1. Introduction

Figure 1. Bombing of May 17, 1938. In Ufficio Storico dell’Aeronautica Militare. Fondo Operazione Militare Spagna., Rome (USAM). Fons Operazione Militare Spagna (OMS), Series 2, Subseries 7, Folder 30. “Obiettivi vari”.

Figure 1. Bombing of May 17, 1938. In Ufficio Storico dell’Aeronautica Militare. Fondo Operazione Militare Spagna., Rome (USAM). Fons Operazione Militare Spagna (OMS), Series 2, Subseries 7, Folder 30. “Obiettivi vari”.

2. Materials and Methods

2.1. Design and Preparation of the Simulation

The design and preparation of the simulation are based on several crucial components: a map of the city of Alicante, narratives and rounds, the identification of targets, the placement of anti-aircraft defenses, the localization of bombings, and the creation of air-raid shelters. Each of these elements is essential to ensure an effective and realistic simulation that enables students to explore various historical aspects and reflect on their contemporary relevance.

A size A0 map of the city of Alicante will be used as the game board for the simulation activity. Students will be organized into pairs or trios and assigned a map divided into neighborhoods identified by different colors. This division aims to facilitate localization and exploration of various areas of the city, helping students to better understand the historical events that took place during the Spanish Civil War. The color-coded division also allows for easy identification and orientation within the map.

Figure 2. Map divided into neighborhoods of the city of Alicante (own creation).

Figure 2. Map divided into neighborhoods of the city of Alicante (own creation).

A narrative has been designed based on the historical events experienced in Alicante during the Civil War, grounded in historical documentation and reference literature [39,40,41]. From this historical narrative, 21 rounds were established for the simulation. Each round presents a historical context introduced through an explanatory video, including maps of the era, archival photos, and other resources. The video is paused at each round to allow students to perform a series of actions on the map using a timer.

The simulation is structured into four phases: Nazi spies studied the area to establish targets, Republican institutions organized anti-aircraft defenses and spotlights, bombings were carried out on military and civilian targets, and air-raid shelters were created by passive defense.

Figure 3. Explanatory brochures for students when conducting the UrbanGame. Own creation.

Figure 3. Explanatory brochures for students when conducting the UrbanGame. Own creation.

Historical documentation indicates that the city of Alicante was studied by Nazi spies to identify targets. A series of maps preserved in the General Military Archive of Ávila (AGMAV C 2126,2/13) [40] have been used to establish a list of elements that students must identify and mark on the map. The goal is for them to become aware of the elements considered vulnerable by enemy forces to weaken the city.

Students are required to identify on the map the locations of anti-aircraft defenses and Republican spotlights using a red marker, imitating the original map.

Figure 4. Documents and maps of supposed military targets in Alicante, drawn by Nazi spies in 1936. Archivo General Militar de Ávila (AGMAV) C 2126,2/13).

Figure 4. Documents and maps of supposed military targets in Alicante, drawn by Nazi spies in 1936. Archivo General Militar de Ávila (AGMAV) C 2126,2/13).

During the Civil War, Alicante suffered numerous bombings that caused significant damage to military and civilian targets [40,41,42]. Bombings that impacted military elements and civilian buildings were selected, with special attention to the bombing of the central market, as it was the most severe among those suffered. This approach helps students better understand the scale and impact of the bombings on the city.

Figure 5. Aerial photograph of the devastating bombing on February 12, 1939. In USAM. OMS. Busta 32: "Obiettivi vari. Raccolta n. 3".

Figure 5. Aerial photograph of the devastating bombing on February 12, 1939. In USAM. OMS. Busta 32: "Obiettivi vari. Raccolta n. 3".

Rectangular stickers will be used to identify air-raid shelters. Students will utilize a dossier that outlines the data for each round and a supplementary sheet where they will record the number of bombs, casualties, and injuries. This method allows them to maintain a detailed record of the bombing consequences and comprehend the importance of having air-raid shelters during the Civil War.

Figure 6. Map with stickers, notepad, and game sheet of the UrbanGame during the validation sessions.

Figure 6. Map with stickers, notepad, and game sheet of the UrbanGame during the validation sessions.

2.2. Validation with Faculty and Activity Design for Students

Currently, educators have the opportunity to implement active methodologies in the classroom, especially when educational regulations require it. In a training course for primary and secondary school teachers and its effectiveness was evaluated through a satisfaction survey and a dialogue among participants. The positive feedback from the attending teachers regarding the content acquisition process was essential for identifying improvements in the activity [43].

Figure 7. Faculty conducting the game during the validation sessions of the UrbanGame.

Figure 7. Faculty conducting the game during the validation sessions of the UrbanGame.

Based on the results obtained from the training course, a methodological proposal was developed to gather detailed information on student performance in the proposed simulation and their perception of it. This proposal includes a specific design for data collection and student evaluation, with the aim of academic improvement and adaptation of the activity to the needs and preferences of the students.

Figure 8. Evolution, focusing on the first and last rounds, of the UrbanGame on the bombings during the Spanish Civil War in Alicante, conducted by some university students. In this case, stickers have been replaced by icons referring to military targets, bombings, and the construction of air-raid shelters.

Figure 8. Evolution, focusing on the first and last rounds, of the UrbanGame on the bombings during the Spanish Civil War in Alicante, conducted by some university students. In this case, stickers have been replaced by icons referring to military targets, bombings, and the construction of air-raid shelters.

For data recording in the simulation, the approach by Franciose and Mehrieng [44] was followed, adapting it to the specific needs of this research. Additionally, the questionnaire was developed following the six-phase procedure described by Thiagaraja [45] and the themes proposed by Kriz and Nöbauer [46]. Data recording was carried out in three phases: game recording, theoretical test, and Google Form questionnaire.

This phase aims to evaluate the activities and performance during the game. Students will take photographs of the urban map after each round to document its evolution and destruction. Subsequently, a video will be created to visualize the evolution and destruction of the urban layout.

The questionnaire aims to assess knowledge acquisition in different areas or phases [45,46].

• Students will be asked to express their emotions, which may vary from empathy, anxiety, anguish, or calm during different moments of the activity, such as bombings, passive defense, construction or absence of shelters, and the threat of espionage.
• The dynamics and roles that have emerged will be evaluated, such as the emergence of a natural leader, the adoption of different roles, the prevalence of certain actions over others, reading documents, moments of dialogue and confrontation of ideas, competitiveness, or passivity.
• The activity also helps students better understand the reality around them, including situations involving women, children, the elderly, or refugees. It will be evaluated whether students empathize more from everyday or institutional perspectives and how they apply what they have learned to real life by learning more about the neighborhoods and urban layout of their city.
• During the simulation, students should identify several learning elements, such as the phases of the war, the massive arrival of refugees, escape by boats, food shortages, and bombings of civilian buildings.
• Students will speculate about different hypothetical scenarios to consolidate historical causality processes. For example, they might be asked what would have happened if the sirens had sounded during the bombing of Alicante's central market or if the ship Stambrook had not arrived at Alicante's port to save the last citizens of the Spanish Republic.
• It is crucial that students understand that situations similar to those simulated can recur in real life. This is the moment to empathize and reflect on behaviors in comparable real-life situations, allowing them to make informed decisions in the future.

The theoretical test is designed to evaluate knowledge acquisition through five multiple-choice questions and two open-ended questions.

3. Results

3.1. Student Opinions on the Relationship Between Learning Strategies and the UrbanGame Activity

To analyze student opinions regarding the relationship between learning strategies in the UrbanGame activity, it is essential to understand the collected data and the provided descriptive statistics.

The evaluated learning strategies included: Collaborative Work, Meaningful Learning, Problem-Based Learning, Discovery Learning, Concentration, and Motivation. In the descriptive statistics, the mean indicates the average responses for each strategy. In this context, the highest means are observed in "Motivation" (5.06) and "Concentration" (5.58), suggesting that these are the most valued or effective strategies according to the students.

Table 2. Mean and Standard Deviation of Learning Strategies Evaluated in the UrbanGame Activity.

Table 2. Mean and Standard Deviation of Learning Strategies Evaluated in the UrbanGame Activity.

Strategy	Mean	Std. Deviation
Collaborative Work	4.1042	2.61940
Meaningful Learning	4.7083	2.46644
Problem-Based Learning	4.1875	2.25649
Discovery Learning	4.3958	2.40336
Oral Expression	3.0625	2.83148
Motivation	5.0625	2.55498
Concentration	5.5833	1.91115

In our research context, the median and variance provide valuable perspectives on the distribution and dispersion of scores across different learning strategies evaluated using UrbanGame. The median indicates the central value of each data set, showing that the strategies of Collaborative Work, Meaningful Learning, Problem-Based Learning, and Discovery Learning have medians of 4. This suggests that, in general, scores tend to cluster around this value, reflecting a positive but not outstanding perception in these areas. In contrast, the strategies of Concentration and Motivation have medians of 7, indicating they are highly valued.

Variance measures the dispersion of scores around the mean. Concentration presents the lowest variance (3.652), indicating that scores are more consistent and less dispersed around the mean. This reinforces the idea that UrbanGame is particularly effective in uniformly improving concentration among students. Collaborative Work shows the highest variance (6.861), suggesting significant variability in responses, indicating notable differences in how students perceive the collaborative work facilitated by UrbanGame. The variances of the other strategies range between 5.092 and 6.528, reflecting moderate dispersion of scores, suggesting variability in student perceptions but not as extreme as in the case of Collaborative Work.

In summary, these results show that UrbanGame is particularly effective and consistently perceived as beneficial for concentration, while effectiveness in other areas such as Collaborative Work, Meaningful Learning, and Problem-Based Learning is perceived more variably among students.

Table 3. Median, Standard Deviation, and Variance of Learning Strategies Evaluated in the UrbanGame Activity.

Table 3. Median, Standard Deviation, and Variance of Learning Strategies Evaluated in the UrbanGame Activity.

Strategy	Median	Std. Deviation	Variance
Collaborative Work	4.0000	2.61940	6.861
Meaningful Learning	4.5000	2.46644	6.083
Problem-Based Learning	4.0000	2.25649	5.092
Discovery Learning	4.0000	2.40336	5.776
Motivation	7.0000	2.55498	6.528
Concentration	7.0000	1.91115	3.652

Standard deviation, as a measure of data dispersion around the mean, provides an understanding of how student scores vary for each learning strategy evaluated with UrbanGame.

The lowest standard deviation is observed in Concentration, with a value of 1.91115. This low standard deviation indicates that student scores in Concentration are tightly clustered around the mean. This reinforces the idea that UrbanGame is uniformly perceived as effective in improving concentration, as student responses do not vary significantly.

Conversely, the highest standard deviation is found in Collaborative Work, with a value of 2.61940. This high standard deviation suggests significant variability in student responses regarding the effectiveness of UrbanGame in enhancing collaborative work. Some students may find UrbanGame very useful for improving collaborative work, while others may not perceive the same level of effectiveness.

The strategies of Meaningful Learning, Problem-Based Learning, and Discovery Learning have intermediate standard deviations, ranging from 2.25649 to 2.55498. These values indicate moderate dispersion of scores around the mean, suggesting mixed student perceptions of the effectiveness of UrbanGame in these areas, with notable but not extreme variability.

The table of frequency percentages for UrbanGame regarding learning strategies reflects the relative effectiveness of different strategies in relation to the UrbanGame activity. The Concentration strategy has the highest frequency at 27.1%, suggesting that UrbanGame significantly promotes students' ability to maintain attention and focus on assigned tasks.

Collaborative Work and Meaningful Learning have a frequency of 22.9%, indicating that UrbanGame also fosters student cooperation and facilitates the connection of new knowledge with prior experiences, which is essential for deep and lasting learning. The 20.8% frequency in Problem-Based Learning highlights UrbanGame's ability to encourage critical thinking and problem-solving, indispensable skills in modern education.

On the other hand, Discovery Learning has a frequency of 10.4%, showing that although this strategy is less frequent, it is still relevant in the UrbanGame context, supporting exploration and self-learning. Motivation, with 6.3%, although less prominent, suggests that UrbanGame contributes to maintaining student interest and willingness to learn.

Overall, the data suggest that UrbanGame is a versatile tool that supports various learning strategies, being especially effective in enhancing concentration and promoting collaborative and meaningful methods.

Table 4. Percentage Distribution of Learning Strategies in the UrbanGame Activity.

Table 4. Percentage Distribution of Learning Strategies in the UrbanGame Activity.

Learning Strategy	Percentage
Collaborative Work	22.9%
Meaningful Learning	22.9%
Problem-Based Learning	20.8%
Discovery Learning	10.4%
Motivation	6.3%
Concentration	27.1%

3.2. Non-Parametric Correlations in Strategies and Their Relationship to Learning Objectives

The data analysis is based on the Kruskal-Wallis Test, a non-parametric test that evaluates whether there are statistically significant differences between multiple groups of an independent variable with respect to an ordinal or continuous dependent variable that is not normally distributed. In this context, three key areas in the UrbanGame are examined: content comprehension, skill acquisition, and value acquisition, in relation to different learning strategies.

3.2.1. Analysis of Content Comprehension

The following groups were analyzed: Collaborative Work, Meaningful Learning, Problem-Based Learning, Discovery Learning, Oral Expression, Motivation, and Concentration. The learning strategies of Meaningful Learning and Problem-Based Learning show significant differences among the levels of comprehension, suggesting that students with higher content comprehension value these strategies more. The other strategies do not show significant differences.

Table 5. Kruskal-Wallis Test Results for Content Comprehension Levels.

Table 5. Kruskal-Wallis Test Results for Content Comprehension Levels.

Strategy	H	p-value	Significance
Collaborative Work	3.076	0.079	No significant differences
Meaningful Learning	3.893	0.048	Significant difference, favors level 5
Problem-Based Learning	5.574	0.018	Significant difference, favors level 5
Discovery Learning	1.778	0.182	No significant differences
Oral Expression	0.976	0.323	No significant differences
Motivation	0.575	0.448	No significant differences
Concentration	1.451	0.228	No significant differences

Figure 9. Kruskal-Wallis H by Group (Understanding Content).

Figure 9. Kruskal-Wallis H by Group (Understanding Content).

3.2.2. Other Key Areas

However, in the analysis of Skill Acquisition, none of the learning strategies show significant differences in skill acquisition across different levels. Similarly, there are no significant differences in the analysis of Value Acquisition.

3.2.3. Practical Relevance of Significant Differences in Content Comprehension

In the analysis of Meaningful Learning, an H statistic of 3.893 and a p-value of 0.048 were obtained, indicating a significant difference in content comprehension. This strategy favored students who valued meaningful learning more, achieving greater content comprehension, which is a positive indicator for its implementation in the educational process. The practical relevance of these strategies suggests that their implementation can have a positive impact on content comprehension, leading to better information retention and a deeper application of knowledge in practical contexts.

Figure 10. Practical Relevance of Problem-based and Meaningful Learning Strategies in Content Understanding and Skill Development.

Figure 10. Practical Relevance of Problem-based and Meaningful Learning Strategies in Content Understanding and Skill Development.

On the other hand, Problem-Based Learning showed an H statistic of 5.574 and a p-value of 0.018, also significant. This strategy favored students engaged in problem-based learning, enhancing their content comprehension. The practical relevance of this methodology suggests that its adoption can help students develop critical and problem-solving skills essential for the practical application of knowledge in real-world situations.

The practical relevance of the significant differences found in the strategies of Meaningful Learning and Problem-Based Learning during the UrbanGame of the Spanish Civil War (Bombings in Alicante) can be evaluated in terms of several factors.

Regarding knowledge retention, strategies that resulted in significant differences can improve long-term knowledge retention. The application of Meaningful Learning helps students connect new knowledge with previous experiences, facilitating better retention.

The application of knowledge is another important factor. Problem-Based Learning encourages a practical approach to problem-solving, preparing students to apply knowledge in real contexts. Students who learn through problem-solving can transfer their critical thinking skills to situations outside the academic environment.

Additionally, the development of critical skills benefits from these strategies. Meaningful strategies not only improve content comprehension but also develop critical skills such as analysis, synthesis, and evaluation. Problem-Based Learning fosters teamwork and collaboration, valuable skills in the professional environment.

Finally, student satisfaction and motivation are crucial. Methods showing significant results can increase student motivation and engagement in the learning process. Meaningful Learning can make students find learning more relevant and satisfying.

Figure 11. Practical Relevance of Significant Differences in Learning Strategies.

Figure 11. Practical Relevance of Significant Differences in Learning Strategies.

The heatmap shows the significance values (p-values) for different learning strategies in three key areas: Understanding Content, Acquiring Skills, and Acquiring Values. The colors in the heatmap represent the magnitude of the p-values, ranging from blue to red, where darker colors indicate higher statistical significance (lower p-values).

In the Understanding Content category, the strategies of "Meaningful Learning" and "Problem-Based Learning" show significant differences in content comprehension. This suggests that these strategies may be more effective in enhancing student understanding.

In contrast, in the areas of Acquiring Skills and Acquiring Values, none of the evaluated strategies show significant differences. This indicates that, according to the available data, there is no clear effect of these strategies on skill or value acquisition.

Figure 12. Heatmap showing significant values (p-values) for different learning strategies in three key areas: content comprehension, skill acquisition, and value acquisition.

Figure 12. Heatmap showing significant values (p-values) for different learning strategies in three key areas: content comprehension, skill acquisition, and value acquisition.

The following line graph shows the Kruskal-Wallis H values and p-values for different learning strategies in the same three areas.

In the upper panel (Figure _a), the Kruskal-Wallis H values by strategy and area are presented. The Kruskal-Wallis H value measures the difference between groups, where higher values indicate a greater difference between group distributions. Each line in the graph represents one of the analyzed areas. It is observed that the strategies of "Meaningful Learning" and "Problem-Based Learning" indeed have the highest H values in Content Comprehension. In contrast, in Acquiring Skills and Acquiring Values, the H values are generally lower, indicating less significant differences among learning strategies.

In the lower panel (Figure _b), the p-values by strategy and area are shown. The p-values indicate the statistical significance of the results, where a p-value less than 0.05 is considered statistically significant. The red horizontal line at 0.05 represents the significance threshold. In Understanding Content, "Meaningful Learning" (p = 0.048) and "Problem-Based Learning" (p = 0.018) are statistically significant, showing differences in content comprehension. "Collaborative Work" is close to the threshold (p = 0.079) but does not reach statistical significance.

In terms of Acquiring Skills, none of the strategies have p-values less than 0.05, indicating no significant differences in skill acquisition. For Acquiring Values, "Concentration" (p = 0.059) is close to being significant but does not reach the threshold. None of the other strategies show significant differences in this area.

Figure 13. Line graph showing the Kruskal-Wallis H values and p-values for different learning strategies in three areas: content comprehension, skill acquisition, and value acquisition.

Figure 13. Line graph showing the Kruskal-Wallis H values and p-values for different learning strategies in three areas: content comprehension, skill acquisition, and value acquisition.

3.3. Qualitative Analysis of Preferences for Learning Strategies

3.3.1. Students of the Innovation and Research in Economics Course, Master's in Secondary Education

After conducting the previous quantitative analysis, we wanted to compare it with the qualitative analysis derived from the open-ended questions of the questionnaire completed by the students and designed for this purpose.

To perform a textual analysis using the QDA method, we assigned codes and subcodes to the various themes or categories identified in the data, primarily focusing on Learning Strategies:

• Code 1: Enhancing Collaborative Work
• Code 2: Enhancing Meaningful Learning
• Code 3: Enhancing Problem-Based Learning
• Code 4: Enhancing Discovery Learning
• Code 5: Enhancing Oral Expression
• Code 6: Enhancing Motivation
• Code 7: Enhancing Concentration

These codes and subcodes facilitated the classification and organization of data in a computer-assisted qualitative analysis using QDA technique in Atlas.ti software. Each case in the dataset was tagged with the corresponding codes and subcodes to reflect its specific characteristics.

Upon analyzing the provided data, several conclusions can be drawn. In general, students of all ages and genders show a tendency towards using collaborative work as a learning strategy, indicating a preference to enhance this methodology in their teaching methods. It is interesting to note that this trend does not appear in the quantitative analysis, where it ranks significantly lower than other more highly valued strategies.

There is also a widespread preference for meaningful learning, which allows students to connect new concepts with their prior knowledge, aiding in better comprehension and retention of information. In this case, it aligns with the results of the quantitative analysis.

Most categories and age groups also show interest in problem-based learning, which involves students in solving real and challenging situations, fostering critical thinking and practical application of knowledge. Although discovery learning is not mentioned as frequently as the previous strategies, some individuals express interest in this approach, which focuses on allowing students to discover concepts through exploration and experimentation.

Figure 14. Qualitative Analysis of Preferences for Learning Strategies among Students of the Innovation and Research in Economics Course, Master's in Secondary Education.

Figure 14. Qualitative Analysis of Preferences for Learning Strategies among Students of the Innovation and Research in Economics Course, Master's in Secondary Education.

Regarding enhancing concentration, not all categories mention this aspect consistently. Nonetheless, some individuals consider it important to foster concentration in teaching methods. In the quantitative analysis, on the other hand, it does appear as a prominent element.

Analyzing the data by age reveals some differences in preferences for learning strategies. The 18 to 25 age group shows a general preference for various learning strategies in the use of urban games (UrbanGame), primarily highlighting meaningful learning. They also mention problem-based learning and discovery learning, although concentration are not emphasized.

The 26 to 35 age group, similar to the previous group, shows interest in a wide range of learning strategies. They relate urban games (UrbanGame) with meaningful learning and discovery learning, additionally emphasizing motivation.

The 36 to 45 age group seems to prefer encompassing all mentioned learning strategies, indicating they would enhance all of them. They do not show specific preferences based on age and appear open to using different methods to promote learning.

The 46 to 55 age group, like the previous group, also mentions that they would generally enhance all learning strategies.

Analyzing the data by gender reveals some differences in preferences for learning strategies. In general, women show an interest in a variety of learning strategies, mentioning meaningful learning, problem-based learning, and discovery learning as strategies to enhance. They also highlight motivation.

On the other hand, men, like women, show interest in meaningful learning and discovery learning. However, oral expression and motivation are not consistently mentioned in this category.

In summary, both men and women show a preference for similar learning strategies. However, women tend to emphasize motivation more in their preferences, whereas men do not mention these aspects as consistently.

3.3.2. Students of the Didactics of History Course, Bachelor's in Primary Education

From the analysis of the provided data, several conclusions can be drawn. In the 18 to 25 age group, women seem to have a broader preference for different teaching methods, such as collaborative work, meaningful learning, problem-based learning, and discovery learning. On the other hand, men in this age group do not consistently mention a preference for specific methods.

No clear differences are observed in the preference for enhancing concentration in any age or gender group.

Figure 15. Differences in Learning Strategy Preferences by Gender and Age among Students of the Didactics of History Course, Bachelor's in Primary Education.

Figure 15. Differences in Learning Strategy Preferences by Gender and Age among Students of the Didactics of History Course, Bachelor's in Primary Education.

Analyzing the data by age identifies some differences in preferences for learning strategies. In the 18 to 25 age group, a general preference for all mentioned learning strategies is observed, without significant differences between men and women.

In the 26 to 35 age group, there is a consistent preference for enhancing collaborative work and meaningful learning, as indicated by the quantitative analysis. In particular, men mention motivation as an important strategy.

In the 36 to 45 age group, no differences in preferences for learning strategies are mentioned. Both men and women in this group indicate that all mentioned strategies are important.

Analyzing the data by gender identifies some differences in preferences for learning strategies. Women mention a variety of learning strategies, highlighting collaborative work, meaningful learning, problem-based learning, discovery learning, and the use of simulations. Additionally, they show a notable preference for discovery learning as an important strategy. In general, women exhibit an inclination towards a wide range of learning strategies.

Figure 16. Qualitative Analysis of Preferences for Learning Strategies by Gender and Age.

Figure 16. Qualitative Analysis of Preferences for Learning Strategies by Gender and Age.

Men also mention various preferences in learning strategies, including collaborative work, meaningful learning, problem-based learning, and the use of simulations. They also emphasize motivation as a relevant strategy. Like women, men show a preference for a variety of learning strategies.

In summary, both women and men have similar preferences regarding learning strategies, highlighting the importance of collaborative work, meaningful learning, and problem-based learning.

3.3.3. General Analysis for the Student Body

Overall, both groups show a concern for implementing strategies that promote collaborative work, meaningful learning and student motivation. Additionally, the Master's in Secondary Education student group places greater emphasis on problem-based learning and discovery learning.

Figure 17. General Analysis for the Student Body.

Figure 17. General Analysis for the Student Body.

3.4. Cultural Promotion of Memory Heritage Following the UrbanGame Activity

After conducting the activity in the classrooms, understanding the preferences for Learning Strategies and Objectives, and with knowledge of the real events that occurred in the city of Alicante during the Spanish Civil War, a series of street activities were organized to deepen not only knowledge but also awareness of the cultural heritage of memory (the air-raid shelters). These activities aim to empathize with the suffering experienced by women, children, and men during those terrible bombings over three hard years (a little more than seventy).

To achieve this, guided tours for students (beyond those scheduled for the general public) were organized and continue to be organized. These tours allow visits to up to nine shelters throughout the city, as well as the War Interpretation Center in Alicante.

Figure 18. Marvá Air-Raid Shelter, and Spanish and Foreign Students Accessing the Shelter Before the Visit.

Figure 18. Marvá Air-Raid Shelter, and Spanish and Foreign Students Accessing the Shelter Before the Visit.

Similarly, the aim has been for student involvement to go beyond merely visiting the air-raid shelters. One of the shelters (Marvá) has been temporarily allocated to nearby Public Secondary Education Institutes for various activities related to Memory: poetry recitals, debates, etc. Additionally, students studying tourism modules at IES Miguel Hernández have been authorized to design and conduct guided tours as part of their practical training. Furthermore, these students have organized several exhibitions inside the air-raid shelter on themes related to the Spanish Civil War, World War II, the Jewish Holocaust, etc.

Figure 19. Educational Activities and Exhibition by Students of IES Miguel Hernández at the Marvá Shelter.

Figure 19. Educational Activities and Exhibition by Students of IES Miguel Hernández at the Marvá Shelter.

To deepen this knowledge and raise awareness among young people, and as a powerful educational didactic tool, a comic has been created and disseminated. It narrates the events of one of the worst bombings against civilian populations in contemporary European history before World War II: the bombing of the Central Market of Alicante on May 25, 1938, by Italian fascist planes. This attack instantly killed more than 300 people, primarily women and children who were shopping for food. The death toll rose to over a thousand in the following weeks due to the severe injuries inflicted on numerous victims of that terrible bombing. This comic is supplemented by a model of the bombing, which is permanently installed at the aforementioned Interpretation Center.

Figure 20. Comic Panels Created with Script by Pablo Rosser and Drawings by Nain Sousa. Own Creation.

Figure 20. Comic Panels Created with Script by Pablo Rosser and Drawings by Nain Sousa. Own Creation.

4. Discussion

References

1. Deterding, S.; Dixon, D.; Khaled, R.; Nacke, L. From Game Design Elements to Gamefulness: Defining" Gamification". In Proceedings of the 15th international academic MindTrek conference: Envisioning future media environments; Lugmayr, A., Franssila, H., Safran, C., Hammouda, I., Eds.; ACM: Brighton, England, 2011; pp. 9–15.
2. Zichermann, G., & Cunningham, C. (). " ". Gamification by Design: Implementing Game Mechanics in Web and Mobile Apps; O’Reilly Media: Sebastopol, CA, 2011.
3. Doney, I. Research into Effective Gamification Features to Inform E-Learning Design. Research in Learning Technology 2019, 27. [CrossRef]
4. Shannon, R.E. Systems Simulation : The Art and Science; Prentice-Hall: Englewood Cliffs, N.J., 1975.
5. Wright-Maley, C. Beyond the “Babel Problem”: Defining Simulations for the Social Studies. The Journal of Social Studies Research, 2015, 39, 63–77. [CrossRef]
6. Abt, C. Serious Games; Viking Press: New York, NY, 1970.
7. Klopfer, E.; Osterweil, S.; Salen, K. Moving Learning Games Forward; MA: The Education Arcade: Cambridge, 2009.
8. Salen, K.; Zimmerman, E. Regras Do Jogo: Fundamentos Do Design de Jogos; Blucher: São Paulo, 2012; ISBN 9788521206279.
9. Steinkuehler, C.; Squire, K.; Barab, S. Games, Learning, and Society: Learning and Meaning in the Digital Age; Cambridge University Press, 2012; ISBN 9780521196239.
10. Gee, J.P.; Hayes, E. Nurturing Affinity Spaces and Game-Based Learning. Games, learning, and society: Learning and meaning in the digital age 2012, 123, 1–40.
11. McGonigal, J. Reality Is Broken; Penguin Press: New York, 2011.
12. Squire, K. Video Games and Learning Teaching and Participatory Culture in the Digital Age; Teachers’ College Press: New York, NY, 2011.
13. Lin, H.; Sun, C.-T. Problems in Simulating Social Reality: Observations on a MUD Construction. Simul. Gaming 2003, 34, 69–88. [CrossRef]
14. Williams, R., Dickinson, A., & Hulme, R. (). . London. New Technology in the Classroom. New History and New Technology Present into Future. The Historical Association/Council for Educational Technology 1986, 8–27.
15. Martín-Antón, J.; Valdés-Gonzaléz, A.; Rodríguez, D.J. Elección, análisis y uso de herramientas didácticas para la enseñanza de las Ciencias Sociales. La importancia de la detección de anacronismos y falta de rigor histórico en un videojuego. RIAICES 2021, 3, 17–26. [CrossRef]
16. Romero, J. ¿Herramientas o Cacharros?. Los Ordenadores y La Enseñanza de La Historia En La ESO; Servicio de Publicaciones de la Universidad de Cantabria, 1997.
17. Valverde, J. Aprendizaje de La Historia y Simulación Educativa. Tejuelo 2010, 9, 83–99.
18. Garris, R.; Ahlers, R.; Driskell, J.E. Games, Motivation, and Learning: A Research and Practice Model. Simul. Gaming 2002, 33, 441–467. [CrossRef]
19. Huizenga, J.; Admiraal, W.; Akkerman, S.; ten Dam, G. Mobile Game-Based Learning in Secondary Education: Engagement, Motivation and Learning in a Mobile City Game. Journal of Computer Assisted Learning 2009, 25, 332–344.
20. Educating the Net Generation; Oblinger, D.G., Oblinger, J.L., Eds.; EDUCAUSE, 2005.
21. Qian, M.; Clark, K.R. Game-Based Learning and 21st Century Skills: A Review of Recent Research. Comput. Human Behav. 2016, 63, 50–58. [CrossRef]
22. Kilgour, P.; Reynaud, D.; Northcote, M.; Shields, M. Role-Playing as a Tool to Facilitate Learning, Self Reflection and Social Awareness in Teacher Education. 2015.
23. Zevin, J. Social Studies for the Twenty-First Century: Methods and Materials for Teaching in Middle and Secondary Schools; 5th ed.; Routledge, 2023; ISBN 9780367459567.
24. Castro, S.S.; Sevillano, M.Á.P. Personalización del proceso de adquisición de la competencia en comunicación lingüística mediante el empleo de los serious games. Diferencias en función del género. Edutec 2022, 149–165. [CrossRef]
25. Michael, D.R.; Chen, S. Serious Games: Games That Educate, Train and Inform; Thomson Course Technology, 2006; ISBN 9781592006229.
26. Annetta, L.A. The “I’s” Have It: A Framework for Serious Educational Game Design. Rev. Gen. Psychol. 2010, 14, 105–113. [CrossRef]
27. Sitzmann, T. A Meta-Analytic Examination of the Instructional Effectiveness of Computer-Based Simulation Games. Pers. Psychol. 2011, 64, 489–528. [CrossRef]
28. Aldrich, C. Simulations and the Future of Learning: An Innovative (and Perhaps Revolutionary) Approach to e-Learning; John Wiley & Sons: Milton, QLD, Australia, 2003.
29. Nafukho, F.M. Simulations and the Future of Learning: An Innovative and Perhaps Revolutionary Approach to E-Learning. Human Resource Development Quarterly 2004, 15, 253–257.
30. Primack, B.A.; Carroll, M.V.; McNamara, M.; Klem, M.L.; King, B.; Rich, M.; Chan, C.W.; Nayak, S. Role of Video Games in Improving Health-Related Outcomes: A Systematic Review. Am. J. Prev. Med. 2012, 42, 630–638. [CrossRef]
31. Arnab, S.; Lim, T.; Carvalho, M.B.; Bellotti, F.; de Freitas, S.; Louchart, S.; Suttie, N.; Berta, R.; De Gloria, A. Mapping Learning and Game Mechanics for Serious Games Analysis. Br. J. Educ. Technol. 2015, 46, 391–411. [CrossRef]
32. Martínez, C.; Montero, R.; Arias, G.; Salcedo, M.A. Los Juegos Serios, Su Aplicación En La Seguridad y Salud de Los Trabajadores. Med. Segur. Trab. 2019, 65, 87–100. [CrossRef]
33. Pellegrini, M. Richard E. Mayer, Computer Games for Learning. An Evidence-Based Approach. Cambridge & London: The MIT Press (2014). Form@re 2014, 14, 96–97. [CrossRef]
34. Belkin, J. Urban Dynamics: Applications as a Teaching Tool and as an Urban Game. IEEE Transactions on Systems, Man, and Cybernetics 1972, 2, 166–169. [CrossRef]
35. Flanagan, M. Critical Play: Radical Game Design; The MIT Press, 2009; ISBN 9780262258760.
36. Innocent, T.; Leorke, D. (De)Coding the City Analyzing Urban Play through Wayfinder Live. American Journal of Play 2020, 12, 270–304.
37. Pramaputri, N.I.; Gamal, A. Urban Planning Simulation Game and the Development of Spatial Competence. IOP Conference Series. Earth and Environmental Science 2019, 396, 12016-. [CrossRef]
38. Larissa, H.; Adriana, de S. e.; Klare, L. The Routledge Companion to Mobile Media Art; 1st ed.; Routledge, 2022; ISBN 9781032399959.
39. Olmo, M.; Rosser, P.; Soler, S.; Poveda, C.; Oña, J.J.; Candela, V.F. Guerra Civil y Memoria Histórica En Alicante; Archivo Histórico Provincial de Alicante., 2016.
40. Rosser, P. Bombas Sobre Alicante. Los Diarios de La Guerra y Las Comisiones Internacionales de No Intervención e Inspección de Bombardeos; Universidad de Alicante, 2023; ISBN 9788413022215.
41. Rosser, P.; Soriano, R. Alicante En Guerra; Ayuntamiento de Alicante: Alicante, 2018; Vol. 1 y 2;.
42. Moreno, R.M.; Viñas, Á.; López, P.P.; Rosser, P. La Aviación Fascista y El Bombardeo Del 25 de Mayo de Alicante; Colección Alacant, ciutat de la memòria; Universidad de Alicante-Ajuntament d’Alacant, 2018.
43. Soler, S.; Rosser, P. Empatizar Con Los Conflictos Bélicos Para Trabajar El ODS 16. Creación de Una Situación de Aprendizaje a Partir de La Simulación Urbana. In Hacia una Educación con basada en las evidencias de la investigación y el desarrollo sostenible; DYKINSON, 2023 ISBN 9788411704274.
44. Franciosi, S.J.; Mehring, J. What Do Students Learn by Playing an Online Simulation Game? In Critical CALL–Proceedings of the 2015 EUROCALL Conference, Padova, Italy; Research-publishing.net, 2015; pp. 170–176.
45. Thiagarajan, S. How to Maximize Transfer from Simulation Games through Systematic Debriefing. In The Simulation and Gaming Yearbook; Percival, F., Lodge, F., Saunders, D., Eds.; Kogan Page Science: London, England, 1993; pp. 45–52.
46. Kriz, W.C.; Nöbauer, B. Teamkompetenz. Konzepte, Trainingsmethoden, Praxis; Vandenhoeck & Ruprecht: Göttingen, Germany, 2002; Vol. 2.
47. Wijnia, L.; Smeets, G.; Kroeze, M.J.; Van der Molen, H.T. Is Problem-Based Learning Associated with Students’ Motivation? A Quantitative and Qualitative Study. Learning Environments Research 2018, 21, 173–193. [CrossRef]
48. Alexander, E. S., White, A. A., Varol, A., Appel, K., & Lieneck, C. Team- and Problem-Based Learning in Health Services Education. Literature Review of Recent Initiatives in the United States. Education Sciences 2024, 14, 515.
49. Albadi, A.; David, S.A. The Impact of Activity Based Learning on Students’ Motivation and Academic Achievement: A Study among 12th Grade Science and Environment Students in a Public School in Oman. Specialty Journal of Knowledge Management 2019, 4, 44–53.
50. Rossi, I.V.; de Lima, J.D.; Sabatke, B.; Nunes, M.A.F.; Ramirez, G.E.; Ramirez, M.I. Active Learning Tools Improve the Learning Outcomes, Scientific Attitude, and Critical Thinking in Higher Education: Experiences in an Online Course during the COVID-19 Pandemic. Biochemistry and Molecular Biology Education 2021, 49, 888–903.
51. Papadopoulou, A.; Mystakidis, S.; Tsinakos, A. Immersive Storytelling in Social Virtual Reality for Human-Centered Learning about Sensitive Historical Events. Information 2024, 15, 244. [CrossRef]
52. The Role of Game Design in Cultural Preservation: Promoting Heritage through Gaming 2024.
53. Kang, S.J. Emotional Exhibition, Visitors’ Empathy, and History Education and Heritage Education in the Museum. The Korean History Education Review 2022, 161, 181–214. [CrossRef]
54. Wu, P.; Fan, K.; Chiang, H.; Ye, A. Exploring the Relationship of User ’ s Emotions and Immersive Experience in a Virtual Environment of Local Culture. 2016.
55. Archeu gamification euprojects erasmusplus activity Available online: https://www.linkedin.com/posts/archeu_gamification-euprojects-erasmusplus-activity-7170712183977316354-dLd2/ (accessed on 23 June 2024).
56. Exploring the Benefits of an Immersive Curriculum Available online: https://www.talespin.com/reading/exploring-the-benefits-of-an-immersive-curriculum (accessed on 23 June 2024).
57. Alam, A.; Mohanty, A. Educational Technology: Exploring the Convergence of Technology and Pedagogy through Mobility, Interactivity, AI, and Learning Tools. Cogent Engineering 2023, 10, 2283282. [CrossRef]
58. Announcing the OpenExO Experience Available online: https://fox40.com/business/press-releases/ein-presswire/651735212/announcing-the-openexo-experience-nurturing-exponential-mindsets-through-engagement/ (accessed on 23 June 2024).
59. Nofal, E.; Panagiotidou, G.; Reffat, R.M.; Hameeuw, H.; Boschloos, V.; Vande Moere, A. Situated Tangible Gamification of Heritage for Supporting Collaborative Learning of Young Museum Visitors. J. Comput. Cult. Herit. 2020, 13, 1–24. [CrossRef]
60. Youth Cultural Heritage Grant Program - Alaska State Council on the Arts Available online: https://arts.alaska.gov/Youth-Cultural-Heritage-Grant-Program (accessed on 23 June 2024).
61. Anonymous Heritage Education Programme Toolkit. For Heritage Clubs in Uganda’s Secondary Schools and Other Young Ugandans; CCFU (The Cross-Cultural Foundation of Uganda), 2019.
62. UNESCO World Heritage Centre Activities Available online: https://whc.unesco.org/en/activities/id_keywords=484&action=list (accessed on 23 June 2024).
63. Celebrating African World Heritage Day Available online: https://awhf.net/2371-2/ (accessed on 23 June 2024).
64. Cultural Heritage and Education Available online: https://culture.ec.europa.eu/cultural-heritage/cultural-heritage-in-eu-policies/cultural-heritage-and-education (accessed on 23 June 2024).
65. MacDowell, M.; Kozma, L.G. Folkpatterns: A Place-Based Youth Cultural Heritage Education Program. The Journal of Museum Education 2007, 32, 263–273. [CrossRef]
66. Youth on the Trail of World Heritage Available online: https://www.ovpm.org/program/youth-on-the-trail-of-world-heritage/ (accessed on 23 June 2024).