboard created goals that were likely to be associat-
ed with learning but were not themselves learning goals, such as being among the
first in the class to work on their projects, or among the first to figure out how to
upload pictures. As soon as rules/goals are required, they are no longer gamifica-
tion – they become part of learning/academic performance.
18.3.2 Applying Goal-Setting Theory via Conflict/Challenge
Game Elements
Conflict/challenge is a powerful game element in learning contexts (Malone,
1981). Challenge coincides with the “difficult” and “attainable” aspects of
SMART goals. Specifically, people need to feel challenged by goals, but not
overwhelmingly so, in order to find them motivating. If a goal is too easy, it will
be perceived as trivial and will not produce a sense of satisfaction for accomplish-
ing it, yet if a goal is too difficult, it will be perceived as impossible, unfair, and
demotivating. Thus, a balance must be achieved between the two extremes in or-
der to maximize the impact of goals. This is particularly difficult in contexts
where learner skill and preparedness varies greatly; that which is excessively dif-
ficult for one learner may be simple for another.
Conflict/challenge can be implemented into a learning context by carefully
considering the difficulty of specific tasks. For example, a common group discus-
sion activity in the classroom involves breaking into small groups and coming to
consensus within each group on some issue related to class material. Afterward,
each group shares its result. However, the only goals in this context are those self-
imposed by the learners. One group may be filled with high-achievers, hoping to

9
impress the instructor by developing as perfect a response as possible. Another
group may be filled with low-achievers, who just fabricate an answer on the spot
when asked at the end. By creating a specific, difficult goal for each group to pur-
sue, one takes advantage of goal-setting theory. It communicates to each group
that they are capable of and expected to come up with an answer to a particular
problem, which increases the self-efficacy of group members and targets their ef-
fort. For example, if the goal of group discussion in a government class was orig-
inally to determine, in hindsight, what the “best” course of action was for each
country in Europe at the dawn of World War II, conflict/challenge might be intro-
duced by assigning each member of the group to determine the best course of ac-
tion for his/her assigned country, and then to task the group with coming to a sin-
gle consensus judgment. Because the best action “for Europe” and the best
actions for individual countries are likely to be different, each member of the
group discussion has now been personally challenged. This is likely to increase
group discussion engagement (a mediator, in terms of the theory of gamified
learning) and therefore learning outcomes.
18.4 The Testing Effect
The theory of test-enhanced learning, commonly known as the testing effect
(Roediger & Karpicke, 2006), provides insight into how game elements can affect
learning outcomes by influencing cognition. The testing effect is a phenomenon in
which learners who are tested on learning material retain that material better than
if they had not been tested, even without receiving feedback on their performance
(Roediger & Karpicke, 2006). Testing triggers learners to retrieve previously
learned information. By retrieving that information, they are in essence practicing
remembering, and thus overall retention of the information is improved, strength-
ening future attempts at retrieving that information.
Importantly, testing provides benefits in long-term retention beyond that of
studying. Giving learners the opportunity to either study a lot with no practice
tests or to study a little and take several practice tests instead of studying can re-
sult in differences in learning recall and retention. Roediger and Karpicke (2006)
found that recall of material one week after being randomly assigned to study or
testing sessions was better for those completing tests. Learners who were repeat-
edly tested forgot less information over time than learners subjected to repeated
studying only. There are many possible mechanisms by which this testing effect
might occur, but it is likely due to a number of memory-related mechanisms (see
Rowland, 2014 for a discussion on the plausible mechanisms and their supporting
evidence). For example, retrieval practice may generate additional cognitive
routes to retrieval through cues presented at each retrieval.
Empirical evidence strongly suggests that the testing effect can substantially
improve information recall. According to a meta-analysis conducted by Rowland

10
(2014), the testing effect causes an improvement in recall by a half standard devia-
tion over study-only conditions. The testing effect is much larger for recall as-
sessments (e.g., short answer, fill-in-the-blank) than for recognition assessments
(e.g., multiple-choice; Rowland, 2014), which show the opposite pattern. Specifi-
cally, studying makes multiple-choice easier and recall harder, whereas testing
makes recall easier but multiple-choice harder. Recall is more in alignment with
learning objectives than recognition, as it is a better indicator of learning. For ex-
ample, most instructors would prefer their learners are able to spontaneously re-
member and describe details about the material than to recognize patterns on mul-
tiple-choice tests. The testing effect also demonstrates a clear advantage for long-
term recall, which is the ultimate goal of instruction (i.e., rather than short-term
recall), over studying.
Further, the testing effect improves learning even when feedback is not given
(i.e., when not informing the learner which of their answers were correct), alt-
hough providing feedback to the learner improves learning further (Roediger &
Butler, 2011; Rowland, 2014). Learners must correctly recall the information at
initial tests in order to successfully recall that information at final tests. Providing
feedback in initial testing allows learners to correct errors in memory before final
testing (Roediger & Butler, 2011).
Given this body of research, the key to eliciting the testing effect is providing
opportunities for learners to complete learning-related assessments or exercises as
they progress through learning materials. Receipt of feedback on their perfor-
mance as they complete these assessments is not necessary to elicit the effect but
is further beneficial to learning.
18.4.1 Applying the Testing Effect via Assessment Game Elements
If testing positively impacts learning elements, learning should be gamified in a
way such that game elements impact and prompt that cognition. The most appro-
priate element for prompting testing behavior is assessment. Assessment is de-
fined as the method by which accomplishment and game progress are tracked (see
Table 18.1). Examples of assessment found within games include points, badges,
and leaderboards. Often, assessment of game progress and accomplishment comes
in the form of rewards (e.g., the player reaches the end of a level and is given a
score or summary of performance, or obtains a badge marking the accomplish-
ment). In a learning context utilizing the testing effect, rewards might be distribut-
ed to learners for participation in practice tests in order to encourage practice re-
calling information. For example, a badge might be awarded to the learner for
completing a quiz on a particular subject (Landers & Callan, 2011). Although
these forms of assessment can be motivating (Mekler, Brühlmann, Tuch, & Op-
wis, in press), not all people react the same to these elements of assessment. Some
users may find little value in the gamification if the assessment lacks an appropri-

11
ate context (Montola, Nummenmaa, Lucero, Boberg, & Korhonen, 2009). For ex-
ample, some learners may find the goal of earning points to be subjectively unre-
warding if the points do not translate to some real-world reward. Others may find
that accumulating points and badges is so engaging that it promotes friendly com-
petition with other users (Montola et al., 2009). Assessment elements need mean-
ingful value for the learners in order to promote additional testing behavior.
Learners will not pursue rewards like badges or leaderboard positions if they do
not value what those rewards represent (Landers, Bauer, Callan, & Armstrong,
2015).
To maximize the value of assessment, McDaniel and Fanfarelli (2016) ex-
plained how digital badges can be used to promote completion behavior, such as
taking additional tests. Badges can direct behavior through goal-setting, providing
feedback, and through debriefing. Badges can provide a goal to direct future learn-
ing behavior (e.g., win this badge for completing three bonus quizzes). Badges can
also provide feedback while learning (e.g., taking a practice test and receiving a
badge for receiving a certain score), which then directs future behavior (e.g., per-
sisting with practice tests to win more badges). Badges can prompt reflection as a
form of debriefing (e.g., reflecting about test performance and effort after com-
pleting a learning session in relation to what badges were earned during the ses-
sion), which can them impact future testing behavior. Each of these attitudes and
behaviors – completing extra quizzes, reading extra feedback, and reflecting – are
thus mediational behaviors as described by the theory of gamified learning.
Landers and Callan (2011) provide a practical example for utilizing an assess-
ment element to promote practice testing. The authors awarded badges to learners
in order to increase participation in taking voluntary quizzes in a college course.
Students were awarded badges for completing optional online quizzes over course
content. Students’ grades in the course were neither dependent on quiz participa-
tion nor quiz performance, so participation in the quizzes was entirely voluntary.
Student feedback indicated that students were motivated to complete the quizzes
and earn the badges, which in turn provided more opportunities for testing and
practice recalling content from the course. The gamification implemented in the
course provided meaningful context for the badges to the learners in multiple
ways. First, some students perceived the badges as goals, with goal accomplish-
ment serving as a source of feedback to the learners’ competence (Antin &
Churchill, 2011). Second, the gamification was implemented within a university
social media platform, allowing students to view each other’s badges and provide
an environment for competition and comparison, another source of feedback.
18.5 Presence Theory
Game elements can be used to impact learning by creating a subjective state of
presence within a learning environment. Presence is “the subjective experience of

12
being in one place or environment, even when one is physically situated in anoth-
er” (Witmer & Singer, 1998, p. 225). A state of presence is likely to be experi-
enced, for example, while engaged in a virtual reality experience. A person might
experience a virtual reality or virtual environment based on an ancient city and
culture. The person might navigate a simulation of the city through immersive
technology in order to learn about an ancient people or their culture. Even though
the person is at home in the present day, the person may feel a sense of presence in
the virtual reality – a sense or feeling, even if only momentarily, of truly being in
that ancient city. Although presence is easier to create in an environment as im-
mersive as virtual reality, it is not required. Presence may be induced by any form
of media (e.g., a website).
At a minimum, fostering a sense of presence requires creating both involve-
ment and immersion. Involvement is “the psychological state experienced as a
consequence of focusing one’s energy and attention on a coherent set of stimuli or
meaningfully related activities and events,” (Witmer & Singer, 1998, p. 227).
Immersion, as defined by the presence literature, is “the psychological state char-
acterized by perceiving oneself to be enveloped by, included in, and interacting
with an environment that provides a continuous stream of stimuli and experienc-
es,” (Witmer & Singer, 1998, p. 227). In short, presence depends on a person’s at-
tention and focus shifting from the physical environment to the virtual environ-
ment. In the context of learning, the goal would be to shift a learner’s attention
from his or her physical environment to the subjective experience of the learning
environment. A learner might be sitting in a classroom, but that does not mean that
this person is engaged with the learning environment. The learning environment
should capture the attention of the learner, while also allowing the learner to feel
included in the environment, capable of interacting with it. By immersing learners
in the learning environment, learning-irrelevant distractions can be dismissed, al-
lowing more focus on the learning content.
A related topic to presence is the state of flow (Nakamura & Csikszentmihalyi,
2002). Flow is similar to the state of presence, except that in addition to requiring
concentration, flow requires interest and enjoyment (Shernoff, Csikszentmihalyi,
Schneider, & Shernoff, 2003). From a learning perspective, however, the neces-
sary elements to create a state of both flow and presence are quite similar.
In research on virtual reality and learning, characteristics of virtual reality are
likely to affect learning through perceptions of presence (Salzman, Dede, Loftin,
& Chen, 1999; Lee, Wong, & Fung, 2010). Different features of virtual reality,
such as visual, auditory, or haptic stimuli are theorized to induce a sense of pres-
ence by increasing perceived fidelity of the virtual environment. The immediacy
of interacting with the virtual reality (i.e., how quickly the virtual environment re-
acts to the user’s actions) also affects presence. If interaction with the virtual envi-
ronment is smoother and more immediate, presence will increase. Presence may
affect learning through a higher quantity of higher quality information (i.e., visual-
ization, audio, and haptics give more context to the information presented).
Through interaction with the virtual environment, users can engage in active learn-

13
ing (see Social Constructivism section later in this chapter). Lee et al. (2010) test-
ed this path using structural equation modeling and found a significant positive re-
lationship between presence and learning outcomes. However, the measure for
presence was a single-item developed by the authors; thus, it is unclear how relia-
ble this measure was and therefore how trustworthy the results are. Although cog-
nitive and affective learning outcomes were included in the study (Kraiger, Ford,
& Salas, 1993), much of the effect of presence on learning seems to be due to the
affective outcomes (e.g., satisfaction with learning). It is difficult to determine
how much of a role presence may play in learning in non-virtual reality learning
contexts. Lee and colleagues (2010) found that a desktop computer program is
enough to induce a state of presence and affect learning, but these results should
be interpreted with caution, as other factors may have played a larger role in learn-
ing (e.g., learner control or reflective thinking).
18.5.1 Applying Presence Theory via Action Language Game
Elements
Action language can be used to improve the sense of presence in order to affect
learning; thus, presence is itself the mediator when this is applied to the theory of
gamified learning. Action language represents how a player communicates with
the game, or how a learner might communicate with the learning environment or
system, and this language can induce a state of presence. For example, in playing
a video game, the action language might include pressing buttons on a video game
controller. Action language in an online learning management system might be us-
ing a computer keyboard and mouse. In playing games, the control interface or ac-
tion language plays a role in affecting the state of presence (Shafer, Carbonara, &
Popova, 2014). To induce presence, action language should be easy to use and
useful, which has been supported by research in virtual reality (Lee et al., 2010).
When using game elements like action language to induce presence, those ele-
ments will also need to be easy to use and perceived as useful by the learner.
When implementing action language in order to induce a state or presence in the
learning environment, it is suggested to use an action language with which learn-
ers are already familiar. Different forms of action language include computer
mouse and keyboard; smartphones, tablets, or other touchscreen computers; and
video game controller (e.g., for PlayStation, Xbox, Wii, etc.).
Different action languages have different features which may be perceived dif-
ferently by users depending upon their familiarity with each interface. For exam-
ple, a mouse is an intuitive way to navigate a computer, but keyboard shortcuts
might be more efficient. Video game controllers are generally more ergonomic
than standard keyboards, but their application is generally more specific (i.e., con-
trollers are used primarily for games whereas a computer keyboard can be useful
for typing, programming, controlling a game, and more). Gerling, Klauser, and

14
Niesenhaus (2011) compared two action languages on their impact on player ex-
perience in a commercial video game. Players reported if they typically played PC
games or console games and were assigned to play either the version with which
they were comfortable or with the action language they did not typically use. Dif-
ferences in presence and immersion measures were small, but a significant, mod-
erate difference in “absorption,” or engagement, was found favoring the unfamil-
iar action language. It is possible that using unfamiliar action language, which is
more challenging (Gerling et al., 2011), prompts players to be more engaged with
the game or task at hand. Players must be more alert when using unfamiliar con-
trols in order to avoid errors. Although this may increase absorption or engage-
ment, this stimulation does not necessarily support immersion in the experience.
This conclusion supports the theory that perceived ease of use and perceived use-
fulness of an action language supports the state of presence.
More immersive action languages than standard computer interfaces are possi-
ble in learning. For example, medical procedure simulations use realistic tools as
controls within a virtual environment. (Wilson et al., 1997; Aggarwal, Morthy, &
Darzi, 2004). Trainees can practice making maneuvers typical of a procedure us-
ing a computer screen and standard medical instruments (e.g., laparoscopic in-
struments). By creating a virtual experience similar to a real life medical proce-
dure, trainees may feel presence in the virtual medical procedure without
consequences for technical errors. In addition to specialized action languages like
medical instruments, new technology also provides outlets for more immersive
experiences. Smartphones, tablets, and other touchscreen computers are becoming
more common, making this action language an increasingly viable option in learn-
ing settings. The interactivity of a touchscreen in combination with the dynamic
nature of a computer screen can create new engaging learning experiences that
were previously not possible. Additionally, video game play is rising globally
(Lofgren, 2015), making video game controllers another viable action language as
well.
One important caveat with the implementation of action languages is that this
may require training for learners to understand how to use those action languages,
which could interfere with learning. Specifically, when learners can process in-
formation with little or no conscious awareness, such as how to use a given action
language, overall cognitive load is reduced, freeing cognitive resources for learn-
ing the content rather than a learning management system, a state called automa-
ticity (Feldon, 2007). If a learner is familiar with an action language already, that
learner will require less practice and cognitive resources to become proficient
enough at the action language to take advantage of its interface with a learning
management system. Care should be taken to either implement action languages
that are already familiar to learners or to ensure that they are trained such that ac-
tion language use becomes automatic before learning with it.

15
18.5.2 Applying Presence Theory via Immersion Game Elements
The second game element that can be used to affect presence is immersion. It is
positioned similarly in the theory of gamified learning – immersion game ele-
ments can be used to create presence (the mediator), in order to affect learning.
Immersion represents the “perceptual and affective relationship of the player with
the game fiction,” which is embodied through players and their representation
(e.g., pieces in a board game, avatars or characters in a digital setting), sensory
stimuli (i.e., visual, audio, haptic), and the sense of safety (Bedwell et al., 2012, p.
742). It is distinct from the psychological state of immersion described earlier.
One way of conceptualizing immersion is in terms of the acceptance, even tempo-
rarily, of the player of the alternate reality presented. If game attributes like player
representation, sensory stimuli, and sense of safety contribute to an immersive ex-
perience, the player will be more likely to suspend his or her disbelief in the reali-
ty presented by the game. Similarly, if these attributes contribute to an immersive
experience in a learning environment, they should contribute to presence, and thus
impact learning.
Each type of immersive game element can be applied in order to facilitate
presence in learning. As the representation of players becomes more realistic and
relevant (i.e., versus abstract representations found in many board games like Sor-
ry or Monopoly), presence should improve. In a video game or virtual environ-
ment, learners should find the content more relevant as they take on a realistic role
in the environment rather than an irrelevant or uninvolved role. As sensory stimuli
increase in fidelity through visual, audio, or haptic enhancements, presence should
improve. This might involve the use of graphics or relevant audio, which would be
easy to implement in a learning context. A simple example might be to include a
video in a lecture in order to demonstrate a concept or procedure. Increasing the
sense of learner safety might also improve presence. Dissociating actions from
consequences (i.e., acting without fear of repercussions) removes concerns for the
real world, allowing learner attention to focus on the virtual learning environment.
This is evident in the example of medical procedure simulations, where trainees
can practice skills where errors do not cause harm to patients. Some consider this
to be the crux of video games; players can try and retry continuously when they
fail at a task. Although these game attributes might contribute to a sense of pres-
ence, they may not necessarily improve learning. Research on fidelity shows that
higher fidelity simulations are sometimes distracting and detract from learning.
Gamified learning environments should be designed in such a way as to utilize a
high quantity of high quality information to improve fidelity only as much as nec-
essary to meet learning objectives, and we recommend consulting the human-
computer interaction research literature for recommendations on this.

16
18.5.3 Applying Presence Theory via Environment Game
Elements
The third element that can be used to affect presence is environment. Environ-
ment represents the location of the player within a game (e.g., playing a physical
game of tag outside in a field or playing a video game set in a fictional galaxy).
This element influences the rules and expectations of the game, and may be real or
fantasy (Bedwell et al., 2012). Different environments may impact presence in
various ways. Depending on the learning context, some environments may be
more relevant than others. Learning about deep sea oil drilling using a virtual oil
rig environment might be more relevant to learning material than the same infor-
mation in a classroom, improving the sense of presence in the learning environ-
ment. Environments might also impact presence differently depending on learn-
er’s preferences for different environments. Learners may prefer to learn technical
skills on a job site rather than in a classroom, whereas others may prefer class-
room learning. Returning to the example of medical trainees, some trainees may
prefer to learn new procedures by watching a video in a classroom, while other
trainees may prefer to learn through trial and error in practicing a new procedure.
If learner preferences for environment are not matched with the learning envi-
ronment, the learners may be distracted by their environment, decreasing their
sense of presence and overall learning. For example, an instructional designer
might use technology like virtual reality or video games to create a digital envi-
ronment to enhance learning. If the objective of learning is to learn about an an-
cient culture, designing a virtual environment based on an ancient city might be an
appropriate environment for enhancing learning. However, if the objective of
learning is to teach algebra, a virtual environment of an ancient city might be in-
teresting but distracting from the learning objectives. Instructional designers
should build different environments using technology to suit the needs of different
learning situations and learner preferences.
18.6 Self-Determination Theory
Self-determination theory suggests that motivation is rooted in the fulfillment of
three basic psychological needs: autonomy, competence, and relatedness (Ryan &
Deci, 2000). As people experience greater control or autonomy over their own
choices and actions, as they feel more competent at what they are doing, and as
they feel more socially connected to other people, their motivation to choose or act
approaches intrinsic motivation. A person is intrinsically motivated when they are
doing something for fun, for its own sake, because it is interesting, satisfying, or
enjoyable (Deci & Ryan, 2000; Ryan & Deci, 2000). Intrinsic motivation is tied to
a variety of positive outcomes, one of which is learning (Deci, Vallerand, Pelle-

17
tier, & Ryan, 1991; Ryan & Deci, 2000). Of particular interest to intrinsically mo-
tivating learning in the context of gamification is the need for autonomy. As learn-
ers have more freedom in how and what they learn, they become more motivated
to learn. Motivation to learn plays a key role in achieving a variety of learning
outcomes (Colquitt, LePine, & Noe, 2000). Thus, instructors should attempt to en-
hance learners’ motivation to learn in order to improve overall learning outcomes.
Although extrinsic motivators such as rewards and deadlines can be motivating,
research on self-determination theory suggests that more intrinsic sources of moti-
vation will lead to greater learning outcomes and supporting autonomy is one way
to do this (Deci et al., 1991). Thus, game elements should be used to provide sup-
port for the autonomy of learners in order to enhance motivation to learn and sub-
sequently overall learning.
In practice, providing autonomy support for learners might be best exemplified
through the concept of learner control. Learner control refers to the extent to
which a learner can affect his or her learning experience by altering features in the
learning environment (Friend & Cole, 1990; Kraiger & Jerden, 2007). There are a
variety of features in learning environments that a learner might adjust in order to
alter his or her learning experience (Kraiger & Jerden, 2007; Karim & Behrend,
2014; Landers & Reddock, in press). For example, a learner might adjust the pace
of learning in a web-based instructional setting. If the learner already knows about
a topic, that learner may choose to spend less time on that topic. Alternatively, if
the learner needs more time to fully comprehend the concepts presented, that
learner may choose to spend more time reviewing the content. Learners might also
be given control over other aspects, such as the time and location of instruction
(Karim & Behrend, 2014). A learner may choose when and where to learn, ac-
cording to what best fits his or her schedule.
Meta-analytic evidence for the effects of learner control on learning outcomes
is mixed (Kraiger & Jerden, 2007; Landers & Reddock, in press). Giving learners
certain types of control over their learning environment is motivating, but this
does not necessarily lead to larger learning gains. Types of control vary in their ef-
fectiveness at improving learning outcomes (Landers & Reddock, in press). For
example, giving learners control over the sequencing of learning material has a
small positive effect on learner satisfaction with the instruction, but has a minimal
effect on knowledge learning. In general, sequence control has a positive effect on
learning, while other types of learner control have neutral or negative effects for
different outcomes. However, it is possible that the relationship between learner
control and learning outcomes is moderated by characteristics of the instruction or
characteristics of the learner, such as the learner’s preference for control (Kraiger
& Jerden, 2007). For example, if a learner is given control over learning, but
would rather have an instructor guide the learner through the content, learner con-
trol might not positively impact learning. Although learner control should theoret-
ically improve motivation to learn by satisfying a need for autonomy (Ryan &
Deci, 2000), it has not been hypothesized in current models of learner control
(Kraiger & Jerden, 2007). It is possible that learner control and learning relation-

18
ship is mediated by motivation to learn, consistent with the theory of gamified
learning (Landers, 2014).
18.6.1 Applying Self-Determination Theory via Control Game
Elements
Control as a game element is conceptually similar to learner control: “the degree
to which players are able to alter the game, and the degree to which the game al-
ters itself in response,” (Landers, 2014, p. 756). By giving learners control, their
need for autonomy is satisfied, increasing their intrinsic motivation to learn, which
should lead to improved learning outcomes (Colquitt et al., 2000). Although learn-
ers may be motivated by having the freedom to set the pace of learning or the
freedom to decide on the sequence of content, the effects of these forms of control
are overall small for learning. Elements of control may be more useful when com-
bined with other game elements.
A learner’s need for autonomy might also be satisfied by combining elements
of control with other elements, such as immersion or human interaction. For ex-
ample, within a computer-based instructional system or learning management sys-
tem, learners might be given control over aesthetic features, a component of im-
mersion (i.e., sensory stimuli or representation, Bedwell et al., 2012). If a learner
were given control over the appearance of an avatar or of the software application
itself, his or her need for autonomy might be satisfied without relegating total con-
trol over learning content to the learner. As another example, if the learning man-
agement system has a social sharing feature, learners might be given autonomy in
how much content they share with other users. If a learner was excited about com-
pleting a particular course module, that learner might want to share his or her
achievement with others. Additionally, learners are not proficient at deciding what
content needs to be learned (DeRouin, Fritzshce, & Salas, 2005; Brown & Ford,
2002), so by giving learners freedom in other ways, instructors can guide learning
while maintaining an intrinsic motivation to learn.
There are many ways to gamify learning with control. Based upon their meta-
analysis, Landers and Reddock (in press) provided nine methods in the context of
web-based instruction, allowing learners to skip material they already know, to
add material for extra study, to change the order of learning material that they ex-
perience, to add or skip knowledge/skill assessments, to receive guidance and de-
termine a course of action in response, to change the stylistic details of the learn-
ing environment, and to control when and where they complete their learning.
However, because effects vary by outcome, it is recommended to choose control
techniques aligned well with learning goals. Outside of web-based instruction,
where the trajectory of a course can change from session to session, there are more
options, some of which are quite simple. For example, if an instructor was trying
to choose between two supplementary videos for class with similar content, one

19
way to provide control to students is to ask them to vote on which video they
would rather see. Such control should improve learner motivation, which should
in turn strengthen the effectiveness of the instructor’s existing instructional ap-
proach (i.e., an example of the moderating path in the theory of gamified learn-
ing).
18.7 The Narrative Hypothesis
The narrative hypothesis provides insight into how a story or narrative element,
which is common to many games, can impact learning outcomes. The narrative
hypothesis suggests that when learning from text, the information from a narrative
genre text is better learned and retained than the information from an expository,
or descriptive text (Graesser, Hauft-Smith, Cohen, & Pyles, 1980; Adams, Mayer,
MacNamara, Koenig, & Wainess, 2012). For example, the narrative hypothesis
suggests that when learning historical information about a world leader, that in-
formation would be better learned when presented as a story rather than as a series
of facts without a narrative. Narrative texts are thought to be more memorable
than expository texts for several reasons, which forms the basis for the narrative
hypothesis. Within narratives, events are more concrete and usually have a causal
ordering. This is in contrast to expository texts, which are generally more abstract,
providing facts and information which may or may not be related to other facts or
information presented in the same passage. Also, narratives might be better under-
stood or better liked by learners (Cunningham & Gall, 1990), which may be an-
other way of explaining its effects on learning retention over expository texts
(Graesser et al., 1980).
The narrative hypothesis has received much empirical support in laboratory
studies (Graesser et al., 1980; Tun, 1989; Best, Floyd, & McNamara, 2008), but
has received less support in actual educational contexts (Cunningham & Gall,
1990). Graesser and colleagues (1980) assessed learner recall of information pre-
sented in various text passages across text genres. Learners recalled more infor-
mation from narrative passages than expository passages, regardless of how famil-
iar the learners were with the content of the passages presented. This finding
suggests that when learning from text, learners will learn better from narratives
than non-narratives. In a later experiment, Graesser and colleagues found that in-
formation presented in narrative texts was also better retained than information
presented in expository passages. In a separate study, Graesser, Hoffman, and
Clark (1980) found that of these same passages, the narrative passages were read
significantly faster than the expository passages. Narrative passages are more easi-
ly understood and processed than expository passages, which makes them easier to
read. Although these findings are optimistic for the use of narrative in learning,
Graesser and colleagues’ (1980; 1980) findings were conducted in a laboratory
setting, which limits their generalizability. The information presented to learners

20
was recalled and retained better in narrative passages than in expository passages,
but none of the information presented was particularly relevant to the learners. It is
possible that expository passages might be just as well recalled and retained
should a learner be sufficiently motivated to learn the content. Further, the passag-
es did not contain equivalent information across genres. The narrative genres in-
cluded stories such as “Noah’s Ark” and “Snow White”, whereas expository pas-
sages were about topics including earthquakes, emotions, energy, and animals
(Graesser, Hauft-Smith, et al., 1980). The nature of the content of the passage
could possibly impact how successful that content is recalled and retained. Narra-
tives should be compared to expository texts, holding informational content con-
stant for a more accurate estimate of the effect of text genre on recall.
Results from field research are more limited. For example, Cunningham and
Gall (1990) compared expository texts to narrative texts in classrooms with mid-
dle and high school students using the same instructional content across genres.
Although narratives were more satisfying to students overall, there were no differ-
ences in learning across text genres. This does not provide strong support for the
narrative hypothesis in terms of improving cognitive outcomes outside of a labora-
tory setting, but more research is needed. Regardless, there appears to be broad
value for the use of narrative in learning overall. Norris, Guilbert, Smith, Ha-
kimelahi, and Phillips (2005) identified 20 of 23 experimental studies conducted
comparing narrative to expository texts that found a positive effect of narrative on
learning outcomes.
18.7.1 Applying the Narrative Hypothesis via Game Fiction Game
Elements
Within the taxonomy presented in this text, narrative is considered a type of game
fiction (Landers, 2014). Game fiction describes the nature of both the game world
and the story, each of which may include elements of fantasy (Bedwell et al.,
2012; Garris, Ahlers, & Driskell, 2002). For example, a game would employ a
fantasy game fiction when using images of fictional worlds, wizards, and dragons
to convey game context to the player (i.e., the game world) or when demonstrating
that the player is a warrior on a mission to defeat a dragon in order to save a king-
dom (i.e., the story). The narrative hypothesis suggests that the part of game fic-
tion involving story elements will be useful for impacting learning.
The use of game fiction may impact learning outcomes directly or indirectly.
Cordova and Lepper (1996) used game fiction to teach math skills to elementary
school students. The authors found that learners reacted more positively (i.e., were
more satisfied and experienced more enjoyment) and learned significantly more in
conditions utilizing game fiction than learners in conditions with no game fiction.
This is consistent with finding by Landers and Armstrong (in press) who found

21
that learners anticipate more satisfaction and enjoyment from narrative-gamified
training compared to more traditional training methods. Although research sup-
porting the narrative hypothesis suggests that the effects of game fiction on learn-
ing are direct, the narrative hypothesis itself suggests that game fiction impacts
learning through mediating processes (Adams et al., 2012). Game fiction may im-
pact the motivation to learn, as narrative is often considered a motivating element
in computer games (Adams et al., 2012). Besides motivation, game fiction could
potentially impact other mediating variables such as learning engagement or dis-
traction. If game fiction increases engagement with the instructional content by
making the content more interesting or relevant to the learner, learning outcomes
should improve. However, if game fiction increases distraction, learning outcomes
might be worse than learning contexts without game fiction. Adding game fiction
to a learning context increases the amount of irrelevant information presented,
which may detract focus from the content itself (Adams et al., 2012).
A simple way of adding game fiction to a learning context is by creating a nar-
rative. Existing instructional content can be converted into a simple story. Thorn-
dyke (1977) provides ten rules that simple stories must follow, which together
create an outline structure for simple stories. First, the setting is established, con-
sisting of characters, location, and time. Next, a theme must be presented, repre-
senting some over-arching goal within the story. The plot of the story consists of
multiple episodes where characters experiences events along the way to the the-
matic goal of the story. Instructional content can be embedded at multiple points
throughout the story. Each episode or event characters experience might present
different content. The actions of the characters might represent different methods
or procedures for completing a process, like a skill-based learning outcome
(Kraiger et al., 1993). This process of embedding content within a story in an en-
gaging way is likely to depend heavily upon writing skill. Practice or experience
with writing, along with strong instructional design principles are needed to make
such a form of gamification effective. Once the narrative has been introduced, its
impact on an anticipated moderating behavior – such as identification with the
protagonist – can be measured and its effect on learning considered.
18.8 Social Constructivism
Constructivism proposes that all meaning from learning is created by learners, ra-
ther than individually (Kraiger, 2008). This is often stated in contrast to objectivist
learning, which involves an instructor transmitting objective facts, with an implicit
assumption that the learner’s goal is to simply absorb information shared by an
expert. From an objectivistic point of view, facts are objective, it is the instructor’s
responsibility to transmit those facts, and learning objectives are the same for all
learners. In contrast, constructivists view knowledge as something that the learner
creates and builds upon individually. Each learner brings different levels of previ-

22
ous knowledge and different motivations for learning, ultimately developing a
personal understanding of course material. From a constructivist scenario, instruc-
tors facilitate (rather than deliver) learning. Ultimately it is the learner who must
choose to build and create meaning from the content.
Constructivism can be broadly divided into two major perspectives: cognitive
and social. Cognitive constructivists believe that the individual is the key, that
each learner constructs their own personal reality using the information they have
learned. In contrast, social constructivists believe that individuals create meaning
from each other, instructors, and their various interactions. Thus, the goal of social
constructivists is to create environments in which learning can be achieved in sev-
eral ways: through learner-content interactions, learner-instructor interactions, and
learner-learner interactions. It is important to note that simply enabling social in-
teraction via technology (e.g., the existence of an interactive chat room or forum)
is not enough: social interaction first requires a psychological environment where
there is group trust and cohesion, and communication is a norm (Kreijns,
Kirschner, & Jochems, 2003).
Existing evidence for social constructivism is sparse because it is a theoretical
perspective in the creation of course materials versus a specific psychological pro-
cess or theory. Instead, social constructivism is realized through the deployment
of socially-oriented educational interventions, intended to encourage peer learning
through expression (Powell & Kalina, 2009). Through this lens, quite a variety of
evidence is available. For example, Durlak, Weissberg, Dymnicki, Taylor, and
Schellinger (2011) found that social and emotional learning programs improved
knowledge and skill development, attitudes, and behavioral change, in a meta-
analysis of 213 educational programs involving 270,034 students. There is less
clarity on the precise mechanism by which such programs influence these out-
comes, although two possibilities have emerged. First, the effects may be due to
social facilitation. For example, students are more motivated in the context of
self-determination theory (discussed earlier) when their behaviors are socially re-
lated to others. This is supported by evidence suggesting motivational aspects to
social elements of learning (Urdan & Schoenfelder, 2006). Second, the effects
may be more cognitive in nature, because the social context of learning actually
makes it easier to learn material. Specifically, because knowledge is socially situ-
ated, learning it in a social context is cognitively simpler than learning it outside
of a social context and then connecting how it is related later.
18.8.1 Applying Social Constructivism via Human Interaction
Game Elements
According to Table 18.1, the human interaction category encompasses “the degree
to which players interact with other players in both time and space.” A high
amount of human interaction in a gamified learning setting consists of learners

23
collaborating, competing, or simply existing alongside other people, in-person or
through technology-mediated communication. An extremely low (or nil) amount
of human interaction in a gamified learning setting would isolate the learner from
communicating with others. Importantly, human interaction refers to interactions
with people in all roles, whether instructor, peer, team member, mentee, or any-
thing else.
Most common learning approaches have some degree of human interaction al-
ready. For example, in a typical lecture, students are still able to observe their
peers’ learning processes and interactions with the instructor. To determine the
impact of human interaction as a game element category, it is important to distin-
guish between the effects of human interaction and the particular interactions
might bring. An instructor delivering course material increase human interaction
but also delivers course material; these effects should be distinguished carefully
when considering gamification with human interaction.
According to social constructivist learning theory, more human interaction dur-
ing the learning process should lead to increased learning outcomes. One way this
can be achieved by creating an environment of learner interdependence and com-
munication. Learners who communicate in order to accomplish their objectives es-
tablish a communication norm, and are thus more likely to communicate casually
or about other topics (Kreijns, Kirschner, Jochems, 2003). In this context, in-
creased communication is a gamified learning mediator, between the use of human
interaction and learning. Another way to gamify with human interaction and in-
crease learner interdependence is via competition, which combines human interac-
tion with conflict/challenge. For example, collaborative teams might compete
against each other in a presentation or quiz. This tactic is commonly seen in
American classrooms when instructors divide the class into teams to play a Jeop-
ardy-style quiz game in preparation for a test. The winning team is sometimes
awarded extra credit points, early recess, candy, or some other token reward, but
such rewards may not be necessary (see the earlier discussion of goal-setting theo-
ry). Winning may be its own reward. In this context, participation in the learning
game might be the key mediator. If the learning environment is structured such
that team interdependence is a must, then competition between teams should
strengthen the communication networks and cohesiveness of the group (remem-
ber, cohesiveness and communication norms are critical to the success of social
constructivism in technologically-mediated learning environments). Thus, learning
environments should be structured in a way that encourages learner-learner inter-
actions.
18.9 Conclusion
In the preceding sections, we have described the theory of gamified learning and
provided specific psychological theories that support particular game element cat-

24
egories in gamification interventions. However, importantly, little empirical re-
search is available supporting these specific pathways. Instead, we have relied on
existing evidence from related fields to support these links. To actually evaluate
the success of such interventions, it is necessary to conduct a rigorous empirical
study of observed effects, and here we describe an outline for such efforts.
In terms of research design, it is critical to determine the particular game ele-
ments targeted, categorize them in terms of the framework displayed in Table
18.1, and then use an appropriate experimental design to isolate the effects of
these elements. True experimentation, where students within a class are randomly
assigned to experience either the gamification intervention (consisting of the iso-
lated element) or no gamification intervention, is the premiere choice. Such an
approach supports conclusions regarding causal effects of the gamification inter-
vention on both the intermediary behavior and on learning. If experimentation is
not possible, quasi-experimentation is the backup choice. There are many ways to
test gamification quasi-experimentally depending on the precise research context,
and this approach is most common in field settings, among practitioners. If this
approach is required, it is recommended to consult sources on quasi-experimental
design for advice on such approaches (e.g., Shadish, Cook, & Campbell, 2002). In
either case, the intermediary variable, whether mediator or moderator, should be
measured, preferably before learning occurs.
In terms of statistical analysis, there are objectives of interest which require dis-
tinct analysis. To address the practical question of overall impact, the learning
outcome targeted should be regressed on a dummy-coded (i.e., 1 or 0) gamifica-
tion variable. This will produce a regression weight (b), which represents the
amount of change expected in the outcome variable as a result of gamification.
Importantly, this result does not need to be statistically significant for the gamifi-
cation to have been successful. However, if the effect size is small, it suggests
that gamification’s effect given the research design is also small. To determine
actual impact, the specific design must be analyzed appropriately. If testing a me-
diational hypothesis, the bootstrapped confidence interval of the indirect mediat-
ing effect must be calculated. Presently, this is commonly done using either struc-
tural equation modeling or by using an SPSS or SAS macro developed by
Preacher and Hayes (2004). If testing a moderation hypothesis, hierarchical mul-
tiple regression should be used to test the incremental effect of moderation, per the
recommendations of Baron and Kenny (1986). Specifically, a model R2 should
first be calculated from the regression of the learning outcome on both the gamifi-
cation dummy variable and moderator variable. Second, a model R2 should be
calculated from the regression of those variables plus an interaction term, calculat-
ed as the product of the dummy variable and moderator. Third, the change in R2
should be evaluated for significance. A significant change in this R2 indicates that
the moderator term adds value in the prediction of learning outcomes.
Overall, it is only through careful, systematic exploration of specific game ele-
ments and their likely psychological mediators and moderators that we will ever
be able to provide consistent, reliable guidance to those working in the field. In

25
the end, the only value gamification can bring is if learning outcomes, whether re-
actions, knowledge gained, or skills gained, actually increase as a result of such
interventions. If we cannot provide such evidence, there is no point in wasting
time on developing gamification interventions, regardless of how easily imple-
mentable researchers and practitioners can make them. It is only through a body
of thoughtfully-designed and empirically supported evidence that we will deter-
mine if gamification is truly a useful instructional design tool or ultimately noth-
ing more than a fad, and we hope that the present chapter provides researchers and
practitioners with the tools to find out.
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