Soft skills development in ICT students: an evaluation of teaching methods by university educators

Cogent Education ISSN: (Print) (Online) Journal homepage: www.tandfonline.com/journals/oaed20 Soft skills development in ICT students: an evaluation of teaching methods by university educators Ariadna Llorens, Enric Trullols, Antoni Pérez-Poch & Nikola Petrović To cite this article: Ariadna Llorens, Enric Trullols, Antoni Pérez-Poch & Nikola Petrović (2025) Soft skills development in ICT students: an evaluation of teaching methods by university educators, Cogent Education, 12:1, 2437906, DOI: 10.1080/2331186X.2024.2437906 To link to this article: https://doi.org/10.1080/2331186X.2024.2437906 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 19 Dec 2024. Submit your article to this journal Article views: 256 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaed20 STEM EDUCATION | RESEARCH ARTICLE Soft skills development in ICT students: an evaluation of teaching methods by university educators Ariadna Llorens a , Enric Trullols a , Antoni P erez-Poch b and Nikola Petrovi c c a Management Department, Universitat Polit ecnica de Catalunya –Barcelona Tech (UPC), Vilanova i la Geltr u, Spain; b Computer Sciences Department, Universitat Polit ecnica de Catalunya –Barcelona Tech (UPC, Barcelona, Spain; c Faculty of Organizational Sciences, Department of Human Resource Management, University of Belgrade, Belgrade, Serbia ABSTRACT Background and context: The study explores the role of teaching methods in fostering soft skills among information and communication technology (ICT) students in university degree programs. As the ICT sector increasingly values soft skills, aligning educational approaches with industry demands has become essential. The research surveyed expert lecturers in telecommunications, computer science, and electronics at various technological universities. Objective: This study aims to evaluate and propose effective teaching models that emphasize soft skills acquisition in ICT degree courses, identifying the skills developed by different teaching methods and determining the most impactful approaches. Method: Utilizing a survey of expert lecturers across different technological fields, the study assessed teaching contexts in on-campus environments. A statistical analysis was conducted to explore the relationship between teaching methods and soft skill development, including a correlation analysis to determine the effectiveness of specific methodologies. Findings: The study concludes that the combination of cooperative learning (group method) and the learning contract (individual method) effectively develops the five key soft skills essential for ICT engineers. Project-based learning (PBL) and problem-based learning (PBL) emerged as the most effective methodologies for fostering these skills, although implementing both simultaneously may be redundant due to their overlapping benefits. However, the skills of innovation, flexibility, creativity, and proactivity are not adequately developed by the examined teaching methods, indicating a need for specific instructional modules. Implications: The findings offer valuable insights for the ICT Education community, proposing adaptable teaching methodologies for ICT degree programs that effectively develop soft skills critical to the technological and business landscapes. These recommendations extend beyond the immediate scope of the study, suggesting broader applications in university-level training. ARTICLE HISTORY Received 11 June 2024 Revised 1 November 2024 Accepted 6 November 2024 KEYWORDS ICT engineering education; active learning; soft skills; employability; university teaching methods; studentcentered learning; skill acquisition; technological education SUBJECTS Study skills; classroom practice; curriculum studies; higher education Introduction There is broad consensus within the academic community on the systemic changes challenging university operations, particularly in academic planning and teaching-learning methods (Graham, 2019). In engineering education, post-pandemic factors have created an uncertain future, prompting some commentators to suggest that universities should prioritize personalized learning and active, studentcentered approaches (Pears, 2022). The lessons learned from COVID-19 have driven many lecturers to reconsider their teaching methods (Tondeur et al., 2023) and validate the changes rapidly implemented during the pandemic, which had been anticipated since the early 2000s (National Academy of Engineering U.S., 2004). European policies have emphasized the need for university strategies that foster teaching innovation, as outlined by the European Commission in January 2022 (European Commission, 2022). This document CONTACT Nikola Petrovi c[email protected] Faculty of Organizational Sciences, Department of Human Resource Management, University of Belgrade, C. Jove Ilica 154, 11000 Belgrade, Serbia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT EDUCATION 2025, VOL. 12, NO. 1, 2437906 https://doi.org/10.1080/2331186X.2024.2437906 highlights the rapid evolution of skill requirements and underscores the imperative for university education to adapt accordingly. As higher education institutions in engineering address future challenges and set guidelines, it is crucial to re-evaluate skills training in the ICT sector (Brynjolfsson & McAfee, 2014; Eurostat Metadata, 2017), with a particular focus on the critical role of active teaching methodologies in curriculum development (Turull i Rubinat, 2020). The European Commission’s Digital Education Action Plan has further driven universities across Europe to integrate more comprehensive digital literacy programs into their curricula, particularly for technological degrees. According to Redecker and Punie (2013), universities offering Computer Science and Telecommunications degrees have revised their curricula to include not only technical knowledge but also a broader set of digital competencies, such as cybersecurity, artificial intelligence, and big data analytics, aligning with the European Commission’s goals of fostering digital transformation across all sectors. A key report by Gaebel et al. (2021) from the European University Association (EUA) underscores the action plan’s role in fostering curriculum innovation in higher education. Universities have increasingly emphasized multidisciplinary approaches, integrating courses on digital ethics, legal frameworks, and the socio-economic impact of technology, which are vital for the digital economy. This broadening of the curriculum is seen as a direct response to European policies that push for more comprehensive preparation of students for the digital labor market. In Computer Science and Telecommunications programs, initiatives under the Digital Education Action Plan have led to increased opportunities for students to participate in EU-funded projects, gaining practical experience through hands-on learning. For example, the Erasmus þprogram, restructured under the Digital Education Action Plan, has offered specific digital skill-building programs in partnership with leading tech companies across Europe, allowing students to apply their academic learning to realworld challenges (European Commission, 2022). A manual on university teaching (Cannon et al., 2000) emphasizes that the only certainty in educational sciences is that learning depends on student actions. They particularly stress the importance of active learning in engaging students and fostering critical skills such as problem-solving and critical thinking, which are essential in ICT education. The manual suggests that methods like group projects, collaborative problem-solving, and hands-on learning are effective ways to help students develop key skills, including communication and teamwork. Moreover, Cannon, Kapelis, and Newble highlight the importance of designing a curriculum that integrates these soft skills, ensuring that students are wellprepared for the modern job market. They also underscore the need for both formative and summative assessments, which are instrumental in embedding soft skills into ICT education. Similarly, Sir Ken Robinson (2022) argued that students, as the main agents of their learning, bear responsibility for their educational outcomes, while educators should strive to create optimal learning conditions. Robinson likened human development to an organic rather than mechanical process, where outcomes cannot be precisely predicted, similar to a farmer who nurtures conditions for growth without guaranteeing results. This perspective frames teaching methodologies as tools that foster environments conducive to effective learning. Since the establishment of the European Higher Education Area (EHEA), European universities have prioritized student-centered learning, positioning lecturers as facilitators of the teaching process (Llorens et al., 2017). Cannon et al. (2000) identified key distinctions between conventional and student-centered learning, such as a more active student role, increased flexibility in teaching methods, and a rise in cooperative methodologies. Studies on Generation Z (individuals born from 2000 onwards and now entering universities) highlight their preference for teamwork, interactive learning, and engagement in the learning process, as well as their adaptability to learning in diverse environments with unrestricted access to information (Kozinski, 2017). These students favor personalized, project-based, practical learning approaches, that aligning with contemporary educational paradigms (Fisk, 2017). Other findings emphasize the need to develop critical thinking skills among Generation Z, as they “might not have great critical thinking” (Chan & Lee, 2023), a skill that is of great importance for active learning. Differentiation of teaching methodologies based on student types is not novel. Kolb’s Experiential Learning Model (Kolb, 1984), developed in the 1970s, categorizes students based on their learning 2 A. LLORENS ET AL. preferences. Given these insights, it is crucial to identify suitable teaching methods for technological university programs, such as engineering. Zabalza (1991) proposed criteria for selecting teaching methods, including validity, comprehensibility, variety, relevance, clarity, procedural mastery, and the inclusion of practical exercises. Neciri (1979) categorized teaching methods by reasoning form (deductive, inductive, analogical), student activity level (passive, active), and work type (individual, collective, mixed). Mario de Miguel D ıaz (2006) structured methodologies based on their organizational modality and participation degree of students and teachers. This classification, previously employed by the authors (Llorens et al., 2019), underpins the methodologies considered in this article. Table 1 presents the teaching methodologies commonly used in this study, according to Mario de Miguel D ıaz (2006). This classification encompasses a wide range of teaching strategies, including service-learning, which involves students’active participation in community service and is viewed as an extension of problembased learning (Salam et al., 2019). Similarly, challenge-based learning merges problem-based and project-based learning, focusing on resolving professional challenges within teams (Charosky et al., 2018). Some teaching techniques and modalities are excluded from this study due to their extensive variety, which presents a limitation. As noted in the introduction, validating university teaching methods remains a significant challenge for European universities. This study specifically aims to develop practical and applicable training models for ICT engineering degrees (telecommunications, electronics, and computer engineering) (Llorens et al., 2017). More specifically, this research examines the optimal approaches for cultivating general professional or soft skills –referred to as transversal or generic skills –which are recurrent themes in ICT sector literature. Soft skills encompass knowledge, practical abilities (skills and heuristics), and personal attributes (character and values) (Green et al., 2013). These skills are not confined to traditional classroom settings; ongoing research explores their acquisition through online courses and appropriate learning activities (Reilly & Reeves, 2023). In a volatile, uncertain, complex, and ambiguous (VUCA) environment shaped by advancements like artificial intelligence and the Fourth Industrial Revolution (Schwab, 2023), there is a shifting trend away from professional specialization. European studies show that ICT firms increasingly value soft skills when evaluating candidates’employability (Eurostat Metadata, 2017). Given this labor market trend, it is essential to revise university curricula to develop technical and soft skills for ICT professionals. According to Ausubel (1963), a proponent of constructivism, meaningful knowledge construction requires reflection and internalization, not just explication. Active or student-centered methodologies position students as the focal point of the learning process (Prince, 2004), contrasting with non-active methods that focus on knowledge characterization and memorization. The Horv ath model (Horv ath et al., 2004) further classifies active methodologies based on their focus (individual, group, community) and approach (constructivist, explorative, instructive). This study concentrates on the ICT sector, emphasizing that the acquisition of key soft skills, as demanded by the market, is crucial for the employability of engineers, alongside their technical expertise. Research consistently shows that soft skills, particularly in communication, teamwork, and problem-solving, are vital for ICT graduates (Pa zur Ani ci c et al., 2017). The current study focuses on these essential skills, as identified in prior work by the authors (Llorens et al., 2017), and presented in Table 2, which ranks key soft skills for ICT engineers according to employer feedback. Our study offers a unique contribution by examining soft skills within the context of engineering education –a perspective that remains underexplored. The majority of research on soft skills emerges from fields such as psychology, education, and organizational Table 1. Relationship between modalities and teaching methods (de Miguel D ıaz, 2006). Organizational modalities Most common teaching methods Theory classes Lecture Seminar-workshops Case study Practical classes Problem solving Internship Problem-based learning Tutorials Project-based learning Study and group work Cooperative learning Individual study and work Learning contract COGENT EDUCATION 3 behavior, where the focus is often on theoretical or practical applications outside of technical disciplines. Investigating soft skills from the vantage point of engineering faculty who are actively teaching in universities provides a fresh perspective, emphasizing the need for practical application within technical fields. While research in teaching practices has largely concentrated on teacher education, often highlighting the role of educators as effective designers of ICT-integrated environments and instructional role models (Isteni c Star ci c & Lebeni cnik, 2020), other educational disciplines, particularly in technical fields, have received comparatively limited focus. Methods The primary objective of this research is to propose a teaching model that leverages various learning methodologies to ensure the acquisition of essential soft skills for ICT engineers. Specifically, the study aims to evaluate which teaching methodologies most effectively develop the key soft skills required by the ICT market for degrees in telecommunications, electronics, and computer science. To achieve this, a quantitative study was conducted through a survey of industry experts and academic professionals from various technological universities. The goal is to propose a teaching model that employs diverse methodologies to facilitate the development of these critical skills in the ICT sector. This approach builds upon previous qualitative research conducted by the authors, and the new quantitative method allows for a comparative analysis of the findings. Notably, the timing of this research is significant, as the COVID-19 pandemic has prompted a substantial shift in educational practices, leading to a global reassessment of teaching methodologies. This study builds on prior research by the authors (Llorens et al., de Miguel D ıaz, 2006) to analyze the effectiveness of various teaching methods in developing key soft skills for ICT students. The teaching methods chosen were identified as those most likely to foster critical soft skills, such as teamwork, communication, and problem-solving, based on previous studies by the authors, covering up to 10 essential skills for ICT students. It is crucial that teaching methods are selected based on their alignment with the intended soft skills development (Biggs, 1996), a principle also emphasized by Bloom’s taxonomy, which highlights the importance of aligning teaching methods with learning objectives (Anderson & Krathwohl, 2001). Given the extensive range of learning techniques and teaching methods available, it was not feasible to include them all in this study. To avoid diluting the findings and to maintain a focused analysis, a limited set of seven commonly used teaching methods was selected, as recommended by Cohen et al. (2017), who discuss the benefits of narrowing the research scope for more in-depth analysis. This selection ensures that the impact on soft skills can be comprehensively evaluated without overextending the scope of the research. The chosen methods allow for sufficient attention to be given to the execution and assessment of each, and they are familiar to the 33 expert academics and industry professionals who participated in the survey, ensuring that these methods are already in use within their lessons. The main goal is to demonstrate how active learning methods effectively foster skills commonly highlighted in the literature (Bonwell & Eison, 1991; Prince, 2004), in coherence with the authors’previous work (Llorens et al., 2017, 2019), which underscores the efficacy of these teaching methods in soft skill development for ICT Table 2. Key soft skills for ICT engineers according to employers ranked in order of importance (Llorens et al.). Teamwork 53% Communication 43% Problem solving 42% Planning 39% Learning 34% Proactivity 31% Analytical thinking 28% Customer orientation 28% Information 26% Innovation 24% 4 A. LLORENS ET AL. degrees. The research methodology design employed in this section aligns with the approach previously utilized in the authors’referenced works. Given the targeted nature of this research, random sampling was not employed. Instead, a non-random, judgmental sampling approach (also known as purposive or authoritative sampling) was used, selecting participants based on the researchers’judgment. This technique is appropriate when the target population comprises experts whose detailed insights are critical and when random selection would not be feasible. This careful sampling approach helps to minimize bias and errors, even with a small sample size. The expert sample consisted of seasoned academics and also ICT professionals, including department heads, deans of engineering schools, and rectors of technological universities. All participants have extensive experience in the ICT sector industry but also in teaching innovation and were fully informed of the study’s objectives. Ensuring sample quality and diversity was crucial for minimizing bias and enhancing the generalizability of the results. For logistical reasons, the sample was limited to Spanish universities, potentially introducing geographical or cultural bias. However, given the shared educational and business frameworks within Europe, the findings are assumed to be broadly applicable, particularly within Spain. Data collection occurred between March and April 2023, involving 33 teachers from four Spanish universities (three public and one private). The sample represented specific ICT fields: electronics (3), computer science (19), and telecommunications (11). Data were gathered via an electronic questionnaire (Google Forms) and analyzed using standard statistical software, including R-Studio. Google Forms was selected as the platform for distributing the questionnaire due to its accessibility, ease of use, and ability to collect and organize data efficiently. Building on previous studies by the authors (Llorens et al., 2017, 2019), the questionnaire was designed to assess the effectiveness of each teaching method in fostering critical soft skills among ICT students, as outlined in Table 3. Respondents were provided with definitions of each teaching method alongside the questionnaire. Results The results are summarized in Table 3, showing absolute frequencies of responses (e.g., the number ‘2’ in row 1, column 1 indicates that only 2 out of 33 experts believe that ‘lecture’contributes to developing the ‘teamwork’skill). The final columns and rows (bold and grey) display the sample median and the third quartile for methods and skills, respectively. Large values in the Median and Q3 columns indicate that ‘PBL’and ‘ABP’followed by ‘Cooperative Learning’and ‘Case Study’are the methods that contribute the most to overall skill development. In contrast, ‘Lecture’is the method that covers fewer skills. No biases related to the university or ICT subsector were detected. Figures 1 and 2illustrate the data distribution from Table 3. In these box-and-whisker plots, the boxes represent interquartile ranges (IR), bold lines denote medians, and whiskers extend to data points within 1.5 IR. A box-and-whisker plot consists of a box (which contains 50% of the data), a vertical segment known as a whisker (that indicates the spread of data), and sometimes individual points corresponding to outliers. These plots effectively visualize central values, data dispersion, and outliers, and are widely used in nonparametric statistics where a given statistical distribution cannot be assumed. For example, Figure 1 shows that while the medians for ‘Teamwork’and ‘Analytical Thinking’are similar, the dispersions differ, reflecting non-homogeneous contributions of the methods to achieve these skills. The ‘Learning’skill demonstrates strong homogeneity (methods contribute more or less equally to the achievement of this skill), indicated by the small box size, while the ‘Innovation’skill shows lower central value, suggesting the methods are not suitable for achieving this skill. Figure 2 highlights ‘Project-Based Learning’and ‘Problem-Based Learning’as the most effective methods, with high medians and low dispersion, whereas ‘Lecture’is rated less favorably. The analysis considered only values above the third quartile (Q3) to explore the relationship between teaching methods and skills, as displayed in Tables 4 and 5. Missing values represent instances where skill achievement or method effectiveness falls below Q3. The third quartile criterion offers robustness against outliers, making it more reliable than the ‘mean þstandard deviation’approach commonly used in other studies. COGENT EDUCATION 5 Table 3. Responses in absolute frequencies. Skills Methods Teamwork Communication Problem Solving Planning Learning Proactivity Analytical thinking Customer orientation Information Innovation Flexibility Creativity Median Q3 Lecture 2 9 7 5 17 2 16 5 8 1 5 2 5 8.25 Case Study 31 16 16 13 12 20 26 11 22 7 14 11 15 20.5 Problem solving 5 4 21 7 19 12 28 2 13 6 5 8 7.5 14.5 Problem based learning (ABP) 31 17 21 15 20 20 29 13 23 14 14 16 18.5 21.5 Project based learning (PBL) 26 22 20 28 20 22 26 14 26 16 23 21 22 26 Cooperative learning 33 27 9 19 22 20 16 5 13 10 18 13 17 20.5 Learning contract 3 11 13 23 23 6 8 12 8 3 6 2 8 12.25 Median 26 16 16 15 20 20 26 11 13 7 14 11 14 Q3 31 19.5 20.5 21 21 20 27 12.5 22.5 12 16 14.5 21 6 A. LLORENS ET AL. Table 4 should be read row-wise, indicating which soft skills are developed by each teaching method. For example, the first row shows that ‘lecture’is associated with ‘commitment to learning’(52%), ‘analytical thinking’(48%), and ‘communication’(27%). Project-based learning emerges as the method fostering the widest range of soft skills, whereas proactivity, innovation, flexibility, and creativity are not predominantly developed by any method, according to the experts. Only values above Q3 are shown to emphasize the most important contributions. Table 5 should be read column-wise, showing which teaching methodologies most effectively develop specific soft skills. For instance, ‘teamwork’is nurtured by ‘case study’(94%), ‘problem-based learning’(94%), and ‘cooperative learning’(100%). The data reveal that problem-based and project-based learning are the methods that cultivate the most skills, up to eight each. Only values above Q3 are displayed in order to emphasize de more important contributions. Figures 3 and 4graphically show the relationship between skills and methods in detail. The vertical dashed lines represent the third quartile. The x-axis is the percentage. Figures 5 to 8 further detail how different methods contribute to skill acquisition. To enhance visual clarity, each figure presents only two methods, with absolute frequencies indicated within the circles. A deeper analysis was conducted using the correlation matrix (Table 6). The highest correlation was observed between ‘Case Study’and ‘Problem-Based Learning’(0.92), indicating that both methods develop similar skills with varying intensities (see Figure 7). ‘Lecture’and ‘Problem Solving’also showed a moderate correlation (0.70, see Figure 6). The correlation between ‘Project-Based Learning’and ‘Problem-Based Learning’was 0.55, primarily differing in the development of ‘Planning’and ‘Flexibility’ skills (see Figure 5). Figure 1. Box-and-Whisker plot for soft skills from Table 3. Figure 2. Box-and-Whisker Plot for teaching methods from Table 3. COGENT EDUCATION 7 Table 4. Teaching methods above Q3 in percentages. Skills Teaching methods Teamwork Communication Problem Solving Planning Learning Proactivity Analytical thinking Customer orientation Information Innovation Flexibility Creativity Lecture 27 52 48 Case study 94 79 67 Problem solving 64 58 85 Problem-based learning 94 88 70 Project-based learning 79 85 79 79 Cooperative learning 100 82 67 Learning contract 39 70 70 36 8 A. LLORENS ET AL. Education at this University. He has been Director of the Teachers’Training Postgraduate Degree at UPC (20162024) and Deputy Director of the Institute of Education Sciences (ICE-UPC). He is a member of the funded research group EduSTEAM University Learning Research Group at UPC. His research is focused on Quality and in Active Methodologies in Higher Education. He has published more than 25 papers in academic journals, and.participated in 12 funded research projects related to STEAM Education. Nikola Petrovi cis doctoral candidate specializing in Quantitative Management, with a particular focus on Human Resource Management. He is a Teaching Assistant at the Faculty of Organizational Sciences, University of Belgrade, Human Resources Management department, where delivers instruction across several courses, including Human Resources Management, Training and Development, and e Learning. He also published several scientific papers on the topic of HRM, Learning Analytics and People Analytics. ORCID Nikola Petrovi chttp://orcid.org/0000-0001-5977-7509 References Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman. Ausubel, D. (1963). The psychology of meaningful verbal learning. Grune & Stratton. Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education,32(3), 347–364. https://doi. org/10.1007/BF00138871 Bonwell, C. C., & Eison, J. A. (1991). Active learning: Creating excitement in the classroom. ASHE-ERIC Higher Education Reports. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company. Cannon, R., Kapelis, Z., & Newble, D. (2000). A handbook for teachers in universities and colleges: A guide to improving teaching methods. (4th ed.). Kogan Page. Chan, C. K. Y., & Lee, K. K. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments,10(1), 60. https://doi.org/10.1186/s40561-023-00269-3 Charosky, G., Leveratto, L., Hassi, L., Papageorgiou, K., Ramos-Castro, J., & Brag os, R. (2018 Challenge based education: An approach to innovation through multidisciplinary teams of students using Design Thinking [Paper presentation]. 2018 XIII Technologies Applied to Electronics Teaching Conference (TAEE), La Laguna, Spain. pp. 1–8. https://doi. org/10.1109/TAEE.2018.8476051 Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education (8th ed.). Routledge. de Miguel D ıaz, M. (2006). Metodolog ıas De Ense~ nanza y Aprendizaje Para El Desarrollo De Competencias: Orientaciones Para El Profesorado Universitario Ante El Espacio Europeo De Educaci on Superior. Alianza. European Commission, Directorate-General for Education, Youth, Sport, and Culture. (2022). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee, and the Committee of the Regions on a European strategy for universities [online], COM (2022) 16 final. https://education. ec.europa.eu/sites/default/files/2022-01/communication-european-strategy-for-universities.pdf European Commission. (2022). Digital Education Action Plan. European Commission. https://education.ec.europa.eu/ focus-topics/digital-education/action-plan Eurostat Metadata. (2017). ICT specialist in employment. http://ec.europa.eu/eurostat/cache/metadata/en/isoc_skslf_ esms.htm Fisk, P. (2017). Education 4.0: The future of learning will be dramatically different, in school and throughout life. http://www.thegeniusworks.com/2017/01/future-education-young-everyone-taught-together/ Gaebel, M., Zhang, T., & Bunescu, L. (2021). Digitally enhanced learning and teaching in European higher education institutions. European University Association. https://www.eua.eu/publications/reports/digitally-enhanced-learningand-teaching-in-european-higher-education-institutions.html Graham, R. (2019). Improving University Reward for Teaching: A roadmap for change. https://www.4tu.nl/cee/en/ publications/roadmap-for-changeweb-version.pdf Green, A., De Hoyos, M., Barnes, S. A., Owen, D., Baldauf, B., & Behle, H. (2013). Literature review on employability, inclusion and ICT, Part 1: The concept of employability, with a specific focus on young people, older workers and migrants. Institute for Prospective Technological Studies,Luxembourg . Holzkamp, K. (1983). Learning: Process of Participation. In D. Boud, R. Keogh, & D. Walker (Eds.), Reflection: Turning Experience into Learning. (pp. 18–40). Routledge & Kegan Paul. Horv ath, I., Wiersma, M., Duhovnik, J., & Stroud, I. (2004). Navigated active learning in an international academic virtual enterprise. European Journal of Engineering Education,29(4), 505–519. https://doi.org/10.1080/ 03043790410001716275 COGENT EDUCATION 15 Isteni c Star ci c, A., & Lebeni cnik, M. (2020). Investigation of university students’perceptions of their eductors as role models and designers of digitalized curricula. Human Technology,16(1), 55–91. https://doi.org/10.17011/ht/urn. 202002242163 Kolb, D. A. (1984). Experiential Learning: Experience as the source of learning and development. Prentice Hall. Kozinski, S. (2017). How generation Z is shaping the change in education. https://www.forbes.com/sites/sievakozinsky/2017/07/24/how-generation-z-is-shaping-the-change-in-education/#304059746520 Litecky, A. R., Arnett, K. P., & Prabhakar, B. (2004). The paradox of soft skills versus technical skills in IS hiring. Journal of Computer Information Systems,45(1), 69–76. Llorens, A., Berbegal-Mirabent, J., & Llin as-Audet, X. (2017). Aligning professional skills and active learning methods: An application for information and communications technology engineering. European Journal of Engineering Education,42(4), 382–395. https://doi.org/10.1080/03043797.2016.1189880 Llorens, A., Prat, J. d., & Berbegal-Mirabent, J. (2019). The ICT skills gap in Spain: Before and after a decade of harmonizing the European Higher Education Area. Computer Applications in Engineering Education,27(4), 934–942. https://doi.org/10.1002/cae.22132 National Academy of Engineering, U.S. (2004). The engineer of 2020: Visions of engineering in the new century. National Academies Press. Neciri, I. (1979). Hacia la did actica general din amica. Kapelusz. Pazur Anicic, K., Divjak, B., & Arbanas, K. (2017). Preparing ICT graduates for real-world challenges: Results of a metaanalysis. IEEE Transactions on Education,60(3), 191–197. https://doi.org/10.1109/TE.2016.2633959 Pears, A. (2022). The future of engineering education in a post-pandemic world. In SEFI Annual Conference 2022. Universitat Polit ecnica de Catalunya. Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education,93(3), 223– 231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x Redecker, C., & Punie, Y. (2013). The future of learning 2025: developing a vision for change. Future Learning,2(1), 3– 17. https://doi.org/10.7564/13-FULE12 Reilly, C., & Reeves, T. C. (2023). Tracking transdisciplinary skills in the design of online courses: A design-based research study. Computers & Education,204, 104867. https://doi.org/10.1016/j.compedu.2023.104867 Robinson, K. (2022). Imagine if: Creating a future for us all. Penguin Books. ISBN. 9780143134169. Salam, M., Awang Iskandar, D. N., Ibrahim, D. H. A., & Farooq, M. S. (2019). Service learning in higher education: A systematic literature review. Asia Pacific Education Review,20(4), 573–593. https://doi.org/10.1007/s12564-01909580-6 Schwab, K. (2023). The Fourth Industrial Revolution. Encyclopedia Britannica. https://www.britannica.com/topic/TheFourth-Industrial-Revolution-2119734 Tondeur, J., Howard, S. K., Scherer, R., & Siddiq, F. (2023). Untangling the Great Online Transition: A network model of teachers’experiences with online practices. Computers & Education,203, 104866. https://doi.org/10.1016/j.compedu.2023.104866 Turull I Rubinat, M. (2020). Manual de docencia universitaria. Universitat de Barcelona. IDP/ICE & Ediciones Octaedro. http://hdl.handle.net/2445/166737 Zabalza, I. (1991). Los contenidos: Dise~ no y desarrollo curricular. Ed. Narcea. Madrid. 16 A. LLORENS ET AL.