The Application of Active Methodologies in Spain: An Investigation of Teachers’ Use, Perceived Student Acceptance, Attitude, and Training Needs Across Various Educational Levels

Abstract

Active methodologies emphasize student participation, autonomy, and skill development, offering an innovative approach to education. However, their implementation in Spain faces challenges, including limited teacher training, resource shortages, institutional constraints, and resistance from both educators and students. This research aims to explore teachers’ perceptions regarding the integration of active methodologies into their teaching practices. The research employed a quantitative, non-experimental, descriptive, and cross-sectional survey design to systematically analyze population characteristics. The study utilized a validated questionnaire with Likert-scale items to assess teachers’ use of active methodologies. Data were collected anonymously via Google Forms, ensuring confidentiality and a robust reliability (Cronbach’s alpha 0.942). The study sampled answers provided by 994 Spanish teachers aged 20–65 using stratified probabilistic sampling, ensuring representation across educational levels, disciplines, and urban–rural contexts. This study shows that teachers prefer traditional active methodologies, such as cooperative learning, while emerging technologies like artificial intelligence face limited adoption due to training gaps. Women show a greater inclination towards active methodologies, with minimal impact from geographical context. These findings underscore the importance of targeted teacher training and support the idea that it is of paramount importance to bridge the gaps in active methodology implementation, fostering inclusive, innovative, and student-centered learning environments across diverse educational contexts.

1. Introduction

The conceptualization of active methodologies (AMs) encompasses a range of pedagogical strategies aimed at promoting student participation and autonomy in the learning process. They represent an innovative and student-centered approach to education, which aims to engage students in their own learning process in a meaningful and transformative way. Instead of being limited to the passive transmission of information, AMs seek to make students active agents in the construction of their knowledge, promoting essential skills for integral development.

However, in the scientific literature, a wide variety of problems associated with the implementation of AMs within the educational field in Spain have been identified. Firstly, a high need for the training and professional development of teachers in AMs has been detected, which makes their successful implementation difficult. Many teachers express a need for continuous training to update their pedagogical skills, especially in the use of innovative technologies and teaching methods (Gómez-Hurtado et al., 2020). But there is also resistance to the adoption of AMs due to the habit of using traditional methods, such as lectures. This resistance may be related to a lack of familiarity with AMs, the perception that they are more complicated or laborious to implement, a lack of time to adequately prepare them, and the fact that AM training offers may be limited in quantity, subject matter, and purpose, since there is evidence that new teachers require more in-depth training that provides them with specific teaching resources and methodological strategies to address the different forms of learning or specific educational support needs (Odalen et al., 2019; Higueras-Rodriguez et al., 2020). In addition, there is a shortage of resources and institutional support, since the implementation of AMs often requires additional resources, such as technology, adequate materials, and time to plan and execute the activities. However, many universities and faculties face budgetary constraints and a lack of institutional support to facilitate these resources (Escarbajal Frutos & Martínez Galera, 2023). Although many students may accept AMs, some show resistance due to an increased workload, lack of autonomy, or preference for more traditional teaching methods (Tharayil et al., 2018). Student perceptions of the effectiveness of AMs vary, and some methodologies may be perceived as too demanding and stressful (Cooper et al., 2018).

This study will help to explore in more depth the specific training needs of teachers and how these affect the effective implementation of AMs, as well as those that are most commonly used, along with students’ attitudes towards active learning, as perceived by teachers, which will help to influence their acceptance, as it is a relevant aspect for optimizing the implementation of these methodologies. Therefore, the following research questions are raised:

• RQ1: What are the characteristics and frequency of teachers’ use of AMs in the classroom?
• RQ2: What are teachers’ presumptions pertaining to the value that their students will assign to each of the AMs in class?
• RQ3: What are the main training needs of teachers in relation to the use of AMs in teaching?
• RQ4: What is the general attitude of teachers towards the implementation of AMs in the classroom?
• RQ5: What differences are established based on the sociodemographic variables analyzed in reference to the application of AMs in the classroom?

1.1. Basic Principles of AMs

One of the essential principles of AMs is the student-centered approach, which places students at the core of the educational experience. In this model, the role of the teacher changes from being a transmitter of knowledge to a facilitator who guides and supports autonomous learning. Research by (de Novaes et al., 2021) highlights that this approach fosters autonomy and critical thinking, key skills in lifelong learning. According to Trowler (2020) this paradigm shift allows students to develop a deeper commitment to content, given that they are actively involved in their learning, reflecting and making decisions about their own educational process.

Another key principle of AMs is collaborative learning, which values social interaction and teamwork as means to improve understanding and problem solving. This approach, supported by techniques such as group work and peer discussions, fosters interpersonal skills and allows students to share different perspectives and knowledge (Cosgrove et al., 2024). Johnson et al. (2018) suggest that collaborative learning not only improves academic performance, but also develops social and emotional competencies, such as empathy and teamwork, which are essential in students’ personal and professional lives.

On the other hand, the connection between theory and practice is fundamental in AMs, as this allows students to apply their knowledge in real and meaningful contexts. In accordance with Costa Machado et al. (2024), this approach not only makes learning more relevant, but also encourages the transfer of knowledge to everyday situations. This is observed in methodologies such as project-based learning, in which students work on authentic problems that reflect situations in the world of work or community (Reis et al., 2020). The possibility of connecting knowledge with practical applications motivates students, while strengthening their understanding and retention of information (Cabanillas-García et al., 2023).

Similarly, reflection is an essential component of AMs, as it allows students to evaluate their own learning process, identify strengths and areas for improvement, and build a sense of self-awareness. Consistent with Schön (2017), reflective learning fosters greater self-awareness and helps students to develop a critical and self-critical mindset that benefits their holistic training process. Through reflection activities, such as learning journals or self-assessments, students can deepen their understanding of the topics studied and adjust their strategies to optimize their learning (Moon, 2004).

AMs value diversity and promote inclusion by adapting to individual needs and learning styles. According to Kahu and Nelson (2018), this principle allows teachers to adjust learning activities to align them with the context and specific characteristics of each group, providing a more equitable educational experience. The flexibility of AMs makes it easier for students to advance at their own pace and develop competencies consistent with their personal and professional objectives, promoting personalized learning that respects the heterogeneity of the classroom (Biggs, 2012).

Finally, it should be noted that assessment in AMs is not limited to grading the final result, but is conceived as a continuous process that provides constant feedback to improve learning. Black and Wiliam (2009) highlight that formative assessment allows both students and teachers to adjust teaching and learning strategies in response to the progress observed. This participatory approach, which includes self-assessment and co-assessment, promotes autonomy and responsibility in students, helping them to reflect on their own performance and identify areas for improvement (Ma et al., 2020).

1.2. Concept and Classification of AM

The literature review conducted by Idoiaga Mondragon et al. (2024) mentions, according to Samwel (2010), that teaching methodologies can be divided into the following two main categories: passive, also known as traditional, and active, which is action-oriented. While passive methodologies follow a linear approach centered on teacher exposition and subsequent knowledge evaluation, prioritizing specific outcomes, AMs propose a more dynamic and flexible approach. The latter allow for the design of curricula tailored to students’ interests and emphasize competency-based learning, fostering the development of interpersonal skills (Moreno et al., 2021). In AMs, the focus shifts to student learning activities and processes rather than direct teacher instruction (Paricio et al., 2019).

Among the most relevant conceptualizations of AMs highlighted in the scientific literature, it is noted that they are centered on the direct experience of students, as learning arises from interactions between students and their environment through practical experiences, promoting reflective thinking (Dewey, 1916). This approach engages students actively in their learning through collaborative, experiential, and problem-based activities that encourage interaction and critical thinking (Barkley et al., 2005). These methodologies function as tools for empowerment, where learning becomes an act of creation rather than passive transmission, allowing students to take ownership of their knowledge (Freire, 1970). The goal is to transform the learning experience, involving students in activities that require applying knowledge, reflecting on their learning, and collaborating with others (Zhao & Kuh, 2004). On the other hand, more recent conceptualizations suggest that active learning encourages students to engage in cognitively demanding tasks, such as applying, analyzing, and evaluating information, while engaging more deeply with the content, with or without the use of technology (Abeysekera & Dawson, 2015).

Previous studies, such as the one conducted by Rodriguez et al. (2017), confirm that the use of technology-supported AMs enhances student engagement and significantly improves the comprehension process, while also strengthening teamwork skills and optimizing social interaction. The use of simulation as an AM in higher education provides multiple benefits, including the development of key competencies such as decision making, critical analysis, teamwork, and digital skills. It also fosters a more experiential and practical learning approach, which enhances employability and increases student motivation through gamification (Navas Bethancourth & Blancafort-Masriera, 2022). Moreover, it transforms the role of the instructor into that of a learning facilitator and allows for flexible adaptation based on students’ proficiency levels. This methodology promotes a more dynamic education, aligned with Education 4.0, by integrating technology and meaningful learning (Martínez-López & Sánchez, 2021).

Moreover, gamification facilitates the transition from traditional methodologies to STEM-based approaches by incorporating game-like elements that enhance student motivation, engagement, and participation. By transforming the classroom into an interactive and exploratory environment, gamification enables students to develop key skills such as problem solving, creativity, and critical thinking. Additionally, it promotes collaborative learning and the use of technological tools, reinforcing the integration of ICT in STEM education (Cobos et al., 2021; Arteaga-Marín et al., 2022). Furthermore, active strategies such as augmented virtual reality facilitate the transition from traditional methodologies to technology-driven approaches by providing an immersive, interactive, and meaningful learning experience. This approach increases student motivation and participation, enhances critical thinking and creativity, and improves the understanding of complex concepts through 3D visualization. Moreover, it provides safe practice environments, fosters digital literacy, and effectively integrates ICT into teaching, aligning with the demands of the 21st century (Elias-Ramos et al., 2021; Idrovo-Iñiguez & Moscoso-Bernal, 2022).

This transition is further strengthened by AI-powered tutoring, which, according to Mounkoro et al. (2024), provides personalized learning, real-time feedback, and a greater adaptability to individual student needs. These systems optimize the educational process by adjusting content based on the student’s knowledge level and learning pace, fostering a more autonomous and efficient approach (Rızvı, 2023). Additionally, they facilitate access to high-quality educational resources, reduce teacher workload, and enable continuous progress monitoring (Malami, 2024). However, certain challenges remain, such as the lack of social interaction and the need to improve algorithm quality to provide more meaningful learning experiences (Zohuri & Mossavar-Rahmani, 2024). Based on the findings that support the transitional shift towards AMs with technological implementation, a classification of two types of AM can be established, as follows: traditional active pedagogical strategies, such as cooperative learning and flipped learning, and technology-enhanced active strategies, such as simulation and gamification using virtual reality or artificial intelligence tutors (Palau & Santiago, 2021; Basilotta Gómez-Pablos & García Barrera, 2023). Figure 1 illustrates the conceptualization of recent trends in AM implementation, emphasizing their focus on practical experience, collaboration, and the use of innovative technologies, such as artificial intelligence or augmented reality (Villalobos López, 2024), and traditional active pedagogical strategies.

1.3. Proposals for AM Used and Demanded in Current Education Based on Educational Level

AMs have gained considerable space in education systems at different levels, focusing on active student participation and collaborative learning (Seman et al., 2018; Cabanillas-García et al., 2023). The most common AMs in primary, secondary, vocational training, and higher education are highlighted below, according to the characteristics and needs of each level.

As highlighted in the study carried out by Lara-Lara et al. (2023), the most used AMs in university institutions during the time of the pandemic were flipped learning. This methodology changes the traditional order of instruction, allowing students to study theory at home and reserve class time for hands-on, collaborative activities. This fosters key competencies such as leadership, creativity, autonomy, and teamwork, while increasing student motivation and engagement. This approach makes it easier for students to take a more active and autonomous role in their learning, promoting their ability to critically apply concepts in practical situations, especially in areas where direct experience is essential (Çevikbas & Argün, 2017; Ekineh & Accra-Jaja, 2022). Also, Martínez Valdivia et al. (2023) reinforce the importance of AMs during COVID-19. This paper highlights some of the most used AMs such as the flipped classroom, problem-based learning, case studies, and team-based learning. Moreover, the study carried out by Bazani and Santos (2023) reiterates flipped learning as the most widely used AM, together with problem-based learning and methodologies focused on technologies, concluding that they enhance the professional and emotional skills of students who are in vocational training. The review carried out by (López-Reyes, 2022), found that the most used AMs are problem-based learning, project-based learning, collaborative learning, gamification, competency-based learning, and the flipped classroom, among others.

Conversely, in primary education, digital learning approaches have gained prominence as a widely adopted method (Gómez-García et al., 2022). Teachers often favor interactive classroom activities over pre-session self-regulated study, as combining traditional classroom practices with gamified elements has been shown to improve student performance, according to Ng and Lo (2022). Gamification, particularly through teacher recognition and feedback, is especially effective and well-received among younger students (Parra-González et al., 2021). It tends to be more applicable in earlier educational stages, such as primary and secondary education, while flipped learning yields better outcomes in later stages like secondary education or vocational training. However, in primary education, there is a pressing need to foster active and autonomous learners who are equipped to meet contemporary social challenges, ensuring a smoother transition to secondary education (Guasp et al., 2020).

1.4. Variables That Affect the Use and Implementation of AMs in the Educational Field

The implementation and use of AMs in education reveal significant differences influenced by gender, age, educational level, and area of knowledge from teachers’ perspectives. These factors determine how educators interact with and apply these methodologies, impacting their effectiveness in the classroom. The recent literature highlights that gender is a relevant factor in the adoption of AMs. It has been observed that female teachers tend to implement active learning strategies more frequently compared to their male colleagues. (Arias-Gago & Rodríguez-García, 2020) explain that this trend could be related to pedagogical beliefs, where women tend to value interaction and collaborative work more, which translates into a greater inclination towards cooperative learning and the use of student-centered techniques. In contrast, some studies suggest that male teachers may lean towards more traditional methods, which may be influenced by a different perception of classroom control and structure (Biryukova & Kanska, 2024).

The age and experience of teachers are also factors that affect the use of AMs. Younger educators tend to adopt AMs more easily, probably due to their recent training and familiarity with contemporary educational technologies (Becerra-García et al., 2023). However, studies such as the one carried out by Fernández-de-Castro and Villegas-Pantoja (2024) also appear in the Mexican context, the results of which found no significant differences between men and women. Only a significant difference was observed in the use of the case method with respect to the age group of the teachers, specifically among teachers aged from 35 to 44 years. In this age range, teachers usually have enough experience to apply case studies with real or relevant examples, but at the same time, maintain a predisposition to take active approaches in teaching.

It should be noted that León-Díaz et al. (2020) indicate that, given the lack of training perceived by teachers in AMs, it is worth considering whether the implementation of these innovative proposals in the classroom responds only to a fad and is being carried out without taking into account the principles upon which education is based and the impact on student learning (Pueyo & Alcalá, 2020). Therefore, training in AMs is perceived by teachers as an essential value for the efficient implementation of these practices, since the literature shows that teacher training in AMs must be broad and up-to-date, since the greater the knowledge of these strategies, the greater their use in the classroom (Bazarra & Casanova, 2016). Godinho et al. (2022) emphasize the need for teacher training in technology-supported AMs, highlighting that many educators still lack prior knowledge and require continuous training for effective implementation (Nascimento & Gomes, 2020). Although teachers recognize the value of these approaches, some experience uncertainty and require guidance during their application (Martínez Palmera & Senior-Naveda, 2024). Furthermore, technological advancements demand a more dynamic and interactive teaching process, reinforcing the need to develop specific digital competencies for effective classroom integration (Gormaz-Lobos et al., 2021). Additionally, the adoption of AMs redefines the teacher’s role, shifting from a knowledge transmitter to a learning facilitator who guides students in constructing their own knowledge (Copetti et al., 2018). These findings highlight the importance of implementing teacher training programs that incorporate innovative strategies and pedagogical support to ensure their effective application.

2. Materials and Methods

The development of this research was based on the quantitative method, with the use of a non-experimental, descriptive, and cross-sectional design, by the means of a survey. This methodology focuses on analyzing and detailing the characteristics of a population or phenomenon in its current state. Its objective is to provide a clear and structured view of how the phenomenon under study behaves or works, allowing the information obtained to be systematic and comparable with other similar studies (Alban et al., 2020).

2.1. Objectives and Variables

This research aims to explore teachers’ perceptions regarding the integration of AMs into their teaching practices. To address this objective, the study considers several of the following independent variables: (i) gender (male and female); (ii) age groups (20–35, 36–50, and 51–65 years); (iii) educational levels taught (primary, secondary, vocational training, and higher education); (iv) academic disciplines (arts and humanities, social and political sciences, health sciences, engineering and architecture, and sciences); and (v) teaching environments (urban or rural) (see Figure 2).

The dependent variables in this study are defined as follows: (i) DV-1: the extent to which AMs are employed in teaching, focusing on strategies that prioritize student-centered, participatory learning; (ii) DV-2: teachers’ presumptions pertaining to the value that their students will assign to each of the AMs, reflecting how positively they engage with these approaches; (iii) DV-3: the specific training needs for educators to effectively implement AMs, encompassing essential knowledge and skills; and (iv) DV-4: teachers’ attitudes towards adopting AMs, influenced by their personal experiences and perspectives, which may range from highly favorable to indifferent or resistant.

2.2. Data Collection Instrument

The data collection tool employed in this study was a questionnaire specifically designed for teachers, drawing on the foundational work of Ibáñez-López et al. (2022). The initial questionnaire consisted of two dimensions. The first dimension focused on the knowledge and use of technological tools for teaching innovation, but it was not used, as it did not align with the research objectives. The second dimension, which was selected for data collection, examined the use of AMs in the teaching–learning process, as it directly related to the study’s research goals.

This dimension included items related to the use of AMs, teachers’ presumptions pertaining to the value that their students would assign to each of the AMs, and the need for teacher training in various AM approaches, such as flipped classroom, project-based learning, cooperative learning, problem-based learning, design thinking, thinking-based learning, gamification, competency-based learning, challenge-based learning, adaptive learning, the implementation of augmented and virtual reality technologies, and artificial intelligence. Additionally, it assessed teachers’ attitudes toward the teaching and learning process using AMs, with statements such as the following: “I promote the active participation of students, both individually and in groups, in the search for knowledge and the development of their own abilities during the teaching and learning process through the application of different AM” and “I incorporate topics related to the application of new teaching methodologies supported by technological tools into my teacher training to foster teaching innovation inside and outside the classroom.” The questionnaire employed a five-point Likert scale, with the response options of Never (1), Rarely (2), Occasionally (3), Frequently (4), and Always (5).

The authors of the instrument (Ibáñez-López et al., 2022) ensured content validity by subjecting it to evaluation by a panel of experts. A total of nine specialists (four women and five men) with an average of 21.78 years of experience (SD = 10.92) were consulted. These experts assessed the relevance and clarity of the questionnaire, the wording of the instructions, its overall structure, and the appropriateness of each question and its response options, following the guidelines of Mirete et al. (2019). Based on their evaluations, Kendall’s coefficient of concordance (K) was calculated, yielding values of K = 0.4716 (p < 0.001) for presentation and instructions, K = 0.4480 (p < 0.001) for the questions, and K = 0.5241 (p < 0.001) for the response options, rejecting, in all cases, the null hypothesis of random agreement. Additionally, the experts provided recommendations to improve the wording of the items, suggesting modifications, additions, or eliminations of certain questions. These suggestions were analyzed, and after a thorough review, the necessary refinements were incorporated into the final version of the questionnaire.

Additionally, the validation process included an Exploratory Factor Analysis (EFA), ensuring beforehand that there were no variables with low correlation or multicollinearity issues. The Bartlett’s test of sphericity yielded a significant result, χ² (276) = 10,115.27, p < 0.001, confirming that the correlation matrix was not similar to an identity matrix. Furthermore, the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.93, which is considered to be very good, and the factor loadings ranged from 0.74 to 0.60. To assess model fit, the Diagonal Weighted Least Squares (DWLS) estimator was applied, obtaining a value of 5264.304 with 252 degrees of freedom (p < 0.001), suggesting a good model fit (Beaujean, 2014). Additionally, the Tucker–Lewis Index (TLI) and Comparative Fit Index (CFI) both exceeded the 0.90 threshold, further confirming an adequate fit. However, the Root Mean Square Error of Approximation (RMSEA) was slightly above 0.1, placing it in an intermediate range between poor and acceptable fit.

The internal consistency and reliability of each DV in the questionnaire were analyzed using Cronbach’s alpha, yielding the following results: UAM = 0.850, TpAM = 0.880, TnAM = 0.917, and AtAM = 0.869. These values indicate a high reliability for each DV, confirming the robustness of the instrument.

2.3. Participants

A stratified probabilistic sampling of the teaching population in Spain was carried out, considering the different educational stages taught. These stages include primary education, secondary education, vocational training, higher education, official language school, and special education. To guarantee representativeness, strata were established according to the educational stage and an age range of teachers between 20 and 65 years old. In this way, it was guaranteed that each subgroup of the population was adequately represented in the sample, allowing for a more precise analysis to be carried out and adjusted to the diversity of teachers in the country.

The inclusion criterion was to be a teacher in Spain within one of these educational levels. The final sample included 994 teachers who, anonymously and voluntarily, agreed to participate in the study by providing their informed consent. The questionnaire was digitized through the Google Forms application and sent to the target population through electronic channels, accompanied by a detailed explanation of the objectives of the study. The responses were collected voluntarily and completely anonymously, ensuring both the confidentiality of the participants and the validity of the data obtained.

Table 1. Sociodemographic characteristics of the sample.

Variable	n (%)
Sex
Male	298 (30)
Female	696 (70)
Age
From 20 to 35 years old	127 (12.8)
From 36 to 50 years old	477 (48.2)
From 51 to 65 years old	385 (38.9)
Teaching level
Primary education	327 (32.9)
Secondary education	350 (35.2)
Vocational training	190 (19.1)
Higher education	127 (12.8)
Area of knowledge
Arts and humanities	303 (30.5)
Social and political sciences	351 (35.3)
Health sciences	79 (7.9)
Engineering and architecture	112 (11.3)
Sciences	149 (15)
Teaching sector
Urban	715 (71.9)
Rural	279 (28.1)

n (frequency).

shows the sociodemographic characteristics of the sample. In total, 30% were men (298) and 70% were women (696). In terms of age, 12.8% were between 20 and 35 years old (127), 48.2% were between 36 and 50 years old (477), and 38.9% were between 51 and 65 years old (385). Regarding the educational level at which they taught, 32.9% worked in primary education (327), 35.2% in secondary education (350), 19.1% in vocational training (190), and 12.8% in higher education (127). In terms of knowledge, 35.3% came from social and political sciences (351), 30.5% from arts and humanities (303), 15% from sciences (149), 11.3% from engineering and architecture (112), and 7.9% from health sciences (79). Finally, in relation to the education sector, 71.9% of the participants worked in urban areas (715), while 28.1% worked in rural areas (279).

2.4. Data Analysis

Data analysis was carried out using statistical tests with SPSS version 25. A descriptive analysis of the DVs measured on a scale was performed, evaluating the mean values (M), variance (V), and standard deviation (SD). In addition, a multivariate inferential analysis was performed using non-parametric tests, such as the U Mann–Whitney test for IV-1 and the H Kruskal–Wallis test for IV-2, IV-3, IV-4, and IV-5. The use of nonparametric tests was justified because the data distributions did not meet the normality assumptions necessary to apply parametric tests, which made it more appropriate to use tests that do not require normality in the data to assess differences between groups (Serrano et al., 2023).

3. Results

Figure 3 shows the average values of each of the DVs analyzed. The descriptive data show that, in a sample of 994 cases, the average use of AMs was moderate (2.663), while teachers’ presumptions pertaining to the value that their students would assign to each of the AMs was slightly higher (2.975). Teachers’ attitude towards AMs received the highest rating (3.851), indicating a generally positive perception of these methods. This suggests that, among the analyzed variables, educators were the most receptive and favorable towards AMs, recognizing their potential benefits in the teaching–learning process. Training needs in AMs was also rated relatively highly (3.62), which suggests variability in the preparation received. In general, although there were differences in each category, the impact on learning and training needs had a greater variability.

3.1. Use, Teachers’ Presumptions Pertaining to the Value Their Students Would Assign to Each of the AMs, and Teacher Training Needs in AMs

The results of

Table 2. Descriptive analysis of the use, teachers’ presumptions pertaining to the value their students would assign to each of the AMs, and the training needs of the teaching staff according to the different AM.

Item	UAM			TpAM			TnAM
	M	V	SD	M	V	SD	M	V	SD
Flipped Classroom	2.36	1.563	1.250	2.68	1.924	1.387	3.15	2.146	1.465
Project-Based Learning	3.39	1.506	1.227	3.77	1.445	1.202	3.79	1.617	1.271
Cooperative Learning	3.62	1.261	1.123	3.85	1.278	1.130	3.80	1.614	1.271
Problem-Based Learning	3.09	1.505	1.227	3.28	1.699	1.303	3.76	1.560	1.249
Design Thinking	1.89	1.282	1.132	2.17	1.696	1.302	3.46	2.142	1.464
Thinking-Based Learning	2.33	1.612	1.270	2.43	1.768	1.330	3.61	1.929	1.389
Gamification	2.77	1.563	1.250	3.56	2.140	1.463	3.61	2.005	1.416
Competency-Based Learning	3.52	1.583	1.258	3.26	1.557	1.248	3.76	1.646	1.283
Challenge-Based Learning	3.03	1.637	1.279	3.34	1.808	1.345	3.88	1.519	1.233
Adaptive Learning	2.63	1.897	1.377	2.75	1.981	1.407	3.68	1.770	1.330
Implementation of Augmented and Virtual Reality Technologies	1.68	1.045	1.022	2.35	2.206	1.485	3.40	2.303	1.518
Artificial Intelligence	1.66	0.984	0.992	2.28	2.058	1.434	3.56	2.265	1505

Mean (M); Variance (V); Standard Deviation (SD).

show a descriptive analysis of the use, teachers’ presumptions pertaining to the value that their students would assign to each of the AMs, and the training needs of teachers in various AMs. In terms of use, cooperative learning (3.62) and competency-based learning (3.52) were the most widely used methodologies, while artificial intelligence (1.66) and the implementation of augmented and virtual reality technologies (1.68) had limited use. Regarding teachers’ presumptions pertaining to the value that their students would assign to each of the AMs, cooperative learning (3.85) and project-based learning (3.77) were the most valued, while emerging technologies such as artificial intelligence (2.28) and design thinking (2.17) showed the lowest value.

Regarding the training needs of teachers, the greatest requirements were observed for challenge-based learning (3.88) and cooperative learning (3.80). However, there was also a need for training in innovative methodologies such as augmented/virtual reality (3.40), artificial intelligence (3.56), and design thinking (3.46), which indicates that teachers perceive a need for training in emerging technologies, despite their low use. In general, traditional methodologies were more accepted and used, while emerging ones showed a clear demand for training.

3.2. Attitude Towards AMs

The results reflect that the teachers had a positive attitude towards the application of AM sin teaching (

Table 3. Descriptive analysis of teachers’ attitudes towards AM.

Item	M	V	SD
I promote the active participation of students in a group and individual way, in the search for knowledge and the development of their own capacities during the teaching and learning process through the application of different AMs	4.03	0.731	0.855
I involve in my teacher training issues related to the application of new teaching methodologies supported using technological tools to support teaching innovation inside and outside the classroom	3.78	1.061	1.030
I elaborate and execute the didactic planning articulating all the curricular elements, applying an active methodology based on the reality of the educational institution	3.82	0.963	0.981
I apply communication strategies that enhance and promote interrelation and interaction to the pedagogical proposal	3.88	0.892	0.944
I use technological tools appropriately to create educational resources and innovate the processes of development and execution of classes	3.63	0.988	0.994
I provide feedback on the learning based on the reflection of the doubts and concerns that arise in the class	3.97	0.941	0.970
I use various methods and techniques that allow the expected learning to be evaluated in a differentiated way according to the learning style of the students	3.83	1.052	1.025
Design individual, group or classroom curricular adaptations considering the educational needs and cultural diversity of students	4.03	0.731	0.855

Mean (M); Variance (V); Standard Deviation (SD).

), with mean scores ranging from 3.63 to 4.03. The best-rated items (4.03) indicate that they promote the active participation of students and adapt their educational practices to the diversity of the student body. Didactic planning and differentiated assessment were also well valued (3.82–3.83), although the appropriate use of technological tools had a lower average (3.63), which suggests room for improvement in this area. In addition, the teachers recognized the importance of feedback and communication strategies, with averages of 3.88 and 3.97, which shows a pedagogical approach focused on interaction and reflection.

3.3. Differences as a Function of Sex, Age, Educational Level, Area of Knowledge, and Teaching Sector

The results of

Table 4. Results of inferential data analysis.

Variable	UAM			TpAM			TnAM			AtAM
	M	U	p	M	U	p	M	U	p	M	U	p
Sex
Male	2.57	92,947.5	0.009	2.81	87,728.5	0.002	3.46	81,984.0	0.001	3.67	89,527.0	0.000
Female	2.70			3.04			3.92			3.68
Teaching sector
Urban	2.64	94,572.5	0.203	2.94	92,183.0	0.063	3.61	99,136.0	0.881	3.84	99,171.0	0.888
Rural	2.72			3.07			3.64			3.87
Variable	M	X2	p	M	X2	p	M	X2	p		X2	p
Age
From 20 to 35 years old	2.74	11.331	0.003	3.10	12.744	0.002	3.79	21.773	0.000	3.86	1.563	0.458
From 36 to 50 years old	2.72			3.05			3.73			3.89
From 51 to 65 years old	2.56			2.85			3.44			3.80
Teaching level
Primary education	2.75	12.259	0.007	3.15	19.038	0.000	3.63	12.504	0.006	3.95	16.342	0.001
Secondary education	2.59			2.88			3.48			3.88
Vocational training	2.70			2.93			3.79			3.77
Higher education	2.58			2.85			3.70			3.63
Area of knowledge
Arts and humanities	2.73	14.067	0.007	2.99	18.948	0.001	3.54	7.569	0.109	3.91	10.020	0.040
Social and political sciences	2.72			3.08			3.72			3.89
Health sciences	2.64			3.01			3.79			3.77
Engineering and architecture	2.50			2.64			3.55			3.66
Sciences	2.54			2.93			3.51			3.82

Mean (M); U (U Mann–Whitney); X² (Chi-square); p-value (p).

show significant differences between men and women in terms of the use of AMs (p = 0.009), teachers’ presumptions pertaining to the value that their students would assign to each of the AM (p = 0.002), the need for training (p = 0.001), and the attitude towards AMs (p = 0.000). Women reported higher scores in the four variables analyzed.

All the same, when analyzed depending on the geographical context (urban or rural), no statistically significant differences were found in any of the variables. Teachers in urban and rural areas had similar scores for the use of AMs, teachers’ presumptions pertaining to the value that their students would assign to each of the AMs, the need for training, and attitude towards MA, with p values higher than 0.05 in all cases. This suggests that the geographical context does not significantly influence the implementation and perception of AMs by teachers.

Younger teachers (20–35 years) reported higher use of AMs (M = 2.74) compared to older teachers, with a significant difference (p = 0.003). Also, they had better teachers’ presumptions pertaining to the value that their students would assign to each of the AMs (M = 3.10, p = 0.002) and a greater need for training in AMs (M = 3.79, p = 0.000). No significant differences were observed in the attitude towards AMs according to age (p = 0.458), suggesting that, regardless of age, attitudes were similar.

In terms of teaching level, primary school teachers had a greater use of AM (M = 2.75) and a greater level of teachers’ presumptions pertaining to the value that their students would assign to each of the AMs (M = 3.15), with significant differences in both variables (p = 0.007 and p = 0.000, respectively). They also showed a more favorable attitude towards AMs (M = 3.95, p = 0.001), although their need for training was lower than that of the other levels. On the contrary, higher education teachers demonstrated the lowest values for these variables.

The results show significant differences in the use of AMs between areas of knowledge, particularly in arts and humanities (M = 2.73, p = 0.007), where teachers reported greater use of AMs, as well as teachers’ presumptions pertaining to the value that their students would assign to each of the AMs (p = 0.001) and positive attitudes (p = 0.040). Although teachers of engineering, architecture, and pure sciences reported a lower use of AMs (M = 2.50 and M = 2.54, respectively), the differences in the need for training were not significant (p = 0.109), indicating that, in this aspect, teachers from different areas do not differ considerably.

These results show that factors such as sex, age, educational level, and area of knowledge significantly influence the use and perception of AMs, with younger teachers and those at primary school levels showing greater use and presumptions pertaining to the value that their students would assign to each of these methodologies.

4. Discussion

Addressing RQ1, the results of this research reveal interesting patterns in the use of AMs, teachers’ presumptions pertaining to the value that their students would assign to each of the AMs, and training needs in relation to AMs in education. The prevalence of traditional AMs such as cooperative learning and competency-based learning shows a bias towards approaches that have been shown to encourage participation and collaboration among students. This coincides with previous studies that highlight how these methodologies are widely accepted by students due to their familiarity and collaborative structure, which create a positive and participatory learning environment (Slavin, 2015; Johnson et al., 2018). Then again, emerging technologies such as artificial intelligence and augmented reality, although they offer a great potential in theory, are not yet common in the classroom and require a greater effort to adapt by both students and teachers.

In response to RQ2, the teachers’ poor presumptions pertaining to the value that their students would assign to each of the AMs in class could be linked to a lack of familiarity with these tools, which coincides with research indicating that students, although they are frequent users of technology in their daily lives, may be reluctant when these tools are applied in educational contexts due to their perceived complexity (Zawacki-Richter et al., 2019). In addition, the low use by teachers reflects a reality in which the preparation and confidence to implement these tools are limited, which highlights the need for training programs in innovative technologies.

As a response to RQ3, in terms of training needs, it is notable that teachers perceive training in emerging methodologies such as augmented reality, artificial intelligence, and design thinking as a priority. This perception indicates an awareness of the importance of these tools for future teaching, and aligns with the literature suggesting that teachers need to be continuously updated to respond to the changing demands of learning in the digital age (Schleicher, 2018). The need for training in challenge-based learning and cooperative learning, which also appears to be significant, could respond to a desire to refine the use of methodologies that have shown good results, but in which teachers are still seeking to improve their effectiveness.

This situation reflects what has been observed in research on the professional development of teachers, such as that of (Darling-Hammond, 2017), who affirm that teacher development is more effective when it focuses on specific methodologies and is adapted to the needs detected by the teachers themselves. In the current context, the demand for training in these AMs is shown to be a critical element to improve the implementation of these strategies in the classroom and, ultimately, to positively impact the student experience (Bazarra & Casanova, 2016; León-Díaz et al., 2020).

Regarding RQ4, the results show that, in general, teachers maintain a positive attitude towards the use of AMs in teaching, especially regarding active student participation and adaptation to diversity in the classroom. This finding coincides with previous studies that suggest that AMs are appreciated by teachers, who recognize their benefits in promoting greater involvement and autonomy in the learning process. AMs are often valued for their ability to promote inclusive teaching, where students can actively participate and benefit from instruction tailored to their individual needs (Kahu & Nelson, 2018).

Teachers also gave a high value to didactic planning and differentiated assessment, highlighting their commitment to creating structured and reflective learning environments. The literature confirms that careful planning and adaptive assessment are essential for AMs to be effective, as they allow teachers to respond to the diversity of learning rhythms and styles present in the classroom (Biggs, 2012). In addition, the importance that teachers assign to feedback and communication strategies suggests that they adopt a student-centered approach, recognizing that continuous feedback is a critical factor for active learning and improved understanding, coinciding with the work of (Parra-González et al., 2021).

However, the use of technology tools receives a relatively lower rating, indicating an area for improvement in AM implementation. This may be due to a lack of specific training in educational technology or the perception that the use of technological tools is complex and requires additional technical skills (Redecker, 2017). The integration of technology in AMs, although promising, continues to present challenges due to factors such as accessibility, the appropriate design of tools, and variability in teachers’ digital competence (Cabero-Almenara et al., 2019). However, adequate support and training in technological tools could facilitate their use in the context of AMs, optimizing their potential to favor a richer and more motivating learning experience.

Providing an answer to RQ5, the results of this analysis highlight how sociodemographic and professional variables affect the use and perception of AMs in teaching. A gender difference is reflected in a general tendency for women to score higher in the use of AMs, in teachers’ perceived student acceptance of AMs, in the need for training, and in the attitudes towards these methodologies. This is consistent with previous research suggesting that women in education tend to adopt student-centered and participatory approaches more often than their male counterparts, possibly due to differences in pedagogical beliefs and teaching styles (Arias-Gago & Rodríguez-García, 2020).

Regarding the age variable, younger teachers show a greater inclination towards the use of AMs and show a higher degree of perceived student acceptance of AMs, which may be related to their familiarity with recent pedagogical approaches and their exposure to innovative technological tools during their academic training (Rodríguez-García & Arias-Gago, 2019). This group also reports a greater need for training, which is consistent with the idea that, although they are open to the use of these methodologies, they perceive that they require professional development to implement them effectively (Ertmer & Ottenbreit-Leftwich, 2010). However, attitudes towards AMs do not vary significantly with age, suggesting that, regardless of their age group, teachers share a positive or negative disposition towards these methodologies.

The educational level at which teachers work also seems to play a fundamental role in the use and acceptance of AMs. Primary school teachers show a higher frequency of use and positive perception of teachers’ presumptions pertaining to the value their students will assign to each of the AMs in class, which could be because, at these levels, active and collaborative learning is prioritized to promote basic skills and motivate students from an early age (Hermans et al., 2008). In contrast, higher education teachers have lower scores, which may be related to curricular demands and the nature of content at this level, which often rely more on traditional methods (de la Sablonnière et al., 2009).

On the other hand, teachers in areas such as arts and humanities stand out in the use and positive perception of AMs compared to other disciplines, such as science or engineering. This could be attributed to the more reflective and critical nature of these areas, which favor active and creative pedagogical approaches (Baeten et al., 2010). However, the need for training does not differ significantly between areas of knowledge, suggesting that, regardless of the discipline, teachers perceive a general lack of training in the use of these methodologies.

Finally, the geographical context (urban or rural) does not seem to significantly influence any of the variables analyzed, which could reflect a uniformity in the adoption and perception of AMs among teachers in both contexts. This finding is relevant, as it indicates that differences in AM implementation may be more linked to individual and organizational factors than to the geographical environment.

5. Conclusions

This study reveals that teachers tend to employ traditional AMs, such as cooperative learning, which has positive teachers’ presumptions pertaining to the value that their students would assign to each of the AMs due to their collaborative approach. However, emerging technologies such as artificial intelligence and augmented reality, while promising, have low classroom adoption and are less familiar to faculty, limiting their use. Teachers show a need for training, both in emerging technologies and in traditional methodologies, to improve their application. In addition, although aspects such as planning and feedback are positively valued, the use of technological tools receives a lower rating, indicating an important area for improvement to maximize the impact of these AMs on the learning experience.

Overall, the results underscore the influence of factors such as gender, age, educational level, and area of knowledge on the implementation and perception of AMs. These findings support the idea that the effectiveness of these methodologies depends on a personalized approach that considers the specific characteristics of the teacher and their context and suggest that AM training initiatives could benefit from being adapted to these factors to maximize their impact on teaching practice.

Among the limitations of this research, it is important to note that the results could be significantly influenced by the internal dynamics and work environment of each participating educational center. To achieve a greater understanding and contextualization of these findings, it is essential to continue this line of research based on the individual discourses of the teachers. This would allow the results to be approximated to the reality of each educational institution and to obtain a more precise and contextualized perspective on the variables under study.