1. Introduction
1.1. The Challenges of Current Engineering Education: Theory to Practice
Current engineering education is under growing pressure to adapt academic content and pedagogical approaches to meet the complex and evolving demands of professional practice and industry (Hsu et al., 2025). While most engineering curricula provide strong technical foundations, students often struggle to apply this knowledge to real-world scenarios (Baltà-Salvador et al., 2021). Consequently, a persistent gap is found between academic preparation and professional expectations for engineering graduates (Bae et al., 2022).
This gap is especially significant since real-world problems demand interdisciplinary thinking and holistic problem-solving. While technical knowledge is foundational, employers increasingly prioritize transversal competencies such as leadership, initiative, teamwork, and adaptability (Bae et al., 2022; Frank et al., 2003; Hadgraft & Kolmos, 2020; Michavila et al., 2018; Pedraja Iglesias et al., 2006). These skills are often underemphasized by traditional curricula and teaching methodologies. As concluded in the study carried out by Michavila et al. (2018), widespread deficiencies can be found in leadership and decision-making among graduates. Furthermore, some studies revealed that students frequently fail to perceive the relevance of the contents of courses for real-world engineering, resulting in disengagement and a lack of interest (Baillie et al., 2006).
Given this context, at university level, a key challenge lies in the limited integration between subjects within the curriculum. This results in fragmented learning and a lack of interdisciplinary understanding (Hadgraft & Kolmos, 2020). Hence, students may be highly trained in individual skills—such as stress analysis, welding design, or material selection; however, they are not capable of combining them to solve integrated engineering problems (Wang et al., 2025).
This disconnection leads to significant difficulties when students transition into the workplace, where decision-making, problem-solving, and cross-disciplinary competencies are essential (Vadakalu Elumalai et al., 2020). Research has shown that students frequently experience anxiety (Asgari et al., 2021) and reported low confidence when facing professional responsibilities (Dore & Richards, 2024). Addressing these limitations requires pedagogical strategies that could emphasize applied knowledge, integration across different areas of knowledge, and exposure to real-world complexity (Hsu et al., 2025).
1.2. Fragmentation in Engineering Curricula
Engineering degree programs are often structured in areas of knowledge with their corresponding subjects. While each subject delivers important technical content, the lack of coordination between instructors and course objectives contributes to students’ difficulties in synthesizing knowledge (Hadgraft & Kolmos, 2020). For instance, in the subject of ‘Materials Resistance’, students may learn to perform mechanical calculations related to stress or deformation. However, such exercises often ignore external conditions like corrosion, temperature variation, and environmental exposure. These factors or conditions are addressed in other courses of the curricula such as Materials Science. As a result, students may design a structural beam by correctly applying stress formulas but fail to consider the material suitability for a saline or humid environment. Similarly, students may learn how to calculate welding stress in one course, study welding processes in another, and examine material properties in a third. However, they will not be able to synthesize these elements into a comprehensive and unique engineering solution. These subjects are commonly taught without explicit connections, limiting students’ ability to transfer knowledge and develop cross-disciplinary competence (Raju et al., 2000). Even in well-integrated domains like Mathematics and Physics, students may not fully appreciate the relevance of foundational knowledge to real design tasks (Hsu et al., 2025).
This structural fragmentation contributes to what has been termed ‘inert knowledge’ (Magana et al., 2016). This is a phenomenon in which learners can recall theoretical concepts but are unable to apply them in new or practical contexts. In other words, a type of knowledge based on knowing-about without the knowing-to. Even when students successfully master subject-specific content, they may lack the ability to think holistically or evaluate skills. These are crucial abilities in engineering design and decision-making (Wang et al., 2025).
1.3. Toward Integrated and Practice-Based Learning
To overcome these limitations, educational research has increasingly endorsed active, integrated, and practice-based methodologies (Cantero-Chinchilla et al., 2020; Felszeghy et al., 2019; Huerta-Gomez-Merodio et al., 2024; Jamieson & Shaw, 2020). Approaches such as problem-based learning (PBL), experiential learning, and the CDIO (Conceive, Design, Implement, Operate) framework have proven effective in promoting interdisciplinary thinking, teamwork, and contextualized knowledge application (Gamarra et al., 2022; Lavado-Anguera et al., 2024). The experiential learning theory presented in Kolb (2015) suggested that knowledge arises from cycles of concrete experience, reflection, conceptualization, and active experimentation. This approach was widely adopted in engineering education. In fact, in engineering teaching, hands-on design tasks are used to allow students to connect theory with practical results. As a result, this approach, which is grounded in active experimentation, effectively demonstrates to students how professional practice operates (Felder et al., 2000; Wankat & Oreovicz, 2015).
The CDIO initiative improves this model by embedding learning within the full cycle of engineering practice. The CDIO’s curriculum standards support the integration of disciplinary knowledge with professional competencies, including teamwork, communication, and ethical responsibility (Crawley et al., 2014).
Studies have also shown that teaching methodologies based on interdisciplinary projects can enhance both motivation and knowledge retention. This can be achieved by requiring students to make connections between subjects and to understand their relevance in applied contexts (Duch et al., 2001; Pedraja Iglesias et al., 2006; Raju et al., 2000). Embedding learning within real design challenges was associated with improved problem-solving skills, deeper conceptual understanding, and the development of transferable competencies (Kolmos et al., 2024; Michavila et al., 2018; Moreno Ruiz et al., 2019).
1.4. Scope and Contribution of This Work
This paper presents a methodology designed to address the challenges outlined above through a cross-curricular and practice-oriented approach using digital tools. It has been implemented in the degree in Industrial Design and Product Development Engineering at the University of Cádiz, (Spain). Students have engaged in a real-world engineering task: designing an outdoor clothesline system resistant to environmental exposure and mechanical stress, while considering economic and manufacturing limitations. This has been applied taking into account the concepts from several engineering subjects of the engineering curricula.
To do so, the methodology has incorporated digital platforms such as MILAGE LEARN+ (MILAGE, 2025) (a gamified blended learning tool using mobile devices) and MindMeister (Meister, 2025) (a concept mapping application). They have been used to enhance students’ understanding and retention of key theoretical concepts through interactive explanations, direct feedback, and opportunities to visualize and apply formal knowledge in practical projects. These tools also facilitate the development of autonomous learning skills. The primary objectives of this study are to
Facilitate interdisciplinary learning by integrating multiple subjects into a single project. This is addressed in Section 2.2 via the integration of multiple engineering subjects within realistic project contexts.
Enhance motivation through context-rich and realistic problem-solving. This is developed across Section 2.1, Section 2.2, Section 2.3 and Section 2.4, highlighting constructivist and experiential learning approaches, supported by gamified and project-based methodologies.
Promote autonomy, reflection, and conceptual integration using digital tools. This is detailed in Section 2.3 through the usage of MILAGE LEARN+ and MindMeister for self-paced learning and collaborative concept mapping.
Foster the development of transversal skills such as teamwork, initiative, and communication. This is embedded within the pedagogical framework in Section 2.1 and the interdisciplinary projects and presentations.
Evaluate the educational impact through student surveys, feedback, and engagement with industry. This is covered in Section 2.5 and Section 3, including survey design, administration, and outcomes alongside feedback from internship supervisors.
The novelty of this work lies in the combination of PBL, cross-subject integration, and blended digital learning. The results provide insights into how engineering programs can better prepare students for professional challenges by aligning classroom instruction with workplace expectations.
The present paper is structured as follows: Section 2 details the methodology and the structure of the intervention. The pedagogical design and theoretical key points of the intervention are discussed across Section 2.1, Section 2.2, Section 2.3 and Section 2.4 while the impact assessment is discussed in a dedicated Section 2.5. Section 3 presents the results of implementing the method. Section 4 discusses the implications and limitations of this work. Section 5 shows the conclusions and key points of this study.
2. Methodology and Structure of the Intervention
This section details the design and implementation of a cross-curricular, practice-oriented methodology using digital tools. The goal is to enhance the engineering students’ ability to integrate disciplinary knowledge, to apply theory to practical challenges, and to develop professional competencies. To assess the methodology, it has been implemented in the degree in Industrial Design and Product Development Engineering at the University of Cádiz.
To ensure a clear distinction between the design of the pedagogical innovation and the methodological approach used to assess its impact, this section is divided into two main parts. The first sections (Section 2.1, Section 2.2, Section 2.3 and Section 2.4) present the pedagogical framework, the learning model, the tools, and the case study that form the basis of the cross-curricular, practice-oriented intervention. These subsections detail the rationale and design of the methodology, grounded in educational theory and aligned with the learning objectives described earlier. The second part (Section 2.5) focuses on the evaluation of the methodology. It describes the participants, the assessment instruments, and the data sources used to analyze the effectiveness of the intervention.
To enhance the industry relevance of the intervention, close collaboration has been established with local engineering companies. These partners have contributed to the formulation of realistic design challenges, aligning the curriculum with current professional practices and employer expectations. Their ongoing engagement has extended to providing feedback on student internship performance and identifying critical competencies required in early-career engineers. This partnership can not only enrich the pedagogical approach but also facilitate improved employability outcomes and strengthened university–industry relations.
The main phases of the methodology proposed in this work are presented in Figure 1. These correspond to each of the subsections of this section and they will be described below.
2.1. Pedagogical Framework
The pedagogical framework of the methodology is based on the contemporary educational theories presented in the review of the literature. These are focused on prioritizing active, contextualized, and integrative learning as suggested in Caballé et al. (2014). Hence, the main points of the methodological framework of this work are as follows: Experiential learning (Kolb, 2015). This model understands learning as a cyclical process involving experience, reflective observation, abstract conceptualization, and active experimentation. This type of learning has been applied to this study to engage students with real-world tasks. These tasks required students to apply, to test, and to refine conceptual knowledge through practice. Constructivism and PBL. Based on constructivist principles (Piaget, 1950), students are considered as active agents in their learning. Hence, through this methodology, they build knowledge by engaging with open-ended and multidisciplinary problems. CDIO framework. The methodology is aligned with the CDIO principles (Crawley et al., 2014), requiring students to define problems, design and evaluate solutions, justify decisions, and present outcomes. This approach closely clones professional engineering workflows and supports the development of both technical and transversal skills.
2.2. Integrated Learning Model
As pointed out in the review of the literature, traditional learning for university courses often divides knowledge into different subjects. Consequently, students suffer from not connecting concepts across them. To overcome this limitation, the methodology presented here integrates diverse disciplines into a single and real engineering project. Different subjects have been considered when designing the project to identify overlapping concepts and align learning objectives. These were Resistance of Materials, Materials Science, Materials for Design, and Computer-Aided Design. The case studies have focused on the design, calculation, and material selection for real engineering products, such as a drying rack, a streetlamp, and a playground swing.
2.3. Digital Pedagogy and Gamified Learning Tools
The digital tools used in this methodology have been deliberately selected to support the development of interdisciplinary knowledge, autonomy, and student engagement. Two platforms—MILAGE LEARN+ and MindMeister—have been central to the pedagogical strategy, with more details as follows: MILAGE LEARN+ has been used to reinforce theoretical content through gamified learning experiences. The application allowed students to navigate content by levels of difficulty (basic, intermediate, and advanced), allowing students to engage with materials at their own pace and build competence gradually. Each activity included instant feedback upon submission, helping students to monitor and correct their understanding in real time. A peer evaluation feature required students to assess the work of classmates, encouraging reflection and critical analysis. The completion of tasks awarded students with progress indicators and gamified rewards, such as badges or scores, to further enhance motivation. Additionally, the autonomy to select topics and levels facilitates differentiated and self-directed learning. These gamification principles combine to create a dynamic, engaging environment that transforms passive study into an active, motivating process. They were used in several studies to help students in the study of several courses at university level (Dorin et al., 2024; Figueiredo et al., 2020; Fonseca et al., 2021). In this case, different chapters/units and subchapters have been created which correspond to the theoretical contents presented as videos and presentations. Students must answer different questionaries and evaluate the work of their colleagues. In Figure 2, the initial screen that students will find is presented. Here, they can choose the content they will work on. In Figure 3, an example of a problem is presented in which the student uploads their solution and, after, the correct solution is shown. MindMeister, a collaborative concept mapping tool, has been integrated as an essential element of the cross-curricular project. Students have used this tool to visually map relationships between key theoretical concepts—such as material properties, design constraints, loading conditions, and mechanical stresses—across the disciplines involved. This visual organization helped students to synthesize and integrate knowledge from Resistance of Materials, Materials Science, Materials for Design, and Computer-Aided Design into a coherent, shared understanding of the engineering challenge. By collaboratively constructing and iteratively refining these concept maps, students have engaged in metacognitive reflection on their learning paths, identified knowledge gaps, and fostered autonomous knowledge integration. The shared nature of the maps has facilitated team-based dialog among students, enhancing communication skills and collaborative problem-solving. This activity has also simulated professional engineering practices, emphasizing the importance of conceptual clarity, interdisciplinary integration, and team coordination. Through MindMeister, students have developed higher-order cognitive skills and were better prepared for the complexities of real-world engineering work (Nitchot & Gilbert, 2025). In Figure 4, a partial view of the interactive website with the subjects and concept relation developed with Mindmeister is shown.
2.4. Case Study
The case studies have focused on the design, calculation, and material selection for real engineering products, such as a drying rack, a streetlamp, and a playground swing. These cases have been designed to simulate professional engineering scenarios, allowing students to apply their knowledge to tangible products and understand the role of a junior engineer.
One representative example has centered on the development of an outdoor clothesline system. It must be capable of withstanding exposure to saline and rainy environments and delivering a minimum operating lifespan of ten years. This scenario has been deliberately selected for its relevance and openness—it required students to handle structural calculations, select appropriate materials, analyze costs, consider issues of environmental resistance, and address user needs. The statement presented to students was as follows:
‘The company ‘The Hanging Blanket’, specializing in household items, has launched its new range of wall-mounted clotheslines, designed for balconies (minimum 1.5 m wide and 2 m long) and small rooftops. These products must achieve a minimum lifespan of ten years in saline and rainy environments. Design a clothesline system, calculate its structural elements and justify your design decisions. Estimate the approximate cost of the product’.
This task has integrated content from four core engineering subjects—Resistance of Materials, Materials Science, Materials for Design, and Computer-Aided Design—and has encouraged collaboration across disciplines and instructors. Students have been required to apply theoretical knowledge in structural analysis, material behavior, mechanical performance, and 3D modeling within a unified design solution. Furthermore, this activity could also be extended to include other subjects such as Design Methodology or Mechanisms and Machines, enhancing the cross-disciplinary relevance.
The product images used in the task are shown in Figure 5, illustrating the functional stages of the system and one of the structural failures previously observed in real-world use. These visuals have been essential to contextualize the design problem, highlighting the need for robust material choices and structural reliability.
A summary of the subject-specific contributions is provided in Table 1. In this table, it is outlined how the concepts from each course area have been applied to address the design requirements. In this case, the need to consider technical, economic, and aesthetic factors has been emphasized. Also, students have been required to weigh trade-offs and justify choices based on durability, manufacturability, cost-effectiveness, and environmental performance. The final stage of the project has included oral sessions simulating real-world pitches, helping students develop public speaking, persuasion, and critical self-assessment skills.
2.5. Evaluation Strategy
To assess the effectiveness of the proposed methodology, a multi-faceted evaluation strategy has been implemented focusing on students’ learning outcomes, self-confidence, and preparedness for professional practice. The evaluation has combined quantitative and qualitative methods and it has incorporated several sources of data for triangulation and validity.
This study involved 73 students enrolled in the degree in Industrial Design and Product Development Engineering at University of Cádiz. Participants have been primarily from a single specialization, with an age range of approximately 19 to 25 years (mean = 21 years). The sample included 75% male and 25% female students. Most participants were in their second year of study. Prior project experience varied, with approximately 8% reporting involvement in engineering design or collaborative projects before this course. Many of the students went on to complete internships after the academic period. Internship outcomes were included as one data source in the assessment.
The evaluation strategy included the following components: Structured surveys. Pre- and post-experience surveys have been filled out by the students. These have been designed by the instructional team based on the prior literature in engineering education and employability studies (Bae et al., 2022; Kolb, 2015; Michavila et al., 2018). No pre-existing standardized instrument fully addressed the specific context of interdisciplinary integration and real-world preparedness. Therefore, the survey was constructed to target constructs such as perceived content relevance, self-confidence in professional contexts, anxiety when facing open-ended problems, and self-directed learning capacity. Internal consistency reliability was assessed via Cronbach’s alpha, with values exceeding the accepted threshold of 0.70. The survey included Likert-type items using a 5-point scale (1 = strongly disagree to 5 = strongly agree) and yes/no questions for categorical items. The same set of questions was used before and after the intervention to allow for a direct comparison. These questions covered areas such as ○. ‘I feel confident applying theoretical knowledge to real-world engineering tasks.’ ○. ‘I find it difficult to retain content from subjects I consider irrelevant to my future profession.’ ○. ‘I feel prepared to work independently in a professional engineering setting.’ ○. ‘I am motivated to study when course content is linked to real design challenges.’ Informal feedback. Throughout the experience, students have been encouraged to provide informal feedback through diaries, focus groups, and regular debriefing sessions. This qualitative data has been used to obtain insights into student motivation, the perceived relevance of the learning activities, and suggestions for improvement. Performance assessment. Student works—including design reports, concept maps, and oral presentations—have been evaluated using rubrics aligned with the targeted skills and learning objectives. Peer and instructor assessments have contributed to a holistic evaluation of teamwork, initiative, and communication abilities. Internship outcomes. Educators have asked the supervisors of the internships to provide feedback regarding the students’ ability to transfer classroom knowledge to professional tasks, to demonstrate autonomy, and to adapt to complex, interdisciplinary environments. Longitudinal tracking. When feasible, follow-up contact has been maintained with graduates to explore the longer-term impact of the methodology on employability and career development.
3. Analysis of the Results
The implementation of the interdisciplinary and practice-based methodology has involved 73 students over an academic year. Its impact has been assessed through pre- and post-intervention surveys, informal student feedback, and internship outcomes in collaboration with local industry partners. Both quantitative and qualitative data have been used to evaluate the students’ perception of content relevance, their level of anxiety when facing real-world problems, and their capacity for autonomous learning.
3.1. Pre- and Post-Application Surveys
At the beginning of the course, students completed a diagnostic survey to assess their perceptions towards the academic content, learning motivation, and preparedness for professional environments. These responses have served as a baseline for comparison following the experiment. In Figure 6, the responses to the questions included in the pre- and post-surveys are shown. The key findings prior to the implementation included the following: A total of 78% of students indicated difficulty retaining content from subjects they perceived as difficult or irrelevant to future professional practice. A total of 89% believed that a greater emphasis should be placed on content directly applicable to real-world engineering work. Only 50% felt they were developing sufficient capacity for self-directed learning. A total of 73% of Mechanical Engineering students and approximately 45% of students from other specializations reported feeling unprepared to work independently in professional settings. A total of 92% have expressed anxiety when facing their first real-world engineering problem. They responded that new responsibility and a fear of failure have been their major concerns.
Following the experience, students completed the same survey at the end of the academic year. The results have revealed the following significant improvements: The proportion of students reporting difficulty retaining theoretical content has dropped to 38%. Only 27% have expressed concern about the relevance of their course content to their future careers. A total of 79% of students have stated that they have improved their ability to learn autonomously. The number of students feeling unprepared for professional work has decreased to 34% among Mechanical Engineering students and 21% in other specializations. Student anxiety related to real-world engineering challenges has declined to 57%.
To statistically evaluate these differences, paired t-tests and effect sizes (Cohen’s d) have been calculated (Table 2). It can be confirmed that all observed improvements have been statistically significant (p < 0.001) and practically meaningful. The perceived difficulty in content retention has decreased, t(72) = 7.12, p < 0.001, d = 0.83 (large effect). However, perceptions of content relevance have improved substantially, t(72) = 14.24, p < 0.001, d = 1.67 (very large effect). Autonomous learning capacity has increased significantly, t(72) = 11.11, p < 0.001, d = 1.30 (very large effect), and student anxiety has decreased, t(72) = 7.48, p < 0.001, d = 0.88 (large effect).
Overall, the statistical analysis can demonstrate that the intervention has had a broad and positive impact on student perceptions, reducing barriers to learning while enhancing motivation, confidence, and preparedness for professional tasks. Although some subgroup variations (e.g., by specialization or gender) have been observed, the overall trends have been consistently positive across the group, suggesting that the benefits of the approach can be broadly applicable.
3.2. Informal Feedback and Student Involvement
Throughout the academic year, additional qualitative feedback has been gathered through collaborative discussions and informal interviews with students who have participated. These students have reported that integrating subjects through a shared and realistic project has increased both their engagement and motivation towards the work. They have also highlighted the value of working in groups in a more dynamic and self-regulated environment, especially when supported by digital tools like MILAGE LEARN+ and MindMeister. Also, the shift from an isolated problem-solving to a holistic design task has allowed students to better contextualize their academic learning.
3.3. Qualitative Findings: Thematic Analysis
A comprehensive thematic analysis has been performed on the qualitative data collected from student diaries, focus groups, and interviews. To do so, the methodology developed by Braun and Clarke (2006) has been followed, which included iterative coding, theme development, and validation. Initial codes have been grouped into categories, which have been then organized into three key themes: Authenticity and motivation: Students highlighted that engagement with realistic, professionally relevant projects has increased their motivation and appreciation of course content. For example, one student shared ‘Working on authentic engineering problems helped me see the importance of what we study and motivated me to learn more.’ Collaborative skills through digital platforms: The integration of tools like MindMeister has facilitated enhanced cooperation and communication among team members. A participant observed ‘Using MindMeister made our group discussions more focused and productive, helping us integrate ideas effectively.’ Autonomous learning supported by gamification: The MILAGE LEARN+ platform provided adaptive, gamified learning experiences encouraging self-regulation. One learner noted ‘The ability to choose difficulty levels and get instant feedback boosted my confidence and helped me learn actively.’
To provide a systematic overview, Table 3 presents the themes, coded categories, and representative student quotes derived from the analysis.
Overall, the thematic analysis indicates that the intervention has been experienced by students as being motivating, collaborative, and supportive of self-regulated learning. These findings complement the quantitative improvements reported in the surveys, showing that the intervention not only reduced barriers such as anxiety and content difficulty but also enhanced motivation, collaboration, and students’ sense of autonomy.
3.4. Internship Outcomes and Industry Collaboration
Two local companies have collaborated with faculty members to provide feedback concerning the link between academic training and industry needs. The feedback from collaborating companies has been collected mainly after students concluded their internships, enabling supervisors to assess the students’ performance and application of skills in professional contexts. This post-internship evaluation has provided valuable insights into students’ ability to integrate theoretical knowledge, work autonomously, and communicate effectively within industry teams. Also, these companies have been informally involved from the planning stages of the methodology, contributing to the types of skills and competencies most valued in professional engineering contexts.
By the end of the academic year, several students who participated in the project have been offered internships within these companies. According to the feedback received, this decision has been attributed to the students’ improved ability to understand the practical implications of their academic training and to clearly articulate and defend engineering decisions. Employers have emphasized the students’ technical reasoning, initiative, and readiness to contribute to a multidisciplinary team. Specifically, companies have reported the following strengths among participating students: An enhanced ability to integrate knowledge from different disciplines into cohesive solutions; Greater autonomy and confidence when tackling unfamiliar or complex problems; Improved communication and professionalism during technical discussions; A stronger capacity to prioritize decisions based on constraints such as cost, feasibility, and material selection.
Additionally, one of the companies has expressed interest in developing a more formalized collaboration with the university, noting that the students trained under the new methodology required less time to adapt to their roles and showed early leadership potential.
In the following academic year, the methodology has continued to be applied and initial feedback has suggested that similar positive outcomes are being obtained. While the graduates’ performance is still being tracked, instructors have observed a higher level of student engagement in projects and an increased demand from companies seeking graduates with integrative and practice-based training profiles. These results reinforce the idea that embedding real-world design challenges and transversal skill development within engineering curricula not only enhances student learning but also improves employability and institutional relations with industry. Table 4 shows the relationship between the competencies developed by students and feedback and comments from industry instructors.
4. Discussion
The results of this study align closely with prior research emphasizing the benefits of experiential and integrated learning approaches in engineering education (Caballé et al., 2014; Duch et al., 2001; Kolb, 2015). The observed reduction in student anxiety corresponds with findings that authentic, real-world challenges enhance self-efficacy and preparedness (Baltà-Salvador et al., 2021; Michavila et al., 2018). Furthermore, the development of transversal skills such as communication, teamwork, and initiative is consistent with the established literature highlighting these competencies as critical for employability (Baltà-Salvador et al., 2021; Michavila et al., 2018).
One of the most notable improvements observed has been the significant reduction in students’ anxiety when facing real-world problems (Dunne & Rawlins, 2000). The results showed a 35% drop compared with baseline levels. While previous studies have observed anxiety in early-career engineers (Asgari et al., 2021; Vadakalu Elumalai et al., 2020), the findings of this work suggest that immersive, practice-oriented pedagogy, supported by supportive technologies, may mitigate such challenges more effectively. Hence, the result is that embedding authentic challenges—like the clothesline system—in the curriculum appears to reduce this anxiety and increase self-efficacy, as proposed in Kolb (2015)’s experiential learning cycle.
The integration of subjects within a single, comprehensive design challenge also contributed to improvements in knowledge retention and interdisciplinary thinking. This improvement also relates to the findings from Kolmos et al. (2024). In this work, the authors argued that cross-curricular PBL fosters a deeper conceptual understanding by forcing students to synthesize and apply content in meaningful ways. Likewise, the enhancement in self-directed learning and autonomous problem-solving supports the conclusions of Baltà-Salvador et al. (2021). These findings strengthen the importance of curricular coherence and content integration, as emphasized in CDIO standards and constructivist learning models (Crawley et al., 2014).
Qualitative analyses have further clarified that authentic, real-world projects can enhance student motivation and engagement by contextualizing theoretical knowledge within meaningful engineering challenges. In fact, similarly to Pozzi et al. (2015), students reported that digital tools such as MILAGE LEARN+ and MindMeister appeared to enhance their learning experiences by reinforcing theoretical content and promoting visual and conceptual connections across subjects. Similar results have been reported in Moreno Ruiz et al. (2019), where combining gamified content, formative feedback, and project-based work significantly enhanced learning outcomes. While these findings are based on self-perceived learning rather than objective performance metrics, they suggest that such tools can be effective in supporting metacognitive reflection and autonomous knowledge integration. The use of digital tools such as MILAGE LEARN+ and MindMeister facilitated not only cognitive integration across disciplines but also fostered collaboration and communication skills, as echoed in similar technology-enabled educational interventions (Moreno Ruiz et al., 2019; Nitchot & Gilbert, 2025). Additionally, this confirms the value of digital platforms in enhancing metacognition, self-regulated learning, and curriculum continuity (Hsu et al., 2025; Huerta-Gomez-Merodio & Requena-Garcia-Cruz, 2024).
The development of transversal competencies—including teamwork, leadership, and initiative—has been another key strength of the methodology. Similar results have been observed in other faculties (Frank et al., 2003; Meredith & Burkle, 2008; Molderez & Fonseca, 2018). The collaborative nature of the project, combined with structured deadlines and oral presentations, simulated a workplace environment. Evaluations from industry supervisors corroborated these educational outcomes, noting that participating students exhibited greater autonomy, effective interdisciplinary problem-solving, and professional communication—competencies that address the identified gaps in graduate readiness (Bae et al., 2022; Michavila et al., 2018). Internship assessments also highlighted enhanced prioritization skills under constraints such as cost and feasibility, which are critical to engineering practice. As a result, this intervention helped students to practice skills essential for employability. This directly addresses the gap identified in studies such as Michavila et al. (2018), where employers noted that graduates often lack participatory and professional competencies despite strong academic records. Also, these results support the conclusions concerning the development of transversal competencies (Bae et al., 2022; Michavila et al., 2018).
However, despite the positive outcomes, certain limitations must be acknowledged. The data collected has been based primarily on student self-perceptions and informal industry feedback, rather than on objective performance assessments. The absence of a control group or longitudinal tracking limits the possible comparisons to assess the methodology more rigorously. Future studies should include comparative groups and long-term tracking to assess how well these competencies translate into job performance and career progression.
5. Conclusions
This study presents a teaching methodology designed to bridge the gap between academic instruction and professional competencies in engineering education. Implemented through a real-world design project, the methodology has successfully integrated content across multiple subjects—Resistance of Materials, Materials Science, Materials for Design, and Computer-Aided Design—supported by digital learning platforms and collaborative pedagogical strategies.
The results demonstrate improvements in students’ ability to transfer theoretical knowledge, increased motivation, enhanced confidence in problem-solving, and the stronger development of transversal skills (such as teamwork, leadership, and communication). It has been observed that the proportion of students struggling to retain subject content has decreased from 78% to 38%. However, self-reported autonomous learning capacity has risen from 50% to 79%. The amount of students feeling unprepared for real-world tasks has been reduced from 73% to 34% among mechanical engineering students. Additionally, student anxiety when facing professional scenarios has decreased by 35%. This might suggest a substantial increase in self-efficacy and perceived readiness.
The project has also encouraged greater curriculum cohesion and reinforced the connection between classroom activities with real professional challenges. The methodology presented in this work has been aligned with experiential and constructivist pedagogical frameworks. Also, it has been designed to respond directly to industry expectations, offering a replicable model for other engineering programs seeking to modernize and contextualize their curricula. The inclusion of digital tools, open-ended problem scenarios, and structured peer collaboration makes it particularly adaptable to blended and interdisciplinary learning environments.
Future work should focus on evaluating the long-term impact of this approach, expanding its implementation to additional subjects and degree programs, and exploring opportunities for deeper collaboration with industry partners. Through continued refinement and institutional support, this methodology can contribute to more effective, relevant, and engaging engineering education. Concerning its limitations, this study is based on self-reported data, which highlights the need for future research to include objective performance measures. Moreover, future studies should consider controlled comparisons and longitudinal designs to evaluate the persistence of competencies and their influence on career outcomes.
Conceptualization, M.H.-G.-M. and M.-V.R.-G.-C.; methodology, M.H.-G.-M. and M.-V.R.-G.-C.; software, M.H.-G.-M. and M.-V.R.-G.-C.; validation, M.H.-G.-M. and M.-V.R.-G.-C.; formal analysis, M.H.-G.-M. and M.-V.R.-G.-C.; investigation, M.H.-G.-M. and M.-V.R.-G.-C.; resources, M.H.-G.-M. and M.-V.R.-G.-C.; data curation, M.H.-G.-M. and M.-V.R.-G.-C.; writing—original draft preparation, M.H.-G.-M. and M.-V.R.-G.-C.; writing—review and editing, M.H.-G.-M. and M.-V.R.-G.-C.; visualization, M.H.-G.-M. and M.-V.R.-G.-C.; supervision, M.H.-G.-M. and M.-V.R.-G.-C.; project administration, M.H.-G.-M.; funding acquisition, M.H.-G.-M. All authors have read and agreed to the published version of the manuscript.
Not applicable because no personal data was collected during the surveys carried out in this study.
Informed consent was obtained from all subjects involved in the study.
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).
The authors declare no conflicts of interest.
Footnotes
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Figure 1 Workflow of the method.
Figure 2 MILAGE LEARN+ platform. Students can choose the subject, the chapter/unit, and the subchapter they want to review.
Figure 3 MILAGE LEARN+ platform. Example of a problem developed by a student and the correct solution provided by the application. Below, 1, 2 and 3 refer to the questions of each of the sections 1.1, 1.2, 1.3, 1.4, etc. of the unit.
Figure 4 Interactive website, developed with Mindmeister, with the subjects and concept relation (partial view).
Figure 5 Images of the product design: (a) Outdoor clothesline in different positions: closed, open, and extended; (b) supports of the bar, some of them broken; (c) clothesline with 1 kg clothes, simulating an evenly distributed load; (d) clothesline with 1 kg clothes, simulating a nodal load.
Figure 6 Pre-/post-intervention comparison of student responses across key indicators.
Subject-specific contributions to the drying rack design task.
| Subject | Contributions to the Designing Tasks |
|---|---|
| Computer-Aided Design | Design the shape of the pieces and establish the quantities. Calculation of the structure and application of the subject studied in Resistance of Materials, using the Finite Element Method. Drafting. |
| Resistance of Materials | Calculation of the structure for the maximum loads that are expected to be used, like wet blankets. Dimensioning the parts from a mechanical point of view, to later see if the dimensions of the materials that will actually be used meet these minimum design requirements. This part is totally interrelated with the design of the pieces in the subject of Computer-Aided Design. Selection of the material that absorbs the stresses to which the structure is subjected (comparisons of its elastic modulus, quality of the same, etc.). This is related to materials subjects. |
| Materials Science | Evaluating the environment where the product will be used (saline and high humidity, dry, exposed directly to sunlight, etc.) and study the material whose properties are the most suitable. Deciding the materials to be used, studying the mechanical properties, the cost of making models with different materials or a single one, the environmental impact of materials, or how many materials should be used. |
| Materials for Design | Selection of the cheapest material that can be used correctly in the process (taking into account the cost of raw materials or processing, for instance). If students do not select the cheapest option which is available, they must justify their choice, for example, in terms of durability, better maintenance, ecodesign, hygienic reasons, etc. |
Results of paired t-tests and Cohen’s d for pre- and post-intervention scores (N = 73).
| Variable | Pre-Mean | Post-Mean | Mean Difference | Standard Deviation of Differences | t(72) | p-Value | Cohen’s d | Interpretation |
|---|---|---|---|---|---|---|---|---|
| Difficulty in content retention | 2.2 | 3.2 | 1.0 | 1.2 | 7.12 | <0.001 | 0.83 | Large |
| Relevance of content | 1.8 | 3.3 | 1.5 | 0.9 | 14.24 | <0.001 | 1.67 | Very Large |
| Autonomous learning | 2.3 | 3.6 | 1.3 | 1.0 | 11.11 | <0.001 | 1.30 | Very Large |
| Anxiety | 1.8 | 2.5 | 0.7 | 0.8 | 7.48 | <0.001 | 0.88 | Large |
Themes, coded categories, and representative student quotes derived from the analysis.
| Theme | Category/Code | Representative Quote |
|---|---|---|
| Authenticity and motivation | Value of real-world tasks | ‘Working on authentic engineering problems helped me see the importance of what we study and motivated me to learn more.’ |
| Professional relevance | ‘The project felt useful beyond the classroom—I can see myself applying this in my future work.’ | |
| Collaborative skills through digital platforms | Team communication | ‘Using MindMeister made our group discussions more focused and productive, helping us integrate ideas effectively.’ |
| Knowledge integration | ‘We could visualize everyone’s contributions, which made collaboration smoother and more efficient.’ | |
| Autonomous learning supported by gamification | Self-regulation | ‘The ability to choose difficulty levels and get instant feedback boosted my confidence and helped me learn actively.’ |
| Increased confidence | ‘I felt more in control of my learning, and I was less afraid of making mistakes because I could retry.’ |
Industry feedback on student competencies developed.
| Competency Observed | Industry Feedback/Comments |
|---|---|
| Integration of knowledge | Students combined theory from multiple courses |
| Autonomy and decision-making | Less supervision needed compared to previous cohorts |
| Communication and professionalism | Clear presentations and strong justification of work |
| Prioritization under constraints | Considered material cost, feasibility, and durability |
Asgari, S.; Trajkovic, J.; Rahmani, M.; Zhang, W.; Lo, R. C.; Sciortino, A. An observational study of engineering online education during the COVID-19 pandemic. PLoS ONE; 2021; 16,
Bae, H.; Polmear, M.; Simmons, D. R. Bridging the gap between industry expectations and academic preparation: Civil engineering students’ employability. Journal of Civil Engineering Education; 2022; 148,
Baillie, C.; Goodhew, P.; Skryabina, E. Threshold Concepts in Engineering Education—Exploring Potential Blocks in Student Understanding. International Journal of Education; 2006; 22,
Baltà-Salvador, R.; Olmedo-Torre, N.; Peña, M.; Renta-Davids, A.-I. Academic and emotional effects of online learning during the COVID-19 pandemic on engineering students. Education and Information Technologies; 2021; 26,
Braun, V.; Clarke, V. Using thematic analysis in psychology. Qualitative Research in Psychology; 2006; 3,
Caballé, S.; Mora, N.; Feidakis, M.; Gañán, D.; Conesa, J.; Daradoumis, T.; Prieto, J. CC–LR: Providing interactive, challenging and attractive collaborative complex learning resources. Journal of Computer Assisted Learning; 2014; 30,
Cantero-Chinchilla, F. N.; Díaz-Martín, C.; García-Marín, A. P.; Estévez, J. Innovative student response system methodologies for civil engineering practical lectures. Technology, Knowledge and Learning; 2020; 25,
Crawley, E. F.; Malmqvist, J.; Östlund, S.; Brodeur, D. R.; Edström, K. Rethinking engineering education; Springer International Publishing: 2014; [DOI: https://dx.doi.org/10.1007/978-3-319-05561-9]
Dore, E.; Richards, A. Empowering early career academics to overcome low confidence. International Journal for Academic Development; 2024; 29,
Dorin, A.; Moraes, M. C.; Figueiredo, M. MILAGE LEARN+: Motivation and grade benefits in computer science university students. 2024 IEEE Frontiers in Education Conference (FIE); Washington, DC, USA, October 13–16; 2024; pp. 1-8. [DOI: https://dx.doi.org/10.1109/FIE61694.2024.10892845]
Duch, B. J.; Groh, S. E.; Allen, D. E. The power of problem-based learning: A practical “how to” for teaching undergraduate courses in any discipline: Free download, borrow, and streaming: Internet archive; Stylus Publishing: 2001; Available online: https://archive.org/details/powerofproblemba0000unse (accessed on 1 July 2025).
Dunne, E.; Rawlins, M. Bridging the gap between industry and higher education: Training academics to promote student teamwork. Innovations in Education and Training International; 2000; 37,
Felder, R. M.; Woods, D. R.; Stice, J. E.; Rugarci, A. The future of engineering education II. Teaching methods that work. Chemical Engineering Education; 2000; 34,
Felszeghy, S.; Pasonen-Seppänen, S.; Koskela, A.; Nieminen, P.; Härkönen, K.; Paldanius, K. M. A.; Gabbouj, S.; Ketola, K.; Hiltunen, M.; Lundin, M.; Haapaniemi, T.; Sointu, E.; Bauman, E. B.; Gilbert, G. E.; Morton, D.; Mahonen, A. Using online game-based platforms to improve student performance and engagement in histology teaching. BMC Medical Education; 2019; 19, 273. [DOI: https://dx.doi.org/10.1186/s12909-019-1701-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31331319]
Figueiredo, M.; Martins, C.; Ribeiro, C.; Rodrigues, J. MILAGE LEARN+: A tool to promote autonomous learning of students in higher education. INCREaSE 2019; Springer International Publishing: 2020; pp. 354-363. [DOI: https://dx.doi.org/10.1007/978-3-030-30938-1_28]
Fonseca, C. S. C.; Zacarias, M.; Figueiredo, M. MILAGE LEARN+: A mobile learning app to aid the students in the study of organic chemistry. Journal of Chemical Education; 2021; 98,
Frank, M.; Lavy, I.; Elata, D. Implementing the project-based learning approach in an academic engineering course. International Journal of Technology and Design Education; 2003; 13,
Gamarra, M.; Dominguez, A.; Velazquez, J.; Páez, H. A gamification strategy in engineering education—A case study on motivation and engagement. Computer Applications in Engineering Education; 2022; 30,
Hadgraft, R. G.; Kolmos, A. Emerging learning environments in engineering education. Australasian Journal of Engineering Education; 2020; 25,
Hsu, Y.-P.; Chiang, D. F.; Kehinde, I. Transforming engineering education in the digital era: Findings from a systematic review. Frontiers in Education; 2025; 10, 1568917. [DOI: https://dx.doi.org/10.3389/feduc.2025.1568917]
Huerta-Gomez-Merodio, M.; Fernández-Ruiz, M. A.; Requena-Garcia-Cruz, M. V. Using FastTest PlugIn for the design of remote and hybrid learning environments to improve the engineering skills of university students. European Journal of Education; 2024; 59, e12654. [DOI: https://dx.doi.org/10.1111/ejed.12654]
Huerta-Gomez-Merodio, M.; Requena-Garcia-Cruz, M. V. Application of MS Excel and FastTest PlugIn to automatically evaluate the students’ performance in structural engineering courses. Computer Applications in Engineering Education; 2024; 32,
Jamieson, M. V.; Shaw, J. M. Teaching engineering innovation, design, and leadership through a community of practice. Education for Chemical Engineers; 2020; 31, pp. 54-61. [DOI: https://dx.doi.org/10.1016/j.ece.2020.04.001]
Kolb, D. A. Experiential learning: Experience as the source of learning and development; Pearson Education: 2015.
Kolmos, A.; Holgaard, J. E.; Routhe, H. W.; Winther, M.; Bertel, L. Interdisciplinary project types in engineering education. European Journal of Engineering Education; 2024; 49,
Lavado-Anguera, S.; Velasco-Quintana, P.-J.; Terrón-López, M.-J. Project-Based Learning (PBL) as an experiential pedagogical methodology in engineering education: A review of the literature. Education Sciences; 2024; 14,
Magana, A. J.; Falk, M. L.; Vieira, C.; Reese, M. J. A case study of undergraduate engineering students’ computational literacy and self-beliefs about computing in the context of authentic practices. Computers in Human Behavior; 2016; 61, pp. 427-442. [DOI: https://dx.doi.org/10.1016/j.chb.2016.03.025]
Meister,. MindMeister; 2025; Available online: https://www.mindmeister.com/ (accessed on 1 July 2025).
Meredith, S.; Burkle, M. Building bridges between university and industry: Theory and practice. Education + Training; 2008; 50,
Michavila, F.; Martínez, J. M.; Martín-González, M.; García-Peñalvo, F. J.; Cruz-Benito, J. Empleabilidad de los titulados universitarios en España. Proyecto OEEU. Education in the Knowledge Society (EKS); 2018; 19,
MILAGE. Milage LEARN+—Mathematics blended augmented game; 2025; Available online: https://webmilage.ualg.pt/ (accessed on 1 July 2025).
Molderez, I.; Fonseca, E. The efficacy of real-world experiences and service learning for fostering competences for sustainable development in higher education. Journal of Cleaner Production; 2018; 172, pp. 4397-4410. [DOI: https://dx.doi.org/10.1016/j.jclepro.2017.04.062]
Moreno Ruiz, L.; Castellanos Nieves, D.; Popescu Braileanu, B.; González González, E. J.; Sánchez de la Rosa, J. L.; Groenwald, C. L. O.; González González, C. S. Combining flipped classroom, project-based learning, and formative assessment strategies in engineering studies. The International Journal of Engineering Education; 2019; 35,
Nitchot, A.; Gilbert, L. Comparison of Mytelemap and MindMeister in using competence maps for self-learning. Technology, Pedagogy and Education; 2025; 34,
Pedraja Iglesias, M.; Rivera Torres, P.; Marzo Navarro, M. Las competencias profesionales demandadas por las empresas: El caso de los ingenieros. Revista de Educación; 2006; 341, pp. 643-662. Available online: https://dialnet.unirioja.es/servlet/citart?info=link&codigo=2165289&orden=0 (accessed on 1 July 2025).
Piaget, J. Psychology of intelligence; Routelege & Paul: 1950; Available online: https://www.digilib.unrika.ac.id/index.php?p=fstream-pdf&fid=1868&bid=63039 (accessed on 1 July 2025).
Pozzi, R.; Noè, C.; Rossi, T. Experimenting ‘learn by doing’ and ‘learn by failing’. European Journal of Engineering Education; 2015; 40,
Raju, P. K.; Sankar, C. S.; Halpin, G.; Halpin, G. An innovative teaching method to improve engineering design education. 2000 American Society of Engineering Annual Conference & Exposition; St. Louis, MO, USA, June 18–21; 2000.
Vadakalu Elumalai, K.; Sankar, J. P.; Kalaichelvi, R.; John, J. A.; Menon, N.; Alqahtani, M. S. M.; Abumelha, M. A. Factors affecting the quality of e-learning during the COVID-19 pandemic from the perspective of higher education students. Journal of Information Technology Education: Research; 2020; 19, pp. 731-753. [DOI: https://dx.doi.org/10.28945/4628] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/40807794]
Wang, X.; Ratanaolarn, T.; Sitthiworachart, J. Integrating project-based blended learning and design thinking to enhance creativity and openness to experience. Cogent Education; 2025; 12,
Wankat, P.; Oreovicz, F. Teaching engineering; Purdue University Press: 2015; Available online: https://docs.lib.purdue.edu/purduepress_ebooks/61 (accessed on 1 July 2025).
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