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Article

Impact of Augmented Reality and Game-Based Learning for Science Teaching: Lessons from Pre-Service Teachers

by
Valerie Czok
1 and
Holger Weitzel
2,*
1
Independent Researcher, 88074 Meckenbeuren, Germany
2
Department of Biology, University of Education Weingarten, 88250 Weingarten, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(5), 2844; https://doi.org/10.3390/app15052844
Submission received: 8 January 2025 / Revised: 26 February 2025 / Accepted: 4 March 2025 / Published: 6 March 2025
(This article belongs to the Special Issue Virtual and Augmented Reality Technology in Education and Entertainment)
Browse Figures
Figure 1
<p>Research design with 4 intervention groups. K and CSE were assessed before and after lesson.</p> "> Figure 2
<p>A line graph for the first model with a moderating effect, showing a stronger moderating effect for the non-AR interventions amongst all groups. The lines illustrate the CSE pre–post relationship of the intervention groups with/without AR.</p> "> Figure 3
<p>A line graph for the second model with a moderating effect, showing a stronger moderating effect for the non-AR intervention amongst both non-GAME groups. The lines illustrate the CSE pre–post relationship of the intervention groups with/without AR.</p> "> Figure 4
<p>A line graph for the third model with a moderating effect, showing a stronger moderating effect for the non-GAME intervention amongst both AR groups. The lines illustrate the K pre–post relationship of the intervention groups with/without GAME.</p> ">
Versions Notes

Abstract

:
Technological advancement and growing interest in digitalizing education increased Augmented Reality (AR) use in education. However, previous research findings on AR’s potential for knowledge acquisition are inconclusive. Furthermore, computer self-efficacy has seldom been evaluated. AR is frequently combined with game-based approaches (GAME), yet the specific impact of each feature, “AR” and “GAME”, is often not differentiated in the research design. This work analyzed an AR game-based learning environment for science teaching. It was conducted with German pre-service teachers, assessing “Knowledge” and “Computer Self-Efficacy”. These measures were used to analyze the effect of AR and GAME in four intervention groups. The results showed a significant time effect for all groups in both variables, indicating all intervention designs led to knowledge and self-efficacy gains. However, no interaction effect was found, indicating the groups did not significantly differ in their knowledge and self-efficacy gains over time. The results further indicate no clear advantage of either AR or GAME for the design of science teaching. However, AR and GAME also did not hinder learning and both led to successful knowledge and self-efficacy gains. This indicates that AR and game-based learning support the learning process and strengthen learners’ computer self-efficacy. Combining both features aids in easing the transition toward technology-enhanced learning by providing a playful learning experience, using digital as well as analog components.

1. Introduction

Augmented Reality (AR) has emerged as a transformative technology in science education, offering innovative opportunities to enhance students’ understanding of complex scientific concepts. However, despite the increasing application of AR in the classroom, there is limited consensus on its effectiveness, particularly when combined with game-based learning elements. The present study aims to explore the impact of AR and game-based learning on pre-service teachers’ knowledge acquisition and computer self-efficacy, which are critical components of successful technology integration in science education.
Hence, this research seeks to address the gaps in the current literature by analyzing the influence of two key design features—AR and game-based learning—on the development of knowledge and self-efficacy. The investigation will entail an analysis of the manner in which these technologies interact in educational settings, with the objective of contributing valuable insights into their potential to foster engaging, interactive, and effective learning environments for pre-service teachers. The subsequent section will provide an overview of the extant literature on AR and its application in science education, including the integration of game-based learning and the various methodologies used to assess its impact.

1.1. Augmented Reality in Science and Engineering Education

The theoretical foundation presented herein is based on a literature review conducted using the “Publish or Perish” software (version 7) [1] to search the Google Scholar database. The following search syntax was employed: Augmented Reality AND Chemistry OR Biology OR Physics OR Engineering OR STEM OR STEAM OR Science AND Game OR Game-based Learning OR Game based Learning OR Gamification.
By utilizing the “Publish or Perish” tool, which leverages Google Scholar’s extensive academic database, we were able to access a wide range of scholarly publications, including both freely accessible documents and those behind paywalls. This approach allowed for a thorough exploration of the current state of research in the field, encompassing studies that might not be readily available through traditional academic search engines. Following the definitions of Milgram et al. [2] and Azuma [3], we specified AR as a technology combining real and virtual content, in which reality is supplemented with digital content that is interactive, real-time, and with functional 3D registration. AR allows for the enhancement of the real/physical world by overlaying it with virtual content, such as models, text, and animations, in real-time using head-mounted displays, AR goggles, or regular handheld devices (smartphone, tablet). AR technology provides the learner with additional information and content, enabling better comprehension and/or new perspectives. Combining the real world with digital content can serve as a teaching medium for science education by displaying complex phenomena or topics by visualizing on different levels (micro/macroscopic) [4,5], making the invisible visible [6,7], or as a substitute for otherwise dangerous or expensive content [8,9,10]. Further, AR allows for the integration of digital technology with traditional learning material [11,12]; it creates interactive [13], engaging, and interesting environments [14,15] to enable remote and self-paced learning; and/or allows the inclusion of gamification/game-based learning.
The rising number of AR applications and widening range of topics reflect these benefits. A screening of AR application used for science teaching between 2017 and 2021 [16] showed use of AR in biology for anatomy [11,17,18,19], for “species’ recognition/identification” [8,20,21], for structural/molecular biology [6,7,22,23,24], and for ecology [13,14,15]. In chemistry, AR has been applied to experimentation [25,26] as well as molecular structures [6,27]. AR applications were developed for manufacturing and construction training [28,29], and for physics and electronics [30,31]. Furthermore, AR was increasingly applied in environmental and sustainability education [32,33,34,35].
Altinpulluk et al. [36] reviewed 58 papers on AR and found it to increase interaction, diversify communication, increase motivation, enrich user experience, and foster skills, such as spatial ability or conceptual understanding. A later review by Xu et al. [37] examined the effect of AR on academic achievement. They differentiated disciplines and found the highest impact in earth science, medium to large effects in math and physics, and small to no significant effects in biology. They also stated there is only “little research on the usage of game mechanisms on AR-aided scientific learning”.

1.2. AR and Game-Based Learning

In their analyses of AR apps in science teaching, Czok et al. [16] found that most AR apps integrate game elements. Specifically, in biology, only 1 of 23 did not apply game elements, 6 used at least one of the defined eight game elements, and another 10 even applied four or more. This showed that combining game-based learning with AR technology seems a popular fit and was frequently used. However, in their work (reference), no correlation could be deduced from the setup of such AR app design features (number of game elements, level of interaction, level of adaptivity, etc.) and the constituted learning effects.
Several studies conducted evaluations on AR combined with game-based learning in a control group design but came to divergent results from no difference in learning effectiveness [38] to improved performance and engagement [39] or improved understanding and higher motivation [40].
Integrating game-based learning and AR into science education could foster learning opportunities, such as creating enjoyable, interesting, and engaging learning environments. In their extensive systematic review, Lampropoulos et al. [41] highlighted the potential AR and “gamification” have in education and demonstrated the diverse fields of application for this combination. They found using AR and “gamification” positively impacts the learner’s attitude and behavior, increasing motivation, participation, collaboration, curiosity, performance, etc. Moreover, they pointed out that integrating “gamification” can ease the oftentimes perceived difficult transition toward technology-enhanced education. In a recent publication, Lampropoulos et al. [42] introduced a game-based AR learning environment and assessed several learning effects in a non-control group setting. They report that it enriched the learning process and boosted students’ motivation, engagement, and self-efficacy.

1.3. Potentials of AR for Science Education on Knowledge Acquisition and Self-Efficacy

Integrating AR and game-based learning allows educators to create enriching and supportive learning environments that enhance understanding and foster the learner’s confidence, e.g., [43,44]. Emphasizing the role of a learner’s individual belief in their ability to succeed specific tasks, self-efficacy plays a special part in the learning process [45,46,47]. Game-based AR has the potential to promote learning and mastering tasks in an interactive and immersive manner. It can boost collaboration and engagement, which provides feedback for the learners’ that shows them that success is attainable [45,48].
The nature of AR, by enhancing the physical/real world with additional digital content, allows users to experience this as a seamless environment. Game-based AR has the potential to reduce intrinsic load by incorporating narrative elements and using visualizations [4]. Active engagement by game-based approaches and the explorative character of AR learning environments invite learners to construct meaningful connections and involve themselves in the learning process [31,49]. This new and enhanced perception of educational content improved knowledge and understanding. Many research approaches that investigated the learning effects of AR learning environments for science teaching in a control group design stated a positive influence on learner performance in the AR intervention group compared to the control group [11,31,50,51,52,53]. A few control group designs reported that AR only partially benefits learning gains [19,54,55,56]. However, research also found no statistical difference between the AR intervention and the respective control group [13,57]. Peeters et al. [4] even reported that the AR intervention in question did not lead to progression in knowledge.
To this date, there is no substantiated comprehension of AR’s impact on knowledge. Thus, it is not yet possible to generally postulate AR’s effectiveness in terms of knowledge acquisition for science education as a whole.
Studies investigating self-efficacy beliefs toward AR or VR showed that exposing pre-service teachers to the technology may improve their beliefs [45,48].
Krug et al. [45] reported significant improvement of self-efficacy regarding digital competencies and a positive effect on the attitude toward AR for science. Gundel [48] exposed pre-service teachers to AR for different amounts of time and assessed “teachers’ sense of self-efficacy”. He found both a significant effect on the different exposure levels as well as a significant increase in self-efficacy beliefs.
Moreover, subject-specific self-efficacy using AR-enhanced an learning environment also resulted in improvement: Cai et al. [54] found AR to enhance “physics learning self-efficacy” in their quasi-experimental control group design. Also, Ciloglu and Ustun [58] reported significantly higher “self-efficacy towards biology” in the experimental group, using AR for biology learning.
Pre-service teachers‘ self-efficacy beliefs in using digital media (CSE) are a key driver for using such in future teaching [47]. Pre-service teacher’s CSE can be seen as the basis for the implementation of new technologies, such as AR, in teaching. Exposure and practice in pre-service to digital media and new technologies are inherent to future teachers’ CSE to improve confidence in using digital media in class [44,46,47]. Furthermore, deeper insight into the impact of CSE whilst contrasting the integrated features of AR and game-based learning can be seen as crucial to differentiate where improvement may stem from.

1.4. Teachers as Key Players in Digital Change

Teachers are significant propagators and disseminators of ways of thinking, learning, and taking action. Teachers are compelled and expected to meet these challenges and keep speed with the increasing digitalization and evolving technologies. In the current shift toward digitalization of the educational sector, the attitude and skillset of pre-service teachers are crucial. In their overview, Kaminskienė et al. [59] illustrated the discrepancies between current research and teachers’ competencies in the classroom: research oftentimes reported the potential of technology-enhanced teaching, but frequently in- and pre-service teachers’ competencies and skills to implement such technologies in their teaching were lacking. The integration of technology use in the curriculum of pre-service teachers was a first step to strengthening their abilities as well as their self-efficacy beliefs. Vogelsang et al. [46] established that previous exposure to digital media and technologies in academic environments significantly affected pre-service teachers’ attitudes toward learning with digital technology and their self-efficacy expectations. Therefore, it is of great importance to prepare pre-service teacher’s open-mindedness and digital-affine attitude during the curricula for teaching in the future. In this project, we accommodated the need for pre-service teachers to familiarize themselves with digital learning technologies and to acquire resilience toward new and ever-evolving media.

1.5. Methodological Issues in Measuring AR’s Potential

The evaluation of the impact of AR on learning primarily focused on the technology’s potential to convey knowledge and/or skills. Previous research typically paired performance assessments with qualitative interviews on interest and attitudes toward the technology [4,13,54,56,57]. Some studies also evaluated motivational effects [31,49,53] and impact on cognitive load [31,49,51,57,60]. While the impact on self-efficacy in technology-enhanced learning environments examined—both in terms of subject self-efficacy [54,58,61] and technology-specific self-efficacy—AR [45,48], the body of evidence remains fragmented.
Despite a substantial amount of research focused on knowledge acquisition, the findings were often inconclusive [62] and hard to compare [43,63,64]. Investigation of AR for science teaching combines a versatile technology with a diverse field, comprising several different subjects and topics. Each subject faces specific challenges and calls for its own specific didactical principles. The target group may differ, the duration and scope of the AR learning environment in question may vary, and the design may also have its impact. Additionally, as the definition of learning gain, performance, or academic achievement differs, so do the measuring instruments and methods.
Moreover, for the most part, current research on game-based AR learning for science merely conducted preliminary testing with small sample sizes and no control group designs. In a systematic review of research on educational AR, Koumpouros [63] found that the majority of studies used individual measuring instruments and insufficient research designs (duration, sample size). This impeded comparability generalization of AR’s learning effectiveness. He (eds.) stressed the need for more research using effective evaluation methods.
In their recent extensive literature review, Jiang et al. [43] highlighted that the effects of AR and VR on academic achievement and cognitive load remain inconclusive. The authors suggested that inconsistencies in findings may arise from variations in use, design, participant characteristics, and the measuring instruments and constructs employed.
Furthermore, game-based AR designs under investigation often need to differentiate control group design in the combined components, AR and game. Xu et al. [37] emphasized the need for research on integrating game mechanisms into AR-aided science education. Hallinger et al. [65] investigated the rise in technology-enhanced game-based learning for teaching sustainability. They (eds.) found a lack of empirical research and experimental study designs and pointed out the need for robust research designs in this domain.
Buchner et al. [64] systematically reviewed AR research on performance and cognitive load, concluding that most findings reported AR to boost performance, yet some also contradicted this. Regarding reliable empirical findings, they highlighted the difficulty of media comparison studies, as providing precisely equivalent content and instructional methods in both intervention groups could be challenging to ensure. To investigate quality and tackle the quality of AR designs, value-added studies were proposed. Therefore, this approach compares AR conditions with different added features and provides deeper insight into the potential of different AR designs.
In summary, the current literature revealed gaps in the understanding of AR’s effectiveness in educational contexts, particularly regarding the integration of game-based elements, robust empirical designs, and measuring the instruments employed. This research aims to address these gaps by employing a robust design, differentiating the nuanced impacts of AR and game-based learning on educational variables (knowledge and computer self-efficacy), thereby contributing to a more comprehensive understanding of their potential in science education.

1.6. Scope of Study

Current findings demonstrated the potential of AR, especially for science teaching. However, methodological issues (such as divergent measuring instruments, non-experimental design, intervention group comparison issues, small sample sizes, and a variety of AR applications with vague scopes) impeded the comparability of reported learning effectiveness and any general conclusion about AR’s potential. In their analysis, Czok et al. [16] rated 52 AR applications according to their design and setup. The juxtaposition of the AR rating and corresponding reported learning effects revealed no correlation. There is yet no general comprehension of how design features of AR learning environments, such as including game elements, may affect learning effects.
Building on this, this work addressed the following research questions to investigate the impact of learning with AR game-based approaches in the field of pre-service teachers’ science teaching. Specifically, we analyzed the impact of the two design features, AR and game-based learning (GAME), on the development of knowledge (K) and computer self-efficacy (CSE). Four intervention designs implemented and/or substituted these two design features to provide a deeper insight into the effects of such regarding knowledge and computer self-efficacy.
RQ1: How does intervention design affect knowledge development, when applying or substituting AR technology and/or game-based learning?
RQ2: How does intervention design affect self-efficacy development, when applying or substituting AR technology and/or game-based learning?
RQ3: What are the moderating effects of AR and/or GAME on knowledge development for pre-service teachers?
RQ4: What are the moderating effects of AR and/or GAME on computer self-efficacy development for pre-service teachers?
Based on the literature, the following hypotheses were formed for the pre–post analyses in group settings A, B, C, and D:
H1: 
Among the four intervention groups, the AR + GAME (A) group develops significantly higher K and CSE scores.
Furthermore, we investigated the moderating effects of both design features, AR and game-based learning, for K and CSE gains. The following hypotheses were added to describe the moderating effects of AR and GAME on K pre/post and CSE pre/post in the different group settings:
H2a: 
AR moderates K gains (investigated by RQ3).
H2b: 
GAME moderates K gains (investigated by RQ3).
H3a: 
AR moderates CSE gains (investigated by RQ4).
H3b: 
GAME moderates CSE gains (investigated by RQ4).
This work adds to the current research by investigating game-based AR and its impact on CSE and K acquisition in a control group design. Applying a 2 × 2 design, we aimed to investigate the applied technology separately from the applied learning strategy. Specifically, by contrasting both design features, AR and game-based learning, singularly and combined, we add to the understanding of the effectiveness for learning science. We investigated both K and CSE in a pre/post design. We expanded this by investigating the relationships and moderating effects among the intervention groups and their respective K and CSE development.

2. Materials and Methods

Following an interdisciplinary approach, this learning environment combined the subjects biology, chemistry, and engineering to shed light on the sustainability issue of “microplastics and plastics” from multiple perspectives. It addressed the emergence, use, and disposal of plastics and microplastics. Designed for pre-service teachers, this game-based AR learning environment introduced innovative teaching methods by integrating technology game-based approaches.

2.1. Intervention Design

This study developed a game-based AR learning environment for education for sustainable development (ESD). The learning environment was developed as an application for tablets, which can be used in classrooms by small working groups. An IT agency specialized in game design assisted the process, as well as educators from all involved subjects and game-based learning experts. Following the design-based research approach, the development was split into iterations in which elements were created and iteratively tested with students. Three iterations tested (1) the overall game mechanics, (2) the digital elements, and finally (3) the overall learning environment as well as the applied instruments. Guided group interviews were transcribed and evaluated using qualitative content analysis.
By combining AR with a game-based approach, the objective of this learning environment was to improve pre-service teachers’ K on microplastics and CSE beliefs about using digital media. The aspect of managing and lowering technostress leads to integrating playfulness into AR learning environments [66,67]. This could help lower the threshold for using and handling new technologies. To systematically incorporate game-based learning, we oriented our design on the eight game elements outlined in the framework’s design guidelines for AR [16]: (1) goals and rules, (2) conflict and challenge, (3) control, (4) assessment, (5) action language, (6) human interaction, (7) (game) environment, and (8) story. The game-based interventions A and C therefore were equipped with a main playful storyline narrating an everyday scenario. In this setting, the game’s antagonist, the plastic monster, must be defeated. This was accompanied by a set of rules that challenged players to make decisions and take action, influencing the game’s course and outcome. The conflict was to avoid accumulating harmful plastic points—this was carried out by making decisions on consumption and answering quiz questions. There was no definitive right or wrong decision; players were rather obliged to consider the consequences, reflecting real-life sustainability dilemmas.
To contrast and analyze the effects of both AR and game-based designs, we applied a 2 × 2 design [68] with four different intervention groups: AR in a game-based environment (the original), AR without a game-based environment, a game-based environment without AR, and an environment without either an AR or game-based approach. For future reference, we call them A (AR + GAME), B (AR + non-GAME), C (non-AR + GAME), and D (non-AR + non-GAME).
AR was applied to interventions A and B, which displayed subject-specific content via AR for biology, chemistry, and engineering. In contrast, C and D displayed the same content in a conventional manner using worksheets, microscopes, and physical material. AR was applied in so-called “breakout rooms”, each dedicated to one of the three subjects with three specific thematic emphases. In the biology breakout room, different types of microplastics were depicted and displayed the variety of origins as well as their particle forms, sizes, and shapes. Here, the participants learned about the intake of particles via the intestinal epithelium. This breakout room concluded with the dangers and health risks associated with potential microplastic absorption in the human body. Chemistry experimented with an everyday issue (textile abrasion through washing) and in this breakout room, participants explored methods to filter out microplastic particles. Participants also observed the extracted and filtered particles under an augmented microscope. The engineering breakout room provided an overview of different materials and used AR to augment their molecular structures. It concluded with the purpose and benefits of the particular material characteristics.
GAME, used for interventions A and C, was rendered in a didactic approach. For example, the game element “story(telling)” was implemented as a lifelike narrative with nine thematic chapters (transportation, supermarket, packaging, consumption, waste, NGO, AR: Chemistry, AR: Biology, and AR: Engineering), of which each chapter had respective tasks. The game element “assessment” included quiz questions and points, while the game element “control” engaged the participants by allowing them to make decisions and actively choose pathways to influence the course of the game. In contrast, the non-GAME interventions (B and D) replaced the eight game elements with exercises integrated in the interactive program “H5P”.
Experts in game design as well as didactics were included in the design and conception of all interventions to guarantee homogeneity in both engagement and interactivity of the different interventions A, B, C and D. Teachers of biology, chemistry, and engineering assisted the creation of the learning material to assure both a balance and equivalence of all subjects in all 4 intervention types.
The entire intervention lasted 120 min and was divided into three parts: introduction (10 min), intervention (A, B, C, or D—80 min), and recap (30 min). The intervention itself was portioned into 3 AR breakout rooms, which were each designed to take up about 10 min (depending on the students’ pace) and 6 additional segments. The non-AR intervention groups had 3 subject segments with equivalent content and duration, designed to convey the same information. The game-based groups were designed to convey the same information and knowledge with 6 story chapters, whereas the non-game-based version used 6 equivalent themed exercises. All 6 elements were segmented equally also, assuring all 6 chapters or exercises took about 5 min. Before the thematic introduction, participants completed the pre-questionnaire covering demographics, K, and CSE. Following this, participants were briefly introduced to the topics of plastic use and microplastics. Participants then received the materials and devices and were divided into teams, each composed of smaller groups of four students. During the main part of the intervention, these groups engaged with the tasks and game play. The device’s app guided them through the intervention and gave instructions on what to do. An adviser was available to assist if anything was unclear. Aside from this, participants were free to proceed at their own pace and handling. After completing the intervention, participants filled out and submitted the post-questionnaire covering K and CSE.

2.2. Research Design

We recorded CSE and K before and after the intervention in a pre–post-design. All data were collected as pre-service teachers’ self-assessments. The participants received the paper and pencil questionnaires to complete directly before and after the lesson. The research design is displayed in Figure 1 below.
To assess content-specific knowledge (K), a single-choice questionnaire was developed as a knowledge test covering the 13 thematic sections of the learning environment. Each item had 3 response options of which one is true and two are false. The value for Cronbach’s Alpha of Kpost was α = 0.791. To assess participants’ self-efficacy beliefs toward using digital media (CSE), the questionnaire of Holden and Rada [69] was applied. It features 10 items, each rated on a 10-point Guttman scale (from 1 = “absolutely unsure”; mid = “fairly sure”; 10 = “absolutely sure”). The value for Cronbach’s Alpha of CSEpost was α = 0.924.

2.3. Sample

A total of 219 pre-service teachers from a German university participated in the interventions. The students were randomly assigned to one of eight groups, of which two groups each used one of the four intervention groups, A, B, C, or D. A team of 7 supervisors assisted the students but made sure not to interfere in their work. Two to three supervisors were assigned randomly to the different intervention groups. All supervisors worked randomly in several different intervention groups. Two teachers introduced the students to the topic using the same introduction presentation. At least one of these two teachers was present in every intervention group. Procedure and instructions for the students were written out and incorporated in the game as well as the interactive program (H5P). The data were curated according to the two different variables under investigation: data set 1 for the analysis of Kpre/Kpost and data set 2 for the analysis of CSEpre/CSEpost.
Identification of the missing values and z-standardization for detecting statistical outliers were applied to clean the data sets. A total of 16 cases were removed from data set 1 (Kpre/Kpost) due to missing values, in addition to 5 statistical outliers. A total of 40 cases were removed from data set 2 (CSEpre/CSEpost) due to missing values. See Table 1 below, for descriptive statistics of both data sets.
Since all participants were pre-service teachers in their first semester and we reviewed the curriculum, we could assume they had no prior experience with AR used for teaching during their studies.

2.4. Data Analysis

A repeated measures ANOVA was conducted to investigate the development of K and CSE. We examined whether the four intervention groups showed an increase in their K and CSE following the intervention (main effect: from pre to post) and whether the increase differed according to or was dependent upon the group’s design (interaction effect: group × time).
Moderation analysis further explored the relationships among Kpre and Kpost, and CSEpre and CSEpost. In total, 12 moderation analyses were performed using SPSS version 29.0.0.0 (IBM). The analyses were carried out with the PROCESS macro for SPSS (version 29) [70]. This analysis used a model in which the relationship between two constructs depends on another variable. This variable (the moderator) is suggested to affect the strength and/or direction of the relationship. In this case, we applied the moderator (AR or GAME) to the relationship of the pre–post variables (K or CSE development from pre to post) to unveil any potential effect said feature (AR or GAME) may have had on the participants’ development (from pre to post). This choice of analysis supported the chosen research design and format of contrasting features while investigating a recorded development from before to after the intervention.
AR and GAME were tested for their potential moderating effects on CSE and K gains. A significant moderating effect would imply that the variable in question—in this case, the intervention design conditions AR/non-AR/GAME/non-GAME—is associated with the direction and/or the intensity of the relationship between pre–post variables—in this case, the development of knowledge/computer self-efficacy. To contrast the interventions by AR or GAME, the groups were merged to form AR/non-AR and GAME/non-GAME groups. Moderation analyses were applied to each of the total samples of K (data set 1 N = 196) and CSE (data set 2 N = 177) as well as to the merged groups. An overview of the performed moderation analyses is shown in Table 2.
Due to the lack of normal distribution, the variables Kpre, Kpost, CSEpre, and CSEpost were Box–Cox transformed to perform moderation analyses. The optimal lambdas for transformation were rounded to one decimal. The following lambdas were used for Box–Cox transformation: Kpre λ = 3.2; Kpost λ = 4.9; CSEpre λ = 3.5; and CSEpost λ = 4.4.

3. Results

We present the study results in two parts. First, we depict the results of the pre–post analyses of K and CSE. Next, we delve deeper into these pre–post relationships for a more detailed analysis.

3.1. Interventions Lead to Improvement in Knowledge and Computer Self-Efficacy

To compare the groups, we first examined their entry levels of both CSE and K. For K only, groups A (AR + GAME) and C (non-AR + GAME), as well as groups A (AR + GAME) and D (non-AR + non-GAME), were equivalent. However, for CSE, the different groups’ entry levels were nonequivalent. T-tests for within-testing were conducted to compare pre- and post-intervention values. A significant change from pre to post was observed for all groups in K and CSE.
A significant main effect (time) was found across all groups. In addition to the descriptive statistic’s mean values, this showed that all groups significantly improved their K and increased their CSE. However, no interaction effect (time × group) was found. The results of K are shown in Table 3, and the results of CSE are shown in Table 4. The increase in K and CSE was not in correlation to group membership.

3.2. AR Moderates the Increase in Computer Self-Efficacy

The following section presents the moderation analyses, revealing a significant effect. Each is displayed with a table of model summary, an extended table, and a graph depicting the conditional effects. Analyses revealing no significant moderating effects are presented in Table A1, which is included in the Appendix A with the model summaries.
The first model used CSEpost as the outcome variable, CSEpre as the predictor, and AR as the moderator. This analysis was applied to the total sample, including all intervention groups: A to D (N = 177). The model revealed that 46.2% of the variability in CSEpost was predicted by both CSEpre and AR (R2 = 0.4623, F(3,173) = 49.5704, p < 0.0000). Table 5 (part a) displays the unstandardized regression coefficients. The interaction effect was statistically significant (p = 0.0115), indicating that the condition AR (either applying or substituting AR in the intervention design) moderated the effect of CSEpre on CSEpost. Figure 2 illustrates this moderating effect. The graph demonstrates that the moderator (AR) did not affect the direction of the relationship but had an effect on the potency. The relationship between CSEpre and CSEpost was stronger for participants in the non-AR interventions, and weaker for the participants in the AR interventions. Table 5 (part b) presents the conditional effects of the focal predictor (CSEpre) at the two values of the moderator (AR: on/off).
The second model also used CSEpost as the outcome variable, CSEpre as the predictor, and AR as the moderator. This analysis was applied specifically to the non-GAME groups: C and D (N = 86). The model revealed that 37.8% of the variability in CSEpost was predicted by CSEpre and AR (R2 = 0.3775, F(3.82) = 16.5778, p < 0.0000). Table 6 (part a) displays the unstandardized regression coefficients. The interaction effect was statistically significant (p = 0.0398), indicating that the condition AR (either applying or substituting AR in the intervention design) moderated the effect of CSEpre on CSEpost. Figure 3 illustrates this moderating effect. The graph demonstrates that the moderator did not affect the direction of the relationship but had an effect on the potency. The relationship between CSEpre and CSEpost was stronger for participants in the non-AR intervention and weaker for participants in the AR intervention. Table 6 (part b) presents the conditional effects of the focal predictor (CSEpre) at the two values of the moderator (AR: on/off).
The third model used Kpost as the outcome variable, Kpre as the predictor, and GAME as the moderator. This analysis was applied only to the AR groups: A and C (N = 98). It revealed that 34.3% of the variability in Kpost was predicted by Kpre and GAME (R2 = 0.3426, F(3,94) = 16.3283, p < 0.0000). Table 7 (part a) displays the unstandardized regression coefficients. The interaction effect was statistically significant (p = 0.0388), indicating that the condition GAME (either applying or substituting game-based learning in the intervention design) moderated the effect of Kpre on Kpost. Figure 4 illustrates this moderating effect. The graph shows that GAME did not affect the direction of the relationship but had an effect on its potency. The relationship between Kpre and Kpost was stronger for participants in the non-GAME intervention, and weaker for participants of the GAME intervention. Table 7 (part b) presents the conditional effects of the focal predictor (Kpre) at the two values of the moderator (GAME: on/off).
All other moderation analyses revealed no statistically significant interaction effect. The supplement data set shows each model summary.

4. Discussion

This study analyzed a game-based AR learning environment for ESD. A total of 217 pre-service teachers participated in four different intervention groups: A (AR + GAME), B (AR + non-GAME), C (non-AR + GAME), and D (non-AR + non-GAME). Aimed to investigate the potential of both AR and GAME, the participants’ K and CSE development over time was assessed.
Various studies reported AR improving knowledge acquisition [11,31,50,51,52,53] and self-efficacy beliefs [45,48,54,58,71]. Therefore, we expected that Kpre would positively relate to Kpost (H1) and CSEpre would positively relate to CSEpost (H1), both with significantly better scores in the AR+GAME group (in our case, A as compared to B, C, and D).

4.1. All Interventions Lead to Improved Knowledge and Computer Self-Efficacy

The findings revealed a significant time effect for K and CSE in all intervention groups. In terms of K and CSE, all intervention groups led to improvement of K and an increase in CSE, as indicated by the growth of the arithmetic mean values. However, this progress was not group-related, as no interaction effect was found. Thus, the improvements of K and CSE were not dependent on the specific intervention group. Consequently, K acquisition and increase in CSE could not be associated with the different intervention groups, with the features AR/non-AR and GAME/non-GAME.
All four types of intervention effectively improved K on microplastics. This finding is consistent with some previous findings reporting no difference in performance between the AR and control groups [13,57]. In contrast, other AR studies found that AR outperforms the control groups [11,31,50,51,53]. Furthermore, our findings contradict the conclusion of Peeters et al. [4], who reported no increase in K for the tested AR intervention.
AR is often combined with a game-based approach. However, comparable studies, investigating both AR and game-based approaches in differentiated intervention groups, were inconclusive as to the impact on learning effects [38,39,42]. To address this research gap, we investigated two hypotheses: (a) the K relationship (H2a) and CSE relationship (H3a) will be moderated by AR and (b) the K relationship (H2b) and CSE relationship (H3b) will be moderated by GAME.
Contrasting intervention groups in a 2 × 2 design and integrating AR and GAME can also be found in the work of Chen [39]. However, as opposed to our findings where all groups progress in K, Chen reported only the GAME approach to significantly have improved “learning achievements” (and flow state). Also, their study focused on elementary school science.
Our results also showed that exposing pre-service teachers to different digital media designs strengthened and enhanced CSE in all four types of this learning environment. This is consistent with previous findings [45,48,54,58] stating that the work with AR increased self-efficacy. Our findings, which showed a rise in CSE in all (digital media) intervention groups, add to previous conclusions [44,46,47] which stated that exposure to and use of digital media positively influences technological self-efficacy. It remains unclear whether this improvement in CSE was due to the fact the participants worked in groups, the appeal of the informal design setup, or other factors. Additionally, AR and/or GAME did not impact the overall improvement of K and CSE.

4.2. AR Moderates the Pre–Post Relationship of Computer Self-Efficacy

The analysis further examined whether the implementation or substitution of AR and/or GAME moderated these relationships of the pre–post variables K and CSE (Kpre to Kpost and CSEpre to CSEpost). The results presented in Table 5, Table 6 and Table 7 provide insights into how the moderator variables (GAME and AR) influenced these relationships.
First, as is consistent with the time effects of the repeated measures ANOVA, Kpre and CSEpre were significant predictors of our educational outcomes (Kpost and CSEpost). All moderation models revealed solid coefficients of determination.
We investigated if AR (on/off) moderated the improvement of the participants’ CSE during the intervention. Significant moderating effects of AR on the CSE relationship were found in two moderation analyses. The conditional effects showed a stronger effect on the CSEpre–CSEpost relationship for the non-AR groups compared to the AR groups. This was applicable for both the total sample (groups A–D: significant moderation effect with stronger conditional effect for non-AR) as well as the non-GAME groups (groups C+D: significant moderation effect with stronger conditional effect for non-AR). This indicates that substituting AR components with conventional tasks within this digital intervention design led to a stronger CSE increase, both for the game-based intervention as well as the H5P intervention. However, AR implementation also contributed to an increase in CSE.
In contrast, the moderation analysis with AR as a moderator on the K relationship found no significant effect, indicating the implementation or substitution of AR (AR vs. non-AR) did not influence the Kpre–Kpost relationship. This applied to the three moderation analyses: consideration of all groups (A–D), the GAME groups (A+B), and the non-GAME groups (C+D).
Similarly, the moderation analysis with GAME as a moderator on the CSE relationship also found no significant effect, indicating that the implementation or substitution of GAME (GAME vs. non-GAME) did not influence the CSEpre–CSEpost relationship. This applied to all three moderation analyses: consideration of all groups (A–D), the AR groups (A+C), and the non-AR groups (B+D).

4.3. GAME Moderates the Pre–Post Relationship of Knowledge

Differentiated results were found for the moderation analysis of GAME as a moderator on the K relationship. No significant moderating effects were found for the consideration of all groups (A–D) and the non-AR groups (B+D), indicating that the implementation or substitution of GAME did not affect knowledge acquisition. However, a significant moderating effect was found for the consideration of the AR groups (A+C), with a stronger conditional effect for non-GAME. This indicates a stronger K increase is achieved with AR interventions without a game-based approach. Yet, GAME implementation also led to an increase in K.
Previous studies have shown that both AR and GAME as features of digital learning environments can be engaging, interactive, and interesting. However, our results suggest these components did not universally strengthen the development of K and CSE (relationship between pre–post). Specifically, in the context of digital interventions for science education, removing AR components might unexpectedly promote a greater increase in CSE compared to including them. This outcome contradicts the common expectation that AR enhances learning experiences and outcomes due to its immersive nature. The uniform knowledge gain across all groups suggests that the inclusion of AR in the digital learning environment did not significantly alter pre-service teachers’ knowledge gain from pre- to post-intervention. This aligns with studies like Radu [62], which indicated that AR’s effectiveness can vary significantly across different contexts and content areas.
The moderating effect of GAME on the pre–post knowledge relationship of both AR groups suggests that game elements did not lead to an equally strong increase as when these elements were substituted. This finding implies that in AR-enhanced learning environments, traditional or alternative instructional strategies may be more effective than game-based approaches for imparting knowledge. This is an interesting finding, as it contrasts with common assertions about the benefits of gamification and suggests a potential conflict or distraction effect when combining game elements with AR technology for learning purposes.
The moderated regression analyses showed that the entrance level of K and CSE were a significant predictor of the resulting K and CSE after exposure to a game-based AR learning environment. Yet, moderating effects of the design features under investigation (GAME and AR) did not clearly show any impact on K acquisition. Notably, a moderating effect of AR even dampened CSE increase, compared to substituting this component with traditional learning material (i.e., =non-AR).

4.4. Implications for Future Development

The findings of the present study must be carefully interpreted within the context of the subject domain (education for sustainable development) and the target group (higher education). The study revealed that the combination of AR and game-based learning did not result in a greater increase in knowledge compared to the other groups. However, there was an increase in computer-related self-efficacy among all groups, with the greatest increase observed in those who utilized more conventional digital media (such as H5P applications). Conversely, game-based learning exhibited a detrimental effect on knowledge growth when compared with non-game-based learning methods. What implications could the results of our study have for teacher training?

4.4.1. Emphasizing Self-Efficacy in Technology Use

Pre-service teachers may develop heightened confidence in their ability to utilize digital tools, despite the relatively modest impact on knowledge retention. The study’s primary implication is the necessity for pre-service teacher education programs to place a premium on fostering computer-related self-efficacy. As Bandura (1997) [72] conceptualizes, self-efficacy plays a pivotal role in shaping the beliefs and behaviors of pre-service teachers concerning the integration of technology in their future classrooms. Despite the absence of a statistically significant increase in knowledge levels among the study participants following AR and game-based learning interventions, the observed enhancement in self-efficacy underscores the potential of technology exposure to boost teachers’ confidence in employing digital tools. Consequently, teacher education programs should incorporate opportunities for pre-service teachers to engage with a range of technologies, including AR, alongside conventional digital tools such as H5P applications. This approach will assist pre-service teachers in developing familiarity with diverse tools, thereby fostering their confidence in integrating technology into their teaching practices.

4.4.2. Contextualizing Technology Use in Teacher Education

Given the context of education for sustainable development and higher education, it is imperative for pre-service teacher programs to acknowledge that the efficacy of various technologies may differ based on the subject domain, the learning objectives, and the learner characteristics. For instance, game-based learning may have a limited impact on knowledge acquisition in some contexts, but it may still be valuable in fostering engagement, motivation, and problem-solving skills, which are essential for teaching 21st century skills. Therefore, teacher education programs should equip pre-service teachers with the skills to critically assess and select appropriate technologies based on the specific needs of their students and the subject matter they are teaching.

4.4.3. Fostering Critical Reflection on Technology Integration

An essential takeaway from this study is the need to foster critical reflection among pre-service teachers regarding the integration of technology into teaching. Pre-service teachers should be taught to not only use technology but also to evaluate its effectiveness in achieving learning outcomes. This can be accomplished through course content that emphasizes evidence-based practices, with a focus on how different educational technologies align with established pedagogical theories and frameworks. Teacher education programs could incorporate case studies, simulations, and collaborative discussions that challenge pre-service teachers to reflect on the benefits and limitations of various technological tools, including AR and game-based learning.

4.4.4. Balancing Innovation with Pedagogical Foundations

The finding that game-based learning had a detrimental effect on knowledge growth compared to non-game-based learning methods underscores the necessity for a balanced approach to the integration of game-based learning. While acknowledging the value of game-based learning, it is imperative that pre-service teacher education programs prioritize the integration of pedagogical foundations to ensure that the methodology does not overshadow educational objectives. Pre-service teachers must be trained to utilize methods purposefully, ensuring that the tools they incorporate into their classrooms align with educational goals, enhance learning experiences, and do not detract from essential content delivery.
The findings of this study indicate that pre-service teacher education programs must strike a balance between technological exposure and a robust foundation in pedagogical practices. By focusing on building self-efficacy, critically evaluating technology, and understanding its contextual appropriateness, teacher education programs can better prepare pre-service teachers to navigate the complexities of modern classroom environments and to make informed decisions about the integration of digital tools in their future teaching practices.

4.5. Limitations

This study was designed as a field study to ensure ecological validity, which precluded the randomization of the sample. The focus on ecological validity was prioritized over randomization.
Due to feasibility constraints, the pre–post-intervention assessments were conducted immediately before and after the intervention. The anonymization of participants prevented a follow-up design, which would be valuable for investigating both CSE and K retainment.
The intervention groups were designed to contrast the features AR and GAME. Substituting AR with traditional learning material is distinctive. However, eight game elements were applied or substituted for the latter feature. This approach is systematic in its design, though it does not guarantee the elimination of any playful character or feeling. Even the non-game-based interventions may have led to a playful experience, which could impair/diffuse the categorization of the result in game-based and non-game-based groups.
Furthermore, game design and its experience is highly dependent on design details and players’ attitudes. A slight variation in design may have led to a different outcome. The results of moderation model 3 could be attributed to the quality and design of the game or non-game.
The learning environments were carried out as group work. In all intervention designs participants worked in small teams of four, sharing a tablet and working equipment. Setup into groups of four was random and consistent throughout all interventions. This organizational aspect of team-based learning may have impacted the participants’ experiences and potentially affected participants’ motivation, contribution, and performance [73]. This aspect was not further investigated and integrated into the data analysis due to feasibility and poses a limitation to the findings. The entire questionnaire contained additional learning variables (for different analyses, mentioned in another publication).
Additionally, the accompanying supervisors and teachers may have impacted individual participants. This aspect was not further investigated, as the focus on ecological validity was prioritized and human interaction in the classroom remains an unforeseeable and instable factor.
Questionnaire fatigue may have led to the notable drop-out rate (CSE: N = 217 to 177 and K: N = 217 to 196).
The inconclusiveness of our results and previous findings of various research potentially may have arisen from the various designs of AR environments, divergent target groups, and different applied topics. Additionally, the research design may have varied, and different measuring instruments may have led to stronger or weaker effects. Furthermore, alternative underlying concepts and constructs are hard to compare. Combining different outcomes, such as “performance” or “academic achievement”, under the same category “learning gains”, may paint research findings with a broad brush. This also goes for the term self-efficacy, applied to various sub-domains of it.
Due to this multiplicity of research design considerations (target groups, control groups design, measuring instruments, learning effect variables) as well as the specific design of AR learning scenarios (design, conceptualization, and application of AR technology), the results of this research cannot be generalized for the use of game-based AR in science education.
Computer self-efficacy, investigated to grasp the participants’ beliefs and judgments of themselves, in terms of using and handling digital media, is a subjective measure. Accordingly, the results are no unbiased entity and need to be interpreted with cautious consideration.
Knowledge understood as the learning gained from our intervention could be realized as acquisition of specific skills in other research work or even have alternative underlying didactical principles when AR is evaluated in a different subject of science.

5. Conclusions

Our study presents valuable insights into the complex interplay between digital interventions and educational outcomes in science teaching. It emphasizes the need for a nuanced understanding of how different technologies impact different aspects of learning and calls for further research to explore these relationships in various educational contexts. In line with previous research evaluating AR learning environments in terms of knowledge acquisition and self-efficacy in a control group design, our results show no statistical difference between the AR intervention and the control groups. The findings indicate that all intervention groups successfully imparted knowledge and strengthened computer self-efficacy. Our research design differentiates the frequently blended features of AR learning environments: AR technology and game-based approaches. Contrasting them in four intervention groups, we aimed to reveal any effect each feature has/or rather has not. This type of group setting or even just control group setting (AR versus traditional learning material) is pivotable to distinguish the actual benefit integrating AR into teaching may have. The present study affirms suggestions of previous research that exposure to digital media and technologies may strengthen pre-service teachers’ CSE. We conclude that combining AR and game-based learning can ease the transition to technology-enhanced education. All intervention setups under investigation led to K and CSE gains, proving that playful technology integration did not hinder the learning process. The lack of research for game-based AR learning environments in science education calls for further research to investigate the complex interactions between additional variables, such as motivational or behavioral aspects associated with learning. Our findings add to the current state of research, yet more research and insight into game-based AR for science is needed. In this context, it is worth noting that future developments combining AR and game-based learning may integrate both features to different extents. While this setup implemented game-based learning with a total of eight game elements and AR technology in three sections, it would be plausible to apply only a selection or single game element or expand the use of AR technology in such learning environments. For instance, given the current development of AI-assisted learning, this feature may support AR learning scenarios in composing AR with high adaptivity and situational feedback mechanisms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app15052844/s1, Supplementary Data.

Author Contributions

Conceptualization, V.C. and H.W.; methodology, V.C. and H.W.; software, V.C.; validation, H.W.; formal analysis, V.C.; investigation, V.C.; resources, H.W.; data curation, V.C.; writing—original draft preparation, V.C.; writing—review and editing, H.W.; visualization, V.C.; supervision, H.W.; project administration, H.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by German Ministry of Science, “ARtiste-Augmented Reality Teaching in Science Technology Education”, grant number reference 34-7811.533-4/3/5. The APC was funded by the University of Education Weingarten.

Institutional Review Board Statement

This study was granted exemption by the Ethics Committee of PH Weingarten. The committee concluded that only anonymized quantitative data were collected. The project team’s intended procedure complies with the DFG’s principles of good scientific practice. A further statement by the ethics committee is not required.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study are available from the Supplementary Materials and corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Model summaries of moderated regression analyses resulting in no moderating effect.
Table A1. Model summaries of moderated regression analyses resulting in no moderating effect.
CSEpost = outcome variable; CSEpre = predictor; AR = moderator (groups A+B: N = 917)coefficient of determination R2 = 0.5441
coefficienttp95% CI low95% CI up
Constant2701.330617.34720.00002391.81573010.8454
CSEpre (B)6.01918.60410.00004.62867.4095
GAME (A)−27.3896−0.12510.9008−462.7249407.9458
INT_1 (A × B)−1.8365−1.76980.0803−3.89920.2261
CSEpost = outcome variable; CSEpre = predictor; GAME = moderator (all groups A–D: N = 177)coefficient of determination R2 = 0.4434
coefficienttp95% CI low95% CI up
Constant2753.81324.13520.00002528.39352978.7692
CSEpre (B)3.92156.27780.00002.68865.1545
GAME (A)−50.7378−0.31890.7502−364.7901263.3146
INT_1 (A × B)1.26341.55040.1229−0.34502.8717
CSEpost = outcome variable; CSEpre = predictor; GAME = moderator (groups A+C: N = 84)coefficient of determination R2 = 0.3086
coefficienttp95% CI low95% CI up
Constant2539.789015.12040.00002205.51562874.0623
CSEpre (B)2.28862.25800.02670.27154.3057
GAME (A)157.58600.69450.4894−293.9875609.1595
INT_1 (A × B)1.89391.49580.1386−0.62584.4136
CSEpost = outcome variable; CSEpre = predictor; GAME = moderator (groups B+D: N = 93)coefficient of determination R2 = 0.5676
coefficienttp95% CI low95% CI up
Constant2933.497019.55620.00002635.44293231.5511
CSEpre (B)4.92866.40220.00003.39906.4583
GAME (A)−231.3527−1.07280.2862−659.8320197.1266
INT_1 (A × B)1.09051.05080.2962−0.97163.1526
Kpost = outcome variable; Kpre = predictor; AR = moderator (all groups A–D: N = 196)coefficient of determination R2 = 0.2712
coefficienttp95% CI low95% CI up
Constant38,813.566026.46940.000035,921.326041,705.8059
Kpre (B)27.25375.40960.000017.316737.1907
AR (A)642.88340.31000.7569−3447.29184733.0587
INT_1 (A × B)4.86340.68660.4932−9.107518.8342
Kpost = outcome variable; Kpre = predictor; AR = moderator (groups A+B: N = 103)coefficient of determination R2 = 0.2582
coefficienttp95% CI low95% CI up
Constant38,348.333319.69950.000034,485.722842,210.9437
Kpre (B)30.09884.45210.000016.684543.5132
AR (A)3417.54201.28340.2023−1866.00698701.0909
INT_1 (A × B)−8.2697−0.88340.3791−26.843610.3042
Kpost = outcome variable; Kpre = predictor; AR = moderator (groups C+D: N = 93)coefficient of determination R2= 0.3172
coefficienttp95% CI low95% CI up
Constant39,166.917617.91900.000034,823.811743,510.0234
Kpre (B)25.78953.43860.000910.887040.6920
AR (A)−2073.1902−0.64490.5206−8460.38494314.0044
INT_1 (A × B)16.70941.53510.1283−4.918338.3371
Kpost = outcome variable; Kpre = predictor; GAME (all groups A–D: N = 196)coefficient of determination R2 = 0.2738
coefficienttp95% CI low95% CI up
Constant39,801.006726.18370.000036,802.827942,799.1856
Kpre (B)33.75536.63350.000023.718543.7920
GAME (A)−874.3225−0.41730.6769−5007.13203258.4871
INT_1 (A × B)−7.4112−1.03690.3011−21.50906.6865
Kpost = outcome variable; Kpre = predictor; GAME (groups B+D: N = 98)coefficient of determination R2 = 0.2395
coefficienttp95% CI low95% CI up
Constant39,995.272419.03210.000035,822.757944,167.7870
Kpre (B)25.78953.59480.000511.545140.0339
GAME (A)−3420.9532−1.13890.2576−9384.82662542.9203
INT_1 (A × B)4.30930.41680.6778−16.220624.8393

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Applsci 15 02844 g001
Figure 1. Research design with 4 intervention groups. K and CSE were assessed before and after lesson.
Figure 1. Research design with 4 intervention groups. K and CSE were assessed before and after lesson.
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Figure 2. A line graph for the first model with a moderating effect, showing a stronger moderating effect for the non-AR interventions amongst all groups. The lines illustrate the CSE pre–post relationship of the intervention groups with/without AR.
Figure 2. A line graph for the first model with a moderating effect, showing a stronger moderating effect for the non-AR interventions amongst all groups. The lines illustrate the CSE pre–post relationship of the intervention groups with/without AR.
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Figure 3. A line graph for the second model with a moderating effect, showing a stronger moderating effect for the non-AR intervention amongst both non-GAME groups. The lines illustrate the CSE pre–post relationship of the intervention groups with/without AR.
Figure 3. A line graph for the second model with a moderating effect, showing a stronger moderating effect for the non-AR intervention amongst both non-GAME groups. The lines illustrate the CSE pre–post relationship of the intervention groups with/without AR.
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Figure 4. A line graph for the third model with a moderating effect, showing a stronger moderating effect for the non-GAME intervention amongst both AR groups. The lines illustrate the K pre–post relationship of the intervention groups with/without GAME.
Figure 4. A line graph for the third model with a moderating effect, showing a stronger moderating effect for the non-GAME intervention amongst both AR groups. The lines illustrate the K pre–post relationship of the intervention groups with/without GAME.
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Table 1. Descriptive statistics of data sets 1 and 2.
Table 1. Descriptive statistics of data sets 1 and 2.
Data SetSample Size NSexAgeCourse of Study
1 (K)N = 196Female = 77%
Male = 23%
Diverse = 0%		18–36
M = 20.31
SD = 2.434		elementary school education = 64.3%
secondary (WHRS) = 28.6%
others = 6.6%
missing values = 0.5%
A: n = 55
B: n = 48
C: n = 43
D: n = 50
2 (CSE)N = 177Female = 78%
Male = 22%
Diverse = 0%		18–36
M = 20.27
SD = 2.441		elementary school education = 63.3%
secondary (WHRS) = 28.8%
others = 7.3%
missing values = 0.6%
A: n = 46
B: n = 38
C: n = 45
D: n = 48
Table 2. Description of sample, predictor, moderator, and outcome variable in terms of intervention groups.
Table 2. Description of sample, predictor, moderator, and outcome variable in terms of intervention groups.
Intervention GroupsSampleN=PredictorModeratorOutcome Variable
A, B, C, DTotal177CSEpreARCSEpost
A+CGAME groups91
B+DNon-GAME groups86
A, B, C, DTotal177CSEpreGAMECSEpost
A+BAR groups84
C+DNon-AR groups93
A, B, C, DTotal196KpreARKpost
A+CGAME groups103
B+DNon-GAME groups93
A, B, C, DTotal196KpreGAMEKpost
A+BAR groups98
C+DNon-AR groups98
Table 3. Results for K’s repeated measures ANOVA.
Table 3. Results for K’s repeated measures ANOVA.
Multivariate Test of Repeated Measures ANOVA for K (N = 196)
ValueFHypothesis dfError dfSig.Partial Eta Squared
time0.86230.8341.000192.000<0.0010.138
time × group0.9880.8043.000192.0000.4930.012
Table 4. Results for CSE’s repeated measures ANOVA.
Table 4. Results for CSE’s repeated measures ANOVA.
Multivariate Test of Repeated Measures ANOVA for CSE (N = 177)
ValueFHypothesis dfError dfSig.Partial Eta Squared
time0.88521.7771.000173.000<0.0010.112
time × group0.9980.7263.000173.0000.5380.012
Table 5. (a) Overview of first model’s variables and effects. (b) Details of moderating effects of AR for first model.
Table 5. (a) Overview of first model’s variables and effects. (b) Details of moderating effects of AR for first model.
(a) Summary of moderated regression analysis predicting CSEpost (N = 177)
Coefficienttp95% CI low95% CI up
Constant2836.517926.30460.00002623.67873049.3571
CSEpre (B)5.525610.69700.00004.50606.5452
AR (A)227.5977−1.45400.1478−536.562081.3665
INT_1 (A × B)−2.0411−2.55290.0115−3.6192−0.4630
(b) Conditional effects of the focal predictor (CSEpre)
AREffectSEtp95% CI low95% CI up
0 (nonAR)5.52560.516610.69700.00004.50606.5452
1 (AR)3.48450.61035.70960.00002.27994.6890
Table 6. (a) Overview of second model’s variables and effects. (b) Details of moderating effects of AR of second model.
Table 6. (a) Overview of second model’s variables and effects. (b) Details of moderating effects of AR of second model.
(a) Summary of moderated regression analysis predicting CSEpost (N = 86)
Coefficienttp95% CI low95% CI up
Constant2960.523019.93120.00002665.03443256.0117
CSEpre (B)4.92866.46900.00003.41306.4442
AR (A)−420.6977−1.88200.0634−865.396924.0015
INT_1 (A × B)−2.6400−2.08930.0398−5.1537−0.1263
(b) Conditional effects of the focal predictor (CSEpre)
AREffectSEtp95% CI low95% CI up
0 (nonAR)4.92860.76196.46900.00003.4130
1 (AR)2.28861.00812.27030.02580.28334.2940
Table 7. (a) Overview of third model’s variables and effects. (b) Details of moderating effects of GAME of third model.
Table 7. (a) Overview of third model’s variables and effects. (b) Details of moderating effects of GAME of third model.
(a) Summary of moderated regression analysis predicting Kpost (N = 98)
Coefficienttp95% CI low95% CI up
Constant39,796.041118.13220.000035,438.260744,153.8215
Kpre (B)42.49895.92720.000028.262356.7355
GAME (A)1370.10230.46910.6401−4428.66227168.8669
INT_1 (A × B)−20.6698−2.09510.0388−40.2583−1.0813
(b) Conditional effects of the focal predictor (Kpre)
GAMEEffectSEtp95% CI low95% CI up
0 (non-GAME)42.49897.17025.92720.000028.262356.7355
1 (GAME)21.82916.77643.22140.00188.374535.2838
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Czok, V.; Weitzel, H. Impact of Augmented Reality and Game-Based Learning for Science Teaching: Lessons from Pre-Service Teachers. Appl. Sci. 2025, 15, 2844. https://doi.org/10.3390/app15052844

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Czok V, Weitzel H. Impact of Augmented Reality and Game-Based Learning for Science Teaching: Lessons from Pre-Service Teachers. Applied Sciences. 2025; 15(5):2844. https://doi.org/10.3390/app15052844

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Czok, Valerie, and Holger Weitzel. 2025. "Impact of Augmented Reality and Game-Based Learning for Science Teaching: Lessons from Pre-Service Teachers" Applied Sciences 15, no. 5: 2844. https://doi.org/10.3390/app15052844

APA Style

Czok, V., & Weitzel, H. (2025). Impact of Augmented Reality and Game-Based Learning for Science Teaching: Lessons from Pre-Service Teachers. Applied Sciences, 15(5), 2844. https://doi.org/10.3390/app15052844

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