The Bright and Dark Sides of Gamification

  • Fernando R. H. Andrade
  • Riichiro Mizoguchi
  • Seiji Isotani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9684)

Abstract

Everything in life has a bright and a dark side; and gamification is not an exception. Although there is an increasing number of publications discussing the benefits of gamification in learning environments, i.e. looking into the bright side of it, several issues can hinder learning because of gamification. Nevertheless, it seems that only few researchers are discussing the dark side of using gamification in learning environments and how to overcome it. Thus, in this paper, we discuss some of the problems of gamification, namely, addiction, undesired competition, and off-task behavior. Furthermore, to deal with both bright and dark sides of gamification at the same time, we propose a framework for intelligent gamification (FIG) that can offer the necessary infrastructure for ITS to personalize the use of gamification by monitoring risk behavior, exploring how best use game design elements to avoid their overuse and finally supporting “fading” mechanisms that gradually reduces the use of gamification and help students to concentrate on learning and not only on extrinsic motivators.

Keywords

Gamification Intelligent tutoring systems Addiction Framework 

1 Introduction

Everything has a bright side and a dark side like a coin, which has a head and a tail, and Gamification is not an exception. Usually when people find a good thing, they tend to focus only on its bright side. However, they should always be aware of its dark side, to use it appropriately.

In the past few years, Gamification has been drawing attention from different areas, with the promise of increasing users’ engagement, motivation, and promoting changes in behavior [8]. By introducing mechanics and elements from games, several companies and research groups have been trying to increase learners’ performance, communication between different groups of people, and promote better health care and healthy habits [1]. Specifically, in the educational field, several studies have been studying different techniques and benefits of using gamification to raise students’ engagement level and reach the flow state with significant findings [2, 3, 4].

Although several positive effects of using gamification has been found to date, particularly to improve student’s performance and increase engagement [8], researchers and educators are ambivalent about using game like materials in education since they could cause addiction and increase the externalization of behaviors that can hinder learning [5, 6].

This fear should be taken seriously since many recent empirical research reports the benefits of gamification as unexpected side effects, and not as a result of a well-thought-out design [1, 3, 4]. It shows that the gamification implementation techniques are still unconsolidated. Yet, according to two literature reviews on the topic, there are no studies addressing the potential negative effects of gamification in Intelligent Tutoring System (ITS) or any other kind of Virtual Learning Environment (VLE) [1, 7].

Thus, the main goal of this work is to discuss the potential harms of using game elements in an ITS and propose a general framework to use gamification in an intelligent way. Considering positive and negative aspects and suggesting ways to fade the gamification elements to cope with addiction/dependence on gamification.

The remainder of this paper is structured as follows: Sect. 2 describes the related works. Section 3 discusses the dark side of gamification and the proposed framework. Section 4 presents our envisioning application of the framework and how to use it. Section 5 concludes the paper with our final thoughts and the directions towards the validation of our Framework for Intelligent Gamification (FIG).

2 Background: Gamification, Flow and Addiction

Kapp [8] defines gamification as “Using game-based mechanics, aesthetics and game thinking to engage people, motivate action, promote learning, and solve problems”. The definition of the concept changes slightly according to different authors, but the core idea remains the same, that is, gamification as a tool to “increase engagement in some activity using game features, providing enjoyment and fun” [1, 2, 3, 9, 10].

The motivational background of gamification usually relies on the SDT (Self Determination Theory) [11], which considers that a human being has three basic needs: competence, relatedness, and autonomy. Based on the degree of a person’s needs and the kind of activity, he/she can be more or less motivated to perform some activity. According to this theory, the user levels of motivation [11], vary from amotivated (without any motivation to perform the activity) to intrinsically motivated (when the user doesn’t need any external incentive to perform it). Thus, the gamification theory proposes that by introducing game elements in an environment to satisfy some of the user’s needs, it is possible to make the activities more attractive, even if he/she is not intrinsically motivated.

The most common game mechanic applied in educational environments is the reward system based on fast feedback about the students’ performance in the form of points, trophies and badges and the division of the domain content in small units representing game levels [12, 13]. Furthermore, the use of leaderboards is also a common tool to stimulate competition [1, 14].

One of the main goals of using gamification is to keep users in flow. The flow is a state of deep concentration in which the user becomes so engaged in the task that he/she loses self-awareness, and track of time [15]. Also known as optimum experience flow; a highly desired state by game developers, considering that they want to keep the player entertained and engaged as much as possible.

The idea of using gamification in learning environments to put students in a flow state while they are learning is quite attractive to be implemented [2, 16]. On the other hand, a number of studies has been conducted addressing the flow state as a factor associated to game addiction. For example, Sun [17] conducted a research with 234 users, in which they found evidences that associate addiction in mobile games with perceived visibility and flow. Perceived visibility is related to the notion of being noticed by peers and in a position of social presence. Gamification designers also seek to incorporate this characteristic in the systems, by using leaderboards and sharing user achievements, thus fulfilling the relatedness needs of the students according to the SDT. In another study, Jeong and Lee [6] examined whether Big Five personality traits can affect game addiction according to psychological, social, and demographic factors. To do so, the researchers used data from a survey of 789 game users in Korea, seeking associations and the results showed that the neuroticism trait apparently increases game addiction. They also observed that a general self-efficacy affected game addiction in a negative way, whereas game self-efficacy increased the degree of game addiction. Besides that, loneliness enhanced game addiction, while depression showed a negative effect on the addiction. In the context of education, these findings could mean that a student who is confident in his abilities to perform the task is less prone to addiction than a student without confidence, and if the student only has confidence in his game skills, he is more susceptible to addiction.

3 The Dark Side of Gamification

The gamification approach originates in the industry with a strong appeal from marketing and service [9]. In the context of learning, to increase students’ engagement researchers and professionals have been trying to bring flow experience and immersion to VLE. Even though improving learners’ engagement using game elements is a highly attractive idea, contrary to the marketing perspective, the goal is not to make the student loyal to the system, but rather increase his learning.

Therefore, we believe that gamification can be good, as long as it is controlled and monitored. If such measures are not taken, then this could adversely affect the effectiveness of the system and hinder learning. In the following paragraphs, we will present three problems that may appear by adding game elements and mechanics without careful considerations:

Off-Task Behavior: If the gamification system is untied to the educational outcomes, the game features can be a distraction to the user. In this case, even if the user likes to use the system, he will not learn more from it. For example, the introduction of resources that provides relatedness to users, such as chats and forums. These resources are not directly related to the learning experience, allowing to the student to spend time in the system without focusing on learning. Another example are the customization features, those are a very important to promote immersion, but also, allows spend time in the system without learning.

Undesired Competition: Leaderboards are a common resource to promote competition, and sense of competence. Still, it can be harmful for students with low performance and low self-efficacy, since they can feel forced in a competition with their peers, which can negatively affect their sense of competence and result in the reduction of their interest and engagement.

Addiction and Dependence: Based on the literature [6, 17, 18], some game features and sensations like flow can be regarded as addictive factors. Thus, addiction could be a potential problem in gamified environments. Unlike the behavior of alcoholics or gambling addicts, addiction in such environments should not have greater effects such as loss of personal property or family disruption. However, our concern is the kind of dependency created by the game-like experience in education, as the students can resource to “game the system” in order to get rewards or they may not be able to learn without gamification features.

In the first scenario, the student could change the focus from learning the subject to other aspects provided by the system gamification. For instance, earning points to get a higher position in a leaderboard or unlock one exclusive or rare content in the system and gain visibility with his peers. Typically, high positions in ranks or acquisition of virtual goods in a gamified application depends on the progress of the system main objective, but it is not uncommon for students to seek alternative strategies to get their desired results [19]. In the second case, the student creates a dependence of game elements to stay engaged in the system. In other words, the student is only capable to focus on the system and acquire some knowledge if it has game elements or some kind of extrinsic reward for his effort. To identify this condition, the system demands information about the relationship of the student with the game elements.

Since the evolution in the gamification in a well-designed system is highly correlated to the success and the learning outcomes, the gamification overuse may go unnoticed; therefore, a constant monitoring of the interactions between the user, the system and the gamification features is required.

4 Framework for Intelligent Gamification (FIG)

There are few initiatives towards gamification taken by academics aiming at the improvement and the consolidation of gamification. Previous works on gamification have proposed frameworks with different perspectives, but to our knowledge none of these have discussed how to deal with the negative implications of gamification [20, 21]. However, as discussed before it is crucial to deal with both sides of gamification, not only using its potential to increase the engagement, but also controlling this use of gamification to avoid the creation of new problems.

In order to address this, we propose a framework based on the ITS architecture that considers the information required to implement gamification with personalization and can process its impacts on the students and potential harms. Further, we propose a strategy to reduce the participation of overused elements by fading. Thereby, our framework proposes to increase the engagement aligning the gamification strategies to gamer profiles and also to identify and handle misuses resultant from the gamification in learning environments, which, for the best of our knowledge, was not addressed by neither the academic community nor the industry. In Fig. 1, we present the proposed framework and its components, which are explained in the following subsections.
Fig. 1.

Framework for intelligent gamification.

Gamification Layer.

In this work, we are not approaching the domain content gamification, in this sense the gamification in this framework is a layer independent of the pedagogical objectives proposed by the tutor, allowing dynamical customization. Once it interacts with the student in order to satisfy the motivational needs of competence, relatedness and autonomy, but do not change the pedagogical objectives proposed by the learning designer. Currently, most of the studies only use static elements without or with at least few personalization options, however, the game design literature and also the results of empirical studies provide evidences indicating the need to consider user individual preferences [1, 10].

Data Modules

  1. (a)

    Gamification Model. A game element can be considered as a game component, it will behave according to the game mechanic attached to it, and will interact with the user when a game event is triggered due an action taken by him [2]. The gamification model contains all the possible game events that can be triggered in the system and that are controlled and regulated by the Controller Component.

     
  2. (b)
    Student Model. The main goal of gamification is to affect the students’ motivation and behavior. In order to do so by using an intelligent approach, it is necessary to hold enough information in the Student Model. Thus, we propose a student model divided in five small groups of attributes, as presented in Fig. 2 and explained in the subsequent item.
    Fig. 2.

    Student model.

    1. (b.1)

      Knowledge Attributes. This group contains the traditional information of the Student Model in terms of domain knowledge or skills they learn. There are several ways of representing students data regarding the information used by the ITS Tutor Module to make decisions in order to provide a better quality of content and hints. Thus, it is not in the scope of this study to address the way of representing these data. However, it is important to clarify that there is indeed a need for data on the student’s performance, so the knowledge base should be able to provide these data to considerations about improvement or decreasing of student performance.

       
    2. (b.2)

      Psychological Attributes. It contains information about the student’s personality traits and data on mood. As said in the previous sections, several studies shown that the personality traits influence learning and addiction behaviors, in this sense, the information about the students’ personality trait is a useful tool to provide evidences of an undesirable condition.

       
    3. (b.3)

      General behavior Attributes. They are responsible for storing information about the student’s habits not related to learning. Game addiction shares several symptoms and characteristics with different kinds of addictions, so it is necessary to expand the knowledge about the user in order to obtain evidences of a problem cause-effect relation.

       
    4. (b.4)

      Interactions Patterns Attributes. The system logs record the session length, dates, time between tasks, estimated required time to finish that tasks and the information about the interaction with the game elements. Therefore, the interaction patterns attributes contain the analyzes of those information such as mean of interactions during sections, number of tasks performed by section, mean time to solve tasks, frequent subjects, total amount of logins, mean length of the sessions.

       
    5. (b.5)

      Gamer Profile Attributes. In this framework, we are considering that students may have different gaming habits and preferences in order to provide a suitable set of game elements and mechanics.

       
     
  3. (c)

    Interaction Patterns. The interaction patterns contain the representation of an expected behavior in the system. This model represents the observable data such as time to finish contents, number of interactions, and frequency of system use. The interaction patterns also contain the model of expected interactions with the game elements. This model will vary according to the gamer profile approach and the gamification model, since it has to represent the regular interaction pattern for a student in the case of static gamification model, or the standards for a group of students in the case of a gamification model based on different profiles.

     
  4. (d)

    Psychological Patterns. The psychological patterns represent the information that, when matched with the situation of one student, provide evidence that this student may be in a risk group. It can be represented by a set of rules, preset by experts or by a series of factors that can be used by the Reasoner to inference about the student situation.

     

Operational Modules

  1. (a)

    Assessment Component. The assessment component is responsible for collecting the student’s observable and interactive data and update student model.

     
  2. (b)

    Behavior Reasoner. The Behavior Reasoner is the component responsible for analyzing the student’s data in order to identify risk behavior. To perform this task, the component compares the information contained in the Student Model with the standards model in the Interaction Patterns and Psychological Patterns. When it identifies anomalies in the student behavior, the Reasoner may inform a human system administrator, such as a teacher, to take an action or, as we propose in this paper, to inform the situation to the Controller, triggering changes in the gamification layer.

     
  3. (c)

    Controller Component. The Controller is the component is responsible for the settings of the gamification layer, and in order to do so, the controller needs to cross the information contained in the student model, gamification model, and behavior Reasoner component. In a customizable approach, the student would be able to interact with the controller, changing the suggested gamification components or parameters and, at the same time, giving information to the controller, which will change the student’s gamer profile attributes, if needed. When the Reasoner identifies that a user needs to change his interactions with some elements, the controller may act changing the value attributed to that element in order to fade this element for the user interest. Our definition of Fade represents the change in the attributes of the element in order to make it less attractive or difficult to access, like changing its colors or moving it to an area that receives less attention.

     

5 Envisioned Application

5.1 Information Gathering

  • Gamer Profile: To model the gamer profile, there are several player types in game design literature and some new types are proposed considering gamification applications [22, 23]. The game components in the system have to be consistent with the player/user types in the chosen typology. The gamer profile is composed of player type attributes and the values for each of these attributes are updated by the controller according to the interaction patterns to personalize the gamification and fading for that specific player.

  • Psychological Attributes: Two very common tools for data acquisition about personality traits are the Big Five [24] and the MBTI (Mayers Briggs Types Indicator) [25]. However, several researches criticized the use of MBTI as a psychometric instrument. Our model is composed of the personality traits, and can contain other psychological variables that may be used to identify anomalies in the user behavior. For instance, history of mood changes and history of emotions.

  • General Behavior Attributes: The function of this model is to store complementary information about user habits. To this effect, the use of intelligent agents or chatbots is highly recommended. Such agents can also be used to acquire information about mood modifications and other behavioral attributes.

  • Gamification Model: Each gamer profile has a list of adaptation attributes that correspond to the game components that will be available to that specific profile in the interface. Each attribute can receive a value between 0 (inactive) and 1 (fully active). The Gamification Model contains the standards for these values, and changing these attributes affects the standards for the player types.

  • Interaction Patterns: Normal user behavior can be established by experts, pilot running of the system or by the behavior of the majority of the users in the system.

  • Psychological Pattern: The psychological pattern represents the risk group in the system. In this sense, this model has to contemplate the traits, and the associations with other variables that provide evidences of a risk scenario. E.g. One student that has the trait of irresponsibility, but solves a number of tasks above the mean of the other students, in a much shorter time than the required, should be considered as a candidate for change.

5.2 Operation

Initially the student provides information about his gamer profile and personality traits. After that, the Controller consults the gamification model and adapts the interface to the elements recommended for the student. Then, the assessment component starts to log the user’s interactions and the intelligent agent interacts with the student in predetermined intervals to fill the general behavior model. Once the general behavior model is populated, the Reasoner starts to compare the patterns periodically, in order to identify anomalies.

As the Reasoner becomes more knowledgeable about the anomalies in the student interaction patterns, it generates a list of gamification artifacts1 eligible for fading. To maximize the learners growth capabilities, the fading method has been previously used to minimize user’s reliance on the system’s help [26]. When an artifact hits the predetermined threshold, the Reasoner marks it for the fading process. Once the process starts, the system agent makes an intervention signalizing the excess of interactions with that artifact and tracks the user performance and interactions seeking changes in his behavior. This intervention intends to increase his self-awareness and provides the opportunity for self-regulation. However, if after a certain period the behavior remains same, the system starts to fade away the artifact, up to removal, until the number of interactions go back to normal. After that, the artifact is restored to the original state and the agent informs the student to observe his behavior.

To identify the implications of fading on the user performance and how much he depends of gamification to keep motivated, the student is constantly monitored. If during the fading process the student’s performance declines, the agent makes an intervention in order to find out whether this is due to fading the artifact. If the reason for the decline is inherent to the process, it provides evidence with respect to the student’s dependence on gamification. Nevertheless, in both cases, the element is restored to the original state and the agent informs the user about the importance of keeping focus on learning. The artifact is restored so as not to impair their learning. Furthermore, the intervention will reinforce his self-awareness and provide, once more, the opportunity for self-regulation, which we believe could be more meaningful since the user knows that he can be “punished” somehow for his overuse.

6 Concluding Remarks

Most of the time people tend to focus too much on the bright side and overlook the dark side of matters. Similarly, the interest in gamification has been growing; however, no one seems to have shown interest in its dark side (negative effects). In this paper, we identified addiction as the dark side of gamification and addressed the elements used in gamification that related to this phenomenon and how it occurs in gamified environments. Further, we proposed a framework to monitor and fade with the gamification elements to avoid the negative implications of addiction.

Our next steps include providing a detailed addiction model for learning environments and the experimental evaluation of the fading strategy of gamification elements and the impact of this strategy in terms of engagement and performance.

The ITS architecture was chosen because such systems consider student information to make decisions in order to improve learning. However, we believe that the same reasoning can be applied to any VLE with proper dynamics to interact and retain enough information about the student and the environment.

Footnotes

  1. 1.

    A Gamification artifact is defined as a composition of a visual game element, that directly interacts with the user, and the game mechanic, that define how this element will behave.

Notes

Acknowledgments

We thank CNPq and CAPES for supporting this research.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fernando R. H. Andrade
    • 1
  • Riichiro Mizoguchi
    • 2
  • Seiji Isotani
    • 1
  1. 1.ICMCUniversity of São PauloSão CarlosBrazil
  2. 2.Japan Advanced Institute of Science and TechnologyIshikawaJapan

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