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Article

Augmented Reality 3D Multibase Blocks at the Future Classroom Lab Through Active Methodology: Analyzing Pre-Service Teachers’ Disposition in Mathematics Course

by
Ana Isabel Montero-Izquierdo
,
Jin Su Jeong
* and
David González-Gómez
Departamento de Didáctica de las Ciencias Experimentales y Matemáticas, Facultad de Formación del Profesorado, Universidad de Extremadura, Avenida de la Universidad s/n, 10004 Cáceres, Spain
*
Author to whom correspondence should be addressed.
Educ. Sci. 2025, 15(8), 954; https://doi.org/10.3390/educsci15080954
Submission received: 30 June 2025 / Revised: 18 July 2025 / Accepted: 20 July 2025 / Published: 24 July 2025
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Abstract

The use of augmented reality (AR) tools and innovative learning environments in education have increased over the last few years due to the rapid advancement of technology. In this study, an AR mathematics learning intervention has been proposed which consisted of the creation of 3D multibase blocks to perform AR arithmetic calculations conducted through active methodologies in the future classroom lab (FCL). The aim of this study was to analyze pre-service teachers’ (PSTs) affective domain (emotion, self-efficacy, and attitude), engagement, motivation, and confidence. The sample consisted of 97 PSTs enrolled on the second year of the Primary Education degree, who were attending the “Mathematics and its Didactics” subject. The findings revealed a significant increase in PSTs’ satisfaction, fun, confidence, and pride, and a decrease in uncertainty, nervousness, and concern. Regarding PSTs’ self-efficacy, a significant improvement was observed in knowing the necessary steps to teach mathematical concepts and work in the FCL. No significant differences were found in attitude, engagement, and motivation; however, the PSTs showed a high disposition in all of them before starting the intervention. Additionally, the PSTs reported to be more confident, and it enhanced their knowledge in the use of 3D design and AR applications to create multibase blocks to support the teaching–learning content of arithmetic operations.

1. Introduction

This research analyzed the effect of a mathematics learning intervention involving augmented reality (AR) and multibase 3D blocks on pre-service teachers (PSTs), implemented through active methodologies in a future classroom lab (FCL). The use of new technologies to teach mathematics has certain benefits because it provides an environment for exploration, which facilitates the understanding of abstract mathematical concepts (Dawley & Dede, 2014). However, the introduction of technology alone does not favor the learning of mathematics; a paradigm-shift in the teaching and learning process is also necessary (Moorthy & Arulsamy, 2014). Conversely, many teachers claim that they have not received adequate training in how to use technology in their teaching. AR has the potential to transform educational strategies by creating an environment in which virtual and real elements interact, thus facilitating the understanding of abstract concepts (Shamsudin & Talib, 2023; Volioti et al., 2023). The application of AR technology can improve teaching–learning methods by making learning more engaging, simplifying difficult ideas, encouraging active learning, and improving student motivation (Estapa & Nadolny, 2015; Garzón et al., 2019; Hsu et al., 2017) and their commitment to mathematics (Estapa & Nadolny, 2015; Volioti et al., 2023). To create AR-customized content, 3D design applications can be used. For example, 3D prototyping tools like Tinkercad help students gain a deeper understanding of scientific and mathematical concepts through real-world applications (Barbosa et al., 2024).
During the last decade, lightning-fast technological developments have enhanced the availability and accessibility of AR applications and mobile technologies in education (Serin, 2022). The AR technology generates an improved and heightened reality (Wu et al., 2013), and AR settings can support students’ knowledge and competences development with greater success (Dünser et al., 2012). Using AR is consistent with good teaching practices as it fosters students’ involvement in the learning process (Di Serio et al., 2013; Dunleavy et al., 2009), immerses learners in the topic (K. Lee, 2012), and places learning in a specific context (Dunleavy et al., 2009; Kamarainen et al., 2013). Moreover, students who used AR as a learning tool have indicated strong engagement with the task (Bonnard et al., 2012), developed collaboration and problem-solving competences (Sollervall, 2012), have shown an improvement in curiosity about the learning process (Bursztyn et al., 2017; Qamari & Ridwan, 2017; Yeung et al., 2012), and greater levels of involvement (Dunleavy et al., 2009; Liu et al., 2011). Furthermore, students’ motivation and interest may be enhanced by engaging students with the use of AR and can lead to a deeper and more effective understanding of the educational content (Dawley & Dede, 2014; Wu et al., 2013).
Additionally, innovative and flexible learning spaces have emerged in recent years. The smart learning environments (SLE) concept has a long research trajectory and it is presently at its entire development stage (Gambo & Shakir, 2023). A type of SLE is the FCL, where the use of virtual reality, AR, robotics, and 3D designs are contemplated (Mogas et al., 2022; Toivonen et al., 2018). According to J. P. Zhang and Chen (2011), adapting educational centers’ spaces based on the FCL requires a methodological shift in the educational projects of schools, involving opportunities for innovation, including practical, communicative, and technological skills. This offers a new pedagogy where interaction is the main core to establish a dynamic, creative, and unrestricted classroom environment. The European Schoolnet in Brussels has created the FCL, which is a learning setting designed to support a shift in teaching methodology toward more active methodologies and that favors collaborative activities to support students’ competence development by utilizing a variety of technological tools, such as robotics kits, 3D printers, and interactive whiteboards (Tena Fernández & Carrera Martínez, 2020). Regarding the learning areas within the classroom, the original model comprises six learning zones (investigate, interact, develop, exchange, create, and present) that encourage learners to move around the classroom according to their pedagogical needs and that use an active approach where the student is the center of the learning process (Dúo-Terrón, 2024). Therefore, within these types of active, innovative, and flexible learning spaces, a variety of active learning methodologies can be applied (Talbert & Mor-Avi, 2019).
A variety of student-centered teaching approaches can be included in active learning settings such as the FCL. The teachers’ use of active methodologies including explanation and facilitation strategies correlates with higher student engagement, lower levels of distractibility, and more positive course evaluations (Tharayil et al., 2018) and can be a key factor in teaching mathematics effectively (National Association for the Education of Young Children, 2009; Jeong & González-Gómez, 2025; Vale & Barbosa, 2023). In mathematics education, it is essential that teachers employ a variety of strategies to aid students in comprehending the benefits and practical applications of the subject, while also fostering the development of higher-order thinking skills (Vale & Barbosa, 2023). In addition, psychological, social, and physical environment factors can affect PSTs’ teaching–learning motivations and can result in more permanent learning outcomes (Edwards, 2015; Edwards et al., 2014; Nesin, 2012). Gardner (1983) supported movement and activity, stating that when an individual is actively involved in exploring physical sites and materials, the brain learns better and remembers more. Furthermore, when applying active learning approaches that incorporate cognitive, social, and physical engagement at the same time, students are more likely to enjoy the process while learning the content (Edwards, 2015). The affective dimension (emotion, self-efficacy, and attitude) should be carefully considered in the learning process, as it can increase students’ interest in the subject (Yllana-Prieto et al., 2023).
Therefore, this study analyzed the effect of a mathematics learning intervention, which comprises AR and multibase 3D blocks, on trainee teachers’ emotions, self-efficacy, attitudes, engagement, motivation, and confidence, implemented through active methodologies in a FCL. Some authors have focused on the role of emotions and beliefs as they play a crucial role in mathematical problem solving (Goldin et al., 2016; Goldin, 2000). When trying to solve a non-routine problem, interest and emotions like surprise, frustration, and anxiety are key components since they influence cognitive processes and focus attention (Goldin et al., 2016). The acquisition of knowledge and the process of storing information are connected to favorable or unfavorable emotions. Long-term memory is influenced by emotions, with details being remembered and stored (Gómez-Rios et al., 2023). The most frequent type of positive emotion when using AR is enjoyment from the point of view of the learners’ satisfaction in acquiring knowledge (Oleksy & Wnuk, 2017; Schez-Sobrino et al., 2020). Positive emotions are associated with motivation (Claros-Perdomo et al., 2020), curiosity (Harley et al., 2020; López-Faican & Jaen, 2020; Zheng et al., 2019), engagement (Alzahrani, 2020; Poitras et al., 2019), and interest (Liao & Humphreys, 2015; Rodríguez & Montalván, 2018). Positive emotions such as joy, pride, and hope can have a positive effect on learning, increasing interest and motivation (Gómez-Rios et al., 2023). However, feelings such as sadness, anger, or shame can lead to disinterest or demotivation (Gómez-Rios et al., 2023).
Hence, the types of emotions experienced are directly related to psychological processes, and emotions constitute an integral component of the educational process, influencing students’ efficacy in learning. Self-efficacy is positively related to a learning achievement in mathematics (J. Lee, 2009; Pietsch et al., 2003), as it is the best predictor of academic performance in mathematics, especially in problem solving (Hoffman & Schraw, 2010; Kramarski & Gutman, 2006). Moreover, it seems that self-efficacy is one of the affective components most related to self-regulation in learning (Bandura, 1971). When students are self-confident in mathematics, they are more likely to persevere through challenges and perform better in mathematics (Covington & Mueller, 2001; De Corte, 1995; Pintrich & De Groot, 1990). Therefore, the probability of achieving success in any task is understood to be influenced by one’s level of confidence in mathematics (Goldin et al., 2016). Regarding the learning environment, research in this area has found that there is a relationship between how students perceive the classroom learning environment and their self-efficacy (Dorman & Adams, 2004), as well as their academic performance (Baek & Choi, 2002).
The term attitude refers to a comparatively persistent set of beliefs, emotions, and behavioral responses towards an event (Vaughan & Hogg, 2013). Thus, students should be challenged with tasks that trigger positive attitudes towards mathematics (Hannula, 2001). There are a variety of emotions, both positive and negative, which can influence the learner’s attitude during the learning process. For instance, surprise (a sense of wonder at the unknown) can encourage exploration in learning (De Alda et al., 2019; Gamboa Araya, 2014; González & Blanco, 2008). Similarly, joy (a sense of well-being and satisfaction) influences the motivation to learn (Alamäki et al., 2021). The learning environment is a variable that positively or negatively affects the students’ emotions and thoughts towards the learning process (Dönmez, 2008). The learning space includes the physical setting, the emotional component, and different teaching elements. The quality of the classroom environment influences students’ confidence in their ability to learn and their attitudes towards subjects (Daemi et al., 2017). The attitude of students in relation to the classroom environment influences students’ participation, self-efficacy, learning motivation, and academic achievement (Fauth et al., 2014; Friedel et al., 2010; Wang & Holcombe, 2010).
Numerous studies suggest that the application of AR in educational settings may enhance learning outcomes and fosters student motivation and engagement (Bursali & Yilmaz, 2019; Gutiérrez & Fernández, 2014). Applications based on AR result in the enhancement of learners’ motivation to complete a task (Moreno-Guerrero et al., 2020) and can facilitate learning by enabling communication between teachers and students. Therefore, engaging students in an emotional experience can positively impact cognitive function and foster their motivation (Gómez-Rios et al., 2023). The use of AR has been shown to elicit positive emotions, and one’s satisfaction level is considered to be a key indicator of joy and interest, which in turn leads to motivation (Claros-Perdomo et al., 2020; Fuchsova & Korenova, 2019). On the other hand, students’ negative emotions can lead to disinterest and demotivation (Gómez-Rios et al., 2023). The utilization of AR has been demonstrated to engender a predominantly positive emotional response in students, thereby evoking favorable conduct such as engagement and motivation for learning. Nevertheless, it can also result in negative emotions such as anxiety and stress due to an absence of knowledge regarding AR technology (Gómez-Rios et al., 2023).
Consequently, according to the literature, an enhancement of students’ affective domain indicates the success of the selected approach (Frenzel et al., 2007), but there are also other factors, such as the learning tool and the educational environment, that can influence students’ effectiveness in teaching–learning mathematics. This innovative educational proposal in the field of mathematics didactics combines the use of an AR technological tool and the FCL innovative learning space to positively impact PSTs’ affective domain, and it is a variable that can have an influence students’ mathematics learning. Additionally, it is noteworthy to analyze learners’ engagement and motivation during the pedagogical intervention and its variables, as it might have affective, cognitive, and behavioral implications for the students’ learning process (Betz, 2018; Schüttler et al., 2021). Hence, the research objectives of this study were to examine the impact on the following: (1) PSTs’ emotions regarding the mathematics AR intervention in the FCL, (2) self-efficacy regarding the learning of mathematical concepts, (3) attitude towards the intervention in the FCL, (4) engagement in the mathematics intervention using AR, (5) motivation for learning mathematics with AR, and (6) to identify students’ confidence in the use of 3D design and AR applications to create multibase blocks as a learning strategy to support the teaching–learning content of arithmetic operations in mathematics subjects.

2. Materials and Methods

2.1. Sample

The sample consisted of 97 PSTs enrolled on the second year of the Primary Education degree, who were attending the “Mathematics and its Didactics” subject. The sample size was 45 PSTs and 52 PSTs in the 2022/2023 and 2023/2024 academic years, respectively. The participants were informed about the University Bioethical Protocol (Number 77/2023), which ensures anonymity and confidentiality and stipulates that the data can only be used for research purposes. The PSTs’ demographic data is displayed in Table 1. The average age of the study’s participants was 20.2 years in both academic years. Regarding the 2022/2023 academic year, the sample consisted of 60% females and 40% males. The PSTs’ educational backgrounds were in social sciences or humanities 68.89%, followed by science 22.22%, technology 6.67%, and others 2.22%. In regard to the PSTs’ demographic characteristics of the 2023/24 academic year, 69.23% were female and 30.77% male. A total of 63.46% of the PSTs had a social sciences or humanities background, 21.15% had a scientific background, and 11.54% had a technological background.

2.2. Course Context

Regarding the subject of ‘Mathematics and its Didactics’ and the contents involved in the intervention within the teaching plan of the subject, the topic “arithmetic operations, meaning and mechanism of operations” is included, and the contents that are worked according to the study plan and within the proposed intervention include: meanings and mechanisms of operations, additions and subtractions, divisions, and problem-solving teaching strategies and resources.

2.3. Pedagogical Intervention

The pedagogical intervention consisted of creating a 3D design of multibase blocks to visualize them in AR and perform arithmetic operations. The pedagogical methodology employed was a student-centered and active approach in the FCL learning environment. The intervention guidelines were provided to PSTs on the virtual platform of the Moodle campus and in physical format, which permitted the teachers to act as the facilitators of the students learning during the session. The different FCL learning zones (investigate, interact, develop, exchange, create, and present) were used to perform the educational intervention as described below (see Figure 1).
First, in the (1) ‘investigate’ area, PSTs explored the websites and materials needed for performing the intervention. The students had to access the Merge Cube and Tinkercad official websites, create an account, and download the apps to investigate the pedagogical opportunities offered by these learning tools. Once the information was collected, the teacher provided the materials. Finally, PSTs completed a content questionnaire as a formative activity and assessed their learning process. In the (2) ‘interact’ learning area, students cooperated with their classmates to think about how to design the four elements of a multibase block (units, tens, hundreds, and thousands) on the Tinkercad 3D design website. Then, students needed to display these elements throughout the interactive AR device, Merge Cube. In the (3) ‘develop’ learning zone, students created a 3D design of the four elements of the multibase block on the Tinkercad website. The students needed the following learning tool and application to conduct the activity: Merge Cube and the Object Viewer application. Additionally, an example video tutorial facilitated the PSTs’ completion of the activity, helped to perform different arithmetic operations and represent them with the multibase blocks through AR. In the (4) ‘exchange’ learning zone, students interacted in order to come up with the conclusions and results obtained, PSTs brainstormed ideas with the classmates about the learning outcomes throughout the session, and their ideas about using the technological learning tools in the FCL. Finally, the (5) ‘create’ area was used to record students’ presentations in the Chroma room and elaborate a final video using an editing software. The video was the product that reflected the students’ learning process, and results of this session can be assessed by the teacher to perceive the students’ progress. Finally, in the (6) ‘present’ area, the students orally reported the findings obtained during the intervention (see Figure 2).

2.4. Instrument

The research consisted of a mixed study in which online questionnaires were employed to obtain the results. Regarding the quantitative analyses, the participants were asked to fill out a 33-item questionnaire and a pre- and post-5-point Likert-type questions to be responded to (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree). The goal was to evaluate the influence of this pedagogical intervention on PSTs’ affective domain (emotion, self-efficacy, and attitude) and AR (engagement and motivation) on diverse domains (see Table 2). The instruments have been adapted from previously validated and published research and include affective domain items (Auzmendi-Escribano, 1992; Baroody, 1998; Pekrun et al., 2011; Jeong & González-Gómez, 2021a, 2021b; Jeong & González-Gómez, 2022) and engagement and motivation items regarding AR (Chang et al., 2010). Concerning the qualitative analysis, a question was asked about students’ confidence in the use of 3D design and AR applications to create multibase blocks as a learning strategy to support the teaching–learning content of arithmetic operations in the mathematics subject.

2.5. Data Statistical Analysis

The instrument measured diverse variables, PSTs’ emotions, self-efficacy, attitude, engagement, and motivation in learning mathematics through AR in the FCL. Cronbach’s alpha (α) was calculated through the Jamovi software (version 2.6.26.0) to evaluate the instrument’s internal consistency (see Table 3). The results for affective domain were α = 0.878 (emotion α = 0.883, self-efficacy α = 0.780, and attitude α = 0.786). Regarding the use of AR for mathematics learning items, the α = 0.888 (engagement α = 0.866 and motivation α = 0.772). These findings suggest that the instrument was reliable as α > 0.7 (Taber, 2018).
Likewise, the normality Kolmogorov–Smirnov test was applied, revealing that the data collected was non-normally distributed (p < 0.05) for each construct. Thus, non-parametric statistical tests were applied (U-Mann–Whitney test). Additionally, the software JASP (version 0.19.3.0) was used to obtain the data and elaborate the raincloud plots median comparison of pre- and post-tests. Finally, the qualitative data was collected through online questionnaires, and the responses were analyzed through WebQDA software (version 3.0) to obtain the frequency of words to visualize the results obtained.

3. Results

3.1. Quantitative Results

The quantitative results were obtained using different methods and include descriptive statistics, a non-parametric analysis, and a reliability analysis. First, the effect of the intervention was assessed by analyzing PSTs’ responses to the pre- and post-online surveys on the different measured dimensions: (1) emotions regarding the mathematics AR intervention in the FCL, (2) self-efficacy regarding the learning of mathematical concepts, (3) attitude towards the intervention in the FCL, (4) engagement in the mathematics intervention using AR, and (5) motivation for learning mathematics with AR. The median and standard deviation results of the pre-and post-test for each construct were analyzed when considering the global score (see Table 4. The various global score values of each variable were calculated as follows. The global scores of affective domain items were calculated and defined as follows: the global Positive Emotion score (PE = ∑PEi), global Negative Emotion score (NE = ∑NEi), global Self-Efficacy score (SE = ∑SEi), and global Attitude score (AT = ∑ATi). Concerning AR interest items, the global Engagement score (E = ∑Ei) and global Motivation score (M = ∑Mi) were calculated. Then, the descriptive data regarding the median comparison of the pre- and post-test for each construct was obtained (Table 4).
Additionally, the raincloud plots of the descriptive data regarding the median comparison of the pre- and post-test (95% CI) were displayed (see Figure 3).
The U-Mann–Whitney test was conducted, obtaining the constructs’ p-values and rank-biserial correlation coefficient (rbb). According to the results (see Table 5), the p-value showed significance in the constructs of PE, NE, SE, and AT. Then, the effect size values were analyzed, considering rbb < 0.10 very small, rbb = 0.10–0.29 small, rbb = 0.30–0.49 medium, and rbb ≥ 0.50 a large effect size (López-Martín & Ardura-Martínez, 2023). Thus, consistent with the results, PE and NE were identified as having a medium effect size.
In addition, the U-Mann–Whitney test was conducted for each of the items to meticulously analyze the significance differences (see Table 6).
According to the p-values obtained, 18 items show significance. However, when considering the effect size, 9 items show a medium effect size, referring to satisfaction (EM2), fun (EM4), confidence (EM5), pride (EM7), uncertainty (EM8), nervousness (EM9), concern (EM10), ‘know the steps necessary to teach mathematics’ (SE7), and ‘know how to work in an FCL’ (SE10).

3.2. Qualitative Results

Qualitative research was conducted to identify the students’ confidence in the use of 3D design and AR applications to create multibase blocks as a learning strategy to support the teaching–learning content of arithmetic operations in a mathematics course. The data was collected through online questionnaires and the responses were analyzed through WebQDA software (version 3.0) to obtain the frequency of words and create the word cloud to visualize the results obtained (see Figure 4).
Additionally, the PSTs’ responses about the intervention were analyzed in terms of the most repeated words, and the relative frequency of each word was calculated. Finally, the PSTs’ responses are shown in Table 7, identifying the students with codes to maintain students’ anonymity. Most of the students reported to be more confident in using 3D design and AR educative tools to learn mathematics concepts (66%) and perceived to have gained more knowledge (10%). As well, they found the tools to be interesting (8%) and they enhanced their learning process (5%) (see Figure 5).

4. Discussion

The application of an AR mathematics intervention in the FCL through active methodology regarding the creation of 3D multibase blocks to perform AR arithmetic operations is shown to have a positive impact on the PSTs. After conducting the intervention, considering the effect size obtained, the PSTs experienced significant results in overall positive emotions and negative emotions. Specifically, an increase in satisfaction, fun, confidence, and pride, and a decrease in uncertainty, nervousness, and concern. According to the literature, emotions are a crucial aspect of students’ learning processes, as any learning that contains emotional aspects tends to be fixed much more effectively. When education is based on emotions of joy, confidence, and surprise, which activate attention, motivation, and curiosity, it generates feelings of well-being (Bueno, 2021). Moreover, positive emotions can buffer the sense of failure and enhance performance (Erez & Isen, 2002). A student’s satisfaction with their learning experience is a key indicator of their commitment to learning and future academic performance. In addition, students who are more satisfied report higher academic achievement (G. Zhang et al., 2021). There are three types of interactions that influence student satisfaction in higher education: formal, informal, and student–instructor (Wong & Chapman, 2023). In the FCL learning environment, the active methodologies applied in the intervention are a crucial aspect of providing interactions between the student and teacher which support the learning process. In student-centered learning environments, which are characterized by a focus on student autonomy and self-regulation, enjoyment is positively linked to self-determined motivation (Kulakow & Raufelder, 2020). A student’s high self-confidence has a positive influence on their learning process by increasing engagement, goal setting, interest, and comfort with teachers and classmates and reducing anxiety (Akbari & Sahibzada, 2020). Students’ confidence can be significantly increased in problem solving when working in small groups and receiving regular feedback from the teacher (Gould, 2021). Pride and other positive feelings are essential for both academic performance and personal growth because they decrease anxiety and promote a cycle of accomplishment and self-improvement (Weidman et al., 2016). The overall results of self-efficacy showed significance but had a small effect size. However, the median overall self-efficacy scores highlight a fairly good starting point in PSTs’ understanding of mathematical concepts in order to teach them, capability of answering students’ questions about mathematics content, effort to be able to succeed in teaching mathematics, belief they have the necessary skills to teach mathematics, belief that mathematics is useful for solving everyday problems, belief that mathematics is relevant to obtain a good job, their struggle to explain mathematics concepts, and their belief that motivating teaching spaces achieve good learning results. Additionally, two self-efficacy items resulted in having a significant effect size, specifically in PSTs enhancing their knowledge on the steps necessary to teach mathematics, and in having more self-efficacy to teach mathematical content through AR in the FCL. An improvement in students’ self-efficacy can have a great impact on a student’s mathematical learning (Smith, 1996), and previous experiences also define learners’ perceptions of their own self-efficacy and mathematical competence (Güneş, 2018). Moreover, students’ beliefs about mathematics serve as the most accurate indicator of mathematical academic achievement (J. Lee, 2009; Pietsch et al., 2003), especially in problem solving (Hoffman & Schraw, 2010; Kramarski & Gutman, 2006). The learning environment also had an impact on self-efficacy seeing as, according to some authors, implementing educational interventions in the FCL increased teachers’ self-efficacy (González Lucas, 2022; Montero-Izquierdo et al., 2024; Sourdot & Smith, 2019). Moreover, students’ self-efficacy (Lin & Chen, 2015) and the development of knowledge and competences can be supported more successfully in AR settings (Dünser et al., 2012). The global attitude scores revealed significance but a small effect size. This may be due to the PSTs’ presenting a high attitude towards the FCL. Therefore, PSTs reported to have a preference toward the use of the FCL to a traditional class or traditional lab, and they strongly believe that the FCL fosters interdisciplinarity, creativity, and collaboration. Studies revealed that positive attitudes toward the learning environment can influence students’ engagement, self-efficacy, learning motivation, and academic achievement (Fauth et al., 2014; Friedel et al., 2010; Wang & Holcombe, 2010). A more conventional learning environment, such a typical classroom, can make it more difficult for the students to promote collaborative learning interventions (Ellis & Goodyear, 2016). Additionally, interdisciplinary educative interventions can be successfully conducted in the FCL (Brauchle, 2023; Montero-Izquierdo et al., 2024), and some studies revealed a significant enhancement of students’ attitudes toward creativity in the FCL (Montero-Izquierdo et al., 2024). Moreover, the FCLs can engage in real-world cooperative learning activities (Andreasen et al., 2022). The FCL, which was set up with stations centered around certain artifacts, offered several essential opportunities for collaborative learning (Lazareva & Tømte, 2024). Concerning the engagement and motivation in learning mathematics through AR, no significant results were obtained. However, prior to conducting the intervention, PSTs reported a high degree of motivation and engagement in AR use. According to the literature, some studies revealed that by learning using AR, they can experience positive emotions, which encourages behavior like engagement and motivation (Gómez-Rios et al., 2023). As mentioned above, the results of the quantitative analysis on emotions show a significant improvement in the students in terms of the degree of satisfaction in the activity. Studies on the use of AR have shown that the degree of satisfaction is considered a determining factor that can influence students’ motivation (Claros-Perdomo et al., 2020; Fuchsova & Korenova, 2019). On the other hand, students’ negative emotions can lead to disinterest and demotivation (Gómez-Rios et al., 2023). The overall score of emotions in the PSTs has been significantly reduced in the intervention as mentioned, which may reinforce the students’ motivation in the proposed intervention. In addition, the qualitative findings revealed that the participants were more confident about how to implement AR mathematics interventions, gaining knowledge about the use of 3D design and AR applications to create multibase blocks as a learning strategy to support the teaching–learning content of arithmetic operations in mathematics course. The feeling of confidence has a positive impact in students’ learning, as it is known that a person’s degree of mathematical confidence affects their likelihood of success (Goldin et al., 2016). Therefore, students are more likely to overcome obstacles and improve their mathematics performance when they have confidence in their ability to address mathematical concepts (Covington & Mueller, 2001; De Corte, 1995; Pintrich & De Groot, 1990). Additionally, students reported an increase in PSTs’ knowledge after conducting the intervention. In accordance with the literature about AR in education, its application can improve students’ cognitive load (Akçayır & Akçayır, 2017; Garzón & Acevedo, 2019), motivational learning outcomes, and situational interest (Sun et al., 2023; Zimmerman et al., 2015). Overall, the application of the pedagogical intervention in active learning settings is demonstrably beneficial for students, significantly improving students’ emotions, self-efficacy beliefs, and confidence in mathematics (Dogan, 2012).

5. Conclusions

This research examined the impact of a mathematics learning intervention involving AR and multibase 3D blocks, implemented through active methodologies in an FCL, on trainee teachers’ emotions, self-efficacy, attitudes, engagement, motivation, and confidence. This topic is of great interest and relevance to researchers in this field. Integrating AR into mathematics education, particularly in teacher training, aligns with current educational innovation trends and the promotion of active learning environments. Furthermore, it contributes to the growing body of literature exploring how emerging technologies can enhance pedagogical practices and prepare future educators for active, technology-enriched classrooms. Moreover, this study demonstrated how the various factors considered in the intervention design can support the effectiveness of teaching and learning in mathematics courses. Thus, conducting this intervention can encourage PSTs to implement innovative and active teaching–learning strategies in their future practice. The importance of evaluating the use of active learning methodologies in innovative environments, such as FCL, becomes evident. This evaluation can provide essential insights to support the continuous improvement of these methodologies and their adoption in educational settings. Therefore, this type of intervention could positively impact pedagogical achievements, preparing PSTs to become more effective Primary Education teachers. The relevant implications are the implementation of educational innovative practices in the field of mathematics education, including teacher training in the use of innovative teaching–learning methodologies, the application of 3D design AR tools to support mathematics teaching, and the implementation of similar interventions for future primary school teachers. The limitation of this research is that it is a single practice, which should be replicated over several years to obtain robust and extrapolatable results. Future studies should increase the sample size of participants to reveal more significant results, and a control group could be added to compare and analyze differences in teaching the same mathematical concepts through innovative versus traditional methodologies.

Author Contributions

Conceptualization, A.I.M.-I., J.S.J. and D.G.-G.; Methodology, A.I.M.-I., J.S.J. and D.G.-G.; Software, A.I.M.-I., J.S.J. and D.G.-G.; Validation, A.I.M.-I., J.S.J. and D.G.-G.; Formal analysis, A.I.M.-I., J.S.J. and D.G.-G.; Investigation, A.I.M.-I., J.S.J. and D.G.-G.; Resources, A.I.M.-I., J.S.J. and D.G.-G.; Data curation, A.I.M.-I., J.S.J. and D.G.-G.; Writing—original draft, A.I.M.-I., J.S.J. and D.G.-G.; Writing—review & editing, A.I.M.-I., J.S.J. and D.G.-G.; Visualization, A.I.M.-I., J.S.J. and D.G.-G.; Supervision, J.S.J. and D.G.-G.; Project administration, J.S.J. and D.G.-G.; Funding acquisition, J.S.J. and D.G.-G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Science and Innovation, Research Project (PID2020-115214RB-I00/AEI/10.13039/501100011033) and the APC was funded by Education Sciences as a feature paper.

Institutional Review Board Statement

The study was reviewed and approved by the Bioethical and Biosecurity Committee of the University of Extremadura, Spain (Number 77/2023, 15 June 2023).

Informed Consent Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Universidad de Extremadura (protocol code 77/2023 and 15 June 2023).

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

The author (Ana Isabel Montero-Izquierdo) expresses her appreciation to the Ministry of Universities of the Spanish Government scholarship (FPU22/04217).

Conflicts of Interest

The authors have no competing interests to declare that are relevant to the content of this article.

Abbreviations

The following abbreviations are used in this manuscript:
ARAugmented reality
FCLFuture classroom lab
PSTPre-service teacher

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Figure 1. The different activities in the future classroom lab (FCL) learning zones in relation to the intervention.
Figure 1. The different activities in the future classroom lab (FCL) learning zones in relation to the intervention.
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Figure 2. Intervention in the FCL: Creation of 3D multibase blocks and AR arithmetic operations.
Figure 2. Intervention in the FCL: Creation of 3D multibase blocks and AR arithmetic operations.
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Figure 3. Raincloud plots: Median comparison of pre- and post-tests (95% CI).
Figure 3. Raincloud plots: Median comparison of pre- and post-tests (95% CI).
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Figure 4. Qualitative analysis of the most frequent words analyzed.
Figure 4. Qualitative analysis of the most frequent words analyzed.
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Figure 5. Frequency of the most frequent words analyzed.
Figure 5. Frequency of the most frequent words analyzed.
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Table 1. Demographic characteristics of the PSTs.
Table 1. Demographic characteristics of the PSTs.
Academic year2022/20232023/2024
Participants Number4552
Age20.220.2
Entrance Grade (Max. 10)7.327.25
Background(%)(%)
Social Science68.8963.46
Science22.2221.15
Technology6.6711.54
Others2.220
Gender(%)(%)
Female6069.23
Male4030.77
Table 2. Questionnaire items applied in the research study.
Table 2. Questionnaire items applied in the research study.
Constructs
1. EmotionsMathematics AR intervention in the FCLPositive emotions
EM1. Joy
EM2. Satisfaction
EM3. Enthusiasm
EM4. Fun
EM5. Confidence
EM6. Hope
EM7. Pride
Negative emotions
EM8. Uncertainty
EM9. Nervousness
EM10. Concern
EM11. Frustration
EM12. Boredom
EM13. Fear
EM14. Anxiety
2. Self-efficacyMathematical concepts and FCLSE1. Understanding mathematics concepts to teach them
SE2. Capable of answering students about mathematics content
SE3. Effort to be able to succeed in teaching mathematics
SE4. Believe to have the necessary skills to teach mathematics
SE5. Believe that mathematics is useful for solving everyday problems
SE6. Believe that mathematics is relevant to obtain a good job
SE7. Know the steps necessary to effectively teach mathematics
SE8. Struggle to explain mathematics concepts
SE9. Believe that motivating teaching spaces achieve good learning results
SE10. Know how to work in an FCL
3. AttitudeFCL learning environmentAT1. Prefer FCL to a traditional class
AT2. Prefer FCL to a traditional lab
AT3. Interdisciplinarity
AT4. FCL creativity
AT5. FCL collaboration
4. EngagementThe use of AR in mathematicsE1. Enjoy learning activities using AR application in mathematics
E2. Engagement using AR in mathematics
5. MotivationLearning mathematics with ARM1. Interest in learning mathematical content through AR applications.
M2. Desire to use AR to learn topics related to mathematics.
6. ConfidenceConfidence about how to apply 3D design and AR in mathematics subjectQualitative question. Reflect on whether after the activity you feel more confident about how to apply activities on 3D design and AR in the future in your elementary classes.
Table 3. Cronbach’s alpha (α) of the items analyzed.
Table 3. Cronbach’s alpha (α) of the items analyzed.
αItemsα
Affective domain0.878Positive emotion0.919
Negative emotion0.845
Self-efficacy0.780
Attitude0.786
Interest AR0.888Engagement0.866
Motivation0.772
Table 4. Median results of the pre- and post-test for each construct.
Table 4. Median results of the pre- and post-test for each construct.
DescriptivesGroupsPENESEATEM
MedianPre-test3.572.003.904.404.004.50
Post-test4.291.714.004.604.504.50
Standard deviationPre-test0.7540.7470.4830.5940.8010.711
Post-test0.6240.6440.4860.6010.6480.591
Table 5. U-Mann–Whitney test p-values, rank-biserial correlation coefficient (rbb), and effect size.
Table 5. U-Mann–Whitney test p-values, rank-biserial correlation coefficient (rbb), and effect size.
Item ConstructsUprrbEffect Size
Positive emotion (PE)2442< 0.0010.4527Medium
Negative emotion (NE)2903< 0.0010.3494Medium
Self-efficacy (SE)33920.0040.2398Small
Attitude (AT)35780.0170.1982Small
Engagement AR (E)41200.3390.0766Very small
Motivation AR (M)38560.0930.1358Small
Table 6. U-Mann–Whitney test for each questionnaire item analyzed.
Table 6. U-Mann–Whitney test for each questionnaire item analyzed.
ConstructItemsUprbbEffect Size
PEEM16287.500<0.0010.27Small
EM25696.000<0.0010.40Medium
EM36104.000<0.0010.29Small
EM42915.500<0.0010.46Medium
EM53125.000<0.0010.41Small
EM63143.000<0.0010.28Small
EM74113.000<0.0010.37Medium
NEEM84168.500<0.0010.35Medium
EM93273.000<0.0010.30Medium
EM103370.500<0.0010.30Medium
EM115269.5000.3100.08Very small
EM125239.0000.3610.07Very small
EM134304.000<0.0010.27Small
EM145138.500<0.0010.25Small
SESE14335.0000.0230.18Small
SE24697.0000.0270.17Small
SE36192.0000.6400.04Very small
SE44365.0000.0590.15Small
SE54095.5000.7130.03Very small
SE65884.5000.5100.05Very small
SE75084.500<0.0010.39Medium
SE85296.5000.7880.02Very small
SE95300.5000.2690.08Very small
SE104703.000<0.0010.32Medium
ATAT14588.0000.0680.14Small
AT25039.5000.0170.19Small
AT34955.5000.0160.19Small
AT44632.0000.4470.05Very small
AT54915.0000.6840.03Very small
EE16287.5000.0920.13Small
E25696.0000.1460.11Small
MM16104.0000.6230.04Very small
M22915.5000.1880.10Small
Table 7. Frequency of the most frequent words analyzed.
Table 7. Frequency of the most frequent words analyzed.
WordsRepetitionRelative Frequency
Confident5566%
Knowledge810%
Interesting78%
Learning45%
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Montero-Izquierdo, A.I.; Jeong, J.S.; González-Gómez, D. Augmented Reality 3D Multibase Blocks at the Future Classroom Lab Through Active Methodology: Analyzing Pre-Service Teachers’ Disposition in Mathematics Course. Educ. Sci. 2025, 15, 954. https://doi.org/10.3390/educsci15080954

AMA Style

Montero-Izquierdo AI, Jeong JS, González-Gómez D. Augmented Reality 3D Multibase Blocks at the Future Classroom Lab Through Active Methodology: Analyzing Pre-Service Teachers’ Disposition in Mathematics Course. Education Sciences. 2025; 15(8):954. https://doi.org/10.3390/educsci15080954

Chicago/Turabian Style

Montero-Izquierdo, Ana Isabel, Jin Su Jeong, and David González-Gómez. 2025. "Augmented Reality 3D Multibase Blocks at the Future Classroom Lab Through Active Methodology: Analyzing Pre-Service Teachers’ Disposition in Mathematics Course" Education Sciences 15, no. 8: 954. https://doi.org/10.3390/educsci15080954

APA Style

Montero-Izquierdo, A. I., Jeong, J. S., & González-Gómez, D. (2025). Augmented Reality 3D Multibase Blocks at the Future Classroom Lab Through Active Methodology: Analyzing Pre-Service Teachers’ Disposition in Mathematics Course. Education Sciences, 15(8), 954. https://doi.org/10.3390/educsci15080954

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