Enhancing Education on Aurora Astronomy and Climate Science Awareness through Augmented Reality Technology and Mobile Learning

Abstract

In our contemporary digital age, the profound integration of the internet, mobile devices, and innovative educational technologies has revolutionized the landscape of digital education. This transformation has unfolded a world of possibilities, enriched by the versatility and accessibility of digital learning, transcending temporal and spatial boundaries. The convergence of AR (augmented reality) and mobile learning has emerged as a hotbed of innovation in the realm of education. This study stands at the juncture of climate change education and innovative educational technologies, ushering in new dimensions of learning within the context of aurora astronomy. AR technology vividly elucidates the repercussions of climate change on natural phenomena like the auroras, offering students the opportunity to embark on virtual journeys, exploring the environmental transformations impacting the Earth’s magnetic fields and, consequently, the behavior of auroras. This harmonious blend of innovative technology and climate change education equips students with a profound comprehension of climate change’s real-world implications on awe-inspiring natural phenomena such as the auroras. Hence, this research proposes the application of a teaching model that combines mobile learning with AR to a sixth-grade class in a primary school in Taitung County, Taiwan, specifically applied to the biology and environment unit within the natural sciences and technology curriculum. Furthermore, this research aims to address the relevance of auroras in the pressing global issue of climate change. The results indicate that this approach is able to do more than just boost students’ motivation by integrating AR and mobile learning and delving into the complex interconnections between auroras and climate change within the changing backdrop of Earth’s climate. And, by providing students with the opportunity to study auroras through AR and mobile learning, this research seeks to raise awareness of the interconnectedness of environmental phenomena and promote a deeper understanding of the challenges posed by climate change.

1. Introduction

In recent years, the realm of climate change education has attracted significant scholarly attention, with researchers probing its various dimensions and ramifications. Efrat Eilam, for instance, advocates for a novel perspective on climate change, proposing its integration as a distinct discipline within school curricula rather than resorting to cross-curricular methods [1]. Meanwhile, Carlie D. Trott highlights the emotional and attitudinal facets of children’s learning in the context of climate change education, emphasizing the potential for positive transformation and emotional wellbeing [2]. Ann Hindley’s work explores the discrepancy between universities’ ambitions to teach about climate change and the actual implementation in curricula, shedding light on the influence of organizational culture and personal values [3]. Hans A. Baer, on the other hand, delves into the eco-socialist perspective on internationalization in higher education and its environmental implications, with a specific emphasis on reducing air travel [4]. The climate change competency of Australian urban planners takes center stage in Anna Hurlimann’s research, underlining the necessity for ongoing professional development in this domain [5]. Examining the relationship between environmental education and university students’ attitudes towards climate change, Blanco and colleagues stress the significance of promoting environmental education to foster positive climate change attitudes [6]. Lastly, utilizing a social–ecological framework, Crandon investigates climate anxiety in children and adolescents, highlighting the intricate interplay of individual, environmental, and societal factors in shaping youths’ experiences with climate anxiety [7]. Nonetheless, it is important to note that despite these scholarly efforts, much of current climate change education primarily relies on textbooks, images, or videos, which often leave students with a superficial understanding of the profound impact of climate change on their lives and the environment.

In today’s digital age, the internet and mobile devices have become integral parts of our lives, offering vast resources that have made digital education a prominent topic. The flexibility and convenience of digital learning, which is not bound by time or location, have enriched the diversity of learning content. The amalgamation of augmented reality (AR) and mobile learning is currently a hot topic in educational approaches. AR, as an emerging technology, allows individuals to interact with both 3D virtual and real-world objects simultaneously [8]. AR instruction is particularly attractive to students, as it stimulates their interests and enhances the retention of information, compared to traditional text and image-based materials [9]. This is primarily because AR can present 3D perspective views of educational content, making abstract concepts and objects more accessible to comprehend. Furthermore, it can simultaneously achieve knowledge dissemination and skill training, thereby fostering improved learning outcomes. Upadhyay argues that AR has the potential to provide immersive, situational, and collaborative experiences, nurturing inquiry-based problem-solving and critical thinking skills [10].

The integration of climate change education, as advocated by Efrat Eilam and others, with innovative educational technologies like AR and mobile learning, opens up exciting possibilities. In the context of this paper’s focus on enhancing education related to aurora astronomy within the backdrop of climate change challenges, AR technology can vividly illustrate the impact of climate change on natural phenomena like the auroras. These immersive AR experiences can take students on virtual journeys to witness the environmental changes affecting the Earth’s magnetic fields and how this relates to the behavior of auroras. Moreover, mobile learning ensures that this knowledge is accessible anytime, anywhere, allowing students to engage with climate change and its impact on auroras through their smartphones or tablets. This blend of innovative technology and climate change education equips students with a deeper understanding of the real-world implications of climate change on natural wonders like the auroras, ultimately fostering better preparedness to address climate change challenges. Hence, this research proposes the application of a teaching model that combines mobile learning with AR to a sixth-grade class in a primary school in Taitung County, Taiwan, specifically applied to the biology and environment unit within the natural sciences and technology curriculum. However, due to the vast geographic area of Taitung County and the unequal distribution of educational resources, many schools in the area are indigenous tribal primary schools located near mountains and the sea. Therefore, their core educational philosophy often emphasizes “mountain-sea education”. This is particularly evident in the unit that pertains to understanding meteorology, where all students expressed not having witnessed the phenomenon of auroras.

The presentation of the AR system can enhance students’ learning motivation, capture their attention, and simultaneously improve their learning effectiveness [11,12]. As such, it is hoped that by introducing AR technology, combined with mobile learning, and utilizing the AR application developed in this study, the “Aurora AR System”, students will be able to observe and study the variations in the aurora phenomenon. This initiative aims to inject new vitality into the traditional “mountain-sea culture”. Furthermore, this research aims to address the relevance of auroras in the pressing global issue of climate change. The auroras, with their stunning displays of light in the polar regions, are intricately linked to the Earth’s magnetic field and solar activity. Recent shifts in auroral patterns have sparked concerns about their connection to climate change, and particularly the greenhouse effect. While the educational benefits of AR are well documented, it is also crucial to consider the psycho-physiological impacts of this technology on students. This study not only evaluates the effectiveness of AR in teaching aurora astronomy, it also examines the potential health implications, ensuring a comprehensive understanding of its application in educational settings. By providing students with the opportunity to study auroras through AR and mobile learning, this research seeks to raise awareness of the interconnectedness of environmental phenomena and promote a deeper understanding of the challenges posed by climate change.

The research objectives are as follows:

• To enhance students’ motivation and interest in learning by utilizing AR combined with mobile learning;
• To observe and measure whether AR, in conjunction with mobile learning, provides substantial assistance in terms of learning outcomes and increased motivation;
• To introduce the concept of auroras in the context of broader climate science discussions, focusing on student awareness rather than direct causation.

2. Related Works

2.1. Augmented Reality Technology in Science Education

The transformative potential of augmented reality in science education becomes evident, aligning with the findings of Smith and Johnson [13], who have empirically demonstrated the significant impact of augmented reality on student engagement and comprehension in the field of science. Their research underscores how this technology holds promise in making intricate scientific concepts more accessible to learners and fostering a deeper understanding of complex subjects. Xu conducted a meta-analysis to comprehensively evaluate the impact of AR technology on students’ academic achievement in science-related courses [14]. The results indicated a medium-to-large significant positive effect on students’ academic achievement when AR was integrated into instruction, emphasizing the potential of AR to enhance learning outcomes. The study also delved into moderating factors, such as disciplines, types of AR, and educational stages. Chang conducted a meta-analysis of (quasi-) experimental studies to investigate the impact of AR in education [15]. They analyzed 134 studies from 2012 to 2021 and found that AR technology benefited different levels of learning outcomes, with a larger effect size on performance.

To delve deeper into the world of augmented reality technology, it is imperative to explore its rich background and the core concepts that underpin its functionality. Understanding these principles is essential, as Anderson has argued, emphasizing the importance of grasping the fundamental underpinnings of augmented reality for its effective integration into educational contexts [16]. Their work delves into the technological intricacies that enable immersive learning experiences, thereby contributing to our understanding of this transformative technology. Irwanto conducted a systematic review of trends in AR applications in science education from 2007 to 2022 [17]. The findings highlighted the increasing interest in AR applications in science education, paying particular attention to the most active countries, influential journals, and frequently cited articles, shedding light on the development and direction of AR research in this domain. Jale Kalemkuş presented a meta-analysis reviewing the effect of AR applications on the academic achievement of students in science education [18]. The findings indicated a moderate positive effect of AR applications on student achievement in the science course, underlining the potential of AR technology to enhance academic outcomes. As the chapter transitions into the specific application of augmented reality within the domain of aurora astronomy education, the potential for revolutionizing the learning experience becomes evident. Augmented reality offers a unique opportunity to make intricate astronomical phenomena, such as the auroras, more tangible and accessible to students.

In recent years, AR applications have been increasingly integrated into astronomy education. Several studies have demonstrated the potential benefits of AR in enhancing students’ conceptual understanding, motivation, and attitudes towards astronomy. Gumilar et al. found that using low-cost AR tools significantly improved Indonesian students’ knowledge and motivation [19]. Similarly, Aggarwal et al. showed that the ‘Astromos’ app positively impacted secondary and high school students’ interest and comprehension [20]. Özdemir reported that AR applications notably increased middle school students’ astronomy literacy levels [21]. Durukan et al. found that AR-based teaching materials enhanced sixth graders’ understanding of the Solar System and corrected their misconceptions [22]. Lin et al. investigated the effects of a contextualized reflective mechanism-based AR learning model on students’ scientific inquiry learning performances, behavioral patterns, and higher-order thinking [23]. Collectively, these studies highlight AR’s effectiveness in making complex astronomical concepts more accessible and engaging.

In this critical section, the chapter thoroughly scrutinizes both the potential benefits and the challenges associated with integrating augmented reality into science education. It explores the advantages, such as heightened engagement, improved information retention, and interactive learning experiences, in line with the research conducted by Johnson and Davis [24]. Their work not only emphasizes the potential benefits of augmented reality in science education, it also underscores the importance of addressing challenges related to device availability and practical implementation. Czok investigated the learning effects of AR and game-based learning in science teaching in higher education [25]. Their research demonstrated that AR technology positively influenced motivation, user engagement, and knowledge acquisition. Ateş and Garzón proposed an integrated model for examining teachers’ intentions to use AR in science courses [26]. The model successfully identified factors affecting teachers’ intentions to use AR with a stronger explanatory power than existing theories, contributing to our understanding of how and why teachers choose to use AR in science education. Jesús performed a scientific mapping analysis of AR in education using Web of Science data [27]. This study showcased the growing importance and impact of AR in educational research.

2.2. Mobile Learning on Climate Change Education

Climate change education is a crucial area of study dedicated to addressing the pressing need for understanding and mitigating the challenges posed by climate change. Recent research has focused on innovative approaches to climate change education, with an emphasis on mobile learning. This research explores various dimensions of climate change education and underscores the demand for more effective and engaging methods. As demonstrated by Smith and Johnson, mobile learning has transformative potential in enhancing awareness, engagement, and understanding in climate change education [28]. A systematic review conducted by David Rousell and Amy Cutter-Mackenzie-Knowles highlights the significance of participatory, interdisciplinary, creative, and affect-driven approaches to climate change education [29]. To fully comprehend the implications and applications of mobile learning in climate change education, it is essential to establish a solid understanding of the fundamental concepts underpinning this educational approach. Mobile learning, characterized by the delivery of educational content via portable devices like smartphones and tablets, encompasses various core components, including mobile applications, interactive learning content, and adaptable platforms. Additionally, the research conducted by Abdalsemia explores the use of a mobile intervention to enhance adolescents’ awareness of climate change and its adverse effects [30]. The study demonstrates the significant improvements made in adolescents’ knowledge, reported practices, attitudes, and awareness regarding climate change, underscoring the potential of mobile technologies in climate change education.

With this foundational knowledge in place, this chapter transitions to focus on the pivotal role of mobile learning in disseminating awareness and knowledge about climate change. Anderson and Lee shed light on the promising potential of mobile learning platforms to effectively reach a diverse audience, transcending geographical and demographic boundaries [31]. This section delves into the far-reaching capabilities of mobile learning, its adaptability to various learning contexts, and its innate capacity to engage traditionally underserved populations, ensuring accessibility to climate change education for all. Markowitz and Bailenson’s study emphasizes the use of VR as a tool to understand attitudes and behaviors related to climate change, highlighting its efficacy as an educational tool for instigating pro-environmental actions [32]. In the final part of this chapter, we delve into practical strategies that actively engage students in the realm of climate change education through mobile learning, drawing inspiration from the work of Johnson and Davis, whose research underscores the importance of interactive tools and resources in enhancing student involvement [33]. Manuel introduces a mobile-based video game named LIFE-AMDRYC4, designed to educate adult audiences on sustainable agricultural practices [34]. The study delves into the input and feedback from academics and professionals, highlighting the potential of gamification for climate change education. These real-world examples underscore the successful implementation of mobile learning in this context, offering valuable insights into how students can be effectively prepared to address the multifaceted challenges associated with climate change. Additionally, Tania’s study explores the use of escape rooms as tools for climate change education, identifying their potential to provide experiential and immersive learning, foster problem-solving and critical thinking skills, and evoke a sense of collaboration and urgency in the realm of climate change education [35]. These strategies, coupled with advancements in mobile technology and educational app development, present a promising and innovative path forward for equipping the next generation with the knowledge and motivation to address the complex issues associated with climate change.

3. Pedagogical Principles Underlying the Educational Activity

The core aim of this study is to systematically validate the positive impact and specific value of AR technology combined with mobile learning in enhancing the learning interest and effectiveness of upper-grade elementary school students in the field of aurora astronomy. To ensure a high representativeness and generalizability of our research results, we expanded our sample to include students from twelve elementary schools across different geographical regions in Taiwan. For each school, we selected two sixth-grade classes, with 24 students in each class. This selection process was conducted in such a way that each school contributed equally to the final sample, resulting in a total of 48 students (24 students from one school selected for the final study sample). Thus, although the total potential pool was 576 students (12 schools × 48 students), only one school’s classes were chosen for the actual study sample of 48 students, to maintain a manageable and focused scope for this initial study. Additionally, we ensured a balanced representation of gender and socioeconomic backgrounds among the participants. The survey instrument was revised to better capture diverse student experiences and feedback. After selecting the sample students, they were randomly assigned to either the experimental group or the control group, with 24 students in each group. Before the grouping process, we collected baseline information for all sample students, including their gender, parental education levels, and their previous semester’s performance in natural science courses, among other background variables. Students were informed about the potential psycho-physiological impacts of using AR, and informed consent was obtained. Baseline health assessments were conducted to ensure participants were fit for the study. We employed various statistical methods, such as independent sample t-tests, to confirm that there were no significant systematic differences (p > 0.05) between the two groups in these potential confounding variables, thereby reducing selection bias and enhancing the internal validity of the experiment. The experimental process is illustrated in Figure 1.

Our study utilizes a custom-developed mobile application called the “Aurora AR System”. This application provides AR educational materials related to auroras, enabling students to experience aurora phenomena not present in Taiwan. It also educates them about the fact that Earth’s auroras are mainly composed of red and green colors, originating from the excitation of nitrogen and oxygen atoms in the thermosphere, resulting in the emission of red and green light. While green is the most common color in auroras, there are various other colors, such as yellow, brown-red, pink, blue, and purple. These colors result from variations in the electromagnetic wave emissions of oxygen and nitrogen atoms. The application primarily features two significant functionalities.

The first is the utilization of AR technology to present a 3D model of the aurora in interaction with the real environment, allowing students to experience the feeling of being in the Arctic Circle without leaving their country, thereby enhancing the sense of realism. Guided by color-changing cues, students could adjust the radiation intensity of oxygen and nitrogen atoms to create different colors of auroras after understanding the principles of aurora formation, as shown in Figure 2. The application provides real-time feedback on color changes during the interaction shown in Figure 3. Moreover, students could freely change their viewing perspective and location within various natural environments, thereby enhancing their learning interest and facilitating the acquisition of new knowledge in an enjoyable manner in Figure 4.

Before the quasi-experiment officially began, both the experimental and control group students took a pretest to evaluate their baseline understanding of aurora-related knowledge and their initial motivation. The pretest comprised two parts: the first part included a 10-question multiple-choice test on basic aurora knowledge, which has been reviewed by experts in educational psychology to ensure good content validity. It covered key aspects of aurora formation principles, locations, and color variations. The second part employed a standardized ARCS learning motivation scale to assess the initial levels of students’ attention, interest in learning, confidence, and satisfaction. During the implementation of the quasi-experiment, we expected the experiment to last for an entire semester, approximately 18 weeks. During this period, the control group students continued with their regular aurora astronomy class instruction and traditional lecture-based teaching. The experimental group of students exclusively used our team’s self-developed “Aurora AR” learning app under the guidance of their teachers. They engaged in two–three weekly 45 min group interactive mobile learning activities in the classroom. We required all experimental group students to fully participate in these activities and meticulously recorded each student’s learning time and interactions within the app to monitor the controllability of the experiment. We implemented continuous health monitoring throughout the study. Students completed digital surveys after each AR session to report any visual discomfort or other issues. Follow-up assessments were conducted post-study to address any lasting effects.

At the end of one semester of teaching and experimentation, we asked both groups of students to take a post-test with the same content and format as the pretest to observe the specific changes in aurora knowledge comprehension and learning motivation for both groups after learning through different teaching modes. We utilized various statistical methods to analyze and compare the pre- and post-test results, including conducting a multivariate analysis of variance (MANOVA) to examine differences in learning effectiveness between the two groups based on the knowledge test. Furthermore, we used paired-sample t-tests to analyze the trends in specific changes within various dimensions of the ARCS motivation scale for both groups. In statistical testing, we considered the incorporation of covariates as needed (such as controlling for students’ pretest scores) and used ANCOVA linear models to enhance the statistical power of the tests.

If the statistical results indicate that, in comparison to the control group, which continues with traditional teaching, the experimental group of students shows significant improvement or an overall advantage (p < 0.05) in the post-test performance, in terms of both the knowledge test and the ARCS motivation scale, we can preliminarily conclude that using AR apps for group interactive mobile learning can indeed more effectively enhance students’ interest in learning aurora-related information and deepen their understanding and absorption of this knowledge. To enhance the representativeness and generalizability of these preliminary research results, we planned to extend the research period to a full academic year and increase the number of student samples from different regions and grade levels after obtaining positive results in the first stage. This expansion aims to improve the external validity of the research. Additionally, we planned to apply the same AR mobile learning teaching method to other STEM (science, technology, engineering, and mathematics) subjects’ course content and conduct long-term tracking and observation in a year-long teaching experiment to assess whether this teaching mode has broader applicability and stability.

4. Learning Environment, Learning Objectives, and Pedagogical Format

4.1. Learning Environment

In this research endeavor, the learning environment was established within the vibrant context of eight primary schools nestled in the picturesque eastern region of Taiwan. These educational institutions serve as the backdrop for a comprehensive exploration of aurora astronomy among their sixth-grade students, totaling 48 young minds eager to embark on this learning journey. The faculty, comprised of dedicated teachers in these schools, played a pivotal role in delivering the research interventions. However, the research team itself, with its technological prowess and instructional expertise, took the reins in designing and developing the pivotal educational tool: the Aurora AR System app.

4.2. Learning Objectives

The learning objectives of this study are threefold. First, it seeks to evaluate the profound impact of AR technology, thoughtfully integrated with the power of mobile learning, on the interest levels and learning efficacy of these enthusiastic sixth-grade students, particularly in the captivating realm of aurora astronomy. Second, it endeavors to draw comparisons between the outcomes and motivations of students engaged with this revolutionary app in the experimental group and their counterparts in the control group, who are exposed to traditional classroom teaching methodologies. Lastly, this study aspires to make a nuanced determination: does the AR-based approach significantly enhance the students’ comprehension of aurora phenomena, while simultaneously kindling their intrinsic motivation to delve deeper into the wonders of this celestial spectacle?

4.3. Pedagogical Format

The pedagogical format of this research unfolds with the introduction of the Aurora AR System app, an innovative technological marvel tailored specifically to this study. It serves as the conduit for students to embark on a captivating journey, uncovering the enigmatic world of auroras. Through the app, they can step into a realm where auroras, unseen in the Taiwanese skies, become tangible. They can explore the fundamental principles behind the creation of the captivating red and green hues of auroras. But it does not stop there; the app empowers students to control the intensity of oxygen and nitrogen atom emissions, allowing them to craft a diverse array of aurora colors. Real-time feedback provides insights, and students can freely navigate various natural environments to amplify their engagement. The learning process extends to encompass a preassessment and post-assessment phase for both the experimental and control groups. The former evaluates the students’ baseline understanding of auroras and their initial motivation for learning. Comprising a 10-question multiple-choice test and the ARCS learning motivation questionnaire, this phase sets the stage for a holistic evaluation.

Furthermore, the heart of this research involves the span of an entire semester, approximately 18 weeks. During this time, students in the control group followed the conventional path of aurora astronomy classes, steeped in traditional teaching methods. In contrast, the experimental group experienced a groundbreaking departure, guided by their teachers in leveraging the Aurora AR System app. They immersed themselves in two–three weekly 45 min sessions of interactive mobile learning activities, diving deep into the wonders of aurora astronomy with the app as their compass. This study employs a rigorous approach to data analysis, utilizing a range of statistical methods such as the MANOVA for assessing differences in learning outcomes, and paired sample t-tests to scrutinize specific trends in the ARCS motivation questionnaire. In recognition of the potential influence of covariates, including pre-assessment scores, this research contemplates their incorporation, thereby elevating its statistical rigor. The culmination of this study will hinge on the statistical findings.

5. Results and Extended Discussion

During the experimental process, we observed that elementary school children in remote areas of Taitung typically have limited exposure to electronic devices. However, when using the Aurora AR app for learning about the aurora unit, they displayed a high level of curiosity and interest in learning. Gamified digital teaching designs like this held great appeal for them, with the majority of students quickly becoming proficient in using the app. This reflects the remarkable learning capacity of these children when confronted with new technologies, and gamified digital education effectively enhances their motivation to learn.

In the quantitative assessment of learning outcomes, we designed pre- and post-tests to evaluate students’ knowledge. However, considering the limited attention span of primary school students when reading text, we streamlined the number of test questions to maintain their engagement. During the actual implementation, we also noted that some students showed less enthusiasm for open-ended questions, possibly due to finding them somewhat tedious. In the future, we can consider adding interactive and game-like elements to assessments to increase participation. The following is an analysis of the quantitative experimental results.

5.1. Pre-Test of Learning Outcomes

Based on the experimental results in

Table 1. Summary of pre-test learning effectiveness scores.

	N	M	SD	df	t	p
Experiment group	24	71.304	15.167	45.00	0.859	0.395
Control group	24	67.083	18.292

and the t-test analysis, the average score of the experimental group before using this learning mode was 71.304 with a standard deviation of 15.167. In contrast, the control group had an average score of 68.083 with a standard deviation of 18.292. The t-value was 0.859, and the p-value was 0.395. Before conducting the experiment, both groups had no significant difference in their initial knowledge levels.

5.2. Post-Test of Learning Outcomes

Based on the experimental results in

Table 2. Summary of post-test learning effectiveness scores.

	N	M	SD	df	t	p
Experiment group	24	84.348	10.369	45.00	3.494 **	0.01
Control group	24	71.250	14.836

** indicates that the difference in average scores between the experimental and control groups is statistically significant with high confidence.

and the t-test analysis, the average score of the experimental group after using this learning mode was 84.348 with a standard deviation of 10.369, while the control group had an average score of 71.250 with a standard deviation of 14.836. The t-value was 3.494, and the p-value was 0.001. After the use of AR combined with gamified digital learning, there was no significant change in the average and variance of the control group. However, the average score of the experimental group significantly increased, and there was a significant difference between the scores of the experimental group and the control group. The learning outcomes of the experimental group greatly surpassed those of the control group, indicating that this learning mode is indeed beneficial for students in terms of learning effectiveness.

Considering the results of both the pre-test and the post-test, we can observe that the use of the Aurora AR app 2.0 for AR mobile learning significantly enhances students’ learning effectiveness in the domain of aurora knowledge. This enhancement may be attributed to the immersive virtual learning environment provided by VR, which enhances children’s understanding of abstract concepts and deepens their knowledge acquisition. Additionally, the gamified interactions strengthen memory retention. The advantages of AR mobile learning are evident when compared to traditional teaching methods. However, it is worth noting that the standard deviation of the experimental group decreased, indicating individual differences in the extent of improvement in learning effectiveness. Future research could focus on analyzing the factors influencing these individual learning differences, such as spatial abilities, gaming proficiency, and other individual characteristics. Additionally, we can attempt to incorporate quantitative records of students’ actual app usage to examine the impact of usage duration on learning effectiveness. Overall, this experiment supports the effectiveness of AR mobile learning in enhancing children’s comprehension of abstract astronomical concepts and their overall learning outcomes.

5.3. Implications for Learning Motivation

Using the ARCS motivation scale results presented in

Table 3. Summary of ARCS pre-test and post-test t-test results.

	Test	M	SD	t
Attention	Pre-test	5.391	1.373	−6.180
	Post-test	7.696	1.146
Relevance	Pre-test	3.652	1.276	−4.343
	Post-test	5.609	0.982
Confidence	Pre-test	4.739	1.288	−5.072
	Post-test	7.130	1.217
Satisfaction	Pre-test	4.913	1.730	−5.947
	Post-test	7.739	1.506

p < 0.01.

, we conducted a thorough analysis by comparing the pre-test and post-test scores of the experimental group students across all four scale dimensions: attention, relevance, confidence, and satisfaction. The outcomes of paired sample t-tests revealed significant improvements in motivation. Specifically, in the attention dimension, the average pre-test score of 5.391 substantially increased to 7.696 in the post-test, reflecting an impressive growth of 2.305 points. Similarly, the relevance dimension exhibited a noteworthy increase, with the pre-test score of 3.652 rising to 5.609 in the post-test, representing an increase of 1.957 points. Moving on to the confidence dimension, the students’ average score improved significantly from 4.739 in the pre-test to 7.130 in the post-test, showcasing a substantial gain of 2.391 points. Lastly, the satisfaction dimension saw remarkable progress, as the pre-test score of 4.913 elevated to 7.739 in the post-test, marking an impressive increase of 2.826 points. It is important to note that all observed changes in scores on each dimension between the pre-test and post-test assessments were statistically significant, with a p-value of less than 0.05. This indicates that the implemented intervention had a substantial impact on students’ motivation, across all dimensions, within the experimental group.

This indicates that, after one semester of using the Aurora AR app for AR mobile learning, students scored higher on all dimensions of the ARCS motivation scale, including attention to the course, perceived relevance, confidence in learning, and overall satisfaction, compared to traditional teaching. This demonstrates the positive impact of gamified mobile learning based on AR on enhancing motivation to learn. It is noteworthy that the attention dimension showed the most significant improvement, with a 2.3-point increase. This may be attributed to the immersive virtual effects of AR providing a more captivating learning experience for children. The gamified interactive design also enhanced students’ attention. The improvements in relevance and satisfaction also indicate that students find meaning in this learning method and derive enjoyment from it.

The results presented in

Table 4. ARCS Multiple Linear Regression Analysis.

	Coefficient	Standard Error	t-Value	p-Value
Intercept	0.320	0.125	2.560	0.015
Attention	0.565	0.087	6.480	<0.001
Relevance	0.412	0.102	4.030	0.001
Confidence	0.635	0.075	8.450	<0.001
Satisfaction	0.723	0.096	7.520	<0.001

offer a comprehensive insight into the impact of implementing the Aurora AR app for AR mobile learning on the ARCS motivation scale. Each dimension of motivation is individually assessed through the ARCS Multiple Linear Regression Analysis, shedding light on the influence of gamified mobile learning based on AR. Starting with the intercept, it serves as a baseline when all ARCS dimension scores are at zero. The intercept’s coefficient is 0.320, with a standard error of 0.125. The associated t-value is 2.560, and the corresponding p-value is 0.015, indicating its statistical significance. Moving on to the attention dimension, the coefficient is 0.565, with a standard error of 0.087. The t-value is 6.480, and the p-value is <0.001, signifying a highly significant increase in students’ attention following the intervention. Within the relevance dimension, the coefficient is 0.412, accompanied by a standard error of 0.102. The t-value is 4.030, and the p-value is 0.001, demonstrating a substantial enhancement in students’ perceived relevance. As for the confidence dimension, the coefficient stands at 0.635, with a standard error of 0.075. The t-value is 8.450, and the p-value is <0.001, indicating a remarkable boost in students’ confidence in their learning abilities. Lastly, the satisfaction dimension reveals a coefficient of 0.723, along with a standard error of 0.096. The t-value is 7.520, and the p-value is <0.001, reflecting a significant increase in students’ overall satisfaction with the learning experience. The findings from this analysis underscore the positive impact of the Aurora AR app for AR mobile learning on various dimensions of students’ motivation, including attention, perceived relevance, confidence in learning, and overall satisfaction. The statistically significant coefficients and t-values suggest substantial improvements in these motivation dimensions as a result of the intervention.

While the overall results are positive, it is worth reflecting on the fact that some students did not show a significant improvement on the ARCS scale. Exploratory analysis of this issue can be conducted in future research, such as examining whether this is related to students’ spatial intelligence or gaming proficiency. Alternatively, qualitative interviews can be employed to understand individual students’ usage experiences. In summary, this experiment supports the use of gamified mobile learning based on AR, significantly boosting the motivation to learn among upper-grade elementary students. However, it is important to remain mindful of individual differences and continually optimize course design.

Table 5. ARCS factor analysis.

Factors	Eigenvalue	Variance Explained (%)
Factor 1 (Attention)	2.58	64.5%
Factor 2 (Relevance)	0.92	23.0%
Factor 3 (Confidence)	0.57	14.2%
Factor 4 (Satisfaction)	0.34	8.5%

presents the results of the ARCS factor analysis, shedding light on the underlying structure of motivation within the context of the ARCS dimensions, which include attention, relevance, confidence, and satisfaction. This factor analysis is a valuable tool for uncovering common factors that elucidate the variance observed within these motivation dimensions. Factor 1, designated as “Attention”, emerges as the most influential factor, boasting the highest eigenvalue of 2.58. It explains a substantial 64.5% of the total variance, underscoring its pivotal role in motivating students. The high percentage of explained variance signifies that Factor 1 adeptly captures the shared variance among items related to attention in the ARCS motivation scale. Factor 2, labeled “Relevance”, is characterized by an eigenvalue of 0.92, explaining 23.0% of the total variance. While it plays a slightly less dominant role than Factor 1, it emphasizes the significance of the relevance dimension in motivating students. Factor 2 effectively accounts for the common variance among items associated with relevance in the scale. Factor 3, denoted as “Confidence”, displays an eigenvalue of 0.57, explaining 14.2% of the total variance. Although it exhibits a lower eigenvalue compared to the preceding factors, it still underscores the importance of confidence in learning motivation. Factor 3 captures the shared variance among items related to confidence within the ARCS motivation scale. Factor 4, labeled “Satisfaction”, exhibits an eigenvalue of 0.34, explaining 8.5% of the total variance. Despite having the lowest eigenvalue among the factors, it remains a noteworthy contributor to overall motivation. Factor 4 encapsulates the common variance among items associated with satisfaction. In summary, the ARCS factor analysis provides a comprehensive understanding of the underlying factors that clarify the variance observed in motivation dimensions. attention takes center stage as the most prominent factor, followed by relevance, confidence, and satisfaction. These findings deepen our comprehension of motivation’s structure within the ARCS framework and shed light on the potential interplay between these dimensions in motivating students in the context of AR mobile learning.

5.4. Extended Research and Implementation

The success of this experiment opens the door to further research and implementation. While this study primarily focused on aurora astronomy, the AR and game-based learning approach could be adapted to other STEM disciplines, broadening its applications. For instance, topics like environmental science, climate change, and sustainability could be taught using similar methods to address the pressing challenges faced by our planet. In terms of implementation, the findings suggest the need for professional development programs for teachers. Educators play a vital role in the effective integration of technology in the classroom. Training programs can help them utilize AR tools, ensuring they can leverage the full potential of these technologies to enhance the learning experience.

5.5. Psycho-Physiological Impacts

The psycho-physiological impact was assessed using brief interviews. This helped gather quantitative and qualitative data on participants’ visual and psychological wellbeing. Our findings indicated that two students reported mild visual discomfort, which correlated with longer durations of AR use. The interviews revealed that most participants found the AR experience engaging but noted occasional eye strain.

5.6. Equity in Education

This research brings attention to the digital divide in education, particularly in underserved regions. As seen in this study, students in remote areas may have limited exposure to electronic devices. The success of this experiment highlights the importance of providing equitable access to technology-driven educational resources. Policymakers and educational institutions should consider initiatives aimed at providing devices and connectivity to students in underprivileged areas to ensure they are not left behind in the digital age.

The experimental results are a testament to the potential of AR and game-based digital learning in the context of aurora astronomy education. The positive impacts on learning outcomes and motivation should motivate further research and implementation in this direction. Ultimately, this innovative approach has the potential to transform the way we educate students, making learning more engaging, accessible, and effective for all.

6. Discussion on the Practical Implications, Objectives, and Lessons Learned

6.1. Enhanced Learning Outcomes and Motivation

The core focus of this research was to explore the impact of AR technology combined with mobile learning on students’ education about aurora astronomy. As we have seen in the earlier sections, the results were highly encouraging. This section provides an extended discussion of the enhanced learning outcomes and motivation observed in this study. The utilization of the Aurora AR System, a novel AR-based environmental education application, transformed the way students engaged with aurora astronomy. Traditional digital learning methods, although informative, often lack the immersive and interactive elements that are crucial for capturing students’ interest, especially in complex scientific subjects. In the pre-test phase, both the experimental group and the control group exhibited similar levels of baseline knowledge. This establishes a fair comparison baseline for assessing the impact of the AR-enhanced learning model. The experimental group was then exposed to aurora astronomy lessons through the Aurora AR System, incorporating the captivating aspects of AR. It allowed students to explore and understand auroras as if they were observing these celestial phenomena in their natural habitat. The post-test results demonstrated a significant improvement in the experimental group’s learning out-comes. Their average scores, when compared to the control group, showed a substantial increase. This difference was statistically significant, with a p-value of 0.001, indicating that the AR-enhanced learning approach had a pronounced impact on students’ understanding of aurora astronomy. This improvement in learning outcomes can be attributed to several key factors.

Engagement: AR technology has the inherent capacity to capture students’ attention and immerse them in the learning material. The combination of visual and interactive elements made learning about auroras exciting and captivating. Students could explore the different colors of auroras, understand the scientific principles behind their formation, and even witness these phenomena virtually, creating a sense of wonder and curiosity.

Interactivity: The Aurora AR System incorporated gamified elements, where students could manipulate the factors affecting aurora colors, providing a sense of agency. This interactivity not only made learning enjoyable, but also facilitated a deeper understanding of the subject matter. Students could experiment with the emission of different colors by controlling the radiation density of oxygen and nitrogen atoms, making learning interactive and hands-on.

Motivation: Beyond the enhancement of learning outcomes, this study revealed a notable boost in students’ motivation. The ARCS motivation questionnaire results were particularly encouraging. Students in the experimental group demonstrated increased attention, relevance, confidence, and satisfaction in their learning. These findings emphasize the importance of creating a learning environment that not only imparts knowledge but also fosters curiosity, self-assuredness, and overall satisfaction with the learning process.

Psycho-physiological impacts: This study underscores the importance of monitoring health impacts when integrating AR into educational practices. Future research should focus on optimizing AR technology to minimize discomfort and enhance user comfort. Educators should consider implementing regular breaks and using ergonomically designed AR tools.

The improved motivation observed in this study could have long-lasting effects on students’ educational journeys. Motivated students are more likely to pursue further studies in related fields and engage in self-directed learning. The combination of AR and game-based learning ignited a passion for aurora astronomy, but the implications reach further, extending to other STEM subjects and an overall love for learning. Further, students in the experimental group demonstrated increased attention, relevance, confidence, and satisfaction in their learning (ARCS motivation scale results), indicating a broader impact on their enthusiasm for learning beyond the subject of aurora astronomy.

6.2. Future Directions

The success of this research project opens up exciting possibilities for future directions. The development of the Aurora AR System is just the beginning. The following are a few key areas where this research can expand.

Diversification of educational content: While this study focused on aurora astronomy, there is immense potential to expand the content to cover a broader range of astronomical phenomena and scientific topics. Introducing topics related to climate change and its connection to auroras would be highly relevant, given the context of this research. By diversifying the educational content, the Aurora AR System could become a comprehensive tool for teaching various scientific concepts.

Assessment and self-evaluation tools: The current application primarily serves as an instructional tool. Future developments could incorporate assessment features, allowing students to evaluate their understanding and progress. This addition would be valuable for both educators and students. Assessments, quizzes, or self-evaluation tools integrated into the app can help students consolidate their knowledge and help teachers gauge the effectiveness of the learning tool.

Professional development for educators: Educators play a pivotal role in implementing technology-enhanced learning methods. Therefore, it is imperative to invest in their professional development. Training programs can be designed to familiarize teachers with the application and its capabilities. This would ensure that teachers can effectively utilize AR and game-based learning to enhance the educational experience in the classroom.

Equity in education: This research highlights the importance of addressing the digital divide in education, especially in underserved areas where access to electronic devices and educational resources may be limited. Policymakers should take note of the potential of technology-driven educational tools and consider initiatives aimed at providing devices and connectivity to students in underprivileged areas. Ensuring equitable access to such tools is essential for a fair and inclusive education system.

In conclusion, the results of this research provide compelling evidence of the efficacy of AR and game-based digital learning in the context of aurora astronomy education. The extended discussion emphasizes the transformative potential of this innovative approach for enhancing learning outcomes and motivation among students. The future of this research holds promise, with opportunities to diversify content, integrate assessment tools, and support educators in adopting this technology. Ultimately, the aim is to create engaging, accessible, and effective educational experiences that inspire a new generation of learners to explore the wonders of science.