Design and Implementation of Gamified Augmented Reality Learning System to Enhance Biodiversity Education ^†

Abstract

As part of our technology-enhanced learning (TEL) strategy, we developed a field-based augmented reality (AR) learning system for biodiversity education among senior elementary school students. Using a 2D illustration style to present the appearance of the species and a situational interactive design, the AR app focused on common wild animals in Taiwan. They also gained insight into wild animal species in outdoor settings, gained knowledge about the phenomenon of roadkill and the rescue of wild animals, and promoted their awareness of ecological conservation. Using the design-based research (DBR) method, we integrated user-oriented design processes and iteratively modified the system functions and interface through expert review and field usability testing. During this activity, 26 senior elementary school students were recruited to participate in an interactive AR game designed for a single player. As part of the learning content, students must collect images of species, recognize roadkill, and learn about wildlife rescue. To evaluate the effect of the activity on knowledge learning and the app’s usability, data were collected through pre- and post-test paper tests, questionnaires, and so on. Based on the research results, this system can significantly enhance students’ learning interests and contextual understanding of biodiversity topics as an effective technology-assisted learning tool. Students reported high levels of immersion and learning motivation, and the teachers agreed that it promoted inquiry-based and independent learning. The results of this study contribute to the field of educational and environmental education. Consequently, context-aware AR tools may enhance students’ situational learning experience and environmental literacy. In addition, it provides a practical design reference for future AR educational applications, demonstrating that gamification and outdoor learning can enhance the learning outcomes of traditional science education.

1. Introduction

Due to the increasing importance of environmental sustainability and biodiversity issues in national education, environmental education plays a significant role. However, traditional classroom teaching methods and paper-based teaching materials lack context and interaction, making it difficult to engage students in wildlife conservation and environmental issues effectively [1]. As augmented reality (AR) technology has been developed in recent years, a new method of learning has been developed that integrates virtual information with physical environments, thereby increasing learners’ understanding of abstract concepts and enhancing their motivation and immersion during learning [2]. Additionally, the introduction of the concept of gamification can enhance learners’ motivation and participation in continuous learning through mechanisms such as task challenges, point rewards, and background upgrades, which are particularly appropriate for senior elementary school children [3]. Even though some studies have shown that gamification can increase motivation and participation in learning, in actual practice, there is still a lack of a systematic framework that combines local wildlife content with outdoor teaching situations [4]. This study aims to address this demand by developing an AR learning system with local ecology at its core, as well as gamification and interactive features.

Design-based research (DBR) is used in this study to conduct systematic development and optimization through the iterative process of design-implementation-testing-improvement [5]. To enhance the field suitability and learning effectiveness, stakeholders, including teachers, students, and designers, are involved in the design process. The research objectives include: (1) constructing an AR system for biodiversity; (2) increasing students’ learning participation and interest through gamification design; (3) evaluating system usability and user experience; and (4) establishing a repeatable design framework to provide reference for subsequent research and practical application. Through the practice of this study, it is expected to make up for the shortcomings of environmental education in the application of interactive technology, and create an AR teaching system with educational value, clear design logic, and practical application for senior elementary school education. Moreover, the results of this study are not only inspirational for the development of digital learning environments but also provide concrete empirical evidence for the development of future related educational technology applications.

Based on the above background, this study develops an AR learning application for senior elementary school students that focuses on biodiversity education. Using outdoor natural fields and local wildlife materials, as well as gamification task design, it enhances students’ knowledge of biodiversity, roadkill issues, and wildlife rescue, as well as promotes their awareness of ecological conservation. To accomplish the above research objectives, we conducted system design and iterative optimization using design-based research (DBR), evaluated the usability of the developed AR learning system, as well as the students’ response to learning engagement, explore if the gamification task design was effective in enhancing student interest in biodiversity issues and contextual understanding of biodiversity issues, and analyzed the feedback received from users after using the AR learning system.

2. Literature Review

2.1. AR

AR technology is an interactive technology that overlays virtual images, sounds, and information over the real world in real time. It is widely used in several fields, including medicine, industry, marketing, and education [6]. According to Garzón and Acevedo, AR has the benefit of promoting the understanding of abstract concepts, enhancing learning motivation, and enhancing practical experience in educational contexts. Especially in the field of science and environmental education, AR can help students visualize natural phenomena that are difficult to observe or simulate [7]. Combining outdoor learning with AR technology can not only provide real-time information supplementation and guidance but also enhance students’ field participation and knowledge internalization [8]. According to Czok et al., field-based AR systems can provide virtual tours, task puzzles, and multi-sensory stimulation without interfering with the natural environment, further stimulating students’ active exploration [9].

2.2. Learning Design Theory of Gamification

A gamification strategy is implemented in education to improve learning motivation and participation through the application of game elements and game mechanisms [10]. A key component of its design is the incorporation of game elements such as points, badges, leaderboards, task challenges, and instant feedback to enhance the fun and interaction of the learning process [4]. The research also indicates that an appropriate design of gamification can meet the psychological needs of learners and promote goal-oriented behavior as well as autonomous learning [11]. As a result, gamification strategies have been widely used in digital learning and technology-assisted teaching, making them an important design methodology to enhance the effectiveness of learning [12].

2.3. DBR

Based on Wang and Hannafin and Barab and Squire, DBR is a research paradigm that integrates educational theory with field practice. Ultimately, it aims to develop a spreadable teaching model and theoretical insights after repeatedly going through the process of design, implementation, and revision in real teaching environments [13]. As part of this research model, a particular focus is placed on the importance of “field context” and the collaborative interaction between researchers and practitioners [14].

Based on the above literature, gamification and AR strategies have been widely used in the field of education. However, system development research focusing on field type, biodiversity theme, and primary school students that uses the DBR method is still relatively rare. The contributions of this study are (1) constructing an outdoor learning system that integrates AR and gamification, (2) implementing a comprehensive design process record using the DBR method (Figure 1), and (3) providing a reference paradigm for the development of future educational technology application systems.

3. Materials and Methods

3.1. DBR Process

We employed the DBR method as the core framework, emphasizing the repeated design, implementation, and revision of teaching interventions in real teaching situations to improve their theoretical value and effectiveness [13,14]. This study follows the four-stage model proposed by Wang and Hannafin, which includes the following:

• Problem analysis: Through interviews, the learning needs of teachers and seniors were collected for outdoor environmental education to summarize the core functions and teaching objectives of the AR learning system, based on the curriculum and relevant literature.
• Design app: A prototype system was developed based on the analysis results, integrating AR identification, roadkill simulations, biological rescue game modules, and instant feedback mechanisms to enhance interactivity and situational immersion [13].
• Implementation: Field tests are conducted with the Taiwan Biodiversity Research Institute, Ministry of Agriculture (TBRI). Students’ behavior, operation difficulties, and learning reactions, and multiple data points through observation and interviews were collected.
• Revision: To ensure that the interface and interaction are optimized based on the test results, pay attention to user feedback and system adaptability, and gradually develop teaching design principles that have an empirical basis through repeated adjustments [15].

As a result, the system has been improved in terms of functionality and usability, as well as meeting the real needs of educational sites and the characteristics of learning environments.

3.2. System Architecture and Learning Functions

The field-based AR learning system developed by this institute is a result of the DBR process, emphasizing three core design concepts: task-oriented interaction, field perception experience, and gamification participation mechanisms. With the implementation of Unity and the AR Foundation integration technology, three core learning modules are designed based on the Android platform, including biological identification, situational roadkill education, and conservation action simulation, as well as tracking of the learning process and achievement rewards.

In functional planning, this system emphasizes particularly the balance between user experience and learning motivation: designing task-based exploration activities to enhance learning participation, integrating real-world AR interaction and reactive feedback to enhance immersion, and guiding students through dual transformations of cognition and behavior. Moreover, the overall system architecture is extensible, allowing it to be modularly modified following different field materials, educational goals, and user needs, demonstrating high potential for practical application.

3.2.1. Species Identification/Roadkill Awareness/Animal Rescue Learning

The design content of the app is presented in 2D illustrations and text introductions to common wild animals in Taiwan (Formosan pangolin, Leopard cat, Formosan black bears, etc.), as shown in Figure 2 and Figure 3. Each AR task is accompanied by different interactive content that enhances the students’ understanding and memory of species identification and ecological knowledge. To enhance learning participation, this function emphasizes sensory stimulation and feedback reinforcement.

3.2.2. AR Field Exploration and Interactive Mechanisms

With the app, you can scan the AR markers (picture cards and stone statues) arranged in the interactive field to collect different appearances of creatures (Figure 4 and Figure 5).

The system generates a different situational animation every time the stone statue is scanned. There are at least two situations for each creature. As shown in Figure 6, participants can accumulate learning experience through multiple playthroughs.

3.2.3. Roadkill Record and Report Learning

Whenever the public discovers injured or deceased wildlife, they should report the incident as soon as possible. The app combines AR camera technology, and users can utilize this function to take photographs of roadkill animals. Figure 7b illustrates how the built-in positioning system and scale function can be used to estimate the size of the species’ carcass.

3.2.4. Learning History Record

Points will be accumulated in accordance with the user’s learning progress and the number of operations performed. Figure 8 illustrates how these points contribute to improving the user’s learning participation, as well as unlocking the richness of the game lobby ecosystem and enhancing the enjoyment and sense of achievement associated with the learning experience.

This design combines self-regulated learning with gamification incentives to enhance learning motivation and willingness to participate (Figure 9). As a result, students are encouraged to continue participating, accumulating achievements, which in turn enhances their motivation for learning and capacity for reflection.

3.3. Participants and Testing

We evaluated the user experience and usability of the system through field testing. Twenty-six students from senior elementary schools (14 boys and 12 girls) participated in the study after obtaining consent from their schools and parents. Tests will be conducted at TBRI. Multiple AR interactive task points are included in the venue configuration, including elements such as species identification, rescue simulation, and roadkill situations. Using tablet devices in a single-person manner, students complete tasks according to the instructions provided by the system. There is a total duration of 120 min for this activity, which covers exploration, learning, and system feedback.

3.4. Tools and Procedures for Data Collection

Data were collected using both a mixed method and a quantitative method to evaluate learning outcomes and user feedback comprehensively. In addition to pre- and post-tests of knowledge, learning motivation, and technology acceptance model scales, brief interviews are conducted to obtain students’ actual operation experiences and subjective feelings.

The knowledge test was developed by teachers and experts based on the teaching tasks; the learning motivation scale was designed based on the ARCS model [16], and the technology acceptance questionnaire, based on the technology acceptance model (TAM) model [17], which covered dimensions such as perceived usefulness, ease of use, and behavioral intent. The quantitative data is statistically processed using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA), while the qualitative data is categorized and summarized into themes to enhance the interpretation of the data.

3.5. Data Analysis Based on DBR

According to the four-stage process of DBR, we recorded the system development process and the design basis. As part of the “problem analysis” and “design construction” stages, teacher and design expert meetings, user testing, etc., were conducted to collect opinions as a basis for optimizing system functions and interfaces.

In the “field implementation” and “reflection and correction” stages, we observed students’ operating behaviors, common error types, and completion rates. They conducted multiple rounds of optimization and adjustment based on observations. As a result of this process, it is easier to clarify the improvement logic of system design and provide the basis for design decisions in the real world.

4. Results

4.1. Pre- and Post-Test Results

To evaluate the learning outcomes of students following the implementation of the AR system developed in this study, a paired sample t-test was used to compare pre- and post-test performance. Analysis results revealed that the average score of the post-test was significantly higher than that of the pre-test, and the difference was statistically significant (t (25) = −6.24, p < 0.001), as shown in

Table 1. Analysis results of pre- and post-test results.

	Pre-Test		Post-Test
	Mean	Standard Deviation (SD)	Mean	SD	t	p
Knowledge score	96.35	11.71	108.08	11.58	−6.24	<0.001

. This system improves students’ understanding and learning of biodiversity-related information.

4.2. Learning Motivation

The four dimensions of the attention, relevance, confidence, and satisfaction (ARCS) model were used for a single-sample t-test to determine students’ learning motivation responses to the AR system developed in this study. Based on the analysis results, the average values of the four indicators were all above 4.5, and all reached statistical significance (p < 0.001), indicating that the system is highly effective in eliciting learning motivation (

Table 2. Analysis results of learning motivation based on ARCS model.

ARCS Dimension	Mean	SD	t	p
Attention	4.680	0.447	53.38	<0.001 ***
Relevance	4.526	0.598	38.62	<0.001 ***
Confidence	4.615	0.487	48.32	<0.001 ***
Satisfaction	4.551	0.588	39.44	<0.001 ***

*** p < 0.001 (Single-sample t-test).

). Following this result, the designed AR learning system has a high level of situational appeal and interactive design, which can effectively promote students’ learning engagement and usage intentions.

4.3. Analysis of TAM

This study used three dimensions of the TAM for analysis to understand students’ acceptance of the AR learning system: perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI). The average values for each dimension were higher than 4.5, and each reached statistical significance (p < 0.001), indicating that students generally found the system helpful for learning, easy to use, and likely to use it again in the future. Based on the results of this study, it can be concluded that the AR system has a good application potential and extension value, and it meets the practical needs of technology-assisted learning tools in the education sector (

Table 3. Analysis results of TAM.

TAM Dimension	Mean	SD	t	p
PU	4.556	0.554	41.98	<0.001 ***
PEOU	4.587	0.574	40.73	<0.001 ***
BI	4.577	0.523	44.60	<0.001 ***

*** p < 0.001 (One-sample t-test, test value = 0).

).

4.4. Analysis of Open Feedback

To gain a deeper understanding of students’ feelings and reactions to AR outdoor learning activities, the research team collected behavioral observation records, activity feedback sheets, and brief interviews. Students were highly engaged in the exploration task, and some students took the initiative to work with their peers to complete the task, showing good interaction and problem-solving skills. On the 26 feedback sheets, more than 90% of students said they had heard of “biodiversity” in the past but had only a superficial understanding of the concept. In open responses after participating in the activity, most students expressed positive opinions such as “the activity was very good”, “I would like to participate again”, and “thank you”. “Taiwan black bear” and “leopard Cat” were among the AR species that students demonstrated great interest in identifying. “Biodiversity Awareness”, “Roadkill Simulation”, and “Rescue Response” were the most popular learning units, indicating that task-oriented AR activities enhance students’ attention and understanding of natural issues.

Teachers and school staff generally expressed their satisfaction with the activity, believing that this type of technology-assisted outdoor learning can effectively deepen students’ environmental awareness and learning impressions. Based on the results of this study, the AR system has a good application potential and extension value, and it meets the practical needs of technology-assisted learning tools in the education sector.

5. Conclusions

Using the design-based research method, we developed and implemented an outdoor learning system that combines augmented reality (AR) and gamification design for biodiversity education for senior elementary school students. Several designs and field tests have shown that this system is both usable and educational and can enhance students’ learning participation and knowledge of ecological concerns.

The system combines outdoor fields with digital technology to demonstrate the application potential of educational technology in environmental sustainability issues. Features include task-oriented exploration, interactive feedback, and immersive operation. Most students find the operation to be easy and interesting, and the task challenges also stimulate their learning motivation and awareness of the environment. This system is based on a modular architecture design that is flexible and scalable. In the future, it may be extended to topics such as aquatic ecology, native plant identification, or disaster education, as well as developed into a cross-domain AR teaching platform. In addition, the learning process record and point accumulation mechanism may also be useful in promoting citizen science education.

To enhance the personalized learning experience, artificial intelligence functions such as automatic identification and learning process analysis should be introduced in the future. It is also possible to conduct long-term tracking and multidimensional evaluation to study the long-term effects of the system on learning outcomes and attitude changes. The research team hopes to expand cooperation with schools, research institutions, and the community in the future to promote the application and practice of educational technology in sustainable development.