Next Article in Journal
Dark Current Noise Correction Method Based on Dark Pixels for LWIR QWIP Detection Systems
Next Article in Special Issue
Evaluating Learner Engagement with Gamification in Online Courses
Previous Article in Journal
Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
Previous Article in Special Issue
Gaming and Resilience: Teaching by Playing Together—Online Educational Competition at School during the Pandemic
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Exploring the Online Gamified Learning Intentions of College Students: A Technology-Learning Behavior Acceptance Model

Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao 999078, China
Student Affairs Department, Foshan University, Foshan 528011, China
School of Applied Science and Civil Engineering, Beijing Institute of Technology, Zhuhai 519088, China
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(24), 12966;
Submission received: 29 November 2022 / Revised: 7 December 2022 / Accepted: 13 December 2022 / Published: 16 December 2022
(This article belongs to the Special Issue Gamification and Data-Driven Approaches in Education)


With the popularity of online education, multiple technology-based educational tools are gradually being introduced into online learning. The role of gamification in online education has been of interest to researchers. Based on learners’ visual, auditory, and kinesthetic (VAK) learning styles, this study uses an empirical research method to investigate the behavioral intention of students to participate in online gamified classrooms in selected universities located in Guangdong province and Macao. The main contributions of this study are to focus on the impact that differences in learning styles may have on the behavioral intentions of learners and to include the “perceived learning task” as an external variable in the theoretical framework. The main research findings are: perceived usefulness and enjoyment are partially mediated between VAK learning styles and the intention to participate in online gamified classrooms; and perceived learning tasks are partially mediated between perceived usefulness and the intention to participate in online gamified classrooms. According to the findings and the Technology Acceptance Model (TAM), this study constructs the Technology-Learning Behavior Acceptance Model (T-LBAM) to explore the intrinsic influencing factors of students’ intention to participate in gamified online classes and makes suggestions for future online gamification teaching.

1. Introduction

With the development of technology, information technology elements are gradually penetrating various fields, including education. Especially in the context of COVID-19, online teaching is being increasingly widely used in higher education settings. As the learning paradigm shifts, the main challenge for students is the change of the learning environment. For example, most students choose to complete their online courses at home, so they need to overcome the distractions of the physical environment, while less classroom interaction and lower learning motivation are also teaching dilemmas that teachers need to face [1,2]. To address the challenges that online education represents for teachers and students, some research has suggested that educators should draw their attention to students’ ongoing motivation to learn [3]. Whether college students engage in online learning and whether they are willing to learn online are the main focus of current research.
In recent years, the forms and platforms of online teaching and learning have diversified step by step, and teachers have been using multimedia technology in the classroom with satisfactory performance. For instance, the use of Kahoot!, an interactive e-learning platform, could increase the enjoyment of online courses, and the friendly competition mechanism presented through its game activities helps to enhance students’ learning motivation and engagement [4]. Dicheva’s study used a mapping analysis to analyze empirical research on the use of gamification in education, which can have a positive learning effect on students [5]. It was shown that affective factors are influenced by personal and environmental sensory experiences, and that online gamified classroom activities can strengthen students’ emotional engagement [6]. Integrating gamification into classroom instruction has become an emerging pedagogical tool. Kasurinen et al. [7] analyzed 1164 gamification studies and found that education was the most common theme among them, and that studying how to improve students’ motivation to learn was a popular research topic in gamification instructional research. Similarly, Dalmina et al. [8] analyzed 70 studies related to the field of gamification, indicating that the concept that gamification could enhance learning motivation is a hot topic and that future research needs to focus on evaluating the effects of gamification instruction. Gamification refers to the inclusion of rules containing game elements or having game characteristics in a non-game context. It can be further categorized as game technology, game practice, and game design, with classic game elements including badges, points, and leaderboards [9]. Furthermore, the concept of game-based instruction not only includes actual teaching practices containing game elements but should also be considered as a concrete manifestation of game-based instruction, where game mechanics are combined with complete curriculum design and post-course assessment tools.
The application of game-based teaching in the offline classroom has received a lot of attention from scholars. Most studies have used empirical research methods to investigate the effects of gamification on students’ classroom performance and motivation, while some studies have used qualitative research interviews to understand teachers’ views on online gamification teaching [10,11]. However, the application of gamification in online education has rarely been considered, and the factors influencing college students’ intention to participate in online gamified learning have not been studied. At the same time, as a new tool for online teaching, gamification intervention has strong technical characteristics. In this study, we attempt to address the following questions: Does the acceptance of college students conform to the characteristics of the Technology Acceptance Model (TAM)? How does the dynamic nature of gamification activities differ from the static technology model? Can gamification participation intention be integrated with TAM model? These are the questions we need to consider deeply. Therefore, in the context of the information technology era, based on the urgent problems in online teaching modes and the characteristics of gamified teaching, this study attempts to explore the behavioral intention of college students to participate in an online gamified classroom through an empirical study. We also discuss the intrinsic influencing factors of students’ willingness to participate in gamified learning from different dimensions.

2. Literature Review

By summarizing the findings and shortcomings of existing studies, this study can purposefully fill the research gaps and propose innovations. For students who already have online learning experience, exploring their behavioral intentions to participate in gamified online classes is largely related to studying their recognition and acceptance of gamified teaching strategies. Therefore, “gamification” and “gamification in online learning” are important topics that are closely related to this study. Since “gamification” has certain technical characteristics, the behavioral intentions of students to accept gamified classrooms can be assessed by referring to relevant studies on the Technology Acceptance Model (TAM). To summarize, we used “gamification”, “Technology Acceptance Model (TAM)”, “TAM in gamification” and “gamification in online learning” as the specific search strings to find relevant studies on the Web of Science, Scopus, IEEE, and Google Scholar search bases.

2.1. Gamification

Game elements in non-game contexts are widely used in various fields, such as marketing, software development, education, and so forth [12]. In marketing, Huotari and Hamari [13] argued that gamification is the process of improving a service by enhancing the user’s gaming experience, and that the game elements chosen are usually in the form of combinations. When the game format is combined with a social interaction type of software, it can attract interest for a short period of time, but there are limitations in maintaining long-lasting interactions between users [14,15]. In the field of education, teachers’ adoption of gamified rewards and interesting scenarios in the classroom can effectively promote interaction, develop students’ teamwork skills, and create a positive learning atmosphere, thus giving students a better learning experience [16]. However, due to differences in gender, personality, prior learning experience, and cultural background, game elements do not always have a positive impact on online education. Therefore, it is necessary to think deeply about how to apply gamification more flexibly to the target audience while taking full advantage of their strengths.

2.2. Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) proposed by Davis [17] is commonly used to analyze the factors that influence behavioral intention when people accept new technologies, which, in turn, provides an analytical framework for predicting the behavior of a target group in terms of using new technologies. Essel [18] used TAM as the foundation theory to investigate college students’ use of a learning management system (LMS) by constructing a structural equation model. The results showed that perceived usefulness was the most important predictor of students’ use of the LMS. On the other hand, Goh [19] used TAM as a theoretical framework to explore students’ perceptions of using LMS in terms of perceived ease of use and usefulness. Two important dimensions of the model are “perceived usefulness” and “perceived ease of use”, which directly influence people’s attitudes toward new technologies and indirectly influence their willingness to use them. Based on different research areas, TAM is often supplemented with additional external variables, such as technology anxiety, self-efficacy, expectations, and trust, among others [20,21,22]. Although the underlying model of TAM is extremely reliable, researchers still need to consider broader environmental factors such as cultural differences and personality habits [23]. Online education is often considered to be a blend of education and technology, and numerous studies have used TAM as a basis for adding additional external variables to explore students’ attitudes toward online learning to further predict students’ behavioral intentions [10,24].

2.3. TAM in Gamification

Since Internet-based gamification elements can be considered a “new technology”, some studies on gamification applications have used TAM as a theoretical foundation to explore participants’ acceptance of gamification [25,26]. Alshammari [27] conducted a controlled experiment with 75 students based on the framework of TAM and found that students who participated in a gamified e-learning system had higher levels of engagement and motivation. When video games are used as learning tools in the classroom, student acceptance thereof should attract the attention of educators. Bourgonjon [28] proposed a path model to predict student acceptance of classroom video games based on the TAM model. It was found that acceptance was influenced by perceived usefulness, perceived ease of use, learning opportunities, and personal experience. It can be seen that TAM is widely used in research on gamified teaching and learning; however, researchers have not adequately considered the distinction between dynamic and static technical models. The external predictor variables based on teaching and learning, as well as gamification characteristics, should be fully considered in the context of actual research questions.

2.4. Gamification in Online Learning

Current research on online gamified education is focused on three perspectives: gamified curriculum development, students’ perspective, and teachers’ perspective. According to existing studies, the impact of gamified instruction on students is mainly focused on two aspects, i.e., learning motivation and classroom engagement [25]. Firstly, the student-centered classroom has been promoted by educators as a place where students can be seen as “customers” and teachers need to tailor the classroom to their students’ characteristics and needs. As a result, teachers can refer to the key points of Customer Relationship Management (CRM) (customer attraction, retention, and development) to improve student engagement in the classroom [29]. In particular, teachers can use game elements to attract students’ attention, guide them to participate in gamified classroom activities, and differentiate instruction according to their characteristics. Tsay et al. [10] designed a gamified curriculum based on self-determination theory (SDT) and conducted research on online gamification with sophomores through an experimental method. The results showed that students who participated in the gamified curriculum had more positive classroom learning engagement, but the facilitation effect of the gamified curriculum on their classroom performance varied due to the different learning experiences, goals, and levels of motivation of the students [10]. Similarly, a study by Hamari et al. [26] also affirmed that gamified classes could increase students’ engagement in online learning but concluded that gamified classroom activities are characterized by assessment difficulties and that different students’ attitudes toward new instructional strategies may have an impact on their classroom performance. While there is no doubt that the integration of game elements with teaching can have positive effects on students, future research will need to include learners from different cultural backgrounds and with different learning styles. Secondly, from the perspective of teachers, Alabbasi [11] conducted in-depth interviews with 47 teachers on the topic of “using gamification technology in online education” and concluded that adding game elements to online courses and online learning management systems will improve students’ concentration and self-directed learning to some extent. However, the competitive nature of the game mechanics may cause learning anxiety, which needs to be considered by educators. Online gamified classroom activities have positive effects on students’ self-efficacy, self-control, motivation, and classroom performance, but further research is needed to determine whether they maintain sustained motivation [30,31].
However, we systematically list the existing research analyzed in this section in Table 1. We found that there has been considerable research on gamification in education, but there is a lack of in-depth research on student participation in online gamified classrooms, and students’ differences have often been overlooked. TAM is not suitable as the ultimate theoretical model for exploring gamified instruction based on the differences between gamification and “technology”. Therefore, this study will modify TAM appropriately to fit the theme of “acceptance in the gamified classroom” and explore the behavioral acceptance model of technology learning by considering differences in perspective among students.

3. Research Model and Hypotheses

Based on the findings and directions of the research on gamification in education, in this section, we used TAM as the theoretical basis and selected the dimension of “perceived usefulness”. Although “perceived usefulness” as an important predictor variable in TAM, the gamified classroom only uses technology as a vehicle for a dynamic activity, namely, it could be called a technology-integrated activity, rather than a purely technological one. Therefore, “perceived ease of use” is not included in the theoretical framework. In this study, the dimension of “perceived ease of use” was changed to “perceived enjoyment” based on the characteristics of gamified activities. Other studies have shown that there is a strong correlation between different learning styles, teaching and learning tasks, and learning activities [33]. Therefore, the three external variables, “visual, auditory, and kinesthetic (VAK) learning styles”, “perceived learning tasks”, and “types of teaching tasks”, associated with gamified activities were also added to the theoretical model. The following section details the five latent variables involved in the model and presents the hypotheses in the study before concluding with an in-depth explanation of the constructed theoretical model.

3.1. Perceived Learning Tasks

According to self-determination theory (SDT), after fully understanding self-needs and environmental information, people can make decisions driven by intrinsic motivation based on three psychological needs: autonomy, competence, and relatedness. [34]. What is more, students’ intrinsic motivation is often related to their interest in the learning task in question. It has been suggested that psychological motivation and cognitive problem solving should receive attention from educators and teachers as important factors influencing students’ engagement in online learning [35]. Due to the variety of activities and classroom arrangements in online gamified classes, students’ perceptions of specific learning tasks may vary according to their learning styles, and such differences may affect students’ willingness to participate. Therefore, we believe it is necessary to explore students’ perceptions and preferences of learning tasks. As such, this study uses “perceived learning tasks” as an external variable to investigate college students’ behavioral intention to participate in gamified classrooms. We propose the following hypothesis:
Hypothesis 1 (H1):
Perceived learning tasks have a significant positive influence on students’ intention to participate in an online gamified classroom.

3.2. Perceived Usefulness

Perceived usefulness, an important factor in TAM, plays a non-negligible role in predicting behavioral intentions [17,36]. In the context of research on online gamified education, students’ perceived usefulness of new learning mode continues to be a part worth focusing on. The inclusion of game elements in an outcome-oriented learning process has been a topic of concern for educators in terms of whether the inclusion of game elements can promote good learning outcomes and learning experiences for students. Likewise, under the condition that game elements are combined with online instruction, learners’ recognition of the “usefulness of online gamified classes” may further influence their behavioral willingness to participate in class. However, in real-life teaching and learning contexts, students’ positive learning attitudes are not always maintained by “perceived usefulness”, and the Task-Technology-Fit plays a significant role in determining the actual usage of game elements in learning situations [37]. As a result, we suggest that students’ subjective perceptions of the usefulness of the gamified instructional model may influence students’ perceptions of classroom learning tasks and their behavioral intentions to participate in gamified online classes. Hence, the following hypotheses are proposed:
Hypothesis 2 (H2):
Perceived usefulness has a significant positive influence on students’ intention to participate in an online gamified classroom.
Hypothesis 3 (H3):
Perceived learning tasks have a mediation effect on the effect of perceived usefulness on students’ intention to participate in an online gamified classroom.

3.3. Perceived Enjoyment

Based on the TAM model, perceived usefulness and perceived ease of use are considered to be important factors influencing attitudes and intention to use. The TAM model has been further improved with the increasing number of studies that used TAM theory as a theoretical foundation. The study by Van der Heijden [37] indicated that “perceived enjoyment” and “perceived ease of use” are strong determinants of behavioral intention to accept technology. In addition, students’ intrinsic motivation is influenced by their learning enjoyment [34]. Consequently, this study will investigate the relationship between students’ perceived enjoyment of and their intention to participate in an online gamified classroom. As such, the following hypotheses are proposed:
Hypothesis 4 (H4):
Perceived enjoyment has a significant positive influence on students’ intention to participate in the online gamified classroom.

3.4. Visual, Auditory, and Kinaesthetic (VAK) Learning Styles

Learning style refers to the behavioral preferences of students in the process of acquiring knowledge. Pashler et al. [38] confirmed, through an experimental approach, that the same instructional strategies have different effects on learners with different learning styles. It is clear that most studies on game-based teaching did not take into account contextual factors, and that differences in students’ cultural backgrounds, learning goals, and learning styles may influence their willingness to participate in game-based classrooms [30,39]. In conjunction with the overall research, in this study, we will focus on the impact of differences in learning styles on behavioral intentions. Since classrooms with gamified activities are more interactive and practical, this study limits the variable “learning style” to “visual, auditory, and kinesthetic (VAK) learning style,” namely, it focuses on the relationship between the extent to which students have a VAK learning style and their intention to participate in online gamified classrooms. VAK learning styles refer to learners’ preference for visual, auditory, or kinesthetic acquisition of information when engaging in learning activities, their expertise in listening, and their experience with acquiring knowledge [40,41]. A common means of gamification in education is through the use of game-based websites, whose user interfaces usually consist of colorful and exaggerated visual images. Some websites also feature cheerful background music. From an objective point of view, these elements are a highly attractive new learning mode for students who are good at perceiving learning content through visual, auditory, and kinesthetic stimuli, and we hypothesize that students with VAK learning styles have a strong behavioral intention. On the other hand, in the TAM, learning style is an external antecedent variable which may have an impact on engagement intention through mediating variables such as usefulness, interest, and learning task arrangement. Therefore, four hypotheses are proposed:
Hypothesis 5a (H5a):
VAK learning style has a significant positive influence on students’ intention to participate in an online gamified classroom.
Hypothesis 5b (H5b):
Perceived learning task has a mediation effect on the effect of VAK learning style on students’ intention to participate in an online gamified classroom.
Hypothesis 5c (H5c):
Perceived usefulness has a mediation effect on the effect of VAK learning style on students’ intention to participate in an online gamified classroom.
Hypothesis 5d (H5d):
Perceived enjoyment has a mediation effect on the effect of VAK learning style on students’ intention to participate in an online gamified classroom.

3.5. Types of Teaching Tasks

Gamified classrooms often quantify student performance through points, badges, and leaderboards to create a more competitive atmosphere [26]. Online education is often delivered via e-learning platforms (e.g., Kahoot!), where teachers only need to select the appropriate question type and answer interface and enter the assessment content to complete the design of the teaching session. Once students have completed the questions, teachers can assess their performance directly based on “rewards” from the electronic platform. These platforms support different types of classroom activities, such as individual (competitive) and group (collaborative) learning tasks, as well as subjective and objective questions. An empirical study by Bovermann et al. [39] found that different types of gamified online learning activities affect the online motivation of university students, with students being more motivated to engage in collaborative tasks. Whereas the enjoyment of gamified activities for online learning is distinct from traditional teaching models, we are more interested in whether some types of instructional tasks can influence the extent to which gamification features affect students’ learning intentions. Therefore, we set “type of instructional task” as a moderating variable and hypothesized that:
Hypothesis 6 (H6):
The type of teaching task moderates the relationship between VAK learning style and students’ intention to participate in an online gamified classroom.
In summary, to explore the behavioral intention of college students to participate in an online gamified classroom, we constructed a theoretical model for this study (Figure 1) which includes five latent variables: Vak learning styles, Perceived learning tasks, Perceived usefulness, Perceived enjoyment, and Types of teaching tasks and Intention. On the whole, the first four variables are related to individual factors, e.g., student learning styles, perception of the classroom, and students’ willingness to engage in learning behavior. Vak learning styles, as the dependent variable, directly influences students’ behavioral intention to participate in online gamified classrooms. Since human behavioral intention can be influenced by a variety of internal and external factors, one or more mediating variables may exist in the causal relationship between students’ Vak learning styles and their behavioral intention. We fully analyzed the original technology acceptance model (TAM) and TAM applied to gamification research and clarified the latent variables and relationships among the variables proposed in previous studies. We finally constructed a model structure of college students’ behavioral intention to participate in online gamification classrooms. The hypotheses in the model will be verified by SmartPLS4 software in the Methodology section.

4. Methodology

In this section, we specify the empirical research methods used in this study in two parts, including questionnaire scale design, participant information, questionnaire return rate, and efficiency, as well as the applied data analysis tools. In addition, this section details the advantages of Partial Least Square Structural Equation Modeling (PLS-SEM) in the field of social science research and the reasons for choosing PLS-SEM for this study.

4.1. Instrument and Participants

This study uses the questionnaire method in empirical research to investigate the intention of online gamified learning behaviors of college students and the scale design by constructing a theoretical model. Since the theoretical model of this study expands on the TAM model according to the characteristics of gamification technology and teaching and learning activities, we will use PLS-SEM to validate the model hypothesis while also fulfilling the function of exploring the expanded theoretical model. The questionnaire consists of two parts: basic personal information (including gender, grade, and major, with five questions) and five-point Likert scale items (including six dimensions with 21 items). The study participants were asked to rate items using the following scale: “Strongly disagree” (1 point), “Disagree” (2 points), “Average” (3 points), “Agree” (4 points) and “Strongly agree” (5 points). The items encompassed the six dimensions included in the theoretical model, namely: VAK Learning Styles; Perceived Learning Tasks; Perceived Usefulness; Perceived Enjoyment; Intention; and Types of Teaching Tasks. All items were adopted from the scales that had been developed, tested, and validated to measure the constructs [42,43,44].
According to the purpose of the study, we conducted research on the online teaching modes of universities in Guangdong province and Macao before the questionnaire was distributed and found that most of the universities did not carry out online gamification teaching. Therefore, this study chose some college students in the Guangdong province and Macao as research participants through purposive sampling.

4.2. Data Collection and Analysis

This study electronically distributed 750 questionnaires to students enrolled in selected universities in Guangdong province and Macao on 4 October 2022; the deadline for the return of the questionnaires was 17 October 2022. We purposively sampled these universities by randomly selecting one class per major as the sampling frame and distributing electronic questionnaires to them through their class web chat groups. The total number of selected classes was 750, instead of the total number of all selected universities, so it could be considered that a total of 750 questionnaires were distributed. In total, 643 questionnaires were returned, with a return rate of 85.7%. After the invalid questionnaires had been processed, 616 valid questionnaires were obtained, with an effective rate of 95.8%.
In this study, model testing was performed using SmartPLS4 software based on PLS-SEM. This was chosen for the following reasons. First, PLS-SEM is a structural equation modeling method that can be used to estimate complex causal relationships in path models with latent variables. Additionally, it has better parameter estimation, model fitting, and statistical power compared to CB-SEM. Second, it can better analyze complex models containing too many latent variables. Additionally, PLS-SEM is suitable for analyzing non-normally distributed data, small sample data, and formative measurement models [45]. Based on these advantages, PLS-SEM is widely used in social science research. SmartPLS4 was selected as the data analysis tool in this research due to the existence of exploratory studies on some latent variables in the reflective measurement models constructed in this study.

5. Results

5.1. Descriptive Statistics

We investigated the background information (gender, grade, school location, place of origin, and major) of the participants in the first part of the questionnaire. The statistical results are shown in Table 2. Among the 616 valid questionnaires returned, 382 were from universities in Guangdong and 234 were from universities in Macao. Since the total numbers of questionnaires that we distributed in the two regions were quite different, the questionnaire response rate was not even, but it was acceptable.

5.2. Evaluating the Reflective Measurement Model

Assessments of reflective measurement models consist of three main steps: internal consistency reliability, convergent validity, and discriminant validity. First, according to Hair et al. [45], the measurement model has good reliability when the factor loadings of each indicator are greater than 0.7, Cronbach’s alpha is greater than 0.8, and composite reliability (CR) is greater than 0.7. The average variance extracted (AVE) is a criterion to test the convergent validity, and an AVE greater than 0.5 can indicate that the model has good convergent validity [46]. Discriminant validity refers to the absence of correlation between a construct and other constructs in the model. There are three means of assessing discriminant validity in PLS-SEM, namely, Fornell-Larcker criterion, Cross loadings, and the Heterotrait-monotrait ratio (HTMT). According to the Fornell-Larcker criterion, the AVE of each latent variable should be greater than the highest squared correlation of that latent variable with any other latent variable [46]. Cross loadings require that each metric has a higher loading on its assigned construct than any other, and that each construct has the highest loading on its metric to infer that the model’s constructs are sufficiently different from each other [47]. HTMT is a criterion for discriminant validity which is specific to the PLS-SEM; a model is considered to have good discriminant validity when the HTMT is less than 0.9.
The factor loadings, Cronbach’s alpha, CR, and AVE of the reflective measurement model constructed in this study are shown in Table 3, while the Fornell-Larcker criterion, Cross loadings, and HTMT are presented in Table 4, Table 5 and Table 6. According to Table 3, due to the factor loadings of each indicator being greater than 0.7, Cronbach’s alpha being greater than 0.8, the CR value being greater than 0.7, and AVE being greater than 0.5, this reflective measurement model has good internal consistency, reliability, and convergent validity. Table 4 presents the Fornell-Larcker criterion. Each value in the diagonal of the table is greater than the other values in the row and column they are in. Table 5 presents the cross-loadings of the indicators; each metric has a higher loading on its assigned construct than any other (i.e., the INT loadings are higher than the other constructs in the first column). Table 6 demonstrates that the HTMT values for all constructs were less than 0.9. As a result, the reflective measurement model has good discriminant validity.

5.3. Evaluating the Structural Model and Hypotheses

The main metrics used to evaluate the structural model are collinearity (VIF), coefficient of determination (R2 value), and predictive relevance (Q2 value). The problem of collinearity may exist when the VIF of each indicator is greater than 5. In this study, the VIF of all indicators is less than 5 (Table 7), so there is no collinearity among constructs [48]. To illustrate the explanatory power of latent variables, the R2 values (0.75, 0.50, and 0.25 are considered substantial, moderate, and weak, respectively) are usually assessed [47]. The R2 values of the three latent variables in this study are all greater than 0.75 and another is close to 0.5, indicating good explanatory power (Table 8). The Q2 value is a set of indicators to assess the predictive relevance of a model; a higher Q2 value (usually suggested to be greater than 0) indicates a higher predictive relevance of the model [49]. The Q2 value of each latent variable in this study was greater than 0 (Table 8), which can indicate good predictive power.
Based on the constructed theoretical model (Figure 1), we used the SmartPLS4 PLS-SEM algorithm to process the returned data. The resulting operational interface is shown in Figure 2. The graphical output shows the six constructs in the reflective measurement model and the indicators pointing to each construct, as well as the relationship between each construct. Figure 2 demonstrates only the path coefficients (the number in the line with arrows) and the R2 of the constructs (the number in the center of each construct), while the significance of the path coefficients needs to be derived by the Bootstrapping algorithm in Table 9.
Table 9 presents the path coefficients, significance, and confidence intervals for the hypotheses proposed in this study. The results indicate that perceived learning task, perceived usefulness, and perceived enjoyment have a significant positive relationship with behavioral intention to participate in an online gamified classroom (H1, H2, and H4 supported), and that VAK learning style has a significant positive effect on behavioral intention to participate in the online gamified classroom (H5a supported). Since both the indirect and direct effects between perceived usefulness and behavioral intention to participate were significant, the perceived learning task was a partial mediator between perceived usefulness and behavioral intention to participate (H3 Supported). The direct and all indirect effects between VAK learning style and behavioral intention to participate were significant, indicating a partial mediation between the two variables. The Variance Accounted For (VAF) value is usually calculated to determine the strength of mediation present, with full mediation when VAF > 80%, partial mediation when VAF < 80%, and no mediation present when VAF < 20% [50,51]. By determining the VAF to be 11.1% for H5b, 28.9% for H5c, and 46.6% for H5d, both perceived usefulness and perceived enjoyment are shown to be partial mediators between VAK learning style and behavioral intention to participate in an online gamified classroom (H5b not supported; H5c and H5d supported). In contrast, the types of teaching tasks showed a negative and non-significant path coefficient between perceived enjoyment and behavioral intention to participate. Therefore, this moderating effect did not exist (H6 not supported).

6. Discussion

According to the obtained results, the main strategic findings of this study are summarized in Table 10, which are fully discussed and analyzed in this section.
Table 10. Major strategic findings.
Table 10. Major strategic findings.
HypothesisSupported (Y)/
Not Supported (N)
H2YPerceived usefulness and perceived enjoyment are important factors that influence students’ participation in online gamified classrooms. Teachers should use the characteristics of game elements to design effective classroom activities that enhance the enjoyment and effectiveness of online learning.
H5cYStudent differences should be taken into account, but even among students with different learning styles, willingness to participate in online gamified classes is indirectly influenced by perceived usefulness and enjoyment.
H3YGamification teaching has both the static characteristics of technology and the dynamic components of teaching. Therefore, we propose the “Technology-Learning Behavior Acceptance Model (T-LBAM)” in Figure 3 based on the results of our study, which will be discussed in detail in the next section.
H6NCollege students are at a high level of cognitive development, and changes in instructional format do not disproportionately affect their participation in gamified classrooms.
Figure 3. Technology-Learning Behavior Acceptance Model (T-LBAM).
Figure 3. Technology-Learning Behavior Acceptance Model (T-LBAM).
Applsci 12 12966 g003
Gamification teaching has not been widely used in the geographic area covered in this study. Understanding students’ behavioral intentions to participate in online game-based classrooms can help teachers adjust their existing online classroom arrangements and teaching strategies. Exploring instructional media will enhance students’ classroom participation and self-efficacy. Based on the fact that previous studies on gamified instruction usually ignore the individual differences of learners, this study takes students’ VAK learning style as an entry point to explore the causal relationship between it and behavioral intention, as well as other factors that influence behavioral intention [52,53]. Both perceived usefulness (H2) and perceived enjoyment (H4) were found to significantly influence students’ behavioral intentions, which is consistent with previous findings [32,54,55]. Additionally, we found perceived usefulness and perceived enjoyment as mediating variables between VAK learning style and behavioral intention, both of which were partially mediated (H5c; H5d). Differences in learning styles can indirectly influence willingness to participate in online gamified classes according to perceived usefulness and perceived enjoyment, which suggests that teachers should take advantage of the sensory stimuli brought by game elements in the classroom and fully explore the intrinsic connections of the class content in order to design a rational classroom. Teachers can present abstract theoretical knowledge through a gamified interface, which can not only attract students’ attention but also deepen their memory. However, students at the higher education level have developed more mature cognitive abilities and do not limit their perception of the formal arrangements in classes; rather, they relate the learning process to their own goals [56]. Therefore, it may be difficult to design “higher-order enjoyment” classroom activities using game elements that are appropriate for students’ cognitive development. The focus of teachers’ teaching is not only on teaching theoretical knowledge from textbooks, but also on developing students’ learning, creative, and problem-solving skills. Hence, how to further stimulate students’ intrinsic motivation and curiosity through gamified classrooms will be a question worth thinking about in future online teaching.
According to the results of the study, the influence of students’ perceived usefulness of the online gamified classroom on their behavioral intentions was mediated by the “perceived learning task” (H3). The “perceived learning tasks” discussed in this study focus on the organization of the gamification sessions in the online classroom, specifically, the sessions in which students expect gamified learning activities to occur. The relationship between perceived usefulness and perceived learning tasks is subtle. They both have a significant positive causal relationship with VAK learning style, but there are differences in the objects perceived by learners. Perceived usefulness was more focused on students’ overall perceptions of “gamification” in the online classroom from a macro perspective, i.e., students’ perceptions of “new technology” dominated this causal relationship, while the perceived learning task dimension was focused on specific learning tasks. However, while the VAK learning style indirectly influenced behavioral intention to participate in the gamified online classroom through perceived usefulness, perceived learning tasks did not mediate between the VAK learning style and behavioral intention to learn. Interestingly, perceived usefulness indirectly influenced learners’ behavioral intention to participate in the online gamified classroom through perceived learning tasks. That is to say, even if students recognize the ‘usefulness’ of gamified learning, the way at which the gamified learning tasks are arranged in the classroom can affect students’ behavioral intentions to be engaged in the gamified online classes. At the teacher level, the incorporation of game elements in online gamified teaching makes the classroom more active than traditional online classrooms, but it also means that changes in the structure and organization of the classroom are needed to improve student engagement and the effectiveness of gamified classroom instruction [57,58]. Although the design and arrangement of online learning activities are teacher-driven, students’ perceptions of the learning tasks should not be ignored. Because students have different cognitive styles and behavioral habits, the same classroom arrangement has different levels of appeal to them [56,59]. Especially when students are trying to accept new forms of learning, it is expected that the appropriate instructional arrangement will add excitement to the classroom. Education is a combination of “teaching” and “learning” in which the actors are the teacher and the students, both of whom are critical to the perception of the classroom. In addition, establishing good communication will help teachers and students adapt to the new teaching mode together, thus achieving the desired classroom effect.
This study found that the types of teaching tasks did not moderate the relationship between perceived enjoyment and students’ learning intention in online gamified classrooms (H6). The main reasons for this could be discussed from both teacher and student perspectives. From the students’ perspective, college students’ cognitive level is higher, and their learning engagement is more related to their individual goals and learning motivation. The subjective cognitive ability of college students is stronger, and their perception of classroom interest and willingness to participate in classroom behavior mainly stems from their attraction to the online gamified teaching model itself, rather than being easily distracted by the colorful format [60]. Thus, the types of teaching tasks are no longer the focus of attracting students’ concentration compared to students at the basic education level. From the teachers’ perspective, differences in teaching style can also affect teaching performance. The teacher’s teaching strategies and presentation style may be more considered by students at the higher education level than the types of teaching tasks.

7. Conclusions

This paper investigates students’ behavioral intention to participate in online gamified classrooms through an empirical study in Guangdong province and Macao and constructs a theoretical model on that based. The findings suggest a correlation between students’ behavioral intention to participate and their VAK learning style, with perceived usefulness and perceived enjoyment as mediating variables. Students’ overall perception of the gamified online classroom and their perceived enjoyment are important factors influencing their participation in the classroom, which has some similarities to the TAM model, both of which include perceived usefulness as an important predictor. Thus, perceived usefulness plays an important role in both the acceptance of purely technological systems and the acceptance of “technology+” teaching models. In the higher education stage, students’ criteria for perceiving the usefulness of a new educational model will refer to the learning objectives they originally set, rather than being limited to the game-based format. Since gamification in online education is not yet popular in most universities in Guangdong province and Macao. As a result, educators need to prioritize how to make the changed classroom best meet students’ intended learning goals and how to enhance the classroom’s enjoyment factor before considering the application of gamification to the classroom, which will help increase students’ engagement in the classroom and thus make the classroom the most effective.
Furthermore, the perceived learning tasks are a mediating variable in the causal relationship between perceived usefulness and behavioral intention to engage in online gamified classrooms, which inspires the need to focus on students’ perceptions of learning tasks [17,61]. In contrast to the Technology Acceptance Model, the focus of this study is not only on “gamification” with technology elements but also on “online education”. Therefore, in the context of education, we cannot ignore the importance of teaching and learning sessions. After the integration of gamification with online teaching and learning, how to give full play to the advantages of gamification in motivating students and creating a gentle competitive atmosphere, and how to make it a “scaffold” in the classroom, a reasonable classroom arrangement, and task assignment cannot be ignored [62,63]. Although the arrangement of classroom sessions may seem a formal aspect of the classroom, it does not mean that it can be casually arranged.

7.1. Theoretical Implication

The theoretical model constructed in this study is based on the Technology Acceptance Model (TAM), which was adapted to analyze students’ online gamified learning behavioral intentions by taking into account the characteristics of online education mode and the important factors of students’ online learning experiences. TAM focuses on the acceptance of “technology” as static, meaning that it lacks flexibility and does not change according to the external user environment [20]. Thus, the behavioral intentions of subjects in the TAM framework are closely related to hardware conditions such as operability and ease of use at the technological level. When gamification is combined with learning tasks, it is “dynamic” in nature. The teacher’s choice of gamification elements, the presentation of the classroom, the actual arrangement of teaching activities, and the student’s participation in learning are all dynamic and changeable. The theoretical model for exploring students’ behavioral intention to participate in online gamified classrooms needs to be different from the original technology acceptance model. The “perceived learning tasks” dimension we introduce focuses on the pedagogical component, incorporating the flexibility and variability that can occur in gamified learning. Students perceived appropriateness of online game-based learning tasks based on their learning habits and preferences can indicate their agreement with dynamic game-based activities.
Accordingly, this theoretical model is more related to a technology-mediated model of learning behavior acceptance than TAM in Figure 3. It incorporates the factors that students should consider when accepting new learning modes, rather than focusing again on the assessment of acceptance of technology systems. With the development of online technology and the popularity of online teaching, more novel e-learning media or tools will emerge in the future, which will enhance the effectiveness of online teaching and classroom enjoyment. Before new technologies are combined with online learning in practice, it is necessary to investigate students’ learning behavior intention, and we expect to further optimize this theoretical model in future research.

7.2. Educational Recommendation

It is worthwhile for teachers to apply game-based instructional models in online courses. The appropriate use of technology can be a driving force in teaching and learning, and the use of game-based strategies can certainly increase the enjoyment of online learning, provide immersive learning opportunities, and develop students’ individual and teamwork skills [64]. At the same time, certain gamification strategies can be motivating for students during their learning process [65]. For instance, teachers could divide a complex learning task into stages and assign different scores to each stage according to the difficulty of the task, so that students can gain a sense of accomplishment from the scores and be motivated to continue with the remaining learning objectives. Even though gamification can help teachers to be more comfortable with the challenges of online teaching, it has a bias toward extrinsic motivation, and how to keep students consistently motivated is another issue that teachers should consider in practice [66].
Teachers need to focus on the instructional population’s learning style preferences, personalities, and other individualized factors before designing an online gamified classroom. Using gamification strategies, classroom tasks can be designed in different styles, such as competitive, cooperative, communicative, and challenging. Due to student personality differences, the same type of learning tasks can have different learning outcomes for students with different learning styles. Therefore, teachers can use gamified teaching tools to provide students with many different types of learning tasks to choose from, thus enhancing students’ classroom engagement and learning motivation. In addition, game elements can facilitate differentiated instruction. For example, designing teaching tasks of different levels of difficulty for the same teaching objective and presenting them in the classroom through gamification means (points, leaderboards, breakthrough mode, etc.) helps students of different ability levels to gain something from the classroom and accomplish learning objectives that are appropriate for them. The “differentiated grouping” of students through games can prevent students from developing an inferiority complex, which is also a teaching strategy to be considered in future online classes.
The learners, as the main subjects of learning, should be the “collaborators” of teachers. Active and effective communication between them can not only help teachers optimize the online gamified classroom arrangement, but also improve students’ online learning effectiveness. Even though teachers will make sufficient preparation for the teaching design before the application of the new teaching mode, various problems will inevitably occur in the real teaching process. This is where timely communication between teachers and students can help identify problems and correct them in later lessons. There are many examples of technology being used in online classrooms, and the process of integrating technology into teaching and learning is not always smooth [67]. Teachers need to consider the relationship between the teaching object and the teaching content, to find a balance situation where they can coordinate and promote each other. Online game-based teaching will go through a phase of gradual improvement in future applications, and effective interaction and feedback between students and teachers may become the “catalyst” for this phase.
Teachers should choose the appropriate means and methods of assessing online gamified instruction. Both online and offline game-based instruction face difficulties in assessing student learning [5,26]. Gamified classroom activities containing quantitative forms such as “points and scores” can inform post-assessment but are unsuitable for direct assessment. Additionally, if multiple gamified activities are performed in the online classroom, their “fragmented” distribution may affect the teacher’s overall assessment of the students. Formative assessment is a common assessment method used by teachers in the classroom, which advocates continuous observation, recording, and evaluation of students during the teaching process to help them identify and correct problems promptly. However, how to make use of the “game elements” or “electronic technology” in the online gamified classroom to assess students’ learning effectiveness efficiently is a question that needs to be considered in future practical teaching.
In summary, gamification in online learning is worth practicing in actual classrooms, and teachers should arrange online gamified learning tasks or classroom sessions that match the learners’ needs and learning habits and think about how to design efficient online gamified classrooms from the perspectives of perceived usefulness and perceived enjoyment, respectively, with a full understanding of the learners’ characteristics [68].

7.3. Limitations and Further Research

First, the investigation of students’ learning styles in this study was limited to VAK learning style; other learning styles should also be included in future study, and subsequent studies could further explore the influence of literacy and logical learning styles on learning behavioral intention in online gamified classrooms. Furthermore, different major categories have their disciplinary characteristics, and most of the questionnaires collected in this study were from social humanities subjects. Future research should collect the opinions of a wide range of students and learn from different majors to draw more generalized research conclusions that could be further applied to future teaching.

Author Contributions

Conceptualization, H.Z. and H.Y.; formal analysis, H.Y. and H.Z.; investigation, H.Z., H.Y., S.S., J.F.I.L. and X.W.; methodology, H.Z. and H.Y.; project administration, H.Z. and J.F.I.L.; resources, H.Z. and H.Y.; writing—original draft, H.Y.; writing—review and editing, H.Y., S.S., X.W. and H.Z. All authors have read and agreed to the published version of the manuscript.


This paper was funded by Macao Polytechnic University (RP/FCHS-02/2022).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing not applicable.


The authors gratefully acknowledge the support of Philosophy and Social Science Planning Project of Guangdong Province of China (GD22XJY16).

Conflicts of Interest

The authors declare no conflict of interest.


  1. Barrot, J.S.; Llenares, I.I.; Del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ. Inf. Technol. 2021, 26, 7321–7338. [Google Scholar] [CrossRef]
  2. Küsel, J.; Martin, F.; Markic, S. University students’ readiness for using digital media and online learning—Comparison between Germany and the USA. Educ. Sci. 2020, 10, 313. [Google Scholar] [CrossRef]
  3. Chiu, T.K.; Lin, T.J.; Lonka, K. Motivating online learning: The challenges of COVID-19 and beyond. Asia-Pac. Educ. Res. 2021, 30, 187–190. [Google Scholar] [CrossRef]
  4. Plump, C.M.; LaRosa, J. Using Kahoot! in the classroom to create engagement and active learning: A game-based technology solution for eLearning novices. Manag. Educ. 2017, 2, 151–158. [Google Scholar] [CrossRef]
  5. Dicheva, D.; Dichev, C.; Agre, G.; Angelova, G. Gamification in education: A systematic mapping study. J. Educ. Technol. Soc. 2015, 18, 75–88. [Google Scholar] [CrossRef] [Green Version]
  6. Dorneles, S.O.; Francisco, R.; Barbosa, D.N.F.; Barbosa, J.L.V. Context Awareness in Recognition of Affective States: A Systematic Mapping of the Literature. Int. J. Hum.-Comput. Int. 2022, 1–19. [Google Scholar] [CrossRef]
  7. Kasurinen, J.; Knutas, A. Publication trends in gamification: A systematic mapping study. Comput. Sci. Rev. 2018, 27, 33–44. [Google Scholar] [CrossRef]
  8. Dalmina, L.; Barbosa, J.L.V.; Vianna, H.D. A systematic mapping study of gamification models oriented to motivational characteristics. Behav. Inf. Technol. 2019, 38, 1167–1184. [Google Scholar] [CrossRef]
  9. Deterding, S.; Dixon, D.; Khaled, R.; Nacke, L. From game design elements to gamefulness: Defining “gamification”. In Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments, Tampere, Finland, 28–30 September 2011; pp. 9–15. [Google Scholar] [CrossRef]
  10. Tsay, C.H.H.; Kofinas, A.; Luo, J. Enhancing student learning experience with technology-mediated gamification: An empirical study. Comput. Educ. 2018, 121, 1–17. [Google Scholar] [CrossRef]
  11. Alabbasi, D. Exploring Teachers’ Perspectives towards Using Gamification Techniques in Online Learning. Turk. J. Educ. Tech. 2018, 17, 34–45. [Google Scholar]
  12. Ariffin, M.M.; Oxley, A.; Sulaiman, S. Evaluating game-based learning effectiveness in higher education. Procedia-Soc. Behav. Sci. 2014, 123, 20–27. [Google Scholar] [CrossRef] [Green Version]
  13. Huotari, K.; Hamari, J. Defining gamification: A service marketing perspective. In Proceedings of the 16th International Academic MindTrek Conference, Tampere, Finland, 17–22 October 2012. [Google Scholar] [CrossRef]
  14. Koivisto, J.; Hamari, J. Demographic differences in perceived benefits from gamification. Comput. Hum. Behav. 2014, 35, 179–188. [Google Scholar] [CrossRef]
  15. Hamari, J.; Lehdonvirta, V. Game design as marketing: How game mechanics create demand for virtual goods. Int. J. Bus. Sci. Appl. Manag. 2010, 5, 14–29. [Google Scholar]
  16. Wilson, D.; Calongne, C.; Henderson, B. Gamification challenges and a case study in online learning. Online Learn. J. 2015, 4, 84–102. [Google Scholar] [CrossRef]
  17. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 1989, 13, 319–340. [Google Scholar] [CrossRef] [Green Version]
  18. Essel, D.D.; Wilson, O.A. Factors affecting university students’ use of Moodle: An empirical study based on TAM. Int. J. Inf. Commun. Technol. Educ. 2017, 13, 14–26. [Google Scholar] [CrossRef] [Green Version]
  19. Goh, W.W.; Hong, J.L.; Gunawan, W. Exploring students’ perceptions of learning management system: An empirical study based on TAM. In Proceedings of the 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Bali, Indonesia, 26–29 August 2013; pp. 367–372. [Google Scholar] [CrossRef]
  20. Marangunić, N.; Granić, A. Technology acceptance model: A literature review from 1986 to 2013. Univ. Access Inf. Soc. 2015, 14, 81–95. [Google Scholar] [CrossRef]
  21. Saadé, R.G.; Kira, D. The emotional state of technology acceptance. Issues Inf. Sci. Inf. Technol. 2006, 3, 529–539. [Google Scholar]
  22. Gefen, D.; Karahanna, E.; Straub, D.W. Trust and TAM in online shopping: An integrated model. MIS Quart. 2003, 27, 51–90. [Google Scholar] [CrossRef]
  23. King, W.R.; He, J. A meta-analysis of the technology acceptance model. Inf. Manag. 2006, 43, 740–755. [Google Scholar] [CrossRef]
  24. El-Sayad, G.; Md Saad, N.H.; Thurasamy, R. How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic. Comput. Educ. J. 2021, 8, 527–550. [Google Scholar] [CrossRef]
  25. Sailer, M.; Homner, L. The gamification of learning: A meta-analysis. Educ. Psychol. Rev. 2020, 32, 77–112. [Google Scholar] [CrossRef] [Green Version]
  26. Hamari, J.; Koivisto, J.; Sarsa, H. Does gamification work?—A literature review of empirical studies on gamification. In Proceedings of the 2014 47th Hawaii International Conference on System Sciences, Waikoloa, HI, USA, 6–9 January 2014; pp. 3025–3034. [Google Scholar] [CrossRef]
  27. Alshammari, M.; Anane, R.; Hendley, R.J. Design and usability evaluation of adaptive e-learning systems based on learner knowledge and learning style. In Proceedings of the IFIP Conference on Human-Computer Interaction, Bamberg, Germany, 14–18 September 2015; pp. 584–591. [Google Scholar] [CrossRef] [Green Version]
  28. Bourgonjon, J.; Valcke, M.; Soetaert, R.; Schellens, T. Students’ perceptions about the use of video games in the classroom. Comput. Educ. 2010, 54, 1145–1156. [Google Scholar] [CrossRef]
  29. Guerola-Navarro, V.; Gil-Gomez, H.; Oltra-Badenes, R.; Sendra-García, J. Customer relationship management and its impact on innovation: A literature review. J. Bus Res. 2021, 129, 83–87. [Google Scholar] [CrossRef]
  30. Park, S.; Kim, S. Is sustainable online learning possible with gamification?—The effect of gamified online learning on student learning. Sustainability 2021, 13, 4267. [Google Scholar] [CrossRef]
  31. Van Dinther, M.; Dochy, F.; Segers, M. Factors affecting students’ self-efficacy in higher education. Educ. Res. Rev. 2011, 6, 95–108. [Google Scholar] [CrossRef]
  32. Van der Heijden, H. User acceptance of hedonic information systems. MIS Quart. 2004, 28, 695–704. [Google Scholar] [CrossRef]
  33. Goštautaitė, D.; Sakalauskas, L. Multi-Label Classification and Explanation Methods for Students’ Learning Style Prediction and Interpretation. Appl. Sci. 2022, 12, 5396. [Google Scholar] [CrossRef]
  34. Gagné, M.; Deci, E.L. Self-determination theory and work motivation. J. Organ. Behav. 2005, 26, 331–362. [Google Scholar] [CrossRef] [Green Version]
  35. Lee, J.; Song, H.D.; Hong, A.J. Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning. Sustainability 2019, 11, 985. [Google Scholar] [CrossRef] [Green Version]
  36. Karahanna, E.; Straub, D.W. The psychological origins of perceived usefulness and ease-of-use. Inf. Manag. 1999, 35, 237–250. [Google Scholar] [CrossRef]
  37. Isaac, O.; Aldholay, A.; Abdullah, Z.; Ramayah, T. Online learning usage within Yemeni higher education: The role of compatibility and task-technology fit as mediating variables in the IS success model. Comput. Educ. 2019, 136, 113–129. [Google Scholar] [CrossRef]
  38. Pashler, H.; McDaniel, M.; Rohrer, D.; Bjork, R. Learning styles: Concepts and evidence. Psychol. Sci. Public Interest 2008, 9, 105–119. [Google Scholar] [CrossRef] [Green Version]
  39. Bovermann, K.; Bastiaens, T.J. Towards a motivational design? Connecting gamification user types and online learning activities. Int. J. Technol. Enhanc. Learn. 2020, 15, 1. [Google Scholar] [CrossRef]
  40. Surjono, H.D. The design of adaptive e-learning system based on student’s learning styles. Int. J. Comput. Sci. Inf. Technol. 2011, 2, 2350–2353. [Google Scholar]
  41. Willis, S. Literature review on the use of VAK learning strategies. STeP J. 2017, 4, 90–94. [Google Scholar]
  42. Chang, C.C.; Yan, C.F.; Tseng, J.S. Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australas. J. Educ. Technol. 2012, 28, 809–826. [Google Scholar] [CrossRef] [Green Version]
  43. Mohammadi, H. Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Comput. Hum. Behav. 2015, 45, 359–374. [Google Scholar] [CrossRef]
  44. Guo, Z.; Xiao, L.; Van Toorn, C.; Lai, Y.; Seo, C. Promoting online learners’ continuance intention: An integrated flow framework. Inf. Manag. 2016, 53, 279–295. [Google Scholar] [CrossRef]
  45. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to Use and How to Report the Results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  46. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  47. Chin, W.W. The partial least squares approach to structural equation modeling. In Modern Methods for Business Research; Marcoulides, G.A., Ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1998; pp. 1295–1336. [Google Scholar]
  48. Cassel, C.M.; Hackl, P. On measurement of intangible assets: A study of robustness of partial least squares. Total Qual. Manag. 2000, 11, 897–907. [Google Scholar] [CrossRef]
  49. Stone, M. Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B Methodol 1974, 36, 111–133. [Google Scholar] [CrossRef]
  50. Hair Jr, J.F.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 2014, 26, 106–121. [Google Scholar] [CrossRef]
  51. Nitzl, C.; Roldan, J.L.; Cepeda, G. Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 2016, 116, 1849–1864. [Google Scholar] [CrossRef]
  52. Fan, K.K.; Xiao, P.W.; Su, C. The effects of learning styles and meaningful learning on the learning achievement of gamification health education curriculum. Eurasia J. Math. Sci. Technol. Educ. 2015, 11, 1211–1229. [Google Scholar] [CrossRef]
  53. Gilakjani, A.P. A match or mismatch between learning styles of the learners and teaching styles of the teachers. Int. J. Mod. Educ. Comput. Sci. 2012, 4, 51. [Google Scholar] [CrossRef] [Green Version]
  54. Wang, Y.; Dong, C.; Zhang, X. Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB. Comput. Appl. Eng. Educ. 2020, 28, 1421–1433. [Google Scholar] [CrossRef]
  55. Ajzen, I. Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior1. J. Appl. Soc. Psychol. 2002, 32, 665–683. [Google Scholar] [CrossRef]
  56. Mayer, R.E.; Massa, L.J. Three facets of visual and verbal learners: Cognitive ability, cognitive style, and learning preference. J. Educ. Psychol. 2003, 95, 833. [Google Scholar] [CrossRef]
  57. Caponetto, I.; Earp, J.; Ott, M. Gamification and education: A literature review. In European Conference on Games Based Learning; Academic Conferences International Limited: Berlin, Germany, 2014; Volume 1, p. 50. [Google Scholar]
  58. Cohen, A.M. The gamification of education. Futurist 2011, 45, 16. [Google Scholar]
  59. Pintrich, P.R. A conceptual framework for assessing motivation and self-regulated learning in college students. Educ. Psychol. Rev. 2004, 16, 385–407. [Google Scholar] [CrossRef] [Green Version]
  60. Hertzog, C.; Kramer, A.F.; Wilson, R.S.; Lindenberger, U. Enrichment effects on adult cognitive development: Can the functional capacity of older adults be preserved and enhanced? Psychol. Sci. Public Interest 2008, 9, 1–65. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  61. Abdullah, F.; Ward, R.; Ahmed, E. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Comput. Hum. Behav. 2016, 63, 75–90. [Google Scholar] [CrossRef]
  62. Alsawaier, R.S. The effect of gamification on motivation and engagement. Int. J. Inf. Learn. Technol. 2018, 35, 56–79. [Google Scholar] [CrossRef]
  63. Xu, J.; Lio, A.; Dhaliwal, H.; Andrei, S.; Balakrishnan, S.; Nagani, U.; Samadder, S. Psychological interventions of virtual gamification within academic intrinsic motivation: A systematic review. J. Affect. Disord. 2021, 293, 444–465. [Google Scholar] [CrossRef]
  64. Doumanis, I.; Economou, D.; Sim, G.R.; Porter, S. The impact of multimodal collaborative virtual environments on learning: A gamified online debate. Comput. Educ. 2019, 130, 121–138. [Google Scholar] [CrossRef]
  65. Saleem, A.N.; Noori, N.M.; Ozdamli, F. Gamification Applications in E-learning: A Literature Review. Tech. Know Learn 2022, 27, 139–159. [Google Scholar] [CrossRef]
  66. Buckley, P.; Doyle, E. Gamification and student motivation. Interact. Learn. Environ. 2016, 24, 1162–1175. [Google Scholar] [CrossRef]
  67. Ferri, F.; Grifoni, P.; Guzzo, T. Online Learning and Emergency Remote Teaching: Opportunities and Challenges in Emergency Situations. Societies 2020, 10, 86. [Google Scholar] [CrossRef]
  68. Noor, N.M.; Aini, M.; Hamizan, N.I. Video Based Learning Embedded with Cognitive Load Theory: Visual, Auditory, And Kinaesthetic Learners’ Perspectives. In Proceedings of the 2014 International Conference on Teaching and Learning in Computing and Engineering, Kuching, Malaysia, 11–13 April 2014; pp. 58–63. [Google Scholar] [CrossRef]
Figure 1. Theoretical Model.
Figure 1. Theoretical Model.
Applsci 12 12966 g001
Figure 2. Graphical output.
Figure 2. Graphical output.
Applsci 12 12966 g002
Table 1. Comparison with existing research.
Table 1. Comparison with existing research.
CategoriesRelated Studied PapersConclusions or Limitations ReachedThe Progress of This Study
Gamification[11]Gamification can enhance user experience.Exploring students’ behavioral intentions after integrating gamified instruction with online learning.
[14]Gamification works better in the short term to attract users.
[15]Whether gamification can stimulate long-term motivation to use is yet to be studied
[16]In the offline classroom, gamification can create a relaxed learning atmosphere for students
Technology Acceptance Model (TAM)[17]The Technology Acceptance Model (TAM) is proposedBased on TAM, the original variables were adjusted to the actual research problem, and new external variables were added (to be discussed in detail in the next section).
[18]“Perceived usefulness” was the most important predictor.
[19]“Perceived usefulness” and “perceived ease of use” directly influence people’s attitudes toward new technologies.
[20,21,22]Due to the different fields of study, other variables are added to TAM (i.e., self-efficacy).
[24]TAM is suitable as a theoretical basis for exploring students’ behavioral intentions toward online learning.
TAM in gamification[25]Gamification can be seen as a “technology”.The distinction between dynamic and static technology models is considered when constructing the theoretical model.
[27]Students who participate in gamified e-learning systems have higher levels of learning engagement.
[28]Learning experiences could influence students’ acceptance of video games in the classroom.
Gamification in online learning[10]Other individual factors such as students’ learning experiences can affect their performance in the gamified classroom.Due to the existence of individual differences, the same gamified learning activities may have different effects on different students; this paper considers students’ learning styles.
[11]The competitive nature of the game mechanics may cause students to experience learning anxiety and thus inhibit their motivation to learn.
[26]Future research will need to include learners from different cultural backgrounds and learning styles.
[32]The ability to maintain continuous learning motivation needs to be further studied.
Table 2. Descriptive Statistics.
Table 2. Descriptive Statistics.
CategoriesFrequenciesPercentages (%)
School LocationMacao38262.1
Place of Student SourceMacao13121.3
MajorHumanities and Social Science34756.3
Public Administration487.8
Science and Technology284.5
Table 3. Assessment of reflective measurement models.
Table 3. Assessment of reflective measurement models.
ConstructsIndicatorsFactor LoadingsCronbach’s AlphaComposite Reliability (rho_A)AVE
Table 4. Fornell-Larcker criterion.
Table 4. Fornell-Larcker criterion.
Table 5. Cross loadings.
Table 5. Cross loadings.
Table 6. HTMT.
Table 6. HTMT.
Table 7. VIF value.
Table 7. VIF value.
Table 8. Coefficient of determination (R2 value) and predictive relevance (Q2 value).
Table 8. Coefficient of determination (R2 value) and predictive relevance (Q2 value).
Table 9. Results of hypothesis testing.
Table 9. Results of hypothesis testing.
HypothesisRelationshipPath CoefficientConfidence Interval
H1PLT → INT0.129 **[0.042, 0.217]
H2PU → INT0.179 ***[0.130, 0.380]
H3PU → PLT → INT0.087 **[0.028, 0.146]
H4PE → INT0.324 ***[0.212, 0.446]
H5aVak-LS → INT0.015 ***[0.323, 0.492]
H5bVak-LS → PLT → INT0.039 *[0.012, 0.072]
H5cVak-LS → PU → INT0.122 **[0.402, 0.517]
H5dVak-LS → PE → INT0.202 ***[0.130, 0.286]
H6TTT*PE → INT−0.006 N[−0.023, 0.011]
Note: * p < 0.05; ** p < 0.01; *** p < 0.001; N: Not significant.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Yan, H.; Zhang, H.; Su, S.; Lam, J.F.I.; Wei, X. Exploring the Online Gamified Learning Intentions of College Students: A Technology-Learning Behavior Acceptance Model. Appl. Sci. 2022, 12, 12966.

AMA Style

Yan H, Zhang H, Su S, Lam JFI, Wei X. Exploring the Online Gamified Learning Intentions of College Students: A Technology-Learning Behavior Acceptance Model. Applied Sciences. 2022; 12(24):12966.

Chicago/Turabian Style

Yan, Haoqun, Hongfeng Zhang, Shaodan Su, Johnny F. I. Lam, and Xiaoyu Wei. 2022. "Exploring the Online Gamified Learning Intentions of College Students: A Technology-Learning Behavior Acceptance Model" Applied Sciences 12, no. 24: 12966.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop