In recent years, university teaching methods have evolved and almost all higher education institutions use e-learning platforms to deliver courses and learning activities. However, these digital learning environments present significant dropout and low completion rates. This is primarily due to the lack of student motivation and engagement. Gamification which can be defined as the application of game design elements in non-game activities has been used to address the issue of learner distraction and stimulate students’ involvement in the course. However, choosing the right combination of game elements remains a challenge for gamification designers and practitioners due to the lack of proven design approaches, and there is no one-size-fits-all approach that works regardless of the gamification context. Therefore, our study focused on providing a comprehensive overview of the current state of gamification in online learning in higher education that can serve as a resource for gamification practitioners when designing gamified systems. In this paper, we aimed to systematically explore the different game elements and gamification theory that have been used in empirical studies; establish different ways in which these game elements have been combined and provide a review of the state-of-the-art of approaches proposed in the literature for gamifying e-learning systems in higher education. A systematic search of databases was conducted to select articles related to gamification in digital higher education for this review, namely, Scopus and Google Scholar databases. We included studies that consider the definition of gamification as the application of game design elements in non-game activities, designed for online higher education. We excluded papers that use the term of gamification to refer to game-based learning, serious games, games, video games, and those that consider face-to-face learning environments. We found that PBL elements (points, badges, and leaderboards), levels, and feedback and are the most commonly used elements for gamifying e-learning systems in higher education. We also observed the increasing use of deeper elements like challenges and storytelling. Furthermore, we noticed that of 39 primary studies, only nine studies were underpinned by motivational theories, and only two other studies used theoretical gamification frameworks proposed in the literature to build their e-learning systems. Finally, our classification of gamification approaches reveals the trend towards customization and personalization in gamification and highlights the lack of studies on content gamification compared to structural gamification.
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Introduction
In recent years, most universities use e-learning platforms to deliver courses. Teaching in the form of e-learning is a modern supplement, and sometimes even an alternative to traditional education (Górska, 2016). Especially since the last few years, with the spread of the Covid-19 crisis, higher education institutions had to shift from traditional teaching to online teaching as an alternative to resume learners' learning (Sofiadin & Azuddin, 2021). However, over time, these digital environments brought several challenges. On one hand, student motivation decreases, resulting in a lack of engagement and participation in courses. On the other hand, instructors struggle to maintain learners’ attention, leading to the eventual abandonment of online education systems. To solve this problem and create engaging e-learning platforms, the gamification technique was proposed.
Game technologies create opportunities for higher education institutions to redesign and innovate their e-learning models to support learning experiences among learners (Alhammad & Moreno, 2018). The introduction and growing expansion of gamification in education and learning contexts promotes critical reflection on the development of projects that transform students’ learning experiences (Garone & Nesteriuk, 2019). However, is it that simple to create effective gamified e-learning systems especially in the context of higher education?
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Early applied work on gamification of educational settings suggested positive-learning, but mixed results have been obtained (Seaborn & Fels, 2015). While gamification in general learning systems is known to have a positive impact on student motivation, evidence on its effectiveness in higher education settings is mixed and still uncertain due to the complicated environment in the higher education context. First, the level of difficulty of study is higher at the university than at lower levels of education, and students are more aware of the importance of education they have chosen (Urh et al., 2015). Moreover, tertiary education is characterized by the variety of students’ profiles, needs and learning methods; thereby, each game element and even each combination of game elements affects each student differently. Given this diversity of features in the higher education context and the increasing number of inter- and multidisciplinary programs, the process of applying gamification is becoming more complex.
The purpose of this systematic review was to provide a comprehensive overview of the current state of gamification in e-learning in higher education. We focused on identifying how designers currently deal with gamification in the digital higher education context, what game elements they use, how these elements are combined, and what gamification theories are used. In addition, this study sought to find data on existing gamification approaches in the literature, especially those suggested to be applied in digital higher education. Our study differs from previous studies in several ways. In our study, we first wanted to compare our results with previous research’s results that addressed the same research questions in terms of trends in the use of game elements, i.e. whether designers who develop gamified e-learning systems still use classic game elements such as points, badges, and leaderboards, or whether they expand the list of game elements used to include deeper game elements like challenges, storytelling, and so on. We then focused on the underpinning gamification theories used in empirical work, and specifically we sought to understand whether empirical research is beginning to use the various gamification frameworks available in the literature, or whether it is still relying on theories and methods that are highly theoretical and do not provide clear guidance to designers when choosing the right set of game elements (Toda et al., 2020). Also, in our study, we sought to find out how game elements are combined in gamified learning systems in higher education. Previous studies have not fully explored this point, with the exception of the study (Dichev & Dicheva, 2017). Finally, we proposed a classification of gamification approaches proposed in the context of e-learning in higher education based on several relevant criteria.
The remainder of this manuscript has the following structure. "Related works" section, briefly reviews some of the most relevant review papers. "Systematic literature review methodology" section, systematic literature review methodology, presents the approach we followed in conducting our paper retrieval. "Results of the search" section, results of the research, presents responses to our defined research questions. "Discussion and limitations" section is dedicated for discussion of the results; and finally, we conclude.
Related works
Prior reviews
This section briefly reviews some of the relevant literature reviews on gamification in higher education related to the topic of our systematic review. The objective is to be able to compare our findings later in the results section to prior reviews’ findings and to shed a more realistic light on any advances in gamification in e-learning in the context of higher education.
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Dichev and Dicheva (2017) critically reviewed the advancement of educational gamification. This review paper was the only one to address the issue of combining game elements in gamified learning systems. The authors found that in all reviewed works, no justification is given for the selection of particular game elements. The study concluded that there is a need for further studies to improve our understanding of how individual game elements are associated with behavioral and motivational outcomes and how they function in an educational context.
Ozdamli (2018) examined 313 studies on gamification in education. It used content analysis to determine trends in gamification research. The study sought to determine the distribution of empirical research based on a variety of criteria, namely: distribution of studies based on years, number of authors, type of publication, paradigms, research sample, environments, theory/model/strategy, learning area and distribution of game components, mechanics and dynamics. The author found that motivational theories are the most frequently used approach in gamification studies and that the most frequently used game components are goals, rewards and progression sticks.
Khalil et al. (2018) reviewed the state of the art on gamification in MOOCs (Massive Open Online Course) by answering eight research questions. One of these questions sought to identify elements of gamification that have been implemented or proposed for implementation in MOOCs. The study found that the most commonly used elements in the application of gamification in MOOCs are badges, leaderboards, progress, and challenges. According to the study, progress and challenges are used more frequently in MOOCs than points.
The paper (Alhammad & Moreno, 2018) studied gamification in the context of software engineering (SE) education. The study sought to understand how gamification was applied in the SE curriculum and what game elements were used. The study identified four gamification approaches from the primary studies analyzed: papers that implemented gamification by following an existing gamification approach in the literature, papers that adapted psychological and educational theories as gamification approaches, papers that designed and followed their own gamification approach, and finally, papers that did not follow any specific gamification approach. In addition, leaderboards, points and levels were found to be the most frequently used gaming components. Similarly, challenges, feedback, and rewards were the most commonly used mechanics, and progression was the most commonly used dynamic.
Majuri et al. (2018) reviewed 128 empirical research papers in the literature on gamification in education and learning. It was found that points, challenges, badges and leaderboards are the most commonly used gamification affordances in education which are affordances that refer to achievement and progression while social and immersion-oriented affordances are much less common.
In the paper (Zainuddin et al., 2020), the authors addressed a research question related to our research area, namely the underlying theoretical models used in gamification research. It was found that in the studies that implicitly mention their theoretical underpinnings, self-determination theory is the most commonly used, followed by flow theory and goal-setting, while the other studies do not provide any theoretical content.
More recently, van Gaalen et al. (2021) reviewed 44 research studies in the health professions education literature. The study addressed the question of what game attributes are used in gamified environments, and sought to understand the use of theory throughout the gamification process. The study used Landers (2014)’s framework to categorize the identified game elements into game attributes and revealed that in most reviewed studies the game attributes ‘assessment’ and/or ‘conflict/challenge’ were embedded in the learning environment. Regarding the use of theory in gamification processes, most of the identified studies on gamification in health professions education were not theory-based, or theoretical considerations were not included or not yet developed.
Finally, the authors of the paper (Kalogiannakis et al., 2021) performed a systematic literature review on gamification in science education by reviewing 24 empirical research papers. A research question related to our field of study was addressed in this review, namely, what learning theory is used, and what game elements are incorporated into gaming apps. The findings of the studyshowed that most articles did not provide details about the theoretical content or the theory on which they were based. The few articles that used theoretical frameworks were based on self-determination theory SDT, flow theory, goal-setting theory, cognitive theory of multimedia learning and motivation theory. In addition, the study found that the most common game elements and mechanics used in gamified science education environments were competitive setup, leaderboards, points and levels.
Systematic literature review methodology
In this paper of systematic review, we followed a methodology to identify how gamification technique has been used in digital learning environments, specifically in higher education. We sought to identify the game elements that have been used the most, the way they have been combined, and the different frameworks proposed in the literature for gamification of e-learning systems in higher education. A systematic literature review is a means of identifying, evaluating and interpreting all available research relevant to a particular research question, or topic area, or phenomenon of interest (Kitchenham, 2004). Kitchenham (2004) summarizes the stages of a systematic review in three main phases: Planning the Review, Conducting the Review, and Reporting the Review. The first phase ‘Planning the Review’ includes the formulation of research questions, identification of key concepts and constructing the search queries. The second phase ‘Conducting the Review’ consists on study selection based on inclusion and exclusion criteria. Finally, the third phase ‘Reporting the Review’ relates to data extraction and responding to research questions. In the following, we detail the main steps of each phase.
Search strategy
We started by identifying the main goal of this systematic literature review by clearly formulating the following research questions:
(1)
Which game elements and gamification theories are used in gamified learning systems?
(2)
How these game elements are combined?
(3)
Which gamification design approaches are available in the literature?
Then, we constructed a list of key concepts that are: gamification, e-learning and higher education. After that, we identified the alternative terms for each of the key concepts as some authors may refer to the same concept using a different term. For the concept of gamification, we identified this list of free text terms: gamify, game elements, game dynamics, game mechanics, game components, game aesthetics and gameful. For the two other concepts of e-learning and higher education, we identified these terms: education, educational, learning, teaching, course, syllabus, syllabi, curriculum, and curricula.
We formulated two search queries based on the terms identified previously:
(1)
For research questions 1and 2:
(gamif* OR gameful OR “game elements” OR “game mechanics” OR “game dynamics” OR “game components” OR “game aesthetics”) AND (education OR educational OR learning OR teaching OR course OR syllabus OR syllabi OR curriculum OR curricula).
(2)
For research question 3:
(gamif* OR gameful OR “game elements” OR “game mechanics” OR “game dynamics” OR “game components” OR “game aesthetics”) AND (education OR educational OR learning OR teaching OR course OR syllabus OR syllabi OR curriculum OR curricula) AND (framework OR method OR design OR model OR approach OR theory OR strategy).
We conducted our research by searching the databases using the search query formulated previously. We performed our search in the Scopus and Google Scholar databases as the first is one of the most professional indexing databases and the second is the most popular, so it helps to identify further eligible studies. The search was performed in December 2021. Although the Scopus database indexed the publication abstracts, most of the articles were not available through Scopus, and the articles were retrieved from the following publishers:
IEEE,
Springer,
ACM,
JSTOR,
SEMANTIC SCHOLAR,
(Hallifax et al. ) SAGE,
Science Direct.
The exception was some articles that could not be accessed. We also performed a backward snowballing search to identify further relevant studies by scanning and searching the references of papers marked as potentially relevant (Dichev & Dicheva, 2017; Mora et al., 2017; Gari & Radermacher, 2018; Khalil et al., 2018; Ozdamli, 2018; Subhash & Cudney, 2018; da Silva et al., 2019; Hallifax et al., 2019a, 2019b; Legaki & Hamari, 2020; Zainuddin et al., 2020; Saleem et al., 2021; Swacha, 2021; van Gaalen et al., 2021) in search of other relevant studies.
Inclusion and exclusion criteria
In the following table, we summarized the inclusion and exclusion criteria that we considered when we screened full text articles (Table 1).
Table 1
Inclusion and exclusion criteria
Criteria
Inclusion
Exclusion
Subject
Gamification (defined as the using of game elements in a non-game context)
Using gamification to refer to game-based learning, serious games, games, video games
Context
Online learning
Conventional learning
Educational level
Higher education
Other settings different from higher education (e.g., work, medicine, elementary school) or no specification about the educational level
Participants
Undergraduate or graduate students
Professors, managerial levels
Study selection
To select the relevant studies for this systematic review, a manual screening was performed. First, we reviewed the titles and abstracts of different records that were retrieved. Then, citations were imported to Endnote and duplicate records were removed. After that, we read the full text of all retained articles for inclusion and exclusion based on the eligibility criteria. In case of uncertainty, discussion was organized with the research team to reach consensus about the articles in question.
Data extraction
We developed a data extraction form that was refined and discussed until consensus was obtained. The extraction form was then used by the review author to extract data from all included studies. In this part of this paper, we have considered two types of papers: papers representing case studies to extract the game elements used in the developed e-learning systems, the underpinning theories behind the gamification process and the way game elements were combined with each other. The second type of retrieved papers is about framework proposals, from which we could identify models, approaches, and design processes proposed in the literature for gamifying digital learning environments in tertiary education level.
Results of the search
General results
In this literature review, we reported the most extensive overview of the empirical research literature on gamification of e-learning in higher education to date. The selection process of relevant studies is shown in Fig. 1. We analyzed a total of 90 papers to respond to the three research questions formulated previously. First, we retrieved 39 papers in the form of empirical studies carried out at university level and analyzed them to identify what game elements are used, what gamification theories are used to guide the gamification process, and how these game elements are combined. We then identified a variety of 51 papers of type theoretical proposals intended to guide the gamification process. Since higher education is part of general learning systems, we included in this review papers that propose gamification approaches for general contexts and general learning systems. Indeed, we identified 16 papers for general application of gamification, 18 papers for gamifying general learning systems and 17 approaches intended to be applied to e-learning systems in higher education.
Fig. 1
Flow diagram of the articles selection process
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Answering research questions
In following, we will answer the three research questions formulated at the beginning of this article:
RQ1
Which game elements and gamification theories are used in gamified learning systems?
Education applications of gamification refer to using game elements for scholastic development in formal and informal settings (Seaborn & Fels, 2015). In our case, we were interested in collecting relevant experimental studies on gamification of e-learning systems in higher education. In the following table (Table 2), we list and examine 39 experimental studies that have implemented a digital learning system at the higher education level to answer RQ1. For each study, we analyzed the game elements that were incorporated and the gamification approaches that were followed during the gamification process. For ease of reference, the game elements that were used in e-learning systems to improve student engagement and the underpinning theories are summarized in Table 2. More detailed descriptions of the 39 empirical studies are presented in “Appendix”.
Table 2
Experimental studies on gamification of e-learning in higher education
A method based on principles established by the paper (Llorens-Largo et al., 2016) was considered when designing the gamified strategies of the 12 MOOCs, namely: Simplicity, feedback, real time, progress, autonomy, individual responsibility. To analyze and evaluate the gamified platform of the energy sustainability-related MOOCs, the study used E-MIGA theoretical taxonomy: Integrated theoretical gamification model in e-learning environments which is proposed by (Dicheva, Dichev et al. 2015)
Avatars, Points, Badges, Feedback, Challenges, Simplified graphical interface, Dynamic graphical interface, Story (narrative), Epic meaning, Social networks and web services, Visualization of all obligations, Advancements within the e-course, E-course completion status, Synchronous communication chat, Asynchronous communication forum, Nonlinear use of teaching materials, Collaboration, Interactive repetition and assessment, Top listing and ranking of students, Detection of systems and teaching materials, Elements of surprises within the e-module, Conditional access to teaching materials, Countdown of time
A conceptual model for gamifying e-learning courses was developed based on the work of authors like (Schonfeld, 2010), (Deterding et al., 2011a, 2011b; Deterding, 2012), (Werbach & Hunter, 2012) and (Chou, 2015)
3. Facey-Shaw et al. (2020)
Badges
A gameful design based on self-determination theory SDT (Ryan & Deci, 2000) which provides a framework for examining human motivation through its focus on innate psychological needs (competence, autonomy and relatedness)
Avatars, Achievements, Challenges, Behavioral momentum, Productivity, Ownership, Points, Bonuses, Introduction with the information, "Combo" effect × 3, Joint collaboration, Regular rewarding, Status, Epic meaning, Surprise, Advancement, Tasks and challenges, Endless duration of the game, Levels, Loss of aversion, Conscious risk, Optimism, "Addiction"/Commitment to the game
Used gamification mechanics and aesthetics derived from (Nielson, 2017) and (Schonfeld, 2010)
A method combining gamification principles for determining additional educational attainments and the framework (Allen, 2007) to balance the difficulty of levels
Challenges, Freedom to fail, Free to choose, Feedback mechanism, Sense of autonomy, Badges, Content unlocking, Leaderboards, Levels of learning tasks, Competition, Cooperation, Social engagement, Time-based activities
Situated motivational affordance theory (Deterding et al., 2011a, 2011b)
Freedom of choice (creating groups), Meaningful purpose (customizing groups: using avatars for each group), Feedback, visibility of progress and path to destination, Ranking score
Quests, Challenges, Feedback, progress bar, Badges using BadgR.io system, Level up! Plugin that extends the use of:
Experience points
Levels
Leaderboards
Avatars
A conceptual design is proposed to model the learner’s journey using gamification elements, this approach talks about the gamification concepts that are suitable to use for each phase of the learning process so as to meet the education sustainable development needs
By analyzing the game elements listed in Table 2, we noticed that PBL elements (points, badges, and leaderboards), levels, and feedback are the most commonly used elements for gamifying e-learning systems in higher education. This is in line with other reviews’ findings, e.g. (Dichev & Dicheva, 2017).
Furthermore, in response to what (Dichev & Dicheva, 2017) stated about the fact that gamification with “deeper game elements” (Enders, 2013) by incorporating game design principles involving game mechanics and dynamics such as challenges, choice, low-risk failure, role-play or narrative is still scarce, we noted in our systematic literature review that recent studies explore new game elements. Indeed, among the 39 studies analyzed in Table 2, there are 20 primary studies that used “deeper game elements” (Enders, 2013) like challenges and storytelling (narrative). Among these, challenges are the most popular ones.
In Seaborn and Fels (2015), the authors noted that till 2015, the majority of applied research on gamification was not grounded in theory and did not use gamification frameworks in the design of the system under study. Likewise, in this systematic review, by analyzing the 39 empirical studies listed in Table 2, we noticed that most studies were not underpinned by gamification theories. This is in line with the findings of other recent studies, such as van Gaalen et al. (2021) and Kalogiannakis et al. (2021). Indeed, of the 39 primary studies analyzed in our systematic review, only nine papers (Smith, 2017; Kyewski & Krämer, 2018; Pilkington, 2018; Tsay et al., 2018; van Roy & Zaman, 2019; De-Marcos et al., 2020; Facey-Shaw et al., 2020; Sanchez et al., 2020; Dikcius et al., 2021) adapted theoretical approaches and used them as gamification approaches. These are a set of social and motivational theories resumed in a variety of six different theories, namely: self-determination theory-SDT, Social comparison theory, social exchange theory-SET, cognitive evaluation theory-CET, situated motivational affordance theory, theory of gamified learning (Landers, 2014) and user-centered design (Nicholson, 2012). Self-determination theory is considerably the most popular one. These findings are correlated with other reviews’ findings such as Zainuddin et al. (2020) and Kalogiannakis et al. (2021). Only two other primary studies Marín et al. (2019) and Dias (2017) used existing theoretical gamification frameworks to build their gamified e-learning systems. For the remaining papers, some built their owngamification design based on guidelines from the literature whereas others did not cite any theory. Hence, we notice that this distribution is in line with (Alhammad & Moreno, 2018)’s review findings regarding the use of four different categories of gamification approaches in primary studies, namely, papers that followed existing gamification frameworks, papers that adapted motivational theories to their needs, papers that built their own approach, and finally, those that didn’t follow any specific approach. We also noticed that motivational theories are the most frequently used approach, as noted in Ozdamli (2018).
RQ2
How these game elements are combined?
For this research question, we sought to identify how game elements are combined in gamified learning systems in higher education. Previous studies have not fully explored this point except the paper (Dichev & Dicheva, 2017). By analyzing the different empirical studies involved in this systematic literature review (listed in Table 2), we noticed the lack of detailed information about how instructors and designers combined different game elements. Indeed, in all reviewed papers, the authors listed only the game elements employed to gamify their learning systems. In addition, no study provided any justification of the choice made about the sets of game elements to use, nor the way they combined them in the gamified learning systems.
In the reviewed collection, five studies employed one single game element (Coleman, 2018; Garnett & Button, 2018; Kyewski & Krämer, 2018; Facey-Shaw et al., 2020; Dikcius et al., 2021), three other studies gamified systems using two game elements (Fajiculay et al., 2017; Smith, 2017; Donnermann et al., 2021), five other studies used three game elements (Hisham & Sulaiman, 2017; Kasinathan et al., 2018; Romero-Rodriguez et al., 2019; Khaleel et al., 2020; Sanchez et al., 2020) while the remaining ones used more than three elements.
This happens due to the lack of studies that provide clear guidelines and justifications for the combination of game elements (Toda et al., 2020).
RQ3
Which gamification design approaches are available in the literature?
In this section, we will approach RQ3. We first synthesize the current literature on gamification approaches in a general context. Then, we present a set of gamification approaches for general learning systems. Finally, we list a set of approaches proposed specifically for higher education within e-learning environments. We briefly described each approach in the table below (Table 3).
A gamification model for gamified system development called the 6D model refering to its six interrelated steps beginning with the letter D: define business objectives, delineate target behaviors, describe your players, devise activity loops, don't forget the fun, deploy appropriate tools
A gamification framework called Octalysis which is based on eight motivational drivers arranged in an octagonal shape. The elements of the Octalysis model that are in the right part represent are related to intrinsic motivation, as opposed to the elements on the left side, which relate to extrinsic motivation (Bernik, 2021). The elements at the top of the system are considered to be positive motivators that encourage the improvement of knowledge and skills through meaning and various incentives, whereas the elements at the bottom of the system are considered negative motivators that encourage bad emotion and should be minimized when planning and implementing the system (Bernik, 2021)
A framework for intelligent gamification (FIG) structured in three layers: gamification layer, tutor layer and data layer.
It is important to note that this model is not approaching the content side of gamification. In this sense the gamification in this framework is a layer independent of the pedagogical objectives proposed by the tutor. This model is based on the following steps: information gathering, operation, assessment and adaptation
This paper provides a framework called SDT (self-determination theory) for examining human motivation through its focus on innate psychological needs (competence, autonomy and relatedness) and the environments fostering or undermining motivation
This paper presents the MDA framework (standing for Mechanics, Dynamics, and Aesthetics) a formal approach developed and taught as part of the Game Design and Tuning Workshop at the Game Developers Conference, San Jose 2001–2004
A method for analysis and application of gamification as a tool to assist the participation and motivation of people in carrying out various tasks and activities
Guidelines for the design of gamified eLearning (can be also applied for training employees in companies so it remains general) systems, using gaming elements like points, achievements, badges, leaderboards, levels and challenge
The paper proposed a framework for gamification in software engineering. This framework is composed of the ontology, a methodology for guiding the process and a support gamification engine. In a case study a company used the framework to gamify the areas of project management, requirements, management and testing
Gamification approaches for general learning systems
A social gamification framework for K-6 learning platform which applies to students from 6 up to 12 years old (K-6). This framework allows teachers to create challenges tailored to students’ level of knowledge; set up different ways to achieve an objective by creating multiple intermediate goals; provide feedback or immediate feedback that allows progress to a new task; to the proper game mechanics to the activities; consider failure as a part of the learning process; enable students to assume different identities and roles; enable recognition of the students’ progress by peers, teachers and parents; and use competition to promote valuable behaviours
This study proposed a Dynamical Model for structural and content Gamification of Learning (DMGL) after reviewing and comparing different models: Game Design Features (GDF), Key Characteristics of a Learning Game (KCLG), ACRS Model and MDA Framework to define four main factors of the proposed model, namely, challenge, fantasy, control and curiosity. In the DGML model, control is considered as the core characteristic. The relation between other factors is defined by authors: curiosity needs to be higher than the challenge. With time, motivation will be reduced, therefore, the ratio of the Challenge and the fantasy has efficient range and the proportion between them must be maintained in order to take the advantage of the educational effectiveness
This paper provides lessons learned from a broad experience in using games and gamification in learning, and after several years of continuous feedback from students, on how to approach the task of gamification
Proposed a conceptual framework design, based on Web 2.0 technology and gamification. The authors offered a guideline for implementing gamification and Web 2.0 technology in e-learning systems. Their framework includes the phases of analysis, design, development, implementation and evaluation
A design process was proposed which is based on machine learning algorithm and personalized content selection.The process is based on Deterding’s framework for gameful design. The paper states that their novel contribution is demonstrating how both a personalization strategy and an algorithm creation process can be used to augment existing design processes, with the algorithm allowing automating the choice of personalization strategies and tasks. In this framework, there are seven design steps:
An approach was proposed which is a personalized gamification model based on ontologies. Focusing on the online process of the approach, three activities are included in the process, namely, data collection (to capture explicit data that consists of students’ information and implicit data by proposing tests to students: player type test, intelligence type test, level test), data exploration (this sub-process is composed of Knowledge representation, Student profiling and Adaptation Recommender) and data reasoning
A new Gamification Framework for Student Engagement was created and implemented allowing practitioners to systematically apply game attribute(s) to a learning experience to implement gamification for purposeful impact on student engagement outcomes in higher education
An innovative gamification framework, called the NEWTON-enhanced gamification model (N-EGM), which was designed as part of the European Horizon 2020 project NEWTON
A design process for adapting gaming features to learners’ player types based on a player model inspired from existing player typologies (this study used the BrainHex typology) and types of gamification elements. The model functions using the principle of recommender systems, by estimating the preference for a feature by a weighted sum of personality traits. Concretely, The model is based on matrix factorization of the matrix representing users’ profiles and the one representing the way in which gaming features match given player types
A design science framework which includes five iterative stages:
1. Problem definition,
2. Identification of desired outcomes,
3. Gamification design,
4. System development,
5. Evaluation.
This model relies on Malone's theory of intrinsically motivating instruction and defines three categories of motivational drivers that are relevant to learning: challenge, curiosity and fantasy. This framework was implemented in practice to create the GAMESIT environment, a gamified system for information technology training
A framework for the design of a gamified personalized learning environment called PeGam for: Personalized Gamification Design Model. This framework is based on user-centered gamification (Nicholson, 2012), and suggests five conceptual elements to be considered: the purpose of personalization, personalization criteria, personalized game elements, personalized gamified intervention, intervention evaluation
A design method for gamifying learning systems using the Design Sprint method and by instantiating the taxonomy proposed by (Toda, Oliveira et al. 2019)
A dynamic adaptive gamification method which takes players’ profiles as initial information and also considers how these profiles change over time based on users’ interactions and opinions
Gamification approaches for e-learning systems in higher education
A model for introduction of gamification into e-learning environments in higher education that consists of the following main elements: management of e-learning, important factors in e-learning, elements of user experience, phases of development (analysis, planning, development, implementation, and evaluation), game mechanics, game dynamics, gamification elements in e-learning and their effects on students.
This study considers the management of e-learning as an important part of the model. The proposed model considers multiple elements for user experience: project management, user research, usability evaluation, information architecture, user interface design, visual design, interaction design, content strategy, accessibility and web analytics.
A systematic incentive model was proposed for motivating students to learn code readability in software engineering, with the combination of both intrinsic (crowdsourcing) and extrinsic (GDEs: points, badges, leaderboards) motivators. This method was implemented as an online platform GamiCRS for students to learn code readability
A gamification design model was proposed based on aspects derived from five motivation theories, namely, flow theory, goal-setting theory, social comparison theory, self-determination theory and behavior reinforcement theory. This model is called the GAFCC design model to goal-access-feedback-challenge-collaboration. In order to implement the model in practice, the paper recommends following the five-stage gamification design procedure of examine, decide, match, launch, and evaluate.
A framework for the design of personalized gamification services. The framework, called FRAGGLE (FRamework for AGile Gamification of personalized Learning Experiences) is based on the use of the Agile methodologies to obtain a fast design ready for testing and being able to iterate. This framework is structured in four phases:
1. Declaration,
2. Creation,
3. Execution,
4. Learning.
In the declaration phase which serves mainly for information gathering, four main key concepts must be sequentially declared: problems, causes (of the previously identified problems; it can be carried out by the “Five Why” technique), user stories (description of the desired outcomes which consist of objectives) and acceptance tests (expected concrete behaviors in the form of when…, then…).
At the creation stage, the appropriate design components are defined: players, game mechanics, stages (discovery, on-boarding, mid-game, and endgame), actions (description of desired and undesired performances) and triggers (to give response to user actions).
In the execution phase, the learner interacts with the developed system. Information about behavior and user’ interactions must be tracked in order to promote the developed system in the future versions. The learning phase serves to test the effectiveness of the developed activities
An adaptive gamification model was developed to guide and implement adaptivity within e-learning platforms. Its key elements are: The Adaptive gamification engine, Management of the E-learning platform, Adaptive game elements techniques and dynamics and adapted gamified course. This work is based on a previous proposed model in the literature (Urh, Vukovic et al. 2015), with a focus on learner individuality
A gamification approach was developed, called: Horses for Courses based on guidelines offered by prior studies. The study is scenario-based, and four gamification affordances were identified from the literature to be implemented in the Horses for Courses application namely, points, levels, leaderboards, and challenges
A conceptual gamification framework to guide developers in the process of incorporating game elements into LMS systems; The framework consists of three components: game elements or gamification components; learning activities; and student engagement components. Game elements, which influence learning activities, are thus grouped and subsequently mapped to the corresponding activities
A gamification model was proposed to be used in blended learning in higher education, using 2 mechanisms: feedback mechanism (points, badges, rewards) and presentation mechanism (Progress bar, Leaderboard)
An engagement framework for guiding developers when gamifying e-learning systems within the higher education context. This work is based on the previous version (Alsubhi & Sahari, 2020)
A conceptual framework for gamification integration in eLearning systems based on the instructional design (ID) model. The stages of this framework are managerial process, analysis, design, development, implementation, evaluation
A gamification model in the e-learning environment in the Libyan higher education context, presented in terms of ADDIE, these five key elements of the ADDIE model include: Analysis, Design, Development, Implementation, and Evaluation
A gamification framework for higher education to assist institutions in designing a gamified e-learning that supports and enables a sustainable education. The key elements of this framework are teaching and learning principles, technology, applications and security & ethics
A Quality Model for Gamified Software Modeling Learning (example: UML modeling), called ModelGame. It serves as a reference framework intended for higher education institutions teaching software modeling
A conceptual model called eRIOOS intended to higher education for gamifying educational e-courses at higher education institutions. The aim of this research was to standardize the gamification elements that can be used in educational e-courses at higher education institutions
A gamification model for university-level distance learning, where game choice is based on skill type and the learning objectives to be attained. The proposed model is composed of the following steps:
1. Choice of the course to be gamified in the subject
2. Set the parameters of the course
3. Choice of gamification technique
4. Development of the course
5. Roll out
6. Results and validation
7. Lessons learned
In the table above, we investigated a total of 51 gamification approaches in three different contexts. The first set of approaches (the first 16 rows of Table 3) was designed for general use, i.e., for all contexts such as learning, health, marketing and entrepreneurship. While the second set of approaches (the next 18 rows of Table 3) targeted general learning contexts, i.e., without any restriction on educational level. Finally, the third set of approaches (the last 17 rows of Table 3) was intended to be applied in a specific context, namely digital higher education.
Given our review’s main interest in e-learning in higher education, we will classify the last 17 approaches of Table 3, which correspond to those designed for e-learning systems in higher education, into several classes based on different relevant criteria that we will detail below. The paper (Saggah et al., 2020) proposes categorizing gamification design frameworks into three categories: scenario-based, high-level approach, and Gamification elements guidance. Inspired by this categorization, we propose our categorization, which will be used to classify the different gamification approaches in e-learning in higher education. A description of each category is given in what follows, and our classification results are shown in Table 4.
Level of detail
High-level approach This group categorizes papers that provide an overview of the design process that serves as a general high-level guideline containing the global phases without detailing which game elements to use and how to implement them.
Gamification elements guidance This group categorizes papers that provide a conceptualization of the gamification elements that can be used in educational environments. These studies can include implementation guidance.
Scenario based This group categorizes papers that provide a descriptive outline of the design process. In other words, these papers propose gamification approaches by describing their application through real empirical studies experimented in real learning environments.
Type from student perspective (adaptive gamification/one size fits all gamification) Adaptive gamification considers that users have different motivations, so it consists of personalizing learning experiences according to each learner profile. Whereas ‘one size fits all’ gamification uses the same gamified system (gamification elements, rules, etc.) for all learners. For ease of use, we will use ‘A’ character for adaptive approaches and x for ‘one size fits all’ ones.
Table 4
Classification of gamification approaches (context of e-learning in higher education)
Profundity from pedagogical perspective (structural gamification versus content gamification) structural gamification refers to the application of game design elements to motivate the learner through an instructional content without changing it (Garone & Nesteriuk, 2019). It can be made by using clear goals, rewards for achievements, progression system and status, challenge and feedback (Garone & Nesteriuk, 2019). Content gamification is the application of elements, mechanics and game thinking to make the content more game-like (Garone & Nesteriuk, 2019). It is a one-time structure created only for a specific content or learning objectives and hence cannot be reused for any content (Sanal, 2019). Garone and Nesteriuk (2019) states that elements that can be used in content gamification are story and narrative; challenge, curiosity and exploration; characters and avatars; interactivity, feedback and freedom to fail (Kapp, 2014). According to Kapp (2014), the combination of both structural and content gamification, is the most effective way to build high engaging and motivating environments. For ease of use, we will use ‘C’ character for content approaches and x for structural ones.
Validation This group categorizes papers that provided a validation of the proposed approach through empirical evidence showing its application to e-learning systems in higher education.
Table 4 represents the results of our classification of gamification approaches in the context of e-learning in higher education. Regarding the level of detail, we noticed that most of the analyzed approaches (with a number of 9 out of a total of 17) are of the type of gamification elements guidance (Urh et al., 2015; Huang & Hew, 2018; Alsubhi & Sahari, 2020; Kamunya et al., 2020; Winanti et al., 2020; Alsubhi et al., 2021; Júnior & Farias, 2021; Sofiadin & Azuddin, 2021; Yamani, 2021). This number is followed by a number of 5 approaches of type scenario based (Mi et al., 2018; Legaki et al., 2020; Al Ghawail et al., 2021; Bencsik et al., 2021; Fajri et al., 2021), and finally, only 2 approaches are categorized as high-level approaches (Carreño, 2018; de la Peña et al., 2021). It is worth saying that scenario-based approaches are, in most cases, the most difficult to reproduce in other educational environments, as they are very specific, and each environment has its own characteristics. In contrast, high-level approaches are more general and need to be tailored according to the context. Finally, gamification elements guidance approaches can strongly help implement gamified learning environments as they provide a handy catalog of elements that can be injected easily into learning environments.
Furthermore, Table 4 shows that most of the suggested design approaches in the literature are not empirically explored (for example, by using a control and comparing gamified and non-gamified systems). Indeed, of the 17 gamification approaches in the context of e-learning in higher education analyzed, only four approaches have been applied and evaluated by empirical evidence (Huang & Hew, 2018; Alsubhi et al., 2021; de la Peña et al., 2021; Júnior & Farias, 2021). Among those four studies, one work was validated with experts (Alsubhi et al., 2021).
Moreover, Table 4 shows that of the 17 gamification approaches proposed for application to online learning systems in the context of higher education, two approaches (Carreño, 2018; Kamunya et al., 2020) fall into the category of adaptive gamification. This shows the trendy nature of personalization in higher education. Finally, Table 4 shows that the 17 approaches that have been proposed to gamify online learning systems in higher education focus solely on structured gamification, neglecting the content side of online learning systems.
Discussion and limitations
Through this systematic review, we identified several papers on the gamification of e-learning in the higher education context. In recent years, the research on gamification in e-learning has been getting traction, and the number of research articles and systematic reviews of research articles is increasing. As a summary of the existing approaches of gamification in e-learning in higher education, we notice the following points:
Gamification of e-learning in higher education: a trending area of research
The systematic review showed that gamification of learning systems is nowadays a hot topic, and research in this field is growing rapidly as well as for e-learning in higher education context, as it is shown by Fig. 2.
Fig. 2
Number of publications per year
×
Gamification design gaps and tendencies
In general, gamification theory helps in training and shaping participant behavior, however, in our systematic literature review, we observed from RQ1 that the majority of applied research on gamification is not grounded in theory and did not use gamification frameworks in the design of the learning system under study. This highlights the fact that there is a real gap between theoretical and applied work on gamification. One reason may be that existing approaches are very theoretical and cannot strongly assist designers and practitioners when gamifying learning systems, as pointed out by Toda et al. (2020). This also explains our results to the second research question RQ2 regarding the lack of detail on the combination of game elements used in the experimental studies and the motivation behind choosing specific game elements over others.
To better understand this phenomenon and to find a rationale for this lack of using theory and, thus, the lack of logic behind the use of certain game elements over others and their random linking and combination in gamified learning systems, we addressed the research question RQ3. In the latter, we analyzed the gamification approaches available in the literature and classified them into different categories based on a variety of criteria. Our results revealed that the gamification elements guidance approaches that provide taxonomies of game elements that can be incorporated into learning systems constitute the majority of the approaches that have been proposed for application in online learning in higher education. Those did not provide the psychological and behavioral changes that correspond to each game element. Instead, the older gamification theory was based simply on the behavioral outcomes that come from using gamification and the motivational needs behind it and did not provide details on how to implement them or details on what elements to use.
Using appropriate game elements can lead to higher levels of user motivation, whereas inappropriate game elements can demotivate users (Hallifax et al., 2019a, 2019b). Thus, it is essential to choose the right combination of game elements that perfectly matches the desired behavior change. To do this, we must first explore the effect of each game element separately (Dichev & Dicheva, 2017). Thus, further studies are needed to improve our understanding of how individual game elements relate to behavioral and motivational outcomes so that we can identify their contribution in studies that mix multiple game elements (Dichev & Dicheva, 2017). An example of such study was provided in the health domain in the paper (Hervas et al., 2017). The latter proposed a taxonomy of gamification elements used in the domain of health by relating them to psychological fundamentals on behavioral changes, like Self-efficacy, Social influence, and Behavioral momentum. This work can facilitate researchers' empirical validation of gamification theory by building contexts and scenarios from ready-made taxonomies of gamification elements that target a specific behavioral outcome.
On the other hand, through our systematic literature review, we can see from RQ3 the recent emergence of data-driven approaches through machine learning techniques (Knutas et al., 2019; Duggal et al., 2021). These techniques help to create gamification designs suitable for the gamified context, especially when it comes to customizing the game elements to be incorporated into the final gamified system to the students' profiles.
In many learning environments, pedagogy assumes that all learners have homogeneous characteristics (Kamunya et al., 2020). However, Schöbel and Söllner (2016) argue that most gamification projects are not working because they are designed for a group of system users without considering the personal needs of each user. Hence the advantage of personalized training to the learner where all learners differ in preference, style and abilities with regard to the learning processes with or without technology mediation (Naik & Kamat, 2015). In this context, we noted the existence of two gamification approaches designed for online learning in higher education (Carreño, 2018; Kamunya et al., 2020). This is put into practice by tailoring the gamification elements to users' individual preferences. A recent related problem is the lack of adaptation of gamification to the content being gamified.
Another recent and relevant issue is the extreme lack of content gamification. Indeed, the motivational impact of certain game elements varies with the user activity or the domain of gamified systems (Hallifax et al., 2019a, 2019b). Therefore, there is a great need for further exploration and experimentation in this immature area to provide a gamified design to satisfy users’ preferences as well as the task at hand. In other words, personalization in gamification should extend to content, as it does with user profiles, for example, by applying machine learning techniques to tailor the choice of game elements to gamified content.
Another common study design issue illuminated by our review is the lack of validation of the proposed gamification approaches through statistical analyses. In addition, most applied research on the gamification of online learning systems in higher education has not explored the gamification frameworks suggested in the literature.
Conclusion and future work
In this work, we conducted a review of the literature on gamification elements used in digital higher education, the way they are combined, and the different gamification approaches proposed in the literature to gamify learning systems. We analyzed a total of 90 papers to answer the three research questions formulated for this study.
This review identified points, badges, leaderboards, levels, feedback, and challenges as the most commonly used game elements in digital higher education. However, in terms of using gamification theory, our review found that the majority of applied gamification research is not theory-based and has not used gamification frameworks in the design of gamified learning systems. Although some experimental studies attempt to adapt psychological and educational theories available in the literature as gamification approaches, the resulting systems are not very clear, and there is no rationale for choosing certain game elements over others. Consequently, it can be concluded that these gamification approaches cannot strongly assist designers and practitioners in gamifying their learning systems. In addition, theoretical gamification approaches in e-learning in higher education should focus on understanding the effect of each single game design element and the behavioral changes that outcome from its use.
Moreover, based on the results of this review, we can observe the trend towards data-driven approaches through the use of machine learning techniques, especially in adaptive gamification approaches. This involves the adaptation of gamification elements to user profiles. On the other hand, although we have noticed the increasing use of gamification elements that are suitable for content gamification and make the content more game-like, such as storytelling and challenges, there is still a lack of gamification approaches that address content gamification. In fact, this is still an immature research area in gamification design in e-learning in higher education. Future works should pay more attention to the pedagogical side of learning systems and the task under gamification. Apart from that, further research is required to compare theory-driven to data-driven gamification approaches, in terms of which one is the better or perhaps evaluate the effectiveness of a combination of the two, and go so far as to propose a hybrid gamification approach, which does not exist yet and might solve several gamification design issues.
Regarding future work, efforts should focus on building a holistic approach by considering all the aspects that constitute the environment. Among those, personalization according to students’ profiles, gamified subject, educational context, learner’s culture, learner’s preferences, level, playing motivations and experience with games.
Finally, we have seen that most of the design approaches suggested in the literature are not empirically explored. Therefore, statistical analyses and comparative studies should be conducted to draw more robust and generalizable conclusions to validate the existing gamification approaches in the literature.
Acknowledgements
Not applicable.
Declarations
Competing interests
The authors declare that they have no competing interests.
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