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. 2022 Dec 31;9(1):e12699. doi: 10.1016/j.heliyon.2022.e12699

Eleven game elements for female nonadaptive gamification courses

Mohammed M Alsofyani 1
PMCID: PMC9826861  PMID: 36632100

Abstract

Based on the HEXAD framework, this study examines the size of female gamification user types and explores universal game elements for developing nonadaptive courses. A HEXAD scale questionnaire completed from 309 female university students. Data analysis was completed using frequency measures and the one-way ANOVA test. The dominant user types and universal game elements related to content, learning activities, and assessment were identified. The study found that game elements of challenge, choice, collection, competition, customisation, guild, leader board, level, lottery, prize, and voting have the highest preference amongst female students according to their gamification user types. When designing courses for female students, designers must focus on the most preferred game elements to improve the engagement of the gamification environment by female students. The study also showed that most female students were philanthropists and disruptors, whereas socializers and achievers were the smallest minority. Philanthropists play for a purpose, and in the context of the study sample, the purpose could be personal need or academic goals. Results were discussed in the light of self-determination theory and personality traits of female university students. Research implications indicate how the findings may be significant for faculties, instructional designers and internal auditing committees in higher education institutions.

Keywords: Online learning, Gamification Hexad user type, Female students, Game elements, Nonadaptive learning

Highlights

  • •

    Explores the preferred game elements for nonadaptive gamification courses by gender.

  • •

    Female participants' responses indicate game elements based on size of gamification user types.

  • •

    Focus on preferred game elements needed to improve use of gamification environment by females.

  • •

    Philanthropist and Disruptors user types' size are the biggest while Socializer and achiever are the smallest.

  • •

    The common game elements among female user types are Choice, Customisation and Level.

1. Introduction

Gamification entails extending the use of games elements into new environments, or ‘the use of game mechanics and experience design to digitally engage and motivate people to achieve their goals’ (” [1];” n. d.). Using such strategy make online learning highly effective since the users' motivation is increased [2]; This is achieved by ensuring that users interact in an environment that matches their preferences and characteristics [3].

An adaptive gamification environment allows flexible learning experiences that match students' preferences and types, given that including all game elements in course design will overload the interface. Since the proliferation of gamification, there has been some success in creating an adaptive gamification environment [4]. We also know now that adaptation can be used in different parts of the learning environment [[5], [6], [7]]. However, the merits of adaptive gamified courses are often met with concerns from faculties. The most serious challenge to such courses is faculties’ limited knowledge of pedagogy and technology [8]; [9,10]. Indeed, designing a nonadaptive learning environment does not require high pedagogical and technological knowledge. Furthermore, such nonadaptive environments offer quick solutions to problems if the game elements that match the preferences of most gamification user types are identified. Thus, exploring the size of gamification user types and the game elements relation simultaneously can yield comprehensive design guidelines for nonadaptive courses, and allow effective but simple design for faculties.

Another factor that influences user experiences in the gamification environment is gender [11,12]. Busch et al. [13] argue that designing a gamification environment by considering gender differences is more efficient than using a unified design. Such gender variation is critical for educators to create learning environments that motivate and engage students. For example, female students are known to have a higher preference for, and thus engagement in, challenging tasks than their male counterparts [14,15]. Female students also report greater positive impact when using game elements such as badges [11], incentives [15], challenges, choices, consequences, gifting, lottery, social discovery, and social network [12]. Although these preferences can be related to the Hexad user types identified by the HEXAD framework, there is scant research on the size of each user type among female students.

This study explores the size of gamification user types among female students in higher education to ascertain which game elements are most appropriate for nonadaptive gamified online courses. Specifically, we pose three research questions: What is the size of each gamification user type among female students? Is there a significant size difference for these gamification user types? What are the most appropriate game elements according to the size of the female gamification user type in none adaptive gamification environments?

2. Literature review

In gamification, users' characteristics can be categorised into six user types. At the same time, game elements can be related to the user's type or gender. These elements, if considered in the gamification environment, can accommodate various types of users in a nonadaptive gamification environment. There is a wealth of literature that explores and has identified the relationship between gamification user types, game elements, and gender differences. This study implements a new technique to condense gamification game elements after assessing the size of female students. A useful limited game elements identified in this study can help nonadaptive gamification environment to create engaging learning for female students in higher education.

2.1. Gamification and HEXAD user types

Students in a gamification environment are not identical; they can be categorised into various types. [16]; for example, identifies Hexad user types: philanthropist, socializer, free spirit, achiever, disruptor, and player. Each category has its own preferences which can increase students’ motivation [3].

This Hexad user types is informed by self-determination theory (SDT) elements, purpose element and change element as an intrinsic motivation [16]. Moreover, extrinsic motivational element was not neglected by Marczewski in the Hexad user type with the inclusive of player type.

Marczewski [16] explained the Hexad user types and their relationship with SDT and game mechanics. First, philanthropists are intrinsically motivated by their purpose and willing to be selfless. They are exhibit caretaking, love sharing knowledge, and look for meaning and trade. Second, socializers are intrinsically motivated by relatedness and willing to be connected to others. They seek social discovery and networking. They also like working in teams, prefer moderate social pressure, and have a positive drive. Third, free spirits are intrinsically motivated by a sense of independence and freedom. They like activities that promote creativity, exploration, and branching choices. Fourth, achievers are intrinsically motivated by competence and like to acquire new skills and promote their capabilities. They prefer levels, quests, and rare content. Fifth, disruptors are willing to make radical changes. They like activities that provide development tools, privacy, and chaotic gameplay. Lastly, players are extrinsically motivated by rewards and prefer to receive appreciation. They like to obtain prizes, badges, and certificates. Thus, it can be said that philanthropist is connected to purpose, socializer to relatedness, free spirit to autonomy, achiever to competence and disruptor to change. Player is connected to extrinsic motivation since they are motivated more with external rewards.

Online learning is predicated on very high user motivation given that the dropout rate of students is higher in this environment than in face-to-face learning [17]. Sustaining intrinsic and extrinsic motivation [18] ensures students are engaged in gamification by having their preferences met. SDT supports intrinsic motivation of autonomous, competent, and relatedness elements [19]. Therefore, sustaining such elements will provide engaging gamification environment.

2.2. Game elements and HEXAD user types

In the literature, we find a reference to at least 23 game elements related to gamification user types; these are badge, challenge, choice, collection, competition, consequence, customisation, feedback, gifting, guild, leaderboard, learning, level, lottery, meaning, point, prize, signposting, social discovery, social network, social status, virtual economy, and voting. Some of the game elements are less preferred by most user types such as strategy, social pressures, and anarchy [12]. Condensing these game elements according to the size of gamification user types will facilitate and accerlate the design of nonadaptive gamification courses.

2.3. Game elements, gender differences and personality traits

A gamification environment that is designed to match and align with user preferences is more likely to be used [20,21]. These preferences may vary by gender, and a large corpus of studies supports this belief [11,14,15,22]. For example, female students show a higher preference for game elements such as challenges [14], badges [22], and prizes [15]. Klock et al. [12] show that female students highly prefer eighteen game elements distributed among the Hexad user types (see Table 1). We believe that recognizing the dominant gamification user types and the highest preferred game elements according the Hexad user types of game elements can help us identify the most appropriate game elements for female students in a nonadaptive gamification course.

Table 1.

Game elements and preference according to female user types.

1. Choice Philanthropist Socializer Free Spirit Achiever Disruptor Player 6
2. Customisation Philanthropist Socializer Free Spirit Achiever Disruptor Player 6
3. Level Philanthropist Socializer Free Spirit Achiever Disruptor Player 6
4. Competition Socializer Achiever Disruptor Player 4
5. Guild Philanthropist Socializer Achiever Player 4
6. Challenge Philanthropist Socializer Disruptor 3
7. Collection Philanthropist Achiever Disruptor 3
8. Leaderboard Socializer Achiever Player 3
9. Lottery Philanthropist Socializer Disruptor Player 3
10. Prize Socializer Disruptor Player 3
11. Voting Philanthropist Disruptor Player 3
12. Signposting Socializer Player 2
13. Social status Socializer Player 2
14. Badges Player 1
15. Consequence Socializer 1
16. Feedback Free Spirit 1
17. point Player 1
18. Virtual economy Player 1
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Also, the Big Five personality traits is informed the design of the Hexad user types [23]. The five personality traits are agreeableness, extroversion, openness, neuroticism and extraversion [24]. These traits have their impact on players [25]. For example, Johnson and Gardner reported that (2010) game elements such as mastery is related to agreeableness and autonomy to new experience is related to openness. Also, Yee et al. (2011) mentioned that the preference for working in group is related to extraversion and compete with other players is preferred by neuroticism. Lastly, game elements such as points, badges and levels are preferred by extroversion [26]. This relationship between the personality traits and the Hexad user types can be used to justify the preference of a particular gamification user types by female students. Especially for the traits that female scored higher than male such as agreeableness, extroversion and neuroticism [24].

3. Research methodology

In this study, he population is higher education students in one of the Saudi University. The sample drawn from this population includes female students studying in different disciplines. This sample serves to purpose in this research. First, it is a single gender so it can be examined thoroughly. Second, it is related to higher education in which faculties have more freedom to design their courses.

Convenient sampling technique [27] was chosen, given that the researcher teaches four groups of female students (N = 330). As the researcher was also the instructor of these groups at the time of the study, the students were directly asked to complete a questionnaire for the purpose of academic research. Voluntary students who participated in this study were informed that their answers will only be used for research purposes and no personal data will be collected. The return response from female university students was 309 out of 330. Table 2 shows the sample disciplines. Lastly, although social-desirability bias of female students’ responses could be extraordinary as the gamification user types topic is not a sensitive social issue. Anonymity, confidentiality and voluntary participation were provided in the research design [28].

Table 2.

Sample discipline.

Discipline N
Computer science 104
Business management 90
Biology 4
Design 83
Law 18
Kindergarten 10
Total 309
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3.1. Data collection and analysis

A research instrument called Hexad user types with 24 items distributed among six categories of user types was adopted from the literature of gamification. This instrument designed specifically for the purpose of identifying the gamer types. It was informed by the SDT theory and five personal traits model. Also, it was validated using The Balanced Inventory of Desirable Responding which evaluate respondents' inclination bias to appear socially acceptable in survey research [23]. The reported reliability and the validity of the instrument was adequate. Therefore, it was selected for the study and translated by the researcher. Then, the instrument was validated by two experts who made minor corrections. A Google Form was created to collect the data from the students during the lecturing time. At the beginning of every lecture, the researcher sent the instrument's Google Form URL to students, and they were given 45 min to complete the questionnaire. The reliability of the instrument was measured with Cronbach α using Microsoft Excel, which measured at 0.812 and is considered high [29].

Both descriptive and inferential statistics were used for data analysis. Means and standard deviations were employed to show the average of every user type among female students. Then, a one-way ANOVA test was used to examine the variation between the Hexad user types. Lastly, Tukey honestly significant difference (HSD) test was implemented to identify the deviant gamification user types [30].

4. Results

Few studies explore the most preferred game elements for nonadaptive gamification courses by gender. Although Klock et al. do identify 18 elements most preferred by female user types, they do not discuss the size of gamification user types and game elements. The responses from female participants in this study indicate the size of gamification user types and help us identify the most relevant game elements to the size of gamification user types.

4.1. Size of gamification user types

As Graph 1 shows, most female students are either philanthropists or disruptors. These two categories are related to selflessness, humanitarianism, and creating change. The participants were least likely to be socializers and achievers, which are related to a preference to work with others and learn new skills, respectively. The four user types show moderate, spread-out standard deviation.

Graph 1.

Graph 1

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Highest female user types.

To examine the difference between female gamification user types, an ANOVA test was conducted [31]. All the assumptions of ANOVA test were met. The observations (female gamification types) are independent. Also, the normality assumption for all the observations is normally distribution since a big size sample is used (n = 309). Lastly, the equality of variances is similar since all the samples' size are equal (n = 309). It yielded a p-value of −4.4 (see Table 3), indicating a statistical difference between the average of some user types.

Table 3.

One-way ANOVA test.

Source DF Sum of square Mean square F statistic P-value
Groups (between groups) 5 329.446777 65.889355 57.924975 −4.44089e-16
Error (within groups) 1848 2102.090321 1.137495
Total 1853 2431.537097 1.312216
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To examine which user type is different, the Tukey HSD test was conducted. The Tukey HSD test revealed that the means of the following pairs were significantly different: philanthropist (x1)–socializer (x2), philanthropist (x1)–achiever (x4), disruptor (x5)–socializer (x2), disruptor (x5)–achiever (x4), disruptor (x5)–player (x6), free spirit (3)–achiever (x4), player (x6)–achiever (x4), and socializer (x2)–achiever (x4) (see Table 4).

Table 4.

Tukey test results.

Pair Difference SE Q Lower CI Upper CI Critical mean p-value
x1-x2 0.363269 0.0606730 5.987326 0.118494 0.608044 0.244775 0.000346771
x1-x4 1.216829 0.0606730 20.055527 0.972054 1.461604 0.244775 1.96187e-10
x2-x4 0.853560 0.0606730 14.068202 0.608785 1.098335 0.244775 1.96244e-10
x2-x5 0.400486 0.0606730 6.600729 0.155711 0.645261 0.244775 0.0000480127
x3-x4 1.050162 0.0606730 17.308556 0.805387 1.294937 0.244775 1.96187e-10
x4-x5 1.254046 0.0606730 20.668930 1.009271 1.498821 0.244775 1.96187e-10
x4-x6 0.978156 0.0606730 16.121768 0.733381 1.222931 0.244775 1.96193e-10
x5-x6 0.275890 0.0606730 4.547163 0.0311149 0.520665 0.244775 0.0166788
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The size of disruptors is different from that of players, socializers, and achievers. Furthermore, the size of philanthropists is different from that of socializers and achievers. Philanthropist user type is related to cooperation while socializer user types is related to collaboration. Female students are preferring to cooperate and support others but not collaborate with other by sharing the same goals as the data here show a significance between philanthropist user type and socializer user type. The differences between other user types are shown in Table 2. Interestingly, the size of achievers is different from that of philanthropists, socializers, free spirits, disruptors, and players, and that of socializers is different from that of philanthropists, disruptors. and achievers. Table 5 shows the user types that have no significance difference in their sizes.

Table 5.

User types with no size significance difference.

Philanthropist – Philanthropist – Philanthropist Philanthropist
Free spirit Free spirit Free spirit – Free spirit Free spirit
- Socializer Socializer – Socializer –
Disruptor – Disruptor – – Disruptor
Player Player Player – Player –
– – – Achiever – –
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The smallest students’ size in some user types is significantly different from other user types. For example, the size of the students is significantly different in achievers compared with all other user types (see Table 4). The number of the students in socializer user type is also significantly different from that of philanthropists, disruptors, and achievers (see Table 5). Hence, the size of the students in the socializer and achiever user types are considered significantly lower than that in philanthropists, free spirits, disruptors, and players, as shown previously in Graph 1.

4.2. Size of game elements according to gamification user types

As noted previously [12], list 18 game elements that are preferred by female students. The game elements in Table 6, however, are not fully preferred by all user types. Some, such as badges and points, are preferred only by one user type. Others, such as signposting and social status, are preferred only by two user types. Such game elements which are preferred by one or two user types were eliminated.

Table 6.

Eighteen game elements preferred by female gamification user types.

1. Choice Philanthropist Socializer Free spirit Achiever Disruptor Player 6
2. Customisation Philanthropist Socializer Free spirit Achiever Disruptor Player 6
3. Level Philanthropist Socializer Free spirit Achiever Disruptor Player 6
4. Competition ● Socializer ● Achiever Disruptor Player 4
5. Guild Philanthropist Socializer ● Achiever ● Player 4
6. Lottery Philanthropist Socializer ● ● Disruptor Player 4
7. Challenge Philanthropist Socializer ● ● Disruptor ● 3
8. Collection Philanthropist ● ● Achiever Disruptor ● 3
9. Leaderboard ● Socializer ● Achiever ● Player 3
10. Prize ● Socializer ● ● Disruptor Player 3
11. Voting Philanthropist ● ● ● Disruptor Player 3
12. Signposting ● Socializer ● ● ● Player 2
13. Social status ● Socializer ● ● ● Player 2
14. Badges ● ● ● ● ● Player 1
15. Consequence ● Socializer ● ● ● ● 1
16. Feedback ● ● Free spirit ● ● ● 1
17. Point ● ● ● ● ● Player 1
18. Virtual economy ● ● ● ● ● Player 1
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Table 4 shows most of the students’ responses to the preference of game user types are similarly distributed among game user types. Hence, eliminating some game elements has little negative impact on the effectiveness of shortening the guidelines for designing nonadaptive courses.

Some game elements, such as choice, customisation, and level, are preferred by all types of users [12], whilst others, such as feedback, badges, and points, are preferred only by single user types. These game elements are preferred only the user type “player.” Furthermore, game elements consequence was only preferred by socializers and feedback was preferred only by free spirits. Thus, the list of game elements as shown in Table 7 can be condensed by excluding these five game elements to include only the highest game elements with big size of gamification user types.

Table 7.

Thirteen game elements preferred by more than two user types.

1. Choice Philanthropist Socializer Free spirit Achiever Disruptor Player 6
2. Customisation Philanthropist Socializer Free spirit Achiever Disruptor Player 6
3. Level Philanthropist Socializer Free spirit Achiever Disruptor Player 6
4. Competition ● Socializer ● Achiever Disruptor Player 4
5. Guild Philanthropist Socializer ● Achiever ● Player 4
6. Lottery Philanthropist Socializer ● ● Disruptor Player 4
7. Challenge Philanthropist Socializer ● ● Disruptor ● 3
8. Collection Philanthropist ● ● Achiever Disruptor ● 3
9. Leaderboard ● Socializer ● Achiever ● Player 3
10. Prize ● Socializer ● ● Disruptor Player 3
11. Voting Philanthropist ● ● Disruptor Player 3
12. Signposting ● Socializer ● ● ● Player 2
13. Social status ● Socializer ● ● ● Player 2
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Note that both socializer and achiever game types are significantly smaller in size compared with other user types among female students. Furthermore, players are the third least user type in size after socializers and achievers. Thus, game elements that matched the least two user types were also eliminated. Table 7 shows that signposting and social status game activities are preferred only by socializers and players. Eliminating these game elements can condense the list for creating nonadaptive gamified courses and make it flexible for faculties. Thus, both signposting and social status were eliminated. Signposting and social status are only preferred by two user types, including socializers, which are amongst the least in number. Table 8 shows eleven game elements.

Table 8.

Highest game elements preferred by more than two user types.

1. Choice Philanthropist Socializer Free spirit Achiever Disruptor Player 6
2. Customisation Philanthropist Socializer Free spirit Achiever Disruptor Player 6
3. Level Philanthropist Socializer Free spirit Achiever Disruptor Player 6
4. Competition ● Socializer ● Achiever Disruptor Player 4
5. Guild Philanthropist Socializer ● Achiever ● Player 4
6. Lottery Philanthropist Socializer ● ● Disruptor Player 4
7. Challenge Philanthropist Socializer ● ● Disruptor ● 3
8. Collection Philanthropist ● ● Achiever Disruptor ● 3
9. Leaderboard ● Socializer ● Achiever ● Player 3
10. Prize ● Socializer ● ● Disruptor Player 3
11. Voting Philanthropist ● ● ● Disruptor Player 3
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The common game elements among all user types are choice, customisation, and level. It is crucial to use these game elements to design effective nonadaptive gamification courses. Designing guidelines for nonadaptive courses based on such results could also motivate faculties to design their own gamified courses, given the technological challenges faced by them when implementing adaptive learning [32].

5. Discussions

Our result that most female university students are philanthropists and disruptors follows the findings in the literature [12,33]. Further, Table 3 shows that challenge, guild, collection, voting, choice, customisation, level, and lottery are the most preferred elements among the female participants across user types.

The main purpose of using gamification in learning contexts is to help learners experience engaging learning environment [34]. When discussing engagement, it is necessary to mention self-determination theory of learners’ motivation in the learning context and five personality traits. People are intrinsic motivated with three elements as self-determination theory suggests. They are competence, autonomy and relatedness [19]. Also, the five personality types [35] have their impact on students in gamification environment [26]. Results in this study of the eleven highest game elements preferred by female students is informed with self-determination theory and personality traits.

First, students need to have sense of mastery in gamification environments, they prefer to collect elements, progress on levels, and get prizes as appear in this study. These three activities require effort from students to be achieved. Completing learning tasks and awarded with elements to collect, levels to progress or getting prizes is an essential part of mastery and achievement for students. Fulfilling this need is an essential component in self-determination theory. Also, students prefer to be autonomous in gamification environments. They want to have a meaningful choice and decide what, when and how to learn. Three game elements out of eleven in this study can be connected to autonomy. They are customisation, choice and voting. For example, when students in learning environment are able to customize the platform interface and decide which avatar to use, they can feel they have full freedom for the appearance of their platform. Also, when students have multiple choice of learning content, activities, and assessment, female students feel of autonomy is fulfilled. Moreover, students need to have sense of relatedness. It is very important to notice here that SDT element relatedness can be either connected to cooperate or collaborate with others. Female students prefer to cooperate with other and relate to them in gamification environment but not collaborate as mentioned earlier. Four game elements out of eleven preferred by students in this study can be connected to relatedness. They are competition, guild, challenge, and leader board. When design gamification courses considering the proposed eleven game elements, the psychological needs for autonomy, mastery and relatedness will be attained and the engagement of the students with nonadaptive gamification courses will increase [3,19].

Second, the eleven game elements identified in this study for female university students can be justified in the light of the Big Five personality traits as the Hexad user types is informed by these traits too [23]. There is evidence that female scored higher than male in three out of five personality traits. These traits are agreeableness, extroversion and neuroticism [24]. These personality traits can be related to the findings of this study. For example, some of the game elements such as levels and leader boards are related to agreeableness trait [26]; D. [25]. That means woman with agreeableness trait need to notice their levels' progress and leader boards’ ranks as they prefer to work with other. Not only these game elements are showing the progress comparing to others but also collection, prize and challenge game elements are related to agreeableness (D. [25]. On the other hand, there are personality traits have similar scoring between female and males can be connected to the identified eleven game elements. They are extraversion and neuroticism personality traits [24]. Extraversion is connected to level and leader board game elements [26] similar to agreeableness mentioned earlier. Also, competition and guild game elements are related to neuroticism and extraversion personality traits [36]. Other game elements such as choice, customisation (D. [25], lottery [26] and probably voting are related to openness trait.

This strong connection between the identified eleven game elements, self-determination theory and the Big Five personality types show the abilities of these game elements to support the engagement of female students in the gamification environment. This impact would take place as a result of fulfilling the psychological need and the alignment with personality traits of female students.

When reviewing previous research about game elements and the size of gamification user types in female higher education institutions, no relevant studies were noticed. The new findings of this research revealed the size of each gamification user types of female students in higher education. For example, the size of philanthropist and disruptors user types are the biggest among female students. Also, a significance lower size of social and achiever's user types was identified comparing to other user types. Lastly, the identified eleven game elements in this study according to the size of female gamification user types is a significance contribution comparing to the eighteen game elements that was mentioned in the literature [12].

6. Conclusion

This study showed that the game elements of challenge, choice, collection, competition, customisation, guild, leader board, level, lottery, prize, and voting have the highest preference amongst female students, according to their gamification Hexad user types. When designing courses for female students, designers must focus on the highest preferred game elements to improve the engagement of the gamification environment by female students. The study also showed that most female students were philanthropists and disruptors, whereas socializers and achievers were the smallest minority.

Overall, based on the results, the following design guidelines are recommended for targeting a gamified environment to female students. When designing the learning content, five game elements preferred by highest user types should be prioritised: guild/team, collection, voting, choices, level, and lottery. A good practical example when designing educational content for female students is to allow students to participate in competition during content navigation and interaction, with free access choice to content elements (e.g. videos and texts). The content could be divided into levels that allow the participants to collect points, win a lottery (surprise element), and vote during their interactions in the environment. When designing the learning and assessment activities, instructors should consider teamwork and various choices of learning activities that are levelled and challenge students and allow them to collect elements. The results also show that socializers and achievers, which are related to a preference for choices and guild/team elements, are the smallest group among female students. Therefore, faculties may design learning content, activities, and assessment that are not too focussed on choice or guild/team activities.

It was not clear before this study that Choice, Customisation and Level are significant game elements for all the Hexad user types. Also, Signposting and Social status game elements were connected to limited user types. On the other hand, achiever and socializer user types have a significance lower size comparing the other four user types for female students in higher education.

Philanthropists play for a purpose, and in the context of the study sample, the purpose could be personal need or academic goals. Therefore, showing the reasons in online activities such as tutorials, forum, peer assessment, and wikis, can lead to higher engagement and motivation to complete such activities.

This study introduces a new technique to condense the game elements list according to the female size of Hexad gamification user type. Results help to design nonadaptive gamification environment for female students in higher education according to eleven rather than eighteen game elements. It is more efficient for faculties in Saudi Arabia and potentially worldwide to work with few game elements that covers most female user types in their courses.

The practical implication of the findings can be effective to design nonadaptive gamification courses focusing to the most critical eleven game elements reported in this study. The eleven game elements will be useful for instructional designers, faculties, and internal auditing committees in higher education institutions. For instructional designers, they can reduce the numbers of game elements in gamification courses from eighteen game elements to eleven. This procedure will help accelerating course completion. Also, game elements such as choice, customisation and levels should get more emphasis in gamification courses as they preferred by all female gamification user types. For faculties, they can focus on the highest game elements preferred by most user types in their design of nonadaptive gamification courses. Game elements such as choice, customisation levels, guild, lottery, and challenge would be sufficient and easily to be developed in nonadaptive courses. For internal auditing committees, they can include the eleven game elements identified in this study as a required elements when assessing female nonadaptive gamification courses. These implications would improve the implementation of gamification's game elements in nonadaptive courses.

6.1. Limitation

To make the analysis and the reflections of the collected data more accurate and clearer, two limitations related to sample selection and game elements were encountered. First, as the researcher intended to monitor the process of questionnaire's answering to increase the accuracy of responding by female university students, the sampling technique used in is study was a convenient sampling. So, findings do not limit the female size of gamification user type to the mentioned numbers. There might be some variation in size of gamification user types with other female students' samples in higher education. Second, for the eighteen game elements preferred by female students included in this study, we do not limit designers to these game elements. There might be other game elements were not mentioned would be preferred by multiple female gamification user types. The new game elements mentioned in recent gamification studies would be considered during nonadaptive gamification courses design.

6.2. Future research

For the future studies, there might be a need to study male students to examine the size of gamification user types and compare the results to the finding this study. Also, researchers interested in this topic might design gamification blueprints according to the findings of this study, to facilitate and accelerate faculties' design of gamification courses for female students. Lastly, a design and development of nonadaptive gamification course informed by the identified eighteen game elements to explore and examine the female students’ engagement according to their user types.

Funding statement

This work was supported by researchers supporting program at Taif University(TURSP-2020/353).

Data availability statement

Data will be made available on request.

Declaration of interest's statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgments

The authors would like to thank researchers supporting program at Taif University for funding this study (TURSP-2020/353).

Footnotes

Appendix A

Supplementary data related to this article can be found at https://doi.org/10.1016/j.heliyon.2022.e12699.

Appendix A. Supplementary data

The following are the supplementary data related to this article:

Multimedia component 1
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.pdf (38.9KB, pdf)
Multimedia component 2
mmc2.pdf (284.6KB, pdf)
Multimedia component 3
mmc3.docx (14.6KB, docx)

Data Availability Statement

Data will be made available on request.

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