A Simulation Game in Mineral Exploration: A Mineral Adventure from Exploration to Exploitation

Abstract

In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation games advances in other scientific fields, such as economics, management, engineering, and mathematics. These simulations offer learners a risk-free virtual platform to apply and refine their knowledge, leveraging animations, graphics, and interactive environments to enrich the learning experience. In engineering, while simulation is widely utilized as a powerful training tool for heavy equipment and process handling, the creation of strategy games for educational purposes is less frequent. This gap primarily stems from the challenge of converting complex engineering concepts and theories into a user-friendly yet comprehensive setup that preserves the more difficult aspects. This study adopts a design-based research approach to develop and evaluate an educational simulation game aimed at enhancing probabilistic and spatial reasoning in mineral exploration. The application generates random scenarios, within which users deploy strategies based on their knowledge, while accommodating the randomness of physical phenomena. The simulation game is adopted as an educational tool in the course “Introduction to Mineral Exploration” in the School of Mining and Metallurgical Engineering of the National Technical University of Athens. Additionally, we present the outcomes of game analytics and a qualitative evaluation derived from three workshops at higher education institutions in Greece.

1. Introduction

The main objective of modern educational systems is to replace traditional methods (passive learning) such as lecturing, assigned textbook readings, and video watching, with active learning [1], allowing analysis, synthesis, evaluation, and reflection [2]. Over the last few decades, the academic community and higher education institutions have focused on developing educational techniques to enhance the active learning process. In this context, recent gamification initiatives in Earth science education have also shown that incorporating competitive elements via quiz-based activities can significantly engage students and enhance learning outcomes [3]. A wide range of strategies for promoting active learning has been described by the Association for the Study of Higher Education. These methodologies are classified by the level of student involvement [4] and vary from classroom debates to global competitions such as the Global Management Challenge which is based on business simulations. The higher levels of the revised Bloom’s taxonomy [2,5] best describe the main tasks of the active learning process and indicate the appropriate activities in the classroom based on the cognitive goals.

In engineering, the most common form of active learning is Problem-Based Learning (PBL) as noted by Stamou et al. [6], which was pioneered as a learning method by Barrows and Tamblyn at the medical school program at McMaster University in Hamilton during the 1960 s [7]. PBL promotes knowledge acquisition through the experience of solving problems, enabling students to develop problem-solving skills, while also increasing their knowledge and understanding [8]. Furthermore, tutors encourage students to determine what they need to learn and how to learn it [9]. The crucial motivation of PBL is its engagement with real-world problems, which closely aligns it with simulation.

Simulation-Based Learning (SBL) is considered an extension of the PBL method, having common educational objectives and criteria of effectiveness, while presenting some additional advantages [10], such as increased post-training self-efficacy and provision of tangible results. In any case, PBL may be mostly considered an education strategy, while SBL is mainly a learning technique. The design of PBL and SBL implementation includes the definition of the problem to be solved, the determination of the prior knowledge and skills to be involved, and the clarification of the learning outcomes of the process. A simulation qualifies as an effective PBL approach if it meets the criteria for PBL effectiveness: the ability to engage students’ interest [11,12] and adequately covers concepts and theories through experiential learning [13,14,15,16,17,18,19].

Today, simulations are intertwined with computer models, which have replaced physical objects to simulate actual objects or systems. Training with computer simulations has been utilized in aviation, military, and medical fields to practice safely, reduce human accident errors, and save the cost of expensive equipment and materials. These fundamental properties have spurred the development of educational simulations in other scientific fields such as economics, management, engineering, and mathematics. Examples of well-known educational simulation games are summarized by [10]. Recent studies [20,21] indicate that simulation games significantly impact students’ learning. In recent years, more sophisticated simulation games have been developed that teach multiple concepts across various domains, involving user cognition and intuition.

In engineering, although simulation is widely used as a powerful educational tool, the creation of interactive environments that motivate learners to formulate strategies and make decisions for managing situations is uncommon. The main challenge is the complexity of programming engineering concepts and theories. Furthermore, the demanding framework for game development, combined with a general lack of expertise among academics, hinders the creation of educational applications of this type. Additionally, while entertainment companies focus on developing commercial applications, the potential sales revenue from educational simulation games remains uncertain [22]. Mayo [23] discuss why educational games often fail to establish successful business models. These challenges often lead to the creation of simplified simulation games that cover only basic aspects within a specific domain.

In the context of geosciences education, additional challenges arise from the inherently spatial and uncertain nature of geological processes. Uncertainty, a fundamental aspect of geoscience, is often overlooked in undergraduate education [24] even though it significantly impacts data reliability and decision-making. Furthermore, fieldwork opportunities in undergraduate geoscience programs may be limited due to logistical, safety, or financial constraints [25]. Therefore, alternative approaches should be employed to enhance the educational experience. In addition, geoscience requires high-level spatial thinking, which is generally improved only slightly, during one academic course [26]. The primary research question guiding this study is: How can a simulation-based learning tool be effectively designed, implemented, and evaluated to promote cognitive engagement with uncertainty, spatial reasoning, and economic trade-offs in mineral exploration education? To address this question, the present work introduces a simulation game in mineral exploration developed by the authors which addresses key gaps in geoscience education. Specifically, the game provides an interactive, scenario-based environment where learners can explore mineral deposits, apply probabilistic reasoning (i.e., Bayes’ Theorem), and engage in decision-making under uncertainty. It also guides learners in developing spatial thinking skills, which are essential in geoscientific problem-solving.

The objective of this study is to iteratively design, implement, and evaluate this simulation-based learning tool using a design-based research framework. This approach ensures that the development process is both theoretically grounded and practically relevant, enhancing the educational effectiveness of the game. Overall, the simulation not only improves conceptual understanding but also replicates the real-world workflow of mineral exploration, making it a novel contribution to active learning in geosciences.

This game aligns with contemporary teaching methods recommended for sustainability education, such as running virtual simulations and conducting cost–benefit analyses, thereby enhancing critical thinking and decision-making skills [27]. By integrating these elements, the simulation game significantly contributes to both academic and personal development, including technical knowledge, soft skills, and sustainable engineering practices. This covers broader educational goals, using simulation games as tools for sustainable education and preparing students to tackle real-world challenges through an interdisciplinary and resource-efficient framework [27]. Such approaches are also consistent with research showing that educational games can spark higher levels of motivation and promote students’ engagement, critical thinking, and problem-solving skills in geosciences [28].

While existing literature highlights the value of simulation in engineering and geoscience education, there remains a lack of tools specifically tailored to the challenges of mineral exploration. The simulation game demonstrated in this study builds upon these educational frameworks by incorporating decision-making under uncertainty, spatial reasoning, and probabilistic inference, which are underrepresented in current teaching tools on mineral exploration.

The present simulation game engages students in an interactive environment where they must strategize and reflect on potential outcomes. Through these activities, we aim to increase both motivation and long-term retention.

In the following section, we outline the crucial features related to the implementation of simulation games. In the Materials and Methods section, we detail the objectives and describe the developed simulation game, along with an explanation of the implementation background. In the Results section, we compile the opinions and suggestions received from participants in three workshops between 2021 and 2024 organized to demonstrate the simulation game. Furthermore, we present some quantitative results from the game evaluation.

2. The Simulation Process

In education, simulations are used to represent either natural or invented systems that interact with user actions. Users may decide to modify parameters to explore the system’s responses or alter system operations to determine the outcomes. Simulations and games share a close relationship, as most games incorporate simulations within their basic architecture; both rely on computer models to allow user interaction and provide at least some degree of user control [22]. However, their purposes diverge significantly in the learning process. The primary objective of simulations is to train users in handling various real or hypothetical situations within certain constraints. Conversely, the purpose of games is to engage the user’s attention for entertainment and motivate competition, as games typically include a competitive element. In the literature, educational simulations are described by various terms such as interactive computer simulation, interactive simulation, interactive exercise, simulation exercise, computerized simulations, and serious games. Nevertheless, the term ‘simulation game’ effectively merges the objectives of both simulations and games within a scientific context, with a priority given to simulation objectives [22].

The development of a simulation game is a demanding task. Instructors should define the objectives of the simulation game, the level of user control (interactivity), the surrounding guiding framework, the system’s parameterization, the management of information, the level of system unpredictability, the simulation game’s reward system, and the learner tracking system. The dimensions for classifying simulation games, including the degree of user control, the guiding framework, the representation of information, and the nature of what is modeled, are proposed by Clark et al. [29] and the National Research Council [22], who also provide application examples in education. The design of a simulation game, including its operations, enjoyment, player effort, and attractiveness, is strongly correlated with the satisfaction and motivation of learners [30,31]. Below, we describe analytically the basic framework for developing a simulation game.

2.1. Objectives of Simulation Game in Education

Simulation games aim to demonstrate course concepts by facilitating interaction with learning materials. Thus, they are used to introduce new concepts, train users in these concepts, and teach them how to manage related information. A simulation game may cover either simple aspects or a broad range of concepts within a discipline. Specifically, instructors should define the desired learning outcomes of the simulation game, such as increasing students’ knowledge of basic concepts, familiarizing them with problem-solving processes, developing analytical and critical thinking skills, enhancing decision-making abilities, and managing information.

2.2. Surrounding Guiding Framework

The prior knowledge required to run the simulation depends on the nature of the course. Usually, introducing simple concepts does not necessitate previous knowledge, and a self-driven application can suffice. Conversely, simulations embedded within a larger sequence of science instruction require prior knowledge. Instructors should identify the essential knowledge from other modules or previous courses and decide how to prepare the user to engage with the simulation game effectively. Moreover, the instructor should determine which evidence will be provided by the simulation game and which knowledge will be acquired during the class lesson. A simulation game can be conducted in a classroom to minimize constraints in the game environment, or, it can be designed to stand alone, focusing on self-identifying information and self-assessment.

2.3. Degree of User Control

Simulation games are classified based on the level of user control as limited, intermediate, or high. Limited user control applications, also known as “targeted” [22], allow control over specific system parameters. These simulations are typically designed to demonstrate simple course concepts and often operate independently. Intermediate user control applications, known as “sandbox” simulations [29], allow the user to direct the process while playing a significant role in determining the simulation’s outcome. Sandbox simulations often integrate multiple concepts of a discipline, aiming to enhance the user decision-making skills and familiarize them with the problem-solving process. Finally, simulations with a high degree of user control allow users to modify variables influencing outcomes or changing the underlying procedures of the simulation.

2.4. System’s Parameterization

The parameterization of the system forms the core of the simulation game, establishing both the educational capabilities and the level of interactivity. It also sets the guiding framework and introduces uncertainty into the underlying system. The instructor is responsible for developing the simulation model and determining its operations, constraints, and processing components. According to Clark et al. [29], the nature of the phenomenon being modeled can be classified into behavior-based, emergent, aggregate, and composite ones. Behavior-based models allow users to assign and modify behaviors or systemic constraints. Emergent models enable interaction with numerous individual agents that lead to an emergent phenomenon. Aggregate models permit user interference with the simulation model, enhancing user engagement. Composite models are utilized for training users in complex tasks, requiring the integration of various data sources to simulate the activities involved in a complicated process for scientific instruction.

2.5. Management of Information

The management of information encompasses the presentation of information, its flow, and user access within the application environment. The way information is represented can significantly influence the game’s objectives. Researchers, such as Plass et al. [32], suggest that applications with more realistic representations are generally more effective for teaching conceptual attributes and do not distract the user from the cognitive process. The information flow, or narrative, is a critical element in the development of a simulation game, as it significantly boosts user engagement. Wilson et al. [33] note that user motivation is positively correlated with the level of mystery in a narrative-driven simulation game. Additionally, information accessibility is a crucial consideration in game design. The purposes of the simulation game, the target audience, the nature of the information, and the user’s response all determine the adaptive features that facilitate the cognitive process.

2.6. Degree of System Unpredictability

Uncertainty can be deliberately introduced into the system operations to affect the system behavior. This unpredictability may relate to the selected scenario or the uncertainty of the simulation model (accuracy of information). For example, dissimilar scenarios might be used with identical structures, or the data may vary due to model uncertainty. Stochastic simulation games offer additional benefits for both instructors and learners. They generate multiple scenarios with varied outcomes. By repeating the simulation game, learners encounter problems that require different treatments. Stochastic simulation games facilitate the analysis of different solutions to a problem, acclimatize users to the uncertainty of the real world, and enhance their conceptual understanding of the process being managed.

2.7. Simulation Game Reward System

The reward system in educational simulation games shares common structures with other games and may include one or more of the following forms: scoring systems, feedback messages, access systems, user experience level systems, and competitive reward systems. The scoring system evaluates learner performance based on their actions within a problem, useful for self-assessment and comparison. Feedback messages provide explanations of scores based on user responses, thereby enhancing knowledge. The access system rewards successful learner actions by allowing users to unlock specific features or access additional information, supporting the principle of ongoing learning [34]. The user experience level system rewards learners for multiple runs of the simulation game, reflecting their time and effort. It provides an overall evaluation of user performance across multiple scenarios. The competitive reward system allows scores to be compared with those of other users and motivates users to return to the game to improve their performance. Tynan [35] discusses these motivation reward systems in games more analytically, focusing on the psychological aspects of the rewards. Moreover, the design of the simulation incorporates motivational elements—such as scoring systems and competition—grounded in gamification theory and principles of self-regulated learning (e.g., [36,37]), which are known to enhance learner engagement and persistence in educational settings.

2.8. Learner Tracking System

The learner tracking system is a vital tool for instructors, tasked with measuring, understanding, and controlling learner experiences. Analyzing learner experiences helps to address critical issues in user actions, select appropriate teaching materials, and identify game weaknesses. Studying learners’ actions, behaviors, and performance aids in enhancing the effectiveness of the simulation game and boosting retention. For remote simulation games, the learner tracking system requires a collection server to transmit data on user-game interactions.

3. Material and Methods

3.1. Design-Based Research Framework

This study follows a Design-Based Research (DBR) methodology [38,39] to guide the iterative development and educational evaluation of the simulation game for mineral exploration. DBR is well suited to educational innovation in authentic contexts, aiming to bridge the gap between theory and practice by refining both the designed artifact and the underlying pedagogy through systematic, real-world testing. The DBR process in this study followed three main phases: (1) Problem Analysis, (2) Iterative Design and Implementation, and (3) Evaluation and Refinement.

In the first phase, we identified key challenges in geoscience education, such as the limited integration of uncertainty and probabilistic reasoning in curricula, restricted fieldwork opportunities, and insufficient development of spatial thinking skills. These findings informed us of the initial design principles of the simulation game.

During the second phase, the game was developed and, since 2018, integrated into the “Mineral Exploration” course, an obligatory part of the fifth semester in the 5-year Master’s program at the School of Mining and Metallurgical Engineering at the National Technical University of Athens (NTUA). The course spans 13 weeks, involves five contact hours per week, and culminates in examinations. It covers topics such as the nature and phases of mineral exploration, uncertainty, and economic risk assessment, statistical methods for risk estimation, geological models of mineral deposits, geophysical methods, exploratory drilling, ore reserves estimation, feasibility studies, and the exploitation of recoverable reserves. Since then, the game has undergone multiple iterations based on informal student feedback, classroom observations, and evolving pedagogical objectives. Key features—including probabilistic reasoning using Bayes’ Theorem, spatial strategy for drilling and exploitation, and economic decision-making through cost–benefit analysis—were progressively implemented and refined.

In the final phase, to assess the game’s broader educational impact, we organized structured workshops involving 190 undergraduate and postgraduate participants from three institutions. These sessions included guided gameplay, system-logged usage data, and structured group discussions to capture engagement, perceptions, and qualitative feedback. This data was analyzed to evaluate the game’s alignment with its learning objectives and to inform future improvements.

3.2. Game Design Rationale and Pedagogical Alignment

To ensure the educational effectiveness of the simulation game, its design is based on the established game and learning theory frameworks. In particular, the Mechanics–Dynamics–Aesthetics (MDA) model [40] and the Learning Mechanics–Game Mechanics (LM–GM) framework [41] were used to align gameplay elements with targeted learning objectives.

The game mechanics—such as probabilistic input using Bayes’ Theorem, spatially strategic drilling, randomization of deposit location, and cost-based decision-making—represent the “mechanics” layer of the MDA framework. These elements initiate and enhance learner interactions (dynamics), which include probabilistic reasoning, planning under uncertainty, and managing limited budgets. In addition, the game offers challenges, surprises, and feedback, reinforcing learner motivation and engagement. Simultaneously, the LM–GM framework was used to align these mechanics with learning mechanics such as experimentation, reflection, and decision-making.

3.3. Simulation Game Objectives

“A Mineral Adventure” is the title of the simulation game demonstrated. As the title suggests, the application immerses users in the field of mineral exploration, enticing them with a playful approach.

The simulation game was developed within the framework of the research program “VirtualMine: A Modeling Tool for Wider Society Learning,” sponsored by the European Institute of Innovation and Technology (EIT), Raw Materials Hub—Regional Center Greece, in 2018. It is available online at www.geostatistics.eu/exercise.html (accessed on 30 September 2025). The aim of the game is to enhance learning by providing an enjoyable learning experience. More specifically, the main objectives are to acquaint students with basic concepts of mineral research, motivate students to develop and test their intuition, and illustrate the challenges of making decisions under uncertainty.

The surrounding framework of the game provides essential evidence to guide players toward achieving a high-performance score, even without specific prior knowledge. However, a high-performance score can be achieved in a single run if the player is able to:

• Remember and understand the principles of a geophysical survey and the concept of the reliability of survey results.
• Apply Bayes’ Theorem to estimate the posterior probability of an orebody’s existence based on prior data and new evidence.
• Evaluate the available data to make informed and strategic decisions regarding the initiation of a drilling campaign.
• Understand and analyze the uncertainty associated with estimating ore reserves and the implications for decision-making.
• Analyze and apply knowledge of cut-off grade to identify which blocks are economically viable for exploitation.
• Apply and evaluate cost and market parameters to estimate an accurate selling price of the extracted ore.
• Understand and analyze the relationship between profit generation and the processes involved in mining and mineral exploration.

3.4. Simulation Game Description

The game begins by outlining the basic concepts of the problem and explaining the user’s goal. Players take on the role of a geoscientist manager tasked with overseeing mineral research activities, starting from preliminary research and potentially leading to the exploitation of an ore body. The primary objective is to maximize profit from the sale of mined ore, adhering to fundamental principles of mineral research. Players are tasked with exploring a designated area measuring 1500 × 1500 m, believed to contain an economically valuable ore body. This area is segmented into 225 blocks, each measuring 100 × 100 m. The a priori probability that an ore body exists in this area is set at 55%, with its expected size approximated to cover about 5 × 5 blocks.

The learner must first decide whether to conduct a geophysical survey of the subsurface, which offers an 80% reliability rate. The outcome of this geophysical exploration can either be positive or negative. Based on these results, the learner then decides whether to proceed to the main exploration campaign or to abandon the process. If opting to continue, the learner must secure funding for exploration drilling. The amount of funding available, and consequently the number of drillholes that can be conducted, is determined by calculating the probability of discovering an ore body, which is based on the results of the geophysical survey and analyzed using Bayes’ Theorem.

The game interface does not provide the Bayesian expression, as understanding and applying it is considered prerequisite knowledge to be assessed through gameplay. However, for clarity, we present below the analytical expressions used to calculate the posterior probability in the cases of both positive and negative geophysical survey results.

i. Case of Positive Geophysical Result:

$$\begin{gathered} P\left(ore body|positive\right) \\ ={\displaystyle \frac{ P\left(positive|ore body\right)P\left(ore body\right)}{P\left(positive|ore body\right) P\left(ore body\right) + P\left(positive|no ore body\right) P\left(no ore body\right)}} \end{gathered}$$

ii. Case of Negative Geophysical Result:

$$\begin{gathered} P\left(ore body|negative\right) \\ ={\displaystyle \frac{ P\left(negative|ore body\right)P\left(ore body\right)}{P\left(negative|ore body\right) P\left(ore body\right) + P\left(negative|no ore body\right) P\left(no ore body\right)}} \end{gathered}$$

The game interface includes a visual explanation (Figure 1) and an interactive calculator (Figure 2) to guide the user through this process. If the user’s calculations are wrong, he/she will receive a penalty reflected in the form of reduced funding.

Next, the user is asked whether they would like to conduct a drilling campaign. Drilling is conducted in a predetermined square grid pattern, with one borehole placed at the center of each block. After the drilling campaign is performed, the grade of the useful ingredient detected is applied to the entire block (Figure 3). The exploration campaign concludes with an estimate of the ore reserves (Figure 4). The learner can calculate the grade of a block as the average of the samples from the first aureole of blocks surrounding it. This calculation is automatically performed by selecting a specific block (Figure 2).

After the exploration campaign, if the learner believes that the results are encouraging, they have the option to proceed with the exploitation of the field. If this path is chosen, the feasibility study reveals that the cut-off grade of the useful ingredient is 10%, the selling price of the ore is €200 per ton, its specific weight is 2 g/cm³, and the extraction cost for each block is estimated at €500,000. By selecting a block, the learner automatically proceeds to extract it and then dispatches it for further processing and sale if its actual grade exceeds the marginal value; otherwise, it is deposited in the pile of barren (Figure 5). The learner can mine up to all the blocks available to them but must also monitor their current balance. They can cease mining at any time, which allows them to be informed about their performance score. The final score is calculated based on the profit from the sale of the ore mined, compared to the optimal potential profit. Consideration is also given to whether they have adhered to the principles governing mineral research (Figure 6).

3.5. Simulation Game Implementation

The simulation game was developed using the JavaScript programming language and is rendered on HTML5. PHP is used to connect to an SQL database, enabling the storage of user actions and displaying the results of competitive users. This programming environment allows the game to operate effectively in web browsers with only basic network accessibility required. To introduce the necessary unpredictability, a scenario is randomly selected at the start of each game session. Ten different scenarios represent the spatial distribution of the ore body in the research area, each with varying ore quality. In four of these scenarios, no ore body is present. All scenarios have an equal probability of occurrence, ensuring a new scenario is used each time the game is repeated. The results from geophysical research incorporate a degree of randomness to reflect both the presence of an ore body and the reliability of the findings.

The application features a graphical user interface inspired by a treasure hunt theme, complete with papyrus and flaming torch visuals. Sound effects enhance user engagement, with specific sounds assigned to actions like button presses, the flaming torches, the geophysical research process, drilling, ore reserve estimation, and the exploitation process. The final score is also accompanied by suitable emotional sound effects. Interactive animations highlight the geophysical research and the extraction of blocks, whether they contain ore or not. The user interface is designed to maintain a level of mystery, aligning the application with serious gaming criteria. It includes step-by-step guidance through visual and textual clues available in a popup sidebar, which also houses a calculator for computing the required a posteriori probability (Figure 2).

The application rewards the user’s successful determination of the a posteriori probability of orebody existence by enabling the conduct of additional boreholes, while an incorrect calculation does not affect the final user scoring. The final score is determined by the profit from the sale of the ore they have mined, relative to the best outcome they could have achieved in the given scenario and the particular outcome of the geophysics campaign, which might be right or wrong. Consideration is also given to whether the user has followed the principles governing mineral exploration in general. Additionally, a list with the top seven users’ scores, along with names, is displayed, and the user can save their scoring performance.

The use of the online database enables the collection of general information about players such as the time of the day and the date of playing, the user’s location, the user’s device, the length of time spent on the game, the unique visits to the game, and the frequency of a user’s return visits to the game. Furthermore, the system saves the player’s actions, the performance, and the optimal outcome for the current scenario. At the end of the game, the player may record their score with their name on a leaderboard, which is displayed after the player’s evaluation and showcases the top seven scores in the simulation game.

3.6. Methodological Approach for Evaluation

As previously mentioned, the Mineral Exploration course is an obligatory part of the fifth semester in the 5-year Master’s program at the School of Mining and Metallurgical Engineering, NTUA. The simulation game has been integrated into this course since 2018 as an additional education tool to support active learning, promote student engagement, and enhance understanding of mineral exploration concepts through experiential gameplay.

According to DBR methodology adopted in this study, the evaluation phase aimed to assess the educational impact of the simulation game in both curricular and broader educational contexts. While DBR often incorporates flexible and iterative evaluation strategies, our approach emphasized authentic settings and real-time learner interaction to derive feedback and guide ongoing refinement.

To evaluate its broader educational impact, a series of workshops were conducted between 2021 and 2024 with undergraduate and postgraduate participants with a geoscience background from three institutions: NTUA (80 participants), Aristotle University of Thessaloniki (45 participants), and the University of Western Macedonia (65 participants), totaling 190 participants.

The evaluation data consisted of three main sources. First, system-generated analytics (e.g., number of sessions played, frequency of replays, save rates) were collected and analyzed to infer engagement patterns. Second, the game scores achieved by participants were also examined as a measure of learning progression. Since the game score reflects a combination of correct probabilistic reasoning, spatial exploration strategy, and informed economic decision-making, it was interpreted as an indirect indicator of cognitive gains in spatial thinking and understanding of uncertainty frameworks. Third, qualitative feedback was gathered through structured group discussions conducted during the final session of each workshop. These discussions focused on perceived learning value, interface usability, challenge balance, and opportunities for improvement. Based on this approach, participant responses were categorized using basic thematic coding, such as usability, engagement, conceptual understanding, and suggestions for improvement.

While this evaluation did not employ standardized survey instruments or comparison groups, it is consistent with DBR’s focus on practical relevance, iterative development, and user-centered refinement. A key limitation of this study is the absence of a control or comparison group. Although our design-based approach emphasizes authentic educational contexts, future research will include comparative studies with students receiving traditional instruction alone. This will help isolate the game’s educational impact and better assess its added value. The results of this evaluation are discussed in the following section.

4. Results

Based on the methodology outlined in the previous section, the workshops were structured into three key sessions designed to maximize participant engagement and feedback. The first session included speeches by guest tutors concerning the risks in mineral exploration. Next, the tutor ran the simulation game demonstrating its features and operations. In the third session, we discussed the strengths, weaknesses, and opportunities of the simulation game. During the last session, the participants ran the game themselves on their mobile devices. Visits to the game were high, while participants played the game multiple times. In the following analysis, we focus on the participants’ opinions and the attributes we should improve as derived from the discussion session of the workshops.

The participants agreed that the application meets the educational objectives by offering an engaging and enjoyable experience. The high participation and multiple runs of the game confirm the application’s attractiveness. Based on the database of the game, 72.63% of the participants played the game, while 88.40% of them reentered the game and 60.14% saved their score. Only 11.59% played the game once, 36.23% twice, and 52.17% more than three times (Figure 7).

In addition to engagement metrics, players score progression was analyzed as an indirect indicator of learning gains. The game’s scoring system reflects performance in probabilistic reasoning, spatial strategy, and exploitation decisions. Among participants who played the game more than once and saved their scores, the average score increased from 72.4 on the first attempt to 94.5 on the final recorded attempt—an improvement of approximately 30.5%, regardless of how many times each user attempted to improve their performance.

Notably, many participants replayed the game with the specific goal of achieving a higher score and being listed among the top scorers. This is supported by system data indicating that 88.40% of users returned to the game after their initial session and that 52.17% played more than three times, suggesting sustained engagement and motivation to improve performance. This behavior aligns with research in educational game design, which highlights that scoring systems and competitive elements can significantly boost motivation, persistence, and self-regulated learning. Importantly, achieving a high score in the game is not easily accomplished through guessing or repeated random attempts. While spatial reasoning is a critical component, particularly in determining effective drilling strategies, players must also assess the economic viability of their decisions to succeed. For instance, it is not sufficient to locate ore-bearing blocks, but learners must evaluate whether the expected grade justifies exploitation, taking into account the fixed extraction cost and the defined cut-off grade. This layered decision-making process reflects real-world complexities and requires a deeper conceptual understanding of geological uncertainty and economic trade-offs. Therefore, score improvements observed over multiple sessions are likely to indicate not merely growing familiarity with the game mechanics, but a deeper comprehension of the spatial-economic reasoning essential for successful mineral exploration.

Thematic analysis of participant discussions revealed three dominant categories: engagement and motivation, conceptual understanding of uncertainty, and recommendations for increased realism and interactivity. First, in terms of engagement, participants highlighted the importance of visual and sound effects, expressing their enthusiasm. Players noted the usefulness of the popup sidebar that holds the most critical information of the game. A motivating challenge was the competitive reward system, as they returned to the game to increase their score and to be listed on the leaderboard with the top seven players. Server data indicates that participants continued to play the simulation game a few days after the workshop, confirming the importance of remote access to the simulation game.

Regarding the conceptual understanding of uncertainty, a strong feature of the game is the incorporation of randomness, which increases the game’s difficulty, motivating players and familiarizing them with the uncertainty of natural phenomena.

Third, participants identified areas for improvement, particularly the simplification of the 2D spatial interface. For instance, a 3D representation would be more suitable for accurately depicting real-world geological complexity and enhancing spatial reasoning. Participants also suggested incorporating a more realistic framework in the exploitation phase by including detailed procedures and increasing learner involvement. Additionally, they proposed adding a countdown timer to enhance player interest and create psychological pressure. A communication form with developers was also recommended to facilitate the incorporation of future suggestions from learners.

5. Discussion and Conclusions

The presented simulation game effectively serves the objectives of PBL and acquaints students with fundamental concepts in mineral exploration. It allows them to apply their understanding of mineral exploration, recognize knowledge gaps, and enjoy the learning process. Through virtual scenarios, students adopt a scientific approach, acting as geoscientist managers directing activities from preliminary research to potential ore body exploitation. Engaging with the game, students not only identify key concepts of mineral exploration and their roles in the exploitation process but also develop mining strategies without actual economic risk, becoming familiar with the costs associated with situational awareness.

This study followed a DBR methodology to guide iterative development, implementation, and evaluation of the simulation game. This framework ensured that the game design remained grounded in authentic educational challenges while being refined through real-world classroom use. To support the pedagogical alignment of game features, the MDA and the LM–GM framework were adopted. These models helped map core mechanics, such as probabilistic reasoning, decision-making under uncertainty, and spatial inference—to targeted cognitive outcomes, ensuring the game’s components served clear educational purposes.

The simulation game addresses a key pedagogical gap in geoscience education: It directly addresses the inherently spatial and uncertain nature of geological processes, an aspect often underrepresented in undergraduate curricula, despite its significant impact on data reliability and decision-making [24]. Lastly, it actively cultivates spatial thinking skills by requiring learners to infer the probable location and extent of the ore body—encouraging them to strategically space drillholes during the exploration phase and to refine their search by targeting adjacent blocks during the exploitation phase. These spatial reasoning skills are essential in geoscience but typically show only limited development over the course of a single academic term [26].

One of the most significant outcomes observed was the motivational effect of the scoring and leaderboard system. According to the game’s database, a substantial majority of participants replayed the simulation, with over 52% playing more than three times. Notably, many did so with the explicit goal of achieving a higher score and securing a top ranking. This behavior aligns with findings in educational game design literature, which emphasize that competitive elements can drive motivation, persistence, and self-regulated learning. These findings align with the principles of Self-Determination Theory [36], which emphasize intrinsic motivation, autonomy, and competence as key drivers of learning. The leaderboard and scoring system supported these drivers by offering feedback, challenge, and a sense of achievement. Moreover, competitive replay behavior observed among participants is consistent with gamification literature (e.g., [37]), suggesting that such features not only promote engagement but also encourage reflective and self-regulated learning.

Based on our experience of the workshops, we are considering several enhancements. These include incorporating more scenarios and the possibility for players to sign up before entering the game. The need for more scenarios arises from the high rate of returning players and could be easily addressed by using random values for ore body reserves or changing their locations. Random values from specific distributions could also be used for the selling price of the ore and the reliability of geophysical research.

Introducing a registration form would provide benefits for both tutors and players. For instance, the player’s username could be linked to their university ID number, which would include personal information such as name and performance in other courses. This would help solve the issue of multiple scores with the same usernames on the leaderboard and allow scores to be linked to individual profiles, thus enhancing the organization of game analytics and improving the evaluation process. Players could review their profiles, track their performance, and even share their scores on social networks like LinkedIn, adding a social component to the game experience.

Further research is necessary to evaluate the educational effectiveness of the simulation game more comprehensively. While this evaluation yielded useful insights through gameplay analytics and participant discussion, future studies would benefit from the use of structured survey instruments, learning assessments, and pre/post-testing to validate learning outcomes more systematically. These evaluation approaches will help validate whether participants have comprehended key concepts related to probabilistic reasoning, spatial thinking, and economic trade-offs, rather than relying on trial-and-error strategies alone. This is particularly important given that score progression, while indicative of engagement, does not necessarily equate to conceptual understanding.

The underlying design principles of the game—such as probabilistic reasoning, spatial decision-making, and uncertainty management—are not limited to mineral exploration. These could be adapted for use in other geoscience domains, such as hydro-geology or environmental risk assessment, and even to other disciplines where spatial economic trade-offs are involved.

Given its web-based architecture, free availability, and minimal technological requirements, the simulation game holds strong potential for integration into both traditional and distance learning programs, across undergraduate and postgraduate curricula in higher education institutions. These insights have important implications for instructional design and educational policy. Institutions aiming to integrate simulation-based learning into STEM curricula should consider incorporating motivational features—such as scoring systems, leaderboards, and real-world decision-making scenarios—to enhance student engagement and persistence. Moreover, accessible and gamified tools like this simulation can support lifelong learning and professional development, particularly in distance or hybrid learning environments.