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Benefits of collaborative learning are established and gamification methods have been used to motivate students towards achieving course goals in educational settings. However, different users prefer different game elements and rewarding approaches and static gamification approaches can be inefficient. The authors present an evidence-based method and a case study where interaction analysis and k-means clustering are used to create gamification preference profiles. These profiles can be used to create adaptive gamification approaches for online learning or collaborative learning environments, improving on static gamification designs. Furthermore, the authors discuss possibilities for using our approach in collaborative online learning environments.
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Creating Student Interaction Profiles for
Adaptive Collaboration Gamification Design
Antti Knutas, Lappeenranta University of Technology, Lappeenranta, Finland
Jouni Ikonen, Lappeenranta University of Technology, Lappeenranta, Finland
Dario Maggiorini, Università di Milano, Italy
Laura Ripamonti, Università di Milano, Milan, Italy
Jari Porras, Lappeenranta University of Technology, Lappeenranta, Finland
ABSTRACT
Benefits of collaborative learning are established and gamification methods have been used to
motivate students towards achieving course goals in educational settings. However, different
users prefer different game elements and rewarding approaches and static gamification
approaches can be inefficient. We present an evidence-based method and a case study where
interaction analysis and k-means clustering are used to create gamification preference profiles.
These profiles can be used to create adaptive gamification approaches for online learning or
collaborative learning environments, improving on static gamification designs. Furthermore, we
discuss possibilities for using our approach in collaborative online learning environments.
Keywords: Collaborative learning, interaction analysis, profiling, k-means clustering,
gamification, adaptive gamification, software engineering education
INTRODUCTION
Collaborative learning, or the cooperative activity of students working together towards a
specific learning goal with the teacher as a facilitator (Bruffee, 1995; Dillenbourg, 1999b), has
become an increasingly topic important in education (Okamoto, 2004). This collaborative
approach to education has been shown to develop critical thinking, deepen the level of
understanding and increase the shared understanding of the material (Gokhale, 1995; D. W.
Johnson & Johnson, 1999; R. Johnson & Johnson, 1994). Computer-supported collaborative
learning (CSCL) extends and facilitates this cooperation by using electronic communication
tools (Dillenbourg, 1999a). CSCL has several benefits, including wider participation for
knowledge building, and improved student productivity and satisfaction (Resta & Laferrière,
2007).
Computer supported collaboration is also essential in software engineering education, because
working and efficiently collaborating teams is at the basis of software engineering industry
(Coccoli, Stanganelli, & Maresca, 2011). The impact of collaboration has been studied in both
physical classrooms (Alaoutinen, Heikkinen, & Porras, 2012) and in online environments
(Dewiyanti, Brand-Gruwel, Jochems, & Broers, 2007) with positive course outcomes. However,
the people who benefit most from this collaboration do not always interact (Knutas, Ikonen, &
Porras, 2013).
In recent studies it has been shown that students can be guided towards educational goals like
collaboration by using gamification (Glover, 2013), which is the application of game-like
elements to non-game environments (Deterding, Dixon, Khaled, & Nacke, 2011; Groh, 2012).
Approaches that use some elements of gamification have been shown to increase student
collaboration (Moccozet, Tardy, Opprecht, & Léonard, 2013) and the motivation towards
achieving course goals (Sheth, Bell, & Kaiser, 2012) in educational settings.
Although we instinctively recognize than games and fun are tightly related, both concept and
their interrelation are quite slippery to define (Caillois, 1961; Crawford, 1984; Huizinga, 1950;
Juul, 2003; Rollings & Adams, 2003; Salen & Zimmerman, 2005). The investigation of these
issues has led neuroscientists and cognitive psychologists to examine how playing a game and
learning are connected (S. Johnson, 2004; Miller, 1956). The basic observation is that humans
have always used games as playgrounds for learning and exercising safely specific skills. During
this process, human brain secretes endorphins (which makes a game an enthralling and fun
activity), is highly focused on recognizing recurring patterns in problems, and on creating
appropriate neural routines to deal with them. Once the pattern is fully caught by the player, the
game becomes boring, but the skill has been accurately acquired. In a certain sense, we could say
that “Fun is the emotional response to learning” (Crawford, 2003) and that the first and main
reason for a (video) game to exist is to provide fun to its players (Koster, 2005), that is achieved
not only through alluring game mechanics, but also by providing an environment that fosters
immersivity (Csikszentmihalyi, 1991; Fullerton, 2008; Salen & Zimmerman, 2004).
Our proposed solution is to use an evidence-based method for deciding which gamification
elements to apply and how to apply them. In this method we build collaborative behavior profiles
for students by using interaction analysis and teamwork profiling surveys. These profiles and the
collected profiles of interactions can be used to model how different students react to
gamification elements and the available goals in order to create or improve adaptive gamification
systems.
In this paper we detail our profiling method and present a case study where we profile the
collaborative behavior patterns of students who participated in a software engineering course.
We also present our plan of how to use these profiles to create custom user-centric gamification
approaches for a gamification system, with an ultimate goal of using them to improve
collaboration in CSCL environments. Specifically, our research questions in this study are:
1. What kind of collaborative interactions are present on a collaborative software engineering
course?
2. Do these interactions have repetitive patterns that can be used for profiling?
3. Which team worker roles and gameplay styles the profiled students prefer?
4. How can these profiles be used to improve gamification systems?
In the next section we review previous approaches to gamification in education. After that we
detail our research setup, methods of analysis and the research results. In discussion we consider
the implication of these results and how these results can be applied to a gamification CSCL
system. The paper finishes with conclusions.
GAMIFICATION APPROACHES IN SOFTWARE ENGINEERING EDUCATION
Gamification, at its core, is the use of game design elements in a non-game environment
(Deterding et al., 2011). However, effective gamification is about using these game elements
along three important principles, in order to satisfy users’ three innate needs for intrinsic
motivation (Groh, 2012), which were originally adapted by Deterding (2011) and Schell (2011)
from Deci and Ryan’s (1985) self-determination theory. The three principles are (Groh, 2012):
Relatedness: The universal need to interact and be connected with others.
Competence: The universal need to be effective and master a problem in a given
environment.
Autonomy: The universal need to control one’s own life.
These elements have been shown to be satisfied by gamification elements by Rigby and Ryan
(2011), where they also demonstrate the connection between the self-determination theory and
gamification.
Gamification is an upcoming method in learning, with ongoing research in its use as
motivation or engagement method (Barata, Gama, Jorge, & Goncalves, 2013; Glover, 2013;
Herranz et al., 2015; Sheth et al., 2012), for fostering collaboration (Moccozet et al., 2013;
Romero, Usart, Ott, & Earp, 2012; Leif Singer & Schneider, 2011; L. Singer & Schneider, 2012)
or engaging by competition (Dubois & Tamburrelli, 2013; Yamawaki, 2013). It has also been
used as an engagement method in the increasingly popular Massive Online Open Courses
(MOOCs) (Romero & Usart, 2013; Tan, 2013). However, the successful application to
gamification methods is not necessarily a straightforward affair, with the application of those
methods sometimes negatively affecting the course (Berkling & Thomas, 2013). The Table 1
summarizes the gamification methods applied in each of the previously mentioned studies.
Table 1. Recent gamification approaches used in education studies
Study
Scope
Methods
Conclusions
Barata et al., 2013.
“Engaging Engineering
Students with
Gamification”
2-year long
study with an
engineering
course
Added experience
points, levels, challenges
and a leaderboard for
progressing through the
course.
Participation and
proactivity increased in
the course. Students
were more engaged.
Berkling and Thomas,
2013. “Gamification of
a Software Engineering
Course and a detailed
analysis of the factors
that lead to it’s failure”
1-year long
study with a
software
engineering
course
Added study paths,
project unlocking, levels,
points and marketplace-
style collaboration.
The game platform
collided with a formal
culture. The students
looked for more strict
structure and did not
always answer to the
gamification-based
challenges.
Dubois and Tamburrelli,
2013. “Understanding
Gamification
Mechanisms for
Software Development”
A software
development
course
Added a competitive
scoring system based on
assignment scores that
was publicized to a
leaderboard.
Results are not
conclusive, but they
suggest that scoring
and leaderboards
increase motivation by
competition.
Moccozet et al., 2013.
“Gamification-based
assessment of group
work”
One year long
bachelor course
Adding rewards for
interaction and sharing
resources: intra-group,
inter-group or at global
class levels. Rewarding
the users for contributing
to the global platform
knowledge.
The system encourages
collaborators to
contribute and interact.
Herranz et al., 2015.
Gamiware: A
Gamification Platform
for Software Process
Improvement
Bachelor level
course in
software
development
and
management
Introducing a role-based
gamification system that
has competition,
cooperation and
challenge based
mechanics.
The introduced
gamification system
increased motivation
and student satisfaction
in a pilot study.
Sheth et al., 2012.
“Increasing Student
Engagement in
Software Engineering
with Gamification”
A software
testing course
Using operant
conditioning by
providing simple
rewards for completing
tasks (experience,
currency and
achievement titles).
Added a MMORPG-like
structure.
Preliminary data shows
indications that
gamification increases
student motivation.
Studies in the field indicate that gamification methods are successful in fostering
collaboration. This applies when some gamification aspects relate to the internal motivational
elements of relatedness, competence or autonomy. This is especially evident in studies by
Moccozet et al. (2013) and Dubois & Tamburrelli (2013), where collaboration was rewarded or
the course participants were able to publish their competence and compare the results to their
peers. Herranz et al. (2015) further propose that when gamifying collaboration it is essential to
have supporting teamwork structures in place in order to have a positive impact on motivation.
The elements of success in these studies are the fact that they connect the users’ achievements to
a meaningful community who share some of the user’s personal goals. Additionally, by
providing variance the approaches in these studies allow the user to retain their personal
autonomy, by allowing the users to choose how exactly to achieve their goal. At the same time
individual elements of gamification have been studied and recent research concludes that simply
applying a single outward aspect of gamification, like badges (Falkner & Falkner, 2014), does
not work and instead gamification has to consider the motivation and goals of the course as a
complete system.
RESEARCH METHOD
In this study we observed 17 students over a five day long intensive format and collaborative
software engineering course. The course had 10 hours of lecturing and 40 to 64 hours (depending
on the student team) of collaborative teamwork around a set task. The topic for the course was to
develop a new mobile or tablet application before Friday’s deadline and the students had no
other courses concurrently. The students spent their time in the same computer classroom, with
each student team sitting at their own table group.
All student interactions that occurred in the classroom were recorded. The video and audio
recordings were combined into multi-angle and surround sound videos that allowed the
researchers reviewing the video to hear several concurrent interactions. This resulted in 40 hours
of video, from which 3366 interactions were recorded for analysis. To gain additional
information about preferred teamwork roles, the students were interviewed and were asked to fill
surveys about their teamwork methods. All 17 students participated in the interviews and 15
students participated in the surveys.
Analysis Process
The main source of data for statistical and network analysis was interaction analysis based on the
classroom interactions. The problem of analyzing classroom interactions into quantitative data
has been approached with interaction analysis and Social Network Analysis (SNA). While SNA
is concerned about who communicates with whom (Otte & Rousseau, 2002), interaction analysis
inspects how people communicate with each other (Jordan & Henderson, 1995). More
specifically, interaction analysis is an interdisciplinary method for investigation of interactions of
human beings (Jordan & Henderson, 1995). Interaction analysis has also been used to examine
student collaboration behavior by identifying common interaction patterns with k-means
clustering and correlation analysis (Serçe et al., 2011). Our research approach combines
interaction analysis and social network analysis by first using interaction analysis to gain
information about communication activities and then using social network analysis to
quantitatively model the collected data as a graph, from which further statistics can be generated.
The activities that were detected by observing interactions were classified according to
activity types defined by Vivian et al. (2013). In their study they also introduced an approach for
coding collaborative interactions in CSCL using team collaboration analysis roles originally
defined by Dickinson and McIntyre (1997). To summarize, these interaction types are: Team
leadership, which involves providing direction, structure and support for other team members.
Team orientation, which refers to attitudes that members have towards one another and the team
task. Social, non-professional communications were included under this category. Monitoring,
which is observing other team members’ performance or activities. Coordination, which
involves process reporting and goal setting. Profession or learning related communications,
which involves the exchange of information in a prescribed manner and by using proper
terminology. Additional behaviors introduced by Vivian et al. (2013) were also used: Seeking,
receiving or giving feedback about performance and seeking help (seeker) or receiving help
(supporter).
The list of analyzed interactions was collated, resulting in a frequency distribution of
interactions by type and by interaction target for each person. These lists give individual
communication profiles between the students, but it is not immediately apparent from these
individual profiles if there are repeating patterns in the student interactions. To find these
patterns k-means clustering was used, which is a statistical analysis method for automatically
partitioning a dataset into a specified amount of groups (Wagstaff, Cardie, Rogers, & Schrödl,
2001).
Finally, in order to gain further insight of which kind of teamwork and gameplay the students
would prefer, two different profiling methods were applied: Belbin’s team work inventory
(2010) and Bartle’s (2004) classification of player types. Belbin’s team role inventory divides the
participants to three major categories (action, people, cerebral) and each of the major categories
into three subcategories. Bartle’s player type classification divides the player types into two
separate axes: Whether the player prefers to act or interact, and whether the player prefers to
interact with other players with the world. Players who act towards other players are called clubs,
players who act towards the world are named diamonds. Players who interact with the world are
named spades and players who interact with other players are hearts. The division of player
types is visualized in the Figure 1, where the player types are divided by the interaction target
and interaction type axes.
Figure 1. The two axes of Bartle player types, adapted to learning environments
Analysis Results
3366 interactions were analyzed based on the student communication interactions, of which
81.79% were internal team interactions and 18.21% were to communications to outside the team
(external). The most common internal interaction type was communication (42.93%) and the
most common external interaction type was team orientation (7.90%).
K-means clustering analysis with Pearson’s correlation coefficient as a distance measure
resulted in four clusters of student profiles that share same communication behaviors. The
average silhouette coefficient for the resulting clusters is 0.64, which means that the data points
group well and are distinct from each other, but not perfectly (Kauffman & Rousseeuw, 1990).
These clusters are detailed in the Table 1, which lists the clusters CL1 to CL4, their members and
the most commonly occurring profiles in these clusters. Individual team members are first
labelled by their group alphabet and then their number within the group. Additionally, each
cluster has been assigned a nickname that characterizes roles the cluster’s profiles tend to take in
collaborative interactions.
Table 2. Student profile clusters
Cluster ID;
(Nodes);
Nickname
Bartle
Profiles
Most Common
Internal Actions
Most Common
External Actions
Members
CL1
(A4, B4)
“Cooperative
workers”
Heart;
Club
Communication
(33.05%);
Team
Orientation
(8.91%);
Coordination
(8.05%)
Communication
(19.54%);
Team
Orientation
(9.48%);
Monitoring
(7.66%)
2
CL2
(A1, A3, C1,
D1, D2)
“Social team
members
Heart;
Club;
Spade
Communication
(32.26%);
Monitoring
(16.04%); Team
Orientation
(13.18%)
Team
Orientation
(11.61%);
Monitoring
(7.37%)
5
CL3
(A2, B3, D5)
Achievement-
oriented
leaders
Diamond;
Club
Communication
(41.74%);
Supporter
(12.26%);
Team Leadership
(11.72%)
Communication
(7.64%);
Team
Orientation
(4.44%)
3
CL4
(B1, B2, C2,
C3, C4, D3,
D4)
“Task-oriented
worker”
Diamond;
Spade
Communication
(54.65%);
Team
Orientation
(10.77%);
Coordination
(7.62%)
Team
Orientation
(6.01%)
7
At a first glance the most common internal and external actions in these profile clusters
appear similar. However, when one looks at the distribution of all tasks, presented in the Figure
2, the specialties and differences in the clusters become more apparent. For example, profiles in
Cluster 2 (“task-oriented workers”) communicate mostly internally and about the task at hand.
Compared to cluster 2, profiles in Cluster 1 (“cooperative workers”) are much more prone to
discuss the task with other teams and monitor what they are doing. While profiles in Cluster 3
(“achievement-oriented leaders”) also engage in professional communication, they are the only
profile type who exhibit significant levels of team leadership or the support type of
communication.
Figure 2. Profile cluster interaction distributions
The communication patterns inside the classroom can also be modelled visually as a graph by
using social network analysis (Bastian, Heymann, & Jacomy, 2009; Scott, 2012), as presented in
the Appendix A. Both the clustering and the graph visualization are based on the same dataset
and the visualization presents the structure and patterns of team communication in a more
interpretable manner. In the visualization each node represents a student and each line
communication between the students. Each node is also tagged with their profile cluster type.
The presented lines, or edges, of communication are unidirectional, with the direction of
communication progressing clockwise. If the communication is bidirectional, there are two edges
connecting the nodes, each circling in a clockwise direction. Each edge is tagged with the most
common type of communication between the connected nodes. For clarity internal team task-
related communication and external task orientation types have been removed from the graph,
because all team members frequently communicated with each other and team orientation does
not relate to the learning or team tasks.
From the social network analysis graph in Appendix A it can be seen that all teams
communicated with each other, with team members communicating about the task at hand or
technical issues, and monitored each other to see possible solutions or how the other teams were
progressing at problem solving. One notable fact is also that the students of cluster type three
(“achievement-oriented leaders”) are distributed evenly, with a maximum of one leader emerging
per team. Another notable feature in the communication patterns is that students do not only seek
help from inside their own team. Several nodes can be seen establishing seeking for help and
support/lead pairs with nodes external to their own teams. This suggests towards the fact that the
right kind of profile types can be encouraged to cooperate and support students outside their
immediate group.
Describing the Detected Profile Clusters
Cluster 1: Cooperative workers
Profiles in Cluster 1 concentrated most on professional communications, which essentially
means that they mostly communicated about software engineering tasks at hand. The difference
to the other worker-type profile cluster, the Cluster 4, is clear: The more collaborative Cluster 1
members identify themselves as social (resource investigator) or coordinator type of team
workers. The second distinctive feature when compared to the other work-oriented profile cluster
is that the profiles of Cluster 1 also cooperated outside their own team and examined
(“monitored”) often what other teams were doing.
Cluster 2: Social team members
The second most distinct cluster is the Cluster 2. These team members identify themselves as
seekers of new information (resource investigator) or team organizers (coordinator). Their
Bartle profile, hearts, describes their activity preferences well. They socialize and watch more
than the more directly productive activities of reporting and organizing. However, each team
needs to stop, reflect and examine their goals and the value of these kind of profiles should not
be underestimated. When considering applicable gamification approaches, these types of profiles
could be motivated to use their social skills more constructively and then report the solutions
forward from other groups to members of the Cluster 3, so that the shared knowledge would
spread further.
Cluster 3: Achievement-oriented leaders
Perhaps the most interesting case are the members of the Cluster 3. These students identify
themselves as people who get things done (implementor), or critical, logical thinkers (monitor
evaluator). However, they exhibited most leadership and supporting actions. They also listened
least to feedback and requested the least amount of help. In short, these people could be
characterized as people who want to get things done and have seized the opportunity to lead
people towards practical goals. Their weakness is getting little input and advice from others. If
there was a reward system for seeking more new information and group feedback, it could be
targeted towards the members of this cluster group, encouraging them to be more inclusive in
decision-making.
Cluster 4: Task-oriented workers
Profiles in Cluster 4 concentrated most on professional communications, which essentially
means that they mostly communicated about software engineering tasks at hand. The difference
to Cluster 1 that the members of the first cluster also collaborated with other team members and
members of the Cluster 4 concentrated almost solely on working with their own team members.
The difference between the cluster teamwork profiles is also clear: The more collaborative
Cluster 1 members identify themselves as social (resource investigator) or coordinator type of
team workers, while the members of the Cluster 4 are more goal-oriented (complete finisher,
implementor). This Cluster 4 is also the largest cluster, containing 41% of the participants. A
possible gamification approach for these profile types would be to encourage reflection and goal
coordination at key points in the project instead of directly proceeding to work.
DISCUSSING THE UTILIZATION POSSIBILITIES OF ADAPTIVE GAMIFICATION
PROFILES IN ONLINE LEARNING ENVIRONMENTS
In the initial research four distinct clusters were detected based on student interaction patterns.
The cluster members were also polled on their teamwork preferences, but these factors were not
taken into account in initial clustering, because the initial sorting is based only on objective
measurements. Despite being based on the students’ subjective views, in many of the cases the
students’ Belbin and Bartle preferences match the observed actions.
Benefits of Adaptive Gamification
Usually in gamification abstract points or achievement levels are used as rewards, but simply
applying points or badges does to make an effective game design (Falkner & Falkner, 2014;
Groh, 2012). Game design can go beyond that. According to Bartle (2004) there are four major
player types that enjoy different activities and also different kinds of rewards in online or
multiplayer games. This leads to the fact that different users enjoy different gamification
approaches. Often games or gamification systems are designed so that each type of player can
find something to interest them. However, a more advanced approach would be to try to profile
the student and then try to present gamification approaches and rewards most suitable to the
student. For example, for an explorer (spade) type of player providing more areas to explore can
be a reward or having other team members benefit from the players’ exploration.
This mirroring of preferences and rewards is of great interest and importance in the
gamification process. The core idea of our research is to borrow approaches, methodologies and
techniques from the field of (video) game design to create - and test an engaging learning
environment, able to foster cooperation among students, to promote positive behaviors and to
impact on their overall learning performances. Beside the “traditional” way to convey teaching,
learning patterns have changed radically (Zyda, 2005): new generations are experiencing new
forms of computer and video game entertainment and this has shaped their preferences and
abilities, while offering an enormous potential for their learning (Prensky, 2006).
The usefulness of games as learning tools is a well-known phenomenon, especially in the first
years of our life (Din & Calao, 2001; Durik & Harackiewicz, 2003; Ritterfeld & Weber, 2006),
and it is demonstrated that games are able to guarantee high learning effectiveness in quite short
time (Koster, 2005; Dario Maggiorini, Previti, Ripamonti, & Trubian, 2013; Laura Anna
Ripamonti & Peraboni, 2010; Squire & Jenkins, 2003; Susi, Johannesson, & Backlund, 2007).
Moreover, games are able to train cognitive functions such as memory (Squire & Jenkins,
2003) , and to teach how to exert control over emotions (Michael & Chen, 2005; Mitchell &
Savill-Smith, 2004; Laura A. Ripamonti & Maggiorini, 2011; Wong & Looi, 2011).
Design Plan for an Adaptive Online Learning System
To exploit positive traits of games, we are planning to combine in different ways the building
blocks used by game designers (summarized in Table 3, and by Fullerton (2008)) to design and
deploy one or more “gamified” learning environment(s) for students. The learning
environment(s) will be built according to the following guidelines:
The “game” should be alluring for different types of Bartle’s player at the same time.
We must take care that the “pattern” learned by the students-players will enforce their
willingness to collaborate and their teamwork skills, independently from their Bartle
type.
Table 3. Building blocks of games
Formal elements
Dramatic elements
Other relevant elements
Players (interaction)
Objectives
Procedures
Rules
Resources
Conflict
Boundaries
Outcomes
Challenge
Play
Premise
Character
Story
Dynamics
Feedback
Rewarding
Core mechanics
Immersivity/flow
By considering the Bartle player types and which kind of game elements the defined player types
find most interesting, we can ensure that the gamified collaborative learning environment has
game elements that are attractive to all player types. The profile clusters, the clusters’ detected
Bartle player types and the most attractive game elements to each player type evaluated using
Bartle’s theory (2004) are listed in the Table 4. This table can be used to help in the requirements
design for a gamification system in order to ensure that the design has elements that are attractive
to the profiles and their player types. An even more advanced design would use an adaptive
approach and use interaction analysis or an introduction questionnaire to target game elements to
those player types who find the elements most interesting.
Table 4. Most attractive elements for profile cluster types
Profile cluster
Exhibited Bartle
player types
Most applicable game elements
CL1: “Cooperative
workers”
Heart, club
Formal elements: Player interaction, rules,
conflict, outcomes.
Dramatic elements: Character, challenge, play.
CL2: “Social team
members”
Heart, club,
spade
Formal elements: Player interaction, rules,
resources, boundaries, outcomes.
Dramatic elements: Premise, story, character,
challenge, play.
CL3: “Achievement-
oriented leaders”
Diamond, club
Formal elements: Player interaction, rules,
procedures, conflict, boundaries, outcomes.
Dramatic elements: Challenge, play.
CL4: “Task-oriented
workers”
Diamond, club,
spade
Formal elements: Rules, conflict, resources,
outcomes.
Dramatic elements: Premise, challenge, play.
Adaptive functionality would enable the next step in gamification by allowing the system to
customize and target the game elements to each player type. For example, a social team member
might not be interested in a scoreboard and the possibility to challenge other teams, but friendly
competition would be attractive to an achievement-oriented leader. An adaptive, or a profile-
aware system, could also provide source material for peer matching tools or automatic group
composition (Rubens, Vilenius, & Okamoto, 2009; van Tienen Marijn & Rothkrantz, 2015).
Research into adaptive gamification systems (Codish & Ravid, 2014) is ongoing, with
Monterrat et al. (2015; 2015) presenting a player model and a design for a generic, pluggable
gamification system design for learning environments. They use predefined player types and
present a player adaptation model, where player preferences and elements are evaluated in a
matrix to target gamification elements. Their evaluation model could also be applied to
collaborative profiles we present in this paper, though it would need to be extended to consider
interaction targets further.
Future Research
Once the gamified learning environment is in place, we are planning to run several activities to
collect data in order to dis/prove our thesis. In particular, on one hand we will run experiments
with small groups of real students in the area of Computer Science. As a side effect, this will
offer us the possibility to verify to what extent their cultural background has an impact on the
perception of the playing experience. Moreover, we will develop and test a model mimicking the
relationships among the students in the learning environment. This will be the basis to build a
large-scale simulation to verify the effects on the composition of the student population (in terms
of Bartle’s types) deriving from variations into the structure of the gamified learning
environment. The simulation will be run both from the perspective of achieving a higher degree
of satisfaction for students and from that of providing leverages to the teachers, useful for
affecting students behaviors (see e.g. Maggiorini, Nigro et al. (2012) for similar approaches).
CONCLUSION
In this case study we studied software engineering student collaboration behavior, sorted them
into profile clusters with the k-means algorithm and found common behaviors among them. We
found similarities in the profile clusters to Bartle player types and presented ways of how to
apply gamification approaches for the defined profile clusters. We also discussed adaptive
gamification approaches for a collaborative learning system that would utilize the presented
profiles to target each user with most attractive game elements and goals.
We defined four profile types using interaction analysis data, determined Bartle player types
for them and found interaction patterns that could be improved using targeted gamification
approaches. Existing literature has established adaptive gamification approaches for the
gamification of learning (Monterrat, Lavoué, et al., 2015). With our design we extend the current
state of research by proposing that adaptive gamification can be applied to collaborative learning
using our profiling method. Furthermore, we present a method where new profiles can be created
with interaction analysis and clustering to improve on predefined player types.
These results cannot be generalized yet because of the limited sample size, but the analysis
method itself is generally applicable and be repeated with the steps detailed in this study in order
to create profiles based for other learning environments. Repeating the analysis on more
extensive datasets would allow defining more general profile types. For future research there are
also behavior studies that use similar teamwork roles (Serçe et al., 2011; Vivian et al., 2013),
which could be used to gain additional and comparative material for the created profiles.
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APPENDIX A. COMMUNICATION VISUALIZED AS SOCIAL NETWORK GRAPH
... Learners react to game mechanisms in a variety of ways. Player typologies, which can be utilized to show the diversity of players' expectations and behaviors [46,51,69,72,82]. ...
... The implementation of Gamification did not achieve the expected results because of the disadvantages of the approach "one size fits all," which was common in the gamification methods proposed in the literature [46]. Yamani [96] mentioned that previous studies on gamification highlighted the existence of a problem of an engagement decline over time and a loss of interest in the perceived challenges. ...
... Ferro et al. [27] analyzed the relationship between player types, personality types, and traits. Knutas et al. [46] proposed a method for creating gamification preference profiles, which can be used for adaptive gamification approaches. Butler [11] presented a framework for determining the efficacy of gamification methods by type of personality. ...
Article
The trend of gamification in online education has grown as technological advancements allow for more digitized learning environments to create interactive and engaging learning experiences. Learners are motivated in various ways, which necessitates an understanding of gamification mechanics and dynamics that produce enjoyment to adapt them to a variety of factors such as personality, needs, values, and motivations of each learner. Furthermore, exploration and advancement in the field of artificial intelligence (AI) allow us to provide an intelligent adaptive gamification environment. The aim of this study is to review the existing literature on adaptive gamification in e-learning, as well as to highlight the scoops and future challenges of adaptive gamification applications. The present research followed the literature review method. For the data collected in this study, we used a qualitative approach. This paper presents in the first part a literature review of studies and a synthesis of the literature on the application of adaptive gamification in online education. The second part deals with the use of AI with adaptive gamification in online education and proposes its different techniques and future adaptation.
... In the second category, Denden et al. present three user studies, two based on a feedback after using a non adapted gamified tool [26,27], and one based on a user survey [25] where participants rated statements based on game elements in order to determine their preference. Knutas et al. [81] analysed videos and interviews with learners in a software engineering project to create clusters of learners based on their interactions. These clusters were then linked to Bartle player types and relevant game elements. ...
... Four papers from the same authors [89,[104][105][106] use the term "game features" to present the same level of implementation. Knutas et al. [81,82] use the terms "game like elements". Mora et al. [108] present different gamification "situations" (that combine different game elements). ...
... This is a straight forward way of implementing dynamic adaptive gamification using static adaptation rules. The systems proposed by Knutas et al. [81,82] use an algorithm that also uses learners' profile and interactions. In both systems, they use the Hexad player profile, and in the more recent one [82] they also use learner skills. ...
Thesis
Full-text available
Gamification, the use of game elements in non game contexts, is becoming widely used in the educational field to enhance learner engagement, motivation, and performance. Many current approaches propose systems where learners use the same game elements. However, recent studies show that learners react differently to different game elements, and that learner motivation, engagement, and performance can vary greatly depending on individual characteristics such as personality, game preferences, and motivation for the learning activity. Results indicate that in some cases game elements that are not adapted to learners can at best fail to motivate them, and at worst demotivate them. Therefore, adapting game elements to individual learner preferences is important. This thesis was part of the LudiMoodle project, dedicated to the gamification of learning resources to enhance learner engagement and motivation. In this thesis, I propose a new system that adapts relevant game elements to learners using individual characteristics, as well as learner engagement. This work is based on previous results in the general gamification field, as well as more specific results from gamification in education. Our main goal is to propose a generic adaptation engine model, instantiated with specific adaptation rules for our educational context. This manuscript presents four major contributions: (1) A general adaptation engine architecture that can be implemented to propose relevant game elements for learners, using both a static and dynamic adaptation approach; (2) A design space and design tools that allows the creation of relevant and meaningful game elements, in collaboration with the various actors of the gamification process (designers, teachers, learners etc.); (3) A static adaptation approach that uses a compromise between both learners' player profile (i.e. preferences for games) and their initial motivation for the learning task; (4) A dynamic learner model built on a trace-based approach to propose an adaptation intervention when an abnormal decrease in engagement is detected. The adaptation engine was implemented in a prototype for the LudiMoodle project, that was used by 258 learners in 4 different secondary schools in France for learning mathematics. To build this prototype we ran a real world study, where learners used this tool as a part of their normal mathematics course. From this study, we ran multiple analyses to better understand the factors that influence the motivational variations of the learners, and how their interaction traces could predict their engagement with the learning task. These analyses served to evaluate the impact of the adaptation of game elements on learner motivation and engagement, and to build the trace based model used for dynamic adaptation.This work represents a significant advancement for the adaptive gamification field, through a generic model for static and dynamic adaptation, with the former based on individual learner characteristics, and the latter on observed learner engagement. I also provide tools and recommendations for designers, to help explore different game element designs. Finally, I discuss these findings in terms of research perspectives, notably with regards to further possible advancements in the dynamic adaptation domain. The full text of my thesis can be found the open archive: https://tel.archives-ouvertes.fr/tel-03125624
... Feedback is the most common element in adaptive gamification models [20,23,25,27,28,30,33,34,35] that can contribute to the development of students' metacognition awareness and monitoring of learning. In [20] the authors describe a framework for user-centered gamification where feedback returns relevant information to the user and generates an engagement cycle. ...
... Quality of feedback provided is a concern in other papers. The model by [27] uses the theoretical guideline of providing positive, competence-related feedback. The authors state that feedback should be meaningful and positive, and should make the user feel capable and not perceived as a punishment [27,35]. ...
... The model by [27] uses the theoretical guideline of providing positive, competence-related feedback. The authors state that feedback should be meaningful and positive, and should make the user feel capable and not perceived as a punishment [27,35]. Immediate and positive feedback is also a guideline for adaptive gamification [33]. ...
Conference Paper
Full-text available
Research on gamification effects on students’ engagement and learning, shows that this should be tailored to its users taking personal characteristics, needs and preferences into account (Barata, 2015; Bockle et al, 2017, Mekler et al., 2017, Hallifax, et al., 2019) rather than applying the standard “one size fits all” design (Böckle et al, 2017) to maximize its effects. Despite a growing body of literature in this area, with the use of machine learning algorithm-based automatization to create personalized designs (Knutas, et al., 2019), Böckle et al. (2017) show that there is little systematic analysis and understanding of what makes up effective approaches to gamification, and the need to explore more complex adaptivity methods. While generally positive, the impact of gamified interventions on student participation varies depending on whether the student is motivated intrinsically or extrinsically (Buckley & Boyle, 2014) and self-regulated learning skills may help increase intrinsic motivation. Self-regulated learning strategies of time management, metacognition, critical thinking, and effort regulation were found to have significant positive correlations with academic success in online settings (Broadbent & Poon, 2015) and to improve students’ satisfaction and learning persistence (Joo, Joung & Kim, 2012). These strategies can be taught to students or promoted by learning environments. In this paper, we present the results of a Systematic Literature Review of scientific papers with empirical evidence from adaptive and personalized learning systems for Higher Education. The aim of the review was to understand how adaptive and personalized gamified learning systems can help higher education students develop self-regulated learning strategies. This was achieved by classifying them on a theoretical framework of dimensions and strategies that can promote self-regulated learning as competence to be developed by learners (Zimmerman, 2011; Kizilcec et al., 2017). This review followed the PRISMA guidelines for systematic reviews. Our analysis focused especially on the role of internal feedback, acquired from tasks, and external feedback aimed at learning processes as an important element on the educational outcomes from the interactions between learners and adaptive learning systems
... User-centered techniques have been proposed to correlate game elements and different user profiles [8,9]. Some of these techniques have focused on specific user characteristics such as motivation [10], personality traits [11,12], learning styles [13], player types [4], and types of interaction with different activities [14]. Others combined different characteristics, such as [15] which took into account learning styles and player types to determine the types of educational activities and game elements to include in a learning pathway. ...
... Even if the questionnaire is valid, the answers may be somewhat random or the results at the beginning may not persist during the experience depending on the moment or the mood of the user. In fact, in addition to questionnaires, some proposals in the literature also gathered user feedback on learning activities [14], or scores on different game elements during the experience. Thus, inspired by these works, we enriched the user model obtained from the initial questionnaire by means of user interactions and opinions during the course of the activity. ...
Article
Full-text available
The design of gamified experiences following the one-fits-all approach uses the same game elements for all users participating in the experience. The alternative is adaptive gamification, which considers that users have different playing motivations. Some adaptive approaches use a (static) player profile gathered at the beginning of the experience; thus, the user experience fits this player profile uncovered through the use of a player type questionnaire. This paper presents a dynamic adaptive method which takes players’ profiles as initial information and also considers how these profiles change over time based on users’ interactions and opinions. Then, the users are provided with a personalized experience through the use of game elements that correspond to their dynamic playing profile. We describe a case study in the educational context, a course integrated on Nanomoocs, a massive open online course (MOOC) platform. We also present a preliminary evaluation of the approach by means of a simulator with bots that yields promising results when compared to baseline methods. The bots simulate different types of users, not so much to evaluate the effects of gamification (i.e., the completion rate), but to validate the convergence and validity of our method. The results show that our method achieves a low error considering both situations: when the user accurately (Err = 0.0070) and inaccurately (Err = 0.0243) answers the player type questionnaire.
... Recently, techniques user-centered have been proposed to correlate the game elements to different user profiles [12] [14]. Some of them focused on specific user characteristics, such as motivation [4], personality treats [7] [8], learning styles [3], player types [16], or type of interaction with different activities [15]. Others combined different characteristics, such as [6] that took into account Learning Styles and Player Typesto end up determining the types of educational activities and game elements to include in a learning pathway. ...
... Even if the questionnaire is valid, the answers may be somewhat random or the results at the beginning may not persistent during the experience depending on the moment or the mood the user. In fact, in addition to questionnaires, some proposals in the literature also gathered user feedback of learning activities [15], or scores on different game elements [10] during the experience. Thus, inspired by these works, we enrich the user model obtained from the initial questionnaire by means of user interactions and opinions during the course of the activity. ...
Chapter
Full-text available
Gamification consists in applying game mechanics in non-game contexts aiming at motivating and shaping behaviours. This paper proposes an adaptive approach for gamification, which takes as initial information players profiles – gathered from Hexad player type questionnaire – and considers also how these profiles change over time based on users interactions. Then, we provide the users with a personalised experience through the use of game elements that correspond to their dynamic playing profile. We present a preliminary evaluation of the approach by means of a simulator that yields promising results when comparing it with baseline configurations, i.e randomized and fixed player profile.
... On one hand, recommendations build from theoretical concerns are commonly inspired by definitions behind criteria and game elements' definitions, using those to link one to another (e.g., users with specific player type are more likely to enjoy specific game elements; [3,6,14,15,22,37,57]). On the other hand, data-driven development procedures are mostly based on surveys, asking users to indicate their preferences based on game elements definitions, storyboards, and prototypes (e.g., [5, 10-12, 18, 47, 60, 61]), as well as exploring users' interactions to implicitly identify which are likely to be the best game elements for them (e.g., [4,7,29]). There is also the recommendation by [63], which relies on both perspectives; that is, exploring both user data and theoretical foundations. ...
... In terms of the game elements selection, studies explored games literature [3,15,29], gamification literature [5,7,22,60,63], informal [4, 10-12, 14, 18, 61] and systematic [24,43] literature reviews, and, in other cases, deliberately made self-selections [6,8,36,57]. In addition, there were cases in which the recommendations were to personalize persuasive [45][46][47] and social influence strategies [48][49][50]. ...
Conference Paper
Personalized gamification has gained substantial interest due to the expectation that it can improve gamification's success. Considering some secondary studies on this topic, they lack to present the characteristics of empirical studies and some aspects on how per-sonalization approaches were designed. In this paper, we present a literature review based on previous research to address these gaps. Based on our analysis, our results provide: insights on how experiments to compare personalized gamification and non-personalized gamification are designed and evaluated; evidence on the effectiveness of personalized gamification found in primary studies; and an overview of how personalization approaches were designed. Our analysis converged in possible guidelines and a research agenda revealing five main needs: i) empirical studies comparing one size fits all and personalized gamification; ii) qualitative user studies; iii) personalization approaches that consider contextual characteristics as well as iv) rely on a broader, unambiguous set of game elements; and v) a benchmark of established resources to increase research reproducibility.
Chapter
Gamification is defined as the use of game design elements in non-game contexts. It is noteworthy that a user preference towards a game mechanic and game element is different as an individual. A common approach to satisfy user expectations is to include multiple game elements to accommodate all the user/player types. However, this approach may cause the user interface to be crowded with irrelevant game elements. This research proposes a method for adaptive gamification design with proper mapping of user/player type and game elements. 915 questionnaires (HEXAD user type) were analysed. Using matrix multiplication/matrix product, we can use correlation analysis to generate two primary relationship output: 1) HEXAD user type with game elements, 2) Six HEXAD user types. The game elements are grouped following Self-Determination Theory (SDT); Competence, Relatedness and Autonomy. Rewards are the fourth category, as extrinsic motivation. The fundamental game components that need to be given extra attention during gamification application development are learning, social comparison/pressure, non-linear gameplay and point features. In the meantime, less attention to leaderboard and creativity tools. The adaptive user types and game elements mapping can be used as a clear guideline for the gamification designer to develop an engaging application.
Article
Game-Based Learning (GBL) is increasingly widespread as a learning technique in the engineering studies. However, this innovative methodology may be difficult to incorporate in some subjects due to their complex contents. This paper aims at combining traditional learning methodologies with game mechanics to analyse the academic performance and motivation in engineering studies. Moreover, an ad-hoc gaming web environment was developed to support and control the learning process. Other modern tools such as mobile phone devices were also employed to support and encourage students.A comparative study is presented in this paper, on which 96 first-year engineering students participated. The students were randomly divided into two groups: Control Group (CG) and Experimental Group (EG). The first group made use of a traditional learning methodology, while the second group combined this traditional methodology with GBL and Information and Communication Technologies (ICT). The obtained results show that EG students significantly improve their academic performance and motivation, expressing a succinctly greater interest in the studied subject. Therefore, the integration of GBL in the teaching methodology of complex technical subjects could have a very positive impact, as well as it could greatly ease the teaching-learning process in engineering studies.
Chapter
Full-text available
The current crisis in Europe has raised the need to increase the entre-preneurship orientation of students and adult citizens. At the same time, Massive Online Open Course (MOOC) has appeared as a disruptive innovation that permits to engage a large number of persons in an online open course available through Internet to anyone aiming to enrol. MOOC has been deployed based on basic technologies such text-based materials, video-lectures and forum based interactions. In this study we introduce the design of a MOOC for Entrepre-neurship education that aims to go one step further by integrating the use of Serious Games as a key part of the methodology for teaching and learning entrepreneurship basics in the context of a MOOC.
Article
Full-text available
Well-designed games are good motivators by nature, as they imbue players with clear goals and a sense of reward and fulfillment, thus encouraging them to persist and endure in their quests. Recently, this motivational power has started to be applied to non-game contexts, a practice known as Gamification. This adds gaming elements to non-game processes, motivating users to adopt new behaviors, such as improving their physical condition, working more, or learning something new. This paper describes an experiment in which game-like elements were used to improve the delivery of a Master’s level College course, including scoring, levels, leaderboards, challenges and badges. To assess how gamification impacted the learning experience, we compare the gamified course to its non-gamified version from the previous year, using different performance measures. We also assessed student satisfaction as compared to other regular courses in the same academic context. Results were very encouraging, showing significant increases ranging from lecture attendance to online participation, proactive behaviors and perusing the course reference materials. Moreover, students considered the gamified instance to be more motivating, interesting and easier to learn as compared to other courses. We finalize by discussing the implications of these results on the design of future gamified learning experiences.
Book
I: Background.- 1. An Introduction.- 2. Conceptualizations of Intrinsic Motivation and Self-Determination.- II: Self-Determination Theory.- 3. Cognitive Evaluation Theory: Perceived Causality and Perceived Competence.- 4. Cognitive Evaluation Theory: Interpersonal Communication and Intrapersonal Regulation.- 5. Toward an Organismic Integration Theory: Motivation and Development.- 6. Causality Orientations Theory: Personality Influences on Motivation.- III: Alternative Approaches.- 7. Operant and Attributional Theories.- 8. Information-Processing Theories.- IV: Applications and Implications.- 9. Education.- 10. Psychotherapy.- 11. Work.- 12. Sports.- References.- Author Index.