Effectiveness of Digital Game Based Learning (DGBL) Tools Usage in Macroeconomics among Tertiary Education Students: ARCS Motivation Model

Effectiveness of Digital Game Based Learning (DGBL) Tools Usage in Macroeconomics among Tertiary Education Students: ARCS Motivation Model

Noormahayu Mohd Nasir^1*, Hafini Suhana Ithnin², Zarul Azhar Nasir³, Muhammad Adidinizar Zia Ahmad Kusairee⁴, Abdul Rahim Ridzuan⁵

^1,2,3,4Faculty of Business and Management, Universiti Teknologi MARA Perak Branch, Malaysia

⁵Faculty of Business and Management, Universiti Teknologi MARA, Malaysia

Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA, Malaysia

Centre for Economic Development and Policy (CEDP), Universiti Malaysia Sabah, Malaysia

Institute for Research on Socio Economic Policy, Universiti Teknologi MARA, Malaysia

Accounting Research Institute (ARI), Universiti Teknologi MARA, Malaysia

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.803389S

Received: 19 October 2024; Accepted: 23 October 2024; Published: 28 November 2024

ABSTRACT

The adoption of digital game-based learning (DGBL) tools in tertiary education is rapidly expanding, driven by digital transformation and the emergence of Education 5.0. This innovative educational paradigm prioritizes personalized learning, collaborative environments, and the integration of advanced technologies to improve educational outcomes. Within this evolving landscape, this research aimed to investigate the relationship between the ARCS motivation model—comprising the components of attention, relevance, confidence, and satisfaction—and the effectiveness of digital game-based learning (DGBL) tools specifically in a Macroeconomics course among tertiary education students. Additionally, the study seeks to identify the most significant determinants among these ARCS components that influence the effectiveness of DGBL tools. Employing a quantitative research design, the study utilizes a structured questionnaire for data collection and applies multiple regression analysis to rigorously examine the relationships among the variables. The findings reveal that high levels of attention, relevance, and confidence are positively and significantly associated with the effectiveness of DGBL tools, while satisfaction was found to be insignificant. Among these, attention emerged as the most impactful factor, highlighting the crucial role of student engagement in maximizing DGBL effectiveness in Macroeconomics courses. This study provides key insights for educators and instructional designers seeking to leverage motivational factors to enhance learning outcomes through digital tools in higher education.

Keywords: DGBL, Macroeconomics, ARCS Motivation Model.

INTRODUCTION

The use of digital game-based learning (DGBL) tools in contemporary tertiary education has been steadily increasing, reflecting advancements in digitalization and the emergence of Education 5.0 (Nasir et al., 2023). The integration of these tools in both online and physical classrooms significantly enhances student engagement, fosters active learning, and improves knowledge retention. Moreover, DGBL tools not only augment interactivity and enjoyment in the educational experience but also actively motivate students to participate, thereby mitigating feelings of boredom (Perini et al., 2018; Schaaf, 2012). Additionally, these tools facilitate the practical application of knowledge through simulations, promote collaboration and teamwork in multiplayer environments, and provide timely feedback to enhance learning. Furthermore, DGBL tools contribute to the development of both hard and soft skills, offering personalized learning experiences through adaptive features and thereby increasing the dynamism and effectiveness of classroom instruction.

However, the use of DGBL tools in education presents several challenges and issues. These challenges include the necessity of aligning games with specific learning objectives and outcomes, the high costs associated with implementation, and the need for modern technological infrastructure (Sera & Wheeler, 2017; Hauge et al., 2022). Teachers may encounter a steep learning curve, and incorporating DGBL into existing curricula can be time-consuming (Palha & Jukić Matić, 2023). Additionally, assessing learning outcomes through traditional methods can be difficult, and students may become overly focused on gameplay, leading to potential distraction (Deubel, 2006). Furthermore, excessive screen time raises health concerns, and some students may resist game-based approaches in favor of traditional learning methods (Ongoro & Fanjiang, 2024). To overcome these barriers and maximize the benefits of DGBL at the tertiary education level, careful planning and adequate support are essential.

To maximize the potential of Digital Game-Based Learning (DGBL) tools in enhancing student engagement and learning outcomes, integrating motivational frameworks such as the ARCS model (Attention, Relevance, Confidence, Satisfaction) is essential. This model has been effectively applied within DGBL tools to foster student engagement and improve overall learning effectiveness. Research indicates that incorporating ARCS elements into multimedia applications and games significantly enhance motivation and perceived enjoyment across various subjects (Lee & Hao, 2015). Additionally, the ARCS model has been successfully employed to create frameworks that sustain student engagement, particularly in communication and language learning contexts (Ghani et al., 2024). Studies have also validated the ARCS model’s effectiveness in blended learning environments, especially for information literacy courses. Key components for achieving effective online learning and favorable outcomes include ARCS-based digital materials, motivational course design, and student-centered learning environments (Chang & Chen, 2015).

Building on this foundation, this study aims to address two key questions related to the Macroeconomics course among tertiary education students: first, what is the relationship between the ARCS motivation model and the effectiveness of Digital Game-Based Learning (DGBL) tools? Second, which determinants most significantly influence the effectiveness of DGBL tools? Consequently, the research objectives are to investigate the relationship between the ARCS motivation model and the effectiveness of DGBL tools, as well as to identify the most significant determinants within the ARCS model that affect the effectiveness of DGBL tools in the context of the Macroeconomics course.

This study is structured into four distinct sections. Section 2 reviews the relevant empirical literature from the past scholars and researchers. Section 3 outlines the methodologies employed in the study. Section 4 presents the findings, and Section 5 concludes with recommendations.

LITERATURE REVIEW

This section presents key findings from the literature that highlight research gaps in ARCS motivation model towards effectiveness of DGBL. Numerous research utilizes the ARCS motivational model in the development of digital game-based learning to enhance student motivation (Hao & Lee, 2019; Travis, 2018; Lu et al., 2018; Huang & Oh, 2018; Wu, 2018; Chang et al., 2017). Chang et al. (2017) integrated the elements of the ARCS motivational design model with a game design concept to develop a Kinect-based immersive gaming framework aimed at enhancing learning motivation and the impact of gaming. The incorporation of numerous gaming characteristics into a learning model facilitated student engagement and enjoyment during the educational process.

Comprehensive strategic planning and teacher instruction in the utilization of digital games in the educational process are anticipated to enhance student comprehension of the subject matter and support teacher instruction. Learning has also become an enjoyable hobby. Khuda et al. (2022) conducted a study utilizing 20 prior investigations on digital game-based learning strategies in higher education, published between 2008 and 2021, selected through certain inclusion and exclusion criteria. The findings indicate that digital game-based learning strategies significantly influence the learning skills of students in higher education contexts. The digital game-based learning technique enhances student engagement and critical thinking skills.

This demonstrates that both intrinsic and extrinsic motivation can be enhanced, leading to a natural propensity among students to engage in learning. Tohidi and Jabbari (2012) assert that motivation is a process that empowers an individual to effectively confront challenges and obstacles to facilitate transformation. Prihartanta (2015) stated that motivation is a crucial element in the approach of a learning session. Various motivation theories and models inform researchers and are tailored to the objectives of their studies.

The ARCS model is one of the motivating frameworks referenced. The ARCS model is a motivational design framework comprising four components: attention, relevance, confidence, and satisfaction (Keller, 1987). Each component encompasses distinct subcategories that examine students’ motivational traits to assist educators in crafting educational environments and tactics that captivate students’ interest in learning. Woo (2014) asserts that DGBL is an educational method that boosts motivation and elevates cognitive load to enhance learning efficacy.  DGBL denotes educational innovations that amalgamate digital games with pedagogical value to enhance learning methodologies. The objective of this DGBL is typically to stimulate students’ interest and motivation to engage with the learning material through games.

The research demonstrated that the ARCS model effectively enhanced motivation for active learning. Hao and Lee (2019) assess an educational augmented reality (AR) game integrated with the ARCS motivational design approach to enhance student motivation in English language learning environments. This investigation indicated that students engaged in AR game lessons exhibited significantly more motivation for learning compared to those who experienced traditional learning methods.

The efficacy of educational sessions utilizing digital games is enhanced by the incorporation of suitable multimedia components. DGBL has demonstrated enhancements in student performance, facilitated enjoyable learning experiences, and elevated students’ attitudes and motivation (Zaman, Khairulamin & Ibharim, 2020). A study by Nusir et al. (2013) indicated that the two-way communication mechanism between students and professors is exemplary.

Additionally, the educational materials using animations aid in stimulating visual perception and augmenting student enthusiasm for learning (Lee & Hao, 2015). The digital game enhances learning by utilizing various multimedia to increase student engagement and emotional involvement (Baskaran & Muhammad Ihsan, 2017). Huang and Oh (2018) discovered in their investigation of the digital game-based educational environment that the attention and self-confidence dimensions of the ARCS motivational design model were much more influential in enhancing student motivation for learning through digital games. Both measures may indicate intrinsic motivational support inside the digital game-based learning environment.

Furthermore, the incorporation of digital games in education can enhance cognitive engagement among students by utilizing multimedia components, including text, graphics, audio, animation, and video. The attributes provided by digital games enhance pupils’ motivation to compete with peers through the acquisition of points or scores. Huang, Johnson, and Han (2013) proposed that attention and confidence can directly forecast total happiness with a digital game-based learning environment. Intrinsic motivation is a crucial and effective aspect of learning, especially when students possess complete autonomy in selecting when, what, and how to learn (Huang & Oh, 2018; Hassan & Ismail, 2020).

Moreover, a study conducted by Wan Daud et al. (2020) demonstrated the greatest mean satisfaction levels. The research assessed the impact of mobile learning on motivation for acquiring the Arabic language. The study investigates interest in mobile Arabic learning. The ARCS model elucidated how mobile app education motivated responses. A total of 273 Malaysian university students utilized mobile learning to acquire Arabic and subsequently completed a motivation questionnaire. The willingness of students to study Arabic via mobile applications was significantly influenced by attention, relevance, contentment, and confidence, with satisfaction yielding the highest mean score. This study was in line with findings by Ammar et al (2024). The study’s findings indicated correlations among all three ARCS components. Consistent class attendance and proactive help-seeking in the satisfaction components of the ARCS Model yield the highest mean score.

Finally, Wertenauer et al. (2024) examine the motivational capacity of students using 21 qualitative semi-structured interviews performed with randomly selected individuals from the AIEDN research. The examined interviews were situated within the ARCS (Attention, Relevance, Confidence, Satisfaction) paradigm of motivation. The findings indicate that the AIEDN AI Learning Assistant can enhance learning motivation. The most crucial elements are new functions and relevant support.

In conclusion, the findings of a literature analysis on DGBL research concerning student motivation indicated the necessity to examine the application of instructional tools utilizing digital games to assess their effectiveness on motivation in the online or physical learning context.

METHODOLOGY

A. Research Framework

Based on the previously discussed literature review, the study framework, illustrated in Figure 1, was designed to assess the relationship between the ARCS model’s components—attention, relevance, confidence, and satisfaction—and the effectiveness of Digital Game-Based Learning (DGBL). Each of these independent variables is grounded in prior research on the ARCS Motivation Theory, with the framework specifically focused on understanding how these factors contribute to the effectiveness of DGBL, the dependent variable in this analysis.

Fig. 1 Framework of the study

B. Questionnaire

This research employed a quantitative method for data gathering, involving the development of a structured questionnaires distributed to selected respondents. According to Sekaran and Bougie (2010), a questionnaire is a structured set of written questions that typically allows respondents to select from a limited range of predefined options. Additionally, using a questionnaire is an efficient method for data collection when the researcher has a clear understanding of the information required and the variables to be measured. The questionnaire items were adapted from previous research to align with the study’s objectives of examining the relationship between the ARCS model components and the effectiveness of DGBL tools, as well as identifying the most impactful determinants.

A five-point Likert scale was employed to measure responses, with options ranging from one to five (1=strongly disagree, 2=disagree, 3=not sure, 4=agree and 5=strongly agree). The questionnaire consists of approximately 24 items, organized into six distinct sections: Part A gathers demographic information, Part B addresses attention factors, Part C examines relevance factors, Part D focuses on confidence factors, Part E explores satisfaction factors, and Part F assesses the effectiveness of Digital Game-Based Learning (DGBL) tools.

Next, the questionnaire was created using Google Forms to facilitate online distribution of the survey. The researcher shared the Google Form link with students across various groups via the WhatsApp application. Prior to distributing the link, the researcher provided a detailed explanation of the survey’s purpose and offered instructions on how to select the appropriate responses.

C. Population and Sampling

This study focuses on accounting students enrolled in the Macroeconomics (ECO211) course at UiTM Perak Branch, Tapah Campus. The course was selected due to observed challenges students face in understanding its content, which includes complex theories and calculations. Many students experience stress as they work to grasp the material and achieve high academic performance. To address these difficulties, Digital Game-Based Learning (DGBL) was introduced as an innovative approach to diversify teaching methods and enhance learning experiences.

Out of a total population of 150 students from five distinct groups (A, B, C, D, and E), all were approached for participation in this study. According to the Krejcie and Morgan table, a minimum sample size of 108 was deemed sufficient for meaningful data collection. Data collection commenced in mid-July, with 138 questionnaires completed, achieving a 92% response rate. The data were initially recorded in an Excel file and subsequently transferred to SPSS for analysis.

D. Data Analysis Procedure

The latest version of SPSS software was utilized to analyze the collected data. The analysis procedures employed multiple regression techniques to examine the relationship between the independent and dependent variables. The significance of the relationship was confirmed by determining whether the significance value was less than 0.05, a threshold commonly accepted in social science research. Furthermore, the factors that contribute the most statistically significant contributions to the dependent variable were identified through the highest beta coefficients.

FINDINGS AND DISCUSSION

A. Respondents’ Profile

According to Pallant (2011), this measurement is necessary to obtain statistics for categorical variables, allowing for a description of the frequency of each response. In this study, four items were examined using frequency analysis, including gender, semester, group, and prior experience with digital game-based learning tools.

Table 1 shows that the respondents consist of 40 males (29.0 percent) and 98 female (71.0 percent), totalling of 138 participants. It indicates that three quarters of respondents are female.

Table 1: Descriptive Analysis (Frequencies) for Gender

In term of semester distribution, most respondents are in semester 4 accounting for 97.8 percent (135 students), while only 2.2 percent (3 students) are in semester 5. This is because most students enrolled in the Macroeconomics (ECO211) course are in their fourth semester. Students in semester 5 are typically repeaters of the course. Table 2 presents the details of respondent distribution in this study.

Table 2: Descriptive Analysis (Frequencies) for Semester

The analysis of the respondent groups, as shown in Table 3, reveals that there are 28 respondents (20.3 percent) from A4AC1104A, 23 respondents (16.7 percent) from A4AC1104B, 31 respondents (22.5 percent) from A4AC1104C, 30 respondents (21.7 percent) from A4AC1104D and 26 respondents (18.8 percent) from A4AC1104E.

Table 3: Descriptive Analysis (Frequencies) for Group

Moreover, respondents were asked about their prior experience with digital game-based learning tools. According to the frequency analysis, 95.7% reported having used digital game-based learning tools, while only 4.3% had not. This information is summarized in Table 4.

Table 4: Descriptive Analysis (Frequencies) for Experience

B. Reliability

This analysis was conducted to assess the scale’s internal consistency by examining Cronbach’s alpha in the reliability statistics (Pallant, 2011). According to DeVellis (2016), a Cronbach’s alpha coefficient above 0.7 is considered acceptable. Table 5 shows that the ARCS factors demonstrate good internal consistency, with a Cronbach’s alpha coefficient of 0.925.

Table 5: Cronbach Alpha Result

C. Correlation Analysis

This research examines the relationship between the independent variables including attention, relevant, confidence and satisfaction and the dependent variable, effectiveness of digital game-based learning tools. According to Pallant (2011), correlation coefficients yield values between -1 and +1, where -1 indicates a perfect negative correlation, +1 indicates a perfect positive correlation, and 0 indicates no correlation. A negative correlation suggests that an increase in one variable corresponds to a decrease in the other, whereas a positive correlation indicates that as one variable increases, the other does as well. Furthermore, the strength of these correlations can be categorized as weak (r = 0.10 to 0.29), moderate (r = 0.30 to 0.49), and strong (r = 0.50 to 1.0) per Cohen’s (1988) guidelines.

Table 6: Correlation Results

Table 6 presents the correlation results for each variable. There was a strong, positive correlation between all the independent variables (attention, relevant, confident and satisfaction) and the dependent variable, effectiveness of digital game-based learning (DGBL), with correlation coefficients (r) ranging from 0.610 to 0.723 (n = 138, p < .001). These results indicate that higher levels of attention, relevance, confidence, and satisfaction are associated with higher levels of effectiveness in digital game-based learning tools.

D. Multiple Linear Regression Analysis

Multicollinearity arises when independent variables in a regression model exhibit high correlations, potentially undermining the interpretability of individual predictor contributions and leading to inflated standard errors. Detecting and addressing multicollinearity is critical to ensure that regression coefficients remain stable and the model results accurately represent the underlying data structure.

Multicollinearity was assessed in this study by analyzing tolerance and Variance Inflation Factor (VIF) values, as presented in Table 7. Tolerance values, calculated as 1−R squared, reflect the proportion of variance in an independent variable not explained by other predictors. A tolerance threshold below 0.10 suggests concerning levels of multicollinearity (Pallant, 2011). The VIF, calculated as the inverse of tolerance, further indicates multicollinearity severity, with values above 10 typically considered problematic.

Table 7: Collinearity Diagnostic

The results indicate that the tolerance values for each independent variable range from 0.309 to 0.374, all remaining well above the critical threshold of 0.10. Therefore, this research does not violate the multicollinearity assumption. This conclusion is further supported by the VIF values, which range from 2.677 to 3.240, all of which are significantly below the threshold of 10. This means that the regression model is reliable, allowing for clear interpretation of how attention, relevance, confidence, and satisfaction contribute to the effectiveness of digital game-based learning tools.

Next, the adjusted R-squared for this model was 0.597 as shown in Table 8. This indicates that 59.7% of the variation in the effectiveness of digital game-based learning tools can be explained by the ARCS factors. This substantial explanatory power underscores the critical role of attention, relevance, confidence, and satisfaction in enhancing the effectiveness of these learning tools.

Table 8: Model Summary for ARCS Factors

a. Predictors: (Constant), Satisfaction, Confident, Relevant, Attention

b. Dependent Variable: Effectiveness

Furthermore, the F-ratio (F = 49.241, p = .001) presented in Table 9 reinforces the significance of the regression model. The F-ratio tests whether the explained variance in the model is significantly greater than the unexplained variance, indicating the overall fit of the model. It is essential to ensure that the ANOVA significance and the p-values for the coefficients fall within the range of p < 0.05 for the model to be considered statistically significant. The results confirm this, as the p-value of 0.001 indicates a robust and significant predictive capability of the model.

Table 9: ANOVA for ARCS Factors

Research Question 1: What is the relationship between the ARCS motivation model and the effectiveness of DGBL tools?

Table 10 presents the results of the multiple regression analysis, examining the influence of the ARCS factors—attention, relevance, confidence, and satisfaction—on the effectiveness of digital game-based learning tools. The results indicate that attention, relevance, and confidence exhibit significant positive regression coefficients, with p-values of 0.001, 0.038, and 0.037, respectively. This suggests that accounting students at UiTM Tapah who score higher in these areas are expected to demonstrate greater motivation to utilize digital game-based learning tools, even when controlling for other variables in the model. In contrast, satisfaction shows a positive but statistically insignificant relationship with the effectiveness of digital game-based learning tools, as evidenced by a significance value of 0.136.

These results are supported by Hao and Lee (2019), who reported that the attention determinant significantly influences students engaged in AR game-based learning. Their findings indicate that when students are interested and motivated to explore further, they are inspired to continue playing, which ultimately enhances attention and elicits a positive response. Additionally, Hao and Lee (2019) emphasize that relevance and confidence also have a substantial impact on the application of AR games. Using familiar stories like Aladdin as teaching material proved to be an effective strategy, as these narratives resonate with students, making the learning experience enjoyable and relevant. Furthermore, the majority of students reported that effective use of the teaching materials helped build their confidence and self-assurance, fostering a sense of success and positive expectations.

Table 10: Coefficients Results for ARCS Factors

Research Question 2: Which determinants most significantly influence the effectiveness of DGBL tools?

In addition, the beta coefficients indicate that the attention factor contributes the largest coefficient, with a value of 0.322. This suggests that attention makes the strongest unique contribution to explaining the effectiveness of digital game-based learning tools when controlling for the variance explained by all other variables in the model. In addition, the beta coefficients for the other independent variables are slightly lower, ranging from 0.135 to 0.220, indicating that they make a lesser unique contribution to the model. Overall, these findings underscore the importance of attention in enhancing the effectiveness of digital game-based learning tools for accounting students at UiTM Tapah while also highlighting the supportive roles of relevance and confidence in this context. These results are supported by Hao and Lee (2019), who reported that the attention determinant significantly influences students engaging in AR game-based learning. Their findings indicate that students become more interested and motivated to explore further, which inspires them to continue playing, ultimately enhancing attention, and eliciting a positive response.

CONCLUSIONS

This research aimed to investigate the relationship between the ARCS motivation model and the effectiveness of Digital Game-Based Learning (DGBL) tools in a Macroeconomics course among tertiary education students. Utilizing a quantitative approach, data were gathered through questionnaires distributed to 138 Accountancy students at UiTM Perak Branch Tapah Campus. Multiple regression analysis was then utilized to test the relationship between the ARCS motivational factors and the effectiveness of the DGBL tools.

The findings reveal that the components of the ARCS model, namely attention, relevance, and confidence, significantly and positively influence the effectiveness of DGBL tools, while satisfaction was found to be insignificant. Among these factors, attention was identified as the most impactful factor, with a beta coefficient of 0.322. This highlights the vital role of sustaining student attention in optimizing the learning experience through DGBL in the context of Macroeconomics courses.

The insights gained from these findings offer various important implications. Firstly, educators and instructional designers should prioritize strategies that capture and maintain student attention. This could involve integrating more interactive elements, such as real-time quizzes, game mechanics, and multimedia content, which have been shown to enhance engagement and motivation. Furthermore, relevance is critical; ensuring that DGBL tools are connected to real-world applications of Macroeconomics can foster deeper understanding and student interest. The findings also suggest that building student confidence in using DGBL tools may enhance their effectiveness, emphasizing the need for supportive resources and training.

In conclusion, this study contributes to the growing body of literature on educational technology, offering valuable insights into the application of motivational theories to enhance learning in higher education. By focusing on attention, relevance, and confidence, educators can refine their instructional strategies and harness the full potential of DGBL tools, ultimately leading to improved learning outcomes in Macroeconomics and beyond. The integration of these insights into pedagogical practices can help create more dynamic and effective learning environments, preparing students to thrive in an increasingly complex economic landscape. It is suggested that future researchers consider incorporating additional variables other than motivation model, such as cognitive complexity, stress, anxiety, the role of teachers, in-game instructions, and prior knowledge, which may influence the effectiveness of digital game-based learning (DGBL).

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