Digital Transformation of Higher Education: A Foresight Study

FIRST EDITION September 2025 AUTHORS Jesús Valverde-Berrocoso Jesús Acevedo-Borrega Alberto González-Fernández COLLABORATORS María Rosa Fernández-Sánchez María del Carmen Garrido-Arroyo María José Sosa-Díaz Alain Presentación Muñoz Isabel Porras-Masero Javier García-Álvarez Research Group «Nodo Educativo». Web: https://nodoeducativo.net Department of Educational Science Faculty of Teacher Training University of Extremadura University Campus – Avda. de la Universidad s/n 10003 – Cáceres – España DOI: https:// doi.org/10.5281/zenodo.17616011 «La transformación digital de las titulaciones universitarias. Las analíticas académicas, las subjetividades y el rendimiento en tiempos prepandémicos y durante la COVID-19» (UNIDIGIT@L). Ref.: TED2021-130743B-I00. Ministerio de Ciencia e Innovación. Proyectos Orientados a la Transición Ecológica y a la Transición Digital, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023, en el marco del Plan de Recuperación, Transformación y Resiliencia. Investigadores principales: Manuel Area Moreira (ULL) y Jesús Valverde Berrocoso (UEX) Índice 1. Introduction.......................................................................................................................................4 2. Method..............................................................................................................................................7 2.1. Participants...............................................................................................................................7 2.2. Development of the sessions...............................................................................................9 2.3. Instruments............................................................................................................................10 2.3.1. Technique: “Organizational Retrospective Analysis”..............................................10 2.3.2. Technique: “SWOT Analysis”......................................................................................11 2.3.3. Base documents for the scenarios............................................................................12 2.3.4. COCD Box technique...................................................................................................19 2.4. Data analysis..........................................................................................................................21 2.4.1. Reinert method..............................................................................................................21 2.4.2. Similarity analysis........................................................................................................23 2.4.3. Specificity analysis......................................................................................................24 3. Results.............................................................................................................................................24 3.1.1. “Growth” future scenario.............................................................................................24 3.1.2. “Collapse” future scenario..........................................................................................36 3.1.3. “Discipline” Future Scenario.......................................................................................43 3.1.4. “Transformation” future scenario..............................................................................54 3.1.5. Closing Session.............................................................................................................67 4. References......................................................................................................................................79 5. Appendices....................................................................................................................................80 5.1. Informed Consent..................................................................................................................80 5.2. Invitation Letter......................................................................................................................81 5.3. Work Plan for the Sessions.................................................................................................82 5.4. Information on the Reinert Method: types of data.........................................................89 5.5. Interpretation of the data for a word (example)..............................................................90 1. Introduction The use of «alternative futures» (or «scenarios») refers to a strategic process applied within an institution or organization to plan and move toward its preferred future. The essential components of a futures thinking process are as follows, in this specific order: 1. Assessment of the past. First, it is essential to develop a shared understanding of the history of the community or group involved, going back, if possible, to «the very beginning» of that community or group, rather than limiting the analysis to the more recent past. It is impossible to think usefully and creatively about the future of something without first understanding its purpose and the many different facets of its past. 2. Understanding the present. Second, the current problems and opportunities must be analyzed. 3. Forecasting aspects of the future. Third, the potential challenges and opportunities of the futures should be analyzed, using a predefined time horizon of approximately 20 to 50 years. 4. Experiencing alternative futures. Fourth, and most crucial of all, one or more of at least four alternative futures should be explored, each based on different combinations of existing trends. 5. Visualizing the futures. Fifth, a futures visualization exercise is carried out, during which participants are better prepared to envision a preferred future for the community or group within the next 20 to 50 years, drawing upon the past, the present, and the alternative futures previously analyzed. Visualizing a preferred future is the primary goal of the entire process. 6. Creating the futures. Sixth, participants discuss and decide what actions should be taken now, and in what sequence, to begin moving the community or group toward its preferred future. 7. Institutionalizing futures research. One of the outcomes of that discussion and decision is the recognition of the need to establish a permanent futures unit that can sustain the ongoing future-oriented process. This should include a continuous exploration process that keeps «looking ahead» to identify emerging challenges and opportunities in both the near and more distant futures, in order to inform the community or group, and its leaders, about them. All the images of the future that exist in the world can be grouped into one of four generic categories: the four alternative futures. At times, certain futures may 4 appear to overlap across two or more categories, but most fit quite naturally into one of the four and not into any others. These four futures are considered «generic» in the sense that the specific varieties of images within each share common theoretical, methodological, and data-based foundations that distinguish them from those of the other three. Each alternative has both «good» and «bad» characteristics; none should be regarded as inherently good or bad. There is no such thing as an «optimal scenario» or an «unfavorable scenario.» Nor is there a «most likely scenario.» In the long run, all four generic forms are equally probable and, therefore, should be considered with equal attention and sincerity. This last point is particularly important. Table 1. Four futures and four phenomena related to the digital transformation of universities Collapse Growth Discipline Transformation Insufficient funding Economic shortage to address digital transformation Smart management Organizational optimization supported by technology Digital control policies Strict administrative regulations Innovation spaces Collaborative research laboratories and centers Digital critics Technophobic attitudes Sustainable Digital Education Application of Universal Design for Learning Standardization Processes regulated by quality standards and monitored by external agencies Technological invisibility 360° connectivity Edu-hyperinflation Excessive and massified university supply Personalized learning Advanced learning platforms Structured learning Homogeneous curriculum applied in predefined environments Learning community Individualized learning pathways developed collaboratively Digital ethics Serious issues of academic dishonesty Digital creativity Teachers and students as co-creators Regulation and standards Application of ethical and legal codes Self-regulation Micro-credentials and lifelong learning 5 Figure 1. Four future scenarios on the digital transformation of Higher Education 6 2. Method This futures study was conducted with the participation of a group composed of nine members of the university community, representing the different sectors that comprise it: teaching and research staff; professional, administrative, and technical staff (PAT staff); and students. The diversity in the group’s composition sought to ensure a plurality of perspectives and to foster the collective construction of visions regarding the institution’s future in relation to its digital transformation. The research was structured around several working sessions in which four future scenarios—Growth, Collapse, Discipline, and Transformation—were explored, along with a final session aimed at visualizing the preferred future. The activities carried out are framed within the tradition of futures studies, which promote reflection on possible and plausible alternatives as a strategy to guide institutional planning and foster processes of strategic anticipation. For the analysis of the session transcripts, the Reinert method was applied using the Iramuteq software. This methodological choice responds to the need to approach the discursive corpus from a perspective that combines quantitative rigor with qualitative interpretive richness. The Reinert method is based on the premise that words are not independent of one another, but rather reflect shared ways of thinking and underlying structures of meaning. Through a lexical analysis grounded in descending hierarchical classification, it allows for the identification of semantic universes, or «lexical worlds,» that emerge within the discourse without imposing predefined categories. Several reasons can be considered to justify the use of this method. First, it allows for the inductive extraction of the latent structure underlying the group’s discourse. Second, it makes it possible to identify dominant lexical patterns that reveal perceptions, tensions, and areas of consensus surrounding the futures under analysis. Finally, it provides a statistical basis that lends validity to the interpretations while also allowing the results to be related to contextual variables such as participants’ institutional role, gender, or field of knowledge, among others. 2.1. Participants A selection of participants was carried out to ensure representation from different sectors of the university community. Invitations were sent to 14 teaching and research staff from various campuses and university centers of the University of Extremadura (UEx), belonging to different fields of knowledge and holding diverse roles (with institutional responsibilities or solely teaching and research positions). Three undergraduate and postgraduate students were also invited, along with two members of the professional, administrative, and technical staff. The invitation letter and the informed consent form can be consulted in the 7 Appendix of this report. A total of nine members of the university community ultimately participated: six professors, two students, and one PAT staff member (see Table 2). Table 2. Description of participants Profile Gender Role Field Professor Male Non-institutional role Sociology Professor Female Non-institutional role Computer Science Professor Male Institutional role Education Professor Male Non-institutional role Education Professor Female Institutional role Law Professor Male Institutional role Education Student Female Undergraduate Education Student Male Postgraduate Education PAT staff Female University services Social Figure 2. Working session with the participant group 8 2.2. Development of the sessions The «Four Futures» exercise was carried out as a one-day workshop or seminar (morning and afternoon). The program developed was as follows (see Appendix): Session 01 – Presentation of the exercise and explanation of the working methodology. (30–40 minutes) Session 02 – Where we come from, where we are. (60–75 minutes) I. Assessment of the past. History of the institution in relation to the digitalization of the teaching and learning process. In particular, participants analyzed the evolution of the university’s virtual campus. II. Understanding the present. Analysis of current problems and opportunities. A SWOT analysis (strengths and weaknesses) may be an appropriate technique. III. Forecasting aspects of the futures. Analysis of the possible challenges and opportunities for the future over the next 10 years. A SWOT analysis (threats and opportunities) may be an appropriate technique. Break – Coffee Session 03 – Future scenario 1: Discipline. (60 minutes) Session 04 – Future scenario 2: Growth. (60 minutes) Break – Lunch Session 05 – Future scenario 3: Transformation. (60 minutes) Session 06 – Future scenario 4: Collapse. (60 minutes) Break – Coffee Session 07 – Visualization of the preferred future. (45 minutes) Figure 3. Workspace for the sessions with the setting of a future scenario 9 Base document for the «Discipline» scenario By the year 2030, Higher Education has adopted a conservative approach to addressing global challenges. This scenario is characterized by strict regulation and the strategic use of digital technology to ensure educational stability and quality. In this «Discipline» scenario, Higher Education is defined by a rigorous and structured approach in which digital technology is used strategically to guarantee quality, sustainability, and equity. Regulation and supervision are key to maintaining the stability and effectiveness of the educational system. Universities have developed highly sophisticated quality assurance systems that include detailed procedures for the design and implementation of teaching activities. Digital technologies play a crucial role in the design and control of all these processes. Moreover, learning platforms follow strict standardization guidelines, allowing for homogeneity and direct transfer of pedagogical design across the university context. Faculty members use complex assessment systems to monitor student learning within a framework of internationally recognized competency indicators. All certification and credentialing processes are regulated by exhaustive administrative standards within a digital system for competency recognition. Institutional ethical codes, aligned with current legislation, govern conduct in the digital domain. -Digital control policies. Universities operate under strict governmental regulations that ensure sustainable and efficient practices. Access to digital infrastructures and services is governed by a robust and tightly controlled security system designed to protect user identity. Student admissions are carefully managed to guarantee the optimal use of available resources. Quality is prioritized over quantity, with a focus on training highly qualified leaders and professionals. Strict policies are implemented to ensure that all students have equitable access to education regardless of their socioeconomic background. Technology is used to identify and support students who require additional assistance. -Standardization. Online learning platforms are standardized and regulated to ensure uniform quality across all institutions. These platforms use artificial intelligence to personalize learning within the limits established by educational policies. Technology is employed to monitor students’ progress and to conduct continuous assessment of their performance. The data collected are used to adapt educational programs and ensure compliance with quality standards, according to the criteria set by national or regional agencies. Universities are part of regulated global networks that facilitate collaboration and knowledge exchange. These networks are monitored to ensure that partnerships meet ethical and quality standards. -Structured learning. Hybrid education follows a structured and disciplined model. Online and face-to-face classes are carefully coordinated to ensure a coherent and high-quality educational experience. Microcredentials and certifications are regulated and aligned with labor market needs. Programs are rigorous and designed to ensure that graduates possess the digital competencies required to contribute effectively to society. Virtual learning communities are moderated to maintain a safe and productive environment. Collaboration is 16 encouraged, but always within a framework of discipline and mutual respect. -Digital regulation. Academic programs are designed to instill a mindset of sustainability and social responsibility in students. The importance of resource conservation and environmental management is emphasized. Comprehensive ethical codes of conduct are implemented in digital environments, with significant academic consequences for those who fail to comply. Base document for the «Transformation» scenario By the year 2030, Higher Education has experienced a radical transformation driven by technological innovations, cultural changes, and new forms of social organization. This scenario is characterized by a significant break with the past and the creation of new educational structures and paradigms. In this «Transformation» scenario, Higher Education not only adapts to the needs of a digital and globalized world but also leads the way toward a more innovative future. Digital technology is the driving force behind this transformation, enabling a more personalized form of education aligned with the socio-labor needs of the immediate context. Higher Education organizations have undergone a radical change, evolving from rigid and isolated institutions into flexible and interconnected centers with a remarkable capacity to adapt to the changing needs of a dynamic society. The integration of digital technologies is so extensive that they become «invisible» and, at the same time, continuously present in academic and research activities. The organization of the teaching–learning process is highly diversified, based on collaboration and on students’ autonomous decision-making when designing their own learning pathways. Networked universities apply a wide variety of methods and procedures for the recognition of students’ competencies. -Innovation spaces. Innovation laboratories and collaborative research centers have been created where students, faculty, and professionals work together on interdisciplinary projects that address global challenges. The new hybrid university campuses integrate the educational offerings of multiple organizations (research groups, learning communities, university departments, faculties, research centers, professional associations, research networks, companies, governmental agencies, and civic associations) that are accessible through highly advanced communicative immersion technologies (videoconferencing + virtual/augmented reality + artificial intelligence). The university is a networked, highly flexible, and rapidly adaptive institution that maintains a collective identity with both physical and virtual components. Physical and virtual mobility of faculty and students is constant within the network to which each university belongs. Institutions are part of global networks that facilitate collaboration and knowledge exchange. Students participate in international projects and have access to educational resources from around the world. -Technological invisibility. Digital infrastructures and services are fully integrated into academic and research activities. Artificial intelligence is used to create highly personalized learning experiences. Intelligent tutoring systems adapt content and activities to the individual needs and 17 preferences of each student. All technological devices are interconnected, allowing for direct and transparent use by any user from the cloud, anywhere. Accessible technologies have been developed to ensure that all students, regardless of their abilities or resources, can continue their education. This includes low-cost devices and subsidized connectivity programs. -Learning community. Online platforms enable the creation of learning communities where students can collaborate, share resources, and support one another regardless of their location. Study programs are modular and customizable, allowing students to design their own educational pathways. -Self-regulation. Universities offer microcredentials and continuing education programs that allow students to acquire new skills and knowledge throughout their professional lives. Questions for the discussion of the four scenarios To guide the discussion, the following set of questions was used: A. General discussion about the future A1. What would be the attitude and behavior of most members of the university community (teaching and research staff, students, and administrative, technical, and service personnel) in a future like this? A2.Which educational–digital issues (e.g., distraction/attention; motivation/interest in knowledge; digital skills/competencies; plagiarism/academic ethics; abuse/cyberbullying, etc.) that concern universities today will have disappeared or become relatively minor? A3.Which organizational–digital issues (e.g., attendance/participation in academic activities; academic calendars/schedules; structure/functioning of the virtual campus/classroom; supervision of teaching practice, etc.) that concern universities today will have disappeared or become relatively minor? A4.What other issues related to digital transformation that concern universities today will have disappeared or become relatively minor? A5.What new issues related to digital transformation, which do not currently exist or are not yet significant, will universities have to address in the future? B. To what extent is the future described in your scenario likely to occur? C. Is the future described in this scenario preferable? That is, to what extent does it resemble the future you would prefer? 18 D. To the extent that the group considers the future described in this scenario preferable, what five actions should be taken now to move toward the desirable aspects of that future? E. To the extent that the group considers the future described in your scenario undesirable, what five actions should be taken now to prevent those undesirable aspects from occurring? 2.3.4. COCD Box technique The COCD Box is an idea prioritization technique developed by the Center for Development of Creative Thinking (COCD) in Belgium. It is mainly used in innovation, creativity, and strategic planning processes, with the objective of organizing and selecting ideas or proposals according to their degree of originality and feasibility. The logic of the technique is represented graphically in a two-axis matrix: (a) the horizontal axis reflects the level of originality or novelty of the idea (ranging from conventional to radical), and (b) the vertical axis reflects the degree of feasibility (ranging from easily achievable to very difficult to implement). By combining both dimensions, ideas are distributed into four main quadrants, represented by three colors (blue, red, and yellow) and a «colorless» box. Each color indicates a specific level of innovation and feasibility. -Blue box: realistic ideas that are easy to implement in the short term. -Red box: feasible ideas that require more time and resources. These ideas do not involve radical innovation but have a positive and achievable impact. -Yellow box: innovative and ambitious ideas that are difficult to implement but have high transformative potential. -Colorless box: ideas that are not considered viable or useful at the moment; they are discarded. This technique integrates creativity with practical implementation, preventing valuable ideas from being discarded for appearing too novel or from prioritizing only those that are easy to execute without considering their innovative impact. 19 Figure 6. Template model for the COCD Box technique Steps in the application of the technique 1. Idea generation Bring the team together and present the problem: «What is the preferable future?» Then, brainstorming techniques can be used to generate as many ideas as possible, without making any judgments or evaluations at this stage. 2. Initial evaluation and classification Each member reviews all the ideas and begins to evaluate them according to two criteria: the level of innovation and feasibility (time, resources, impact). It is important that this initial classification be carried out individually or in small groups to ensure a variety of perspectives. 3. Classification in the COCD Box Organize the ideas within the color-coded chart, assigning each idea to the category that best reflects its degree of innovation and feasibility. -Blue ideas: realistic and easy to implement; therefore, they are placed in the blue box. -Red ideas: feasible and achievable in the medium term with reasonable resources; therefore, they are placed in the red box. 20 -Yellow ideas: highly innovative or transformative but difficult to implement in the short term. They are placed in the yellow box. -Colorless box: ideas that are not viable or that show limited alignment with the current objective. These are placed in the colorless box. 4. Selection of priority ideas Analyze and select the ideas together with the team. In general, blue and red ideas are considered priorities in the short and medium term, while yellow ideas can be planned as long-term objectives. Ideas placed in the colorless box are discarded, although they may be reconsidered in the future if the context changes. 2.4. Data analysis The Iramuteq software was used, which implements the Reinert method along with two complementary analyses: similarity analysis and specificity analysis. These are described below. 2.4.1. Reinert method The textual corpora corresponding to the four future scenarios were analyzed using the Reinert method. This method is based on the premise that words are not independent from one another but reflect a way of thinking about the topic under discussion, as statements acquire meaning through words (Reinert, 1990, 1998). This method belongs to the tradition of quantitative textual analysis, which combines multivariate statistical techniques with principles of discourse analysis and semiotics. The Reinert method is widely used in studies of collective perceptions. Its main strengths lie in its ability to inductively extract the latent structure of discourse, identify dominant lexical patterns without imposing preconceived categories, and combine quantitative rigor with qualitative interpretation. The corpus of interventions from each focus group session was subjected to a lexical analysis (Reinert method) using the Iramuteq software. The text is segmented into Elementary Context Units (ECUs), which are relatively short portions of text (normally between 30 and 50 words) that constitute the basic unit of analysis. The program transforms words into their canonical form or lemma (for example, «working,» «works,» and «worked» are reduced to «work»), removing stopwords to focus on semantically meaningful terms. It then builds a contingency table that records the frequency of each word’s occurrence within each ECU, showing how key terms are distributed throughout the corpus. The core of the method is a descending hierarchical classification algorithm, inspired by the logic of factorial and cluster analysis. This technique groups ECUs into classes or «lexical worlds» that are internally homogeneous but heterogeneous with respect to each other, based on the co-occurrence of words. Each resulting class is 21 interpreted as a characteristic semantic field or «lexical universe.» In addition, the program provides significant associations with variables (for example, age, gender, or institutional role) when metadata have been included. Finally, from the contingency table, a distance matrix is generated that allows for the graphical representation of the proximity or distance relationships between ECUs and classes, identifying the central and peripheral nodes of the discourse (LarruzeaUrkixo et_al., 2021; Martins et_al., 2022). To determine which words are considered relevant in the analysis using the Reinert method, three criteria were applied to ensure that only words that clearly and reliably characterize each text group were taken into account (Fernandez Rotaeche et_al., 2021): a) Sufficient frequency: the word must appear more times than the overall average frequency in the entire analyzed text (i.e., the average number of occurrences for each form across the corpus). b) Expected value: the word must have an expected value greater than 3; that is, it is expected to appear at least three times within the class or text group being studied. c) Significant association: the relationship between the word and the class must be statistically significant. To determine this, a chi-square value equal to or greater than 3.89 is calculated, which indicates that there is less than a 5% probability that this association occurred by chance (p < .05, with 1 degree of freedom). When applying the Reinert method, the Iramuteq software automatically groups the text into «classes» or clusters. Each class consists of (1) keywords that appear frequently and characteristically within that group, and (2) text segments (ECUs) that contain those words. These groupings are based on statistical calculations (frequencies, chi-square values) and are therefore not arbitrary. Consequently, these statistical values make it possible to interpret each «class» as an important theme or subtheme within the corpus. There is a quantitative basis showing that these words and segments are significantly related to each other. Once the classes have been defined, it is possible to analyze which participant characteristics (passive variables1) are associated with each class. In our case, these passive variables were: −Gender: woman/man. −Role within the university: teaching and research staff, student, or professional, administrative, and technical staff. 1 They are not part of the text segmentation itself but are later cross-referenced to analyze patterns (for example, to determine whether a theme is more strongly associated with women than with men). 22 −In the case of teaching and research staff: whether they hold a management position or not, and the academic field to which they belong. −In the case of students: whether they are undergraduate or postgraduate students. The entire analysis process (segmentation, grouping, and calculations) is statistical and reproducible; that is, any repetition of the analysis using the same data will yield identical results. The subjective component arises when interpreting the results and assigning a label to each class. To carry out this labeling, the most characteristic words, the representative text segments, the co-occurrences (i.e., which words appear together), and the distribution patterns of those words within the corpus are taken into account. This final interpretation combines the quantitative foundation with the researchers’ qualitative reading. 2.4.2. Similarity analysis Similarity analysis is a lexical network analysis technique that graphically represents how words are related within a corpus, showing which terms co-occur (appear together) in the same text segments. This type of analysis provides an «Xray» of the overall semantic structure, complementing the detailed class-based perspective offered by the Descending Hierarchical Classification (DHC). The Iramuteq software applies this technique following graph theory principles (Diestel, 2025) and the methodology proposed by Marchand and Ratinaud (2012), as well as the approach of (1996) and Flament (1962) for visualizing networks of social representations. This means that the software represents words as «nodes» within a graph, and the relationships between words as «edges» or lines connecting them. Each edge shows how many times two words appear together within the same text segments (co-occurrences). In this way, a lexical network is generated that reveals which terms are most closely linked to one another and which words act as bridges between different ideas or themes. First, it examines all the Elementary Context Units (ECUs) generated during segmentation. Then, it records how many times each pair of words appears together within the same segment and, finally, calculates a co-occurrence matrix that serves as the basis for constructing a similarity graph: (a) nodes, which are the active words in the corpus; (b) edges (lines), which represent the links between words that co-occur; and (c) edge weight, which corresponds to the number of co-occurrences. In this type of analysis, the larger the node, the higher the frequency of the word; and the thicker the line, the stronger the relationship between those words. This approach helps to understand, both visually and quantitatively, the internal structure of the discourse. Similarity analysis has an exploratory nature, as it complements the Descending Hierarchical Classification by showing the articulation between thematic cores and bridging words that structure the analyzed discourse. At the 23 same time, it is a reproducible analysis, meaning that co-occurrences are calculated objectively. This method allows for the rapid visualization of connections and thematic subfields. It is commonly used in studies of social representations, collective discourses, narratives, and perceptions, making it particularly suitable for futures studies. 2.4.3. Specificity analysis Specificity analysis is a statistical technique that identifies which words are significantly more frequent in each part of a corpus by comparing them with their distribution in the rest of the corpus. Its purpose is to identify the characteristic vocabulary of each subcorpus or variable (for example, participant groups, text genres, or sociodemographic categories). It is based on the calculation of the chisquare (χ²) to determine whether the difference in a word’s frequency is statistically significant or due to chance. The Iramuteq software compares each lexical form within each category to its overall frequency. For each word, a contingency table is generated showing the word’s frequency within a given category and its frequency across the entire corpus. The chi-square test of independence is applied to determine whether the association is statistically significant. By convention, χ² ≥ 3.84 indicates p < . 05; in other words, there is less than a 5 % probability that the concentration of the word in that group occurred by chance; or, stated differently, we have at least 95 % confidence that the word is truly overrepresented in that group in a statistically significant way. Specificity analysis makes it possible to identify which terms clearly distinguish each group, revealing their distinctive discursive features. It is particularly useful in comparative analyses—for example, contrasting the way different participant profiles, interview stages, or text types are expressed. Finally, it complements the Descending Hierarchical Classification (DHC) by showing which words characterize each passive variable, even when they do not form classes. 3. Results 3.1.1. «Growth» future scenario The analyzed corpus is composed of 148 texts, segmented into 262 Elementary Context Units (ECUs) with an average of 26.72 words per segment, following the parameters recommended by the Reinert Method. The total number of lexical occurrences amounts to 7,001, distributed across 947 lemmas, which reflects an adequate level of lexical richness. The Descending Hierarchical Classification (DHC) grouped 67.18% of the segments into four classes with significant lexical associations, using the DHC algorithm and considering 246 active forms with a minimum frequency of three. These results provide a solid 24 statistical foundation for interpreting the predominant themes within the corpus and analyzing their relationship with the «passive» variables. The vocabulary of each class aligns with the main narrative blocks presented prior to the discussion. In fact, the words function as «anchors» that participants used when positioning themselves in relation to the «Growth» future scenario. Table 3. Lexical classes (DHC) and their meaning in the «Growth» future scenario Class % of ECUs Core words (χ² ≥ 8) Thematic core 132.4% growth, issue, economic, scenario, resource… Development and campus: discourse on growth, resources, and perceived shortcomings at the university. 216.5% sustainability, seem, set, word, question… Construction of the concept of sustainability: terminological debate and reflections on its scope. 326.1% methodology, student, class, attend, work, want… Teaching practice: focus on the teacher– student relationship, lesson planning and methodological improvement. 425% know, year, thing, people, time, work… Personal experience and trajectory: lived accounts, the passage of time and everyday observations. **Passive variables with χ² > 3.89 and p < .05 considered significant As shown in the dendrogram in Figure 7, following the cluster analysis division, four main classes or thematic areas were extracted. These refer to the organization (the university) in relation to its evolution and professional development (Classes 1, 2, and 3), as well as to one of its main actors (the teaching and research staff) and their experiences within the university community (Class 4). The dendrogram was divided into two branches: the first branch comprises the first, second, and third classes, which refer to the university as an institution. Specifically, Class 1 addresses the «Development» of the university organization (32.39%). Greater economic growth is linked to a decline in continuous on-campus presence: «We are here in the morning and disappear from campus by 1 p.m. It becomes a wasteland then. Of course, all that life that once fostered certain economic growth is also being lost. And the same happens to the students» (male, teaching and research staff, institutional role, social science education; χ² = 97.54). At the same time, a paradox arises when considering that this scenario may actually suggest the need for «degrowth» to maintain viable university services: «This is supposed to be a growth scenario, but it’s absurd, because sustainability, at least for our university to remain viable, may require it to shrink, to reduce in size, in programs, in buildings, and that’s what makes it sustainable» (male, teaching and research staff, non-institutional role, sociology; χ² = 79.99). Two passive variables are associated with this class: sociology (χ² = 12.54; p < .001) and social science education (χ² = 8.70; p < .005). 25 The graph (Figure 9) shows a discourse with a double polarization: future strategy (growth–sustainability) versus experiential narrative (personal experiences). As can be observed, the graph is organized around a central core (0) containing verbs of argumentation and problem management, which connect all thematic lines. From this nucleus emerge six peripheral communities corresponding to the main discursive focuses of the focus group: (1) Innovation and university resources; (2) Contextualized narrative on institutional development (yellow); (3) Analysis of the scenario’s underlying values; (4) Organization of teaching and the student’s role (cyan); (5) Conceptual and ethical debate on sustainability; and (6) Experiential narratives and everyday experiences. From the reading of the implicit axes in the graph (Figure 9), a left–right opposition emerges between an institutional–strategic pole (growth, sustainability, campus) and an experiential–everyday pole (people, time, talk), and a top–bottom contrast between a conceptual–argumentative plane (node «say» and community 5) and a descriptive–operational plane (communities 6 and 4). The communities are clearly separated, confirming the thematic coherence of each block detected in the CHD (Descending Hierarchical Classification). Class 1 («Development») is represented in lexical communities 1 and 2; Class 2 («Sustainability») in communities 3 and 5; Class 3 («Teaching practice») in community 4; and Class 4 («Experience») in community 6. Specificities analysis: gender As shown in Figure 10, the residual value of +5.0 for the word «sustainability» indicates that male participants mentioned this concept far more frequently than expected. The term «growth» reinforces the same pattern (residual +2.3). In summary, men dominated the discourse on strategies for sustainable expansion. Conversely, women tended to overuse the word «power» (residual +3.0), referring to capability, empowerment, or influence within the future scenario. The noun «problem» was also more frequent among women, suggesting a stronger focus on obstacles or risks. 32 Figure 10. Specificities analysis by the passive variable «gender» The analysis reveals two gender-based lexical emphases: men monopolize the rhetoric of «sustainability and growth,» while women introduce the dimension of «power and problems,» adding critical nuances and a sense of personal agency (that is, the capacity to act intentionally, make decisions, and bring about change in one’s environment). The overfrequency of the word «power» among women suggests that they more strongly express the idea of «I can do or change something.» These specificities complement the gender distribution observed in the CHD classes (men concentrated in the «Sustainability» class, women in the «Everyday experiences» class). Other terms related to the academic community (teacher, student) and attitude verbs (to want, to create or to believe) did not reach statistical significance, indicating similar discourse patterns between genders on those topics. Specificities analysis: teaching and research staff Although the differences do not reach the threshold of statistical significance, the analysis reveals two discursive matrices: one more structured and problemsolving oriented (teaching staff with an institutional role), and another more proactive and focused on the capacity to grow and transform (teaching staff without an institutional role). 33 Figura 11. Análisis de especificidades con la variable pasiva «docente» (con cargo o sin cargo institucional). Regarding the lexical profile of teaching staff with an institutional role, the term «professor» (residual +1.8) indicates that they refer to their formal position more frequently than expected, consistent with their institutional responsibility. They show greater concern for the strategic and environmental dimensions of the scenario («sustainability,» +1.7). Their discourse includes more references to difficulties and student management (+1.2 and +0.8, respectively). However, they speak less about individual empowerment («power,» -1.0) and the concept of «growth» (-1.3). Therefore, teaching staff with an institutional role tend to adopt an institutional register: they speak as teacher-managers who analyze problems and guide sustainability. Regarding the lexical profile of teaching staff without an institutional role, they use the idea of «growth» more frequently (+1.3) and refer less than expected to terms such as «professor,» «student,» «sustainability,» or «problem.» This may suggest a lower focus on organizational aspects and environmental discourse. Thus, teaching staff without an institutional role position themselves within a more goal-oriented discourse, centered on aspirations and future projection. They do not limit themselves to describing the current situation but emphasize what they aim to become («to grow,» «to improve,» «to innovate»). Teaching staff without an institutional role speak more about future possibilities and their own role than about problems, positions, or formal structures. Their discourse focuses on experience and individual initiative rather than institutional logic. 34 Conclusions The analysis of the «Growth» future scenario makes it possible to identify four key strategic axes. First, the issue of growth and resources reflects the need to expand services and campuses, while warning against oversizing that could compromise future sustainability. Second, sustainability emerges as a still poorly defined concept, whose clarification is a priority for its integration into teaching, research, and management dimensions. Third, teaching methodologies reveal a clear demand for practical training and for exchange spaces that foster classroom innovation. Finally, everyday experience provides elements that should not be overlooked: coordination difficulties, emotional burden within the university community, and obstacles that hinder students’ ability to organize into groups. The discussion also reveals key tensions and opportunities. The opposition between growth and sustainability constitutes a central dilemma that may become an opportunity if framed within a discourse of sustainable growth. Similarly, the distance between the macro and the micro levels (institutional strategies versus classroom experiences) highlights the need to establish connecting mechanisms that translate strategic planning into operational actions while ensuring that everyday experiences feed back into institutional planning. The gap between conceptual frameworks and experiential narratives further underscores the importance of integrating both dimensions so that overarching principles remain connected to actual practice. The profile-based analysis reveals relevant differences in discourse. Men tend to adopt a more institutional perspective, emphasizing sustainability and growth, whereas women introduce a critical view focused on power and problems, contributing greater sensitivity to risks. Those holding institutional positions focus on management and difficulties, while teaching staff with non-institutional role express themselves in terms more inclined toward change. Incorporating these diverse perspectives into the planning process is a decisive factor for avoiding bias and enriching future strategies. Based on these findings, several operational recommendations can be established. First, it is necessary to reach a consensus on a broad definition of sustainability that incorporates environmental, social, pedagogical, and economic dimensions. Second, it is recommended to review the university’s growth model, prioritizing selective development based on quality and sustainability criteria. Third, hybrid methodologies should be promoted to link pedagogical innovation with sustainable practices, such as the digitalization of processes. It is also advisable to create teaching communities of practice that foster peer learning and the exchange of successful experiences. Equally important is to give space to the everyday narratives of teaching and research staff, administrative and technical personnel, and students within strategic plans, so that daily experiences are incorporated into institutional planning. Finally, it is suggested to reduce the bureaucracy associated with international initiatives and alliances, such as EU 35 GREEN, so that they are perceived as useful instruments rather than an additional burden. 3.1.2. «Collapse» future scenario The analyzed corpus consists of 137 texts, segmented into 218 Elementary Context Units (ECUs) with an average of 25.20 words per segment, following the parameters recommended by the Reinert Method. The total number of lexical occurrences amounts to 5,493, distributed across 915 lemmas, reflecting adequate lexical richness. The Descending Hierarchical Classification (CHD) grouped 67.43% of the segments into four classes with significant lexical associations, based on 221 active forms with a minimum frequency of three or more. These results provide a solid statistical foundation for interpreting the predominant themes in the corpus and analyzing their relationship with the «passive» variables. The vocabulary of each class aligns with the major narrative blocks provided before the discussion. In fact, the words function as «anchors» that participants used when positioning themselves in relation to the «Collapse» future scenario. Table 5. Lexical classes (CHD) and their meaning in the «Collapse» future scenario Class % of ECUs Core words (χ² ≥ 8) Thematic core 1 15.7% license, Google, decision, system, Microsoft Technological management and licensing costs. Debate on the migration from Google to Microsoft, costs, and the perception of these expenses as «unaffordable.» 2 32.6% here, place, clear, movement, return Territorial anchoring and attractiveness of place: living, working or returning to Extremadura; quality of life versus structural shortcomings. 3 27.2% say, set/put, digital, present, funding Digital change and communication: discourses on technological presentism, funding, and the need to «put in» resources. Debate on digital change and the urgency of financing. 4 24.5% do, good, lack/shortage, instructor, thing Pedagogical action and solutions: emphasis on «doing» in the face of a lack of resources, the role of teaching staff, and continuous improvement. Pragmatic response: «doing» despite limited resources. 36 **Passive variables with χ² > 3.89 and p < .05 considered significant Figure 12. Dendrogram of the «Collapse» future scenario Figure 13 shows the Correspondence Factor Analysis (CFA) of the corpus corresponding to the «Collapse» future scenario. 37 Figure 13. Correspondence Factor Analysis (CFA) map for the «Collapse» scenario This graph projects the four classes (and their most characteristic lemmas) onto two axes that explain 70.5% of the lexical inertia (Factor 1 = 38.9%; Factor 2 = 31.6%): (1) Infrastructure versus Teaching Practice and (2) Territorial Anchoring versus Digital Present. Factor 1 (X) displays a contrast between «infrastructure» and «pedagogical action.» The left pole (red cluster) centers on license and system management, whereas the right pole (blue-purple cluster) focuses on teaching and operational responses. This axis separates the administrative-technological discourse, which concentrates on the migration from Google to Microsoft and on the «unbearable» costs associated with that decision, from the pragmatic narrative that calls for «doing» in order to overcome the shortage of resources. The threat of ICT dependence is discussed from a local perspective. In relation to the future scenario, the far-left pole highlights the university’s structural dependence on ICT providers, while the far-right pole reflects the initiative of teaching and research staff who seek practical solutions despite existing constraints. Factor 2 (Y) shows, at the lower pole (green cluster), references to identity and place, and at the upper pole (cyan-blue cluster), references to the digital present and funding. This axis contrasts the emphasis on territory («being here,» «returning»), which underscores quality of life and the loss of talent, with a digital-present discourse concerned with «providing» resources and «funding» to keep pace with current technological demands. From a futures-oriented perspective, the lower pole underscores the 38 university’s local vocation (remaining in the territory to strengthen it), whereas the upper pole points to the urgency of modernization in response to global technological pressures. The factors reveal two complementary tensions in the focus group discussion, which can be expressed as questions: (1) Do we continue debating technological costs and the soundness or unsoundness of the decisions that have been made, or do we move toward teaching action? (Infrastructure vs. Practice); and (2) Do we reinforce territorial identity or prioritize technological updating? (Local vs. Globaldigital). From these polarities, it can be concluded that there is a need to design strategies that connect technological modernization with the local context and, at the same time, transform diagnostic insights into concrete actions for classroom teaching practice. Overall, the lexical map confirms that the narrative of the «Collapse» future scenario functioned as a cognitive frame: participants reproduced its four major blocks and articulated their responses in line with the axes anticipated by the guiding questions. This reinforces the validity of the foresight method used in the focus group. Table 6. Relationships between the four lexical classes and the «Collapse» future scenario Reinert Class Central 2030 scenario feature linked to it Lexical evidence / excerpt (χ² ≥ 8) Protocol elements that activate it 1. Technological management and licensing (15.65%) Lack of funding to maintain ICT infrastructure leads to a return to face-toface methodologies as a cost-reduction strategy license, Google, Microsoft, unaffordable, system. «The Zoom license is unaffordable…» A3 questions (current organizational–digital problems) and A4 questions (new technological problems) 2. Territorial identity (32.65%) «Returning» to the physical campus in a peripheral region with quality of life but structural shortcomings here, place, return, clearly, movement. «Life here is very good… attracting talent to Extremadura» A1 question (attitudes/behaviors of the university community) + Block E (risks to be avoided) 3. Digital change and funding (27.21%) Emergence of «digital critics» and saturation of tools within the collapse scenario say, provide, digital, present, funding. «Provide resources now… the digital present is overwhelming us» A2 questions (educational–digital problems) and A5 questions (future problems) 4. Pragmatic response (24.49%) The weight of the inperson teacher as the pillar sustaining the system after the do, teacher, lack, good, people. «Less diagnosis and more doing… the teacher Block D (five actions to begin implementing today) 39 Reinert Class Central 2030 scenario feature linked to it Lexical evidence / excerpt (χ² ≥ 8) Protocol elements that activate it collapse holds everything up» The four lexical classes closely reflect the four threats described in the «Collapse» scenario document (funding, digital critics, edu-hyperinflation, ethics). Each lexical block is activated immediately after the corresponding question, demonstrating the effectiveness of the protocol in guiding the discussion. Factor 1 presents the central antithesis of the scenario: expensive technology that collapses versus teaching staff who keep the system running. The correlation between action verbs («do») and nouns denoting shortage («lack») confirms that participants internalized the idea of «returning to chalk and blackboard.» Factor 2 expresses the «in-person versus online provision» tension, which defines the concept of edu-hyperinflation and the scenario’s «return to campus.» The lexical field related to place («here,» «return») shows the interest in establishing territorial differentiation within an oversaturated market. The «digital present» class brings to light the concerns highlighted in the «Digital Critics» section of the narrative, including reactions to fake news, privacy issues, and negative effects on mental health, as reflected in the students’ contributions. Similarity analysis Figure 14 shows, as a result of the similarity analysis, the co-occurrences (words that appear together) and their connection structure. The graph displays the «nodes,» or active words (frequency ≥ 3 in the corpus); the «node size,» which corresponds to the absolute frequency of each word; and the «edges,» that is, the co-occurrences within the same ECU, which are only shown when they appear ≥ 2 times (edge threshold = 2). Finally, the colors identify communities detected through the Louvain algorithm, each grouping words that co-occur more frequently with one another than with the rest. Thick edges indicate stable lexical links (≥ 5 co-occurrences). 40 Figure 14. Results of the lexical similarity analysis by communities for the «Collapse» scenario The similarity map reveals a clearly defined narrative thread: say → do → change. The word say functions as the core of the discourse, linking reflection on the problem with proposed actions. Next, do operates as a bridge between the community that calls for solutions now and the one that emphasizes the reality here, showing the progression from diagnosis to the implementation of changes within the university institution. This discursive axis — say → do → change — shows that the discussion has moved beyond the diagnostic stage. The driver of change lies in the transition from say to do. The word do connects temporal urgency (now) with territorial anchoring (here), revealing a willingness to act immediately within the local context. The discourse analysis developed in this scenario suggests that institutional planning should prioritize 41 here, as concrete anecdotes and dilemmas emerge regarding how to preserve educational excellence without undermining student autonomy. Class 2, Creativity (grey), appears near the origin of the axes, functioning as a semantic bridge that connects the four surrounding areas. Its central position indicates that pedagogical innovation (the freedom to create and adapt content) structures both the macroinstitutional reflections (ethics, big data) and the micro-pedagogical ones (control, quality). Figure 16. Correspondence Analysis (CA) map for the «Discipline» scenario4 The two axes intersect to structure the discourse on «Discipline». Factor 1 (X) contrasts a pragmatic-autonomic dimension (right side), where debates on publication ethics and educational quality emerge, with a technocratic-systemic dimension (left side), focused on big data and institutional control mechanisms. This polarity reflects the tension between the freedom of action of institutional actors (instructors and students) and the implementation of data-driven solutions and formal rules. Factor 2 (Y) opposes a conceptual-reflective discourse (upper part), characteristic of discussions on ethical standards and invisible digital 4 Visual rule: the larger the word, the greater its contribution (χ²) to defining the axis; the closer two words appear, the more frequently they co-occur within the same text segments (ECUs). 48 systems, to a narrative-empirical discourse (lower part), expressed through concrete accounts and examples of practical control and quality-versus-quantity dilemmas in the classroom. This contrast highlights the alternation between the construction of theoretical frameworks and the evocation of lived experience. The preferability questions (Section C: «Is this future desirable?») elicited more conceptual responses, primarily linked to Classes 1 («Ethics») and 5 («Big Data»), where participants reasoned about values, norms and technological systems. In contrast, the risk-avoidance questions (Section E: «What must be done to prevent undesirable aspects?») generated a more narrative-empirical discourse, with numerous examples and anecdotes associated with the control (Class 3) and quality (Class 4) quadrants. In the AFC map, the synergies and tensions emerge from the spatial arrangement of the semantic nuclei along the X and Y axes. On the one hand, the positions of Ethics (Class 1) and Big Data (Class 5) along the horizontal axis reveal a friction between a moral framework centred on academic publishing standards and a technified model of institutional management based on large-scale data analysis. This tension suggests the need to promote integrative debates that foster an approach of «ethical growth with data», combining normative safeguards with transparent information systems. Similarly, the placement of Quality (Class 4) and Ethics (Class 1) on the positive side of the X-axis highlights that academic freedom for instructors and students, together with intellectual legitimacy, occupies a shared normative space. This alignment points to the convenience of designing joint policies that coordinate criteria for educational excellence with scientific integrity. Finally, the central position of Creativity (Class 2) acts as a semantic bridge between macro-systemic dimensions (ethics, big data) and microexperiential ones (control, quality). This reveals that pedagogical innovation —the capacity to redesign and adapt teaching— can reconcile ethical oversight, datadriven management and classroom practices within a cohesive university framework. In the map of passive variables, the variable *sex_hombre appears very close to Class 1 («Ethics»), indicating that male participants placed stronger emphasis on ethical norms and standards in academic publishing. In contrast, *sex_mujer shifts towards Classes 5 («Big Data») and 4 («Quality»), suggesting that women focused more on the implementation of large-scale data systems as well as on educational excellence and student autonomy. Similarly, *est_posgra appears associated with the same pole as «Ethics», whereas *est_grado aligns with «Control» (Class 3), indicating a greater involvement of undergraduate students in discussions on internal regulatory mechanisms. Finally, Class 2 («Creativity») retains a central and transversal position in the map, highlighting its integrative role in connecting macro-institutional dimensions (ethics, big data) with micro-pedagogical ones (control, quality). 49 Table 8. Relationships between the four lexical classes and the «Discipline» future scenario Class Core feature of the 2030 scenario Lexical Evidence / Representative Excerpt Protocol elements that activate it 1. Ethics (16.6%) Ethical standards and the legitimacy of academic publishing article, journal, publish, predatory, set, ethics, knowledge. «The number of predatory journals has doubled or even tripled…» A2 «Which educational–digital problems will have disappeared?», A3 «Which organizational–digital problems…?», E «What needs to be done to avoid…?» 2. Creativity (22.1%) Instructor autonomy to reinvent and adapt the educational model create/believe, capacity, lack, model, adapt, norm. «Academic freedom, each person adapts the content and methodology…» A2 «How will teaching practices change?», D «What should be done to move toward…?» 3. Control (23.2%) Tension between strict regulation and the unpredictability of practice control, regulation, clear, know, go through, go. «Regulation is impossible; strict regulation is like quality…» A3 «Which organizational– digital problems…?», E «What needs to be done to avoid…?» 4. Quality (15.5%) Student perspective: quality vs. quantity and security vs. freedom quality, quantity, student, freedom, indicator. «What do we want, security or freedom?» A1 «What attitudes/behaviors toward the future?», E «What needs to be done to avoid…?» 5. Big Data (22.7%) Implementation of invisible digital systems and largescale data analysis system, big, data, intelligence, scenario, use. «This imperceptible digital virtual control system…» A3 «Which organizational– digital problems…?», D «What should be done to move toward…?» Similarity analysis Figure 17 presents, as a result of the similarity analysis for the «Discipline» scenario, the co-occurrences and their connection structure. The graph displays the nodes, which are the active words (frequency ≥ 3); the size of each node reflects its absolute frequency; and the edges indicate co-occurrences within the same contextual unit (threshold ≥ 2 joint appearances). The thick edges indicate especially stable links (≥ 5 co-occurrences). Finally, the colors identify the lexicometric communities detected through the Louvain algorithm, grouping words that co-occur preferentially with one another. The graph (Figure 17) reveals a doubly polarized discourse: on the one hand, a macro-institutional dimension versus a micro-pedagogical one, and on the other, a contrast between a conceptual-reflective register and a narrative-empirical one. At 50 its centre lies an articulating core formed by two linking verbs, go and say, which act as bridges across all thematic lines. From this core emerge five peripheral communities, each corresponding to one of the lexical classes extracted through CHD: (1) Ethics, a debate on academic publishing and the need for clear codes (article, journal, predatory); (2) Creativity, the autonomy of instructors to create and adapt the training model (create/believe, capacity, model); (3) Control, the tension between strict regulation and practical unpredictability (control, regulation, know); (4) Quality, the student perspective on the quantity vs. quality and freedom vs. security dualities (quality, quantity, student, freedom); and (5) Big data, the implementation of invisible digital systems and large-scale data analysis (system, big, data, intelligence). The relative position of these modules confirms that Quality and Big data form a coherent thematic block, while Ethics, Control and Creativity cluster into a second block, maintaining at the same time strong connections with the central core. Figure 17. Results of the lexical similarity analysis by communities for the «Discipline» scenario The implicit axes in the graph (Figure 17) reveal a clear polarity between two semantic blocks: in the lower area, the communities of Ethics, Control and Creativity, which share a more normative-reflective discourse; and in the upper area, the communities of Quality and Big Data, strongly interconnected and 51 focused on the implementation of outcomes and the large-scale use of data. Along the horizontal axis, a conceptual-argumentative plane can be observed (the Ethics and Big Data communities, on the right side of the graph), characterized by terms that refer to codes, principles and systems; in contrast with a narrative-operational plane (the Control and Quality communities, on the left side), where words describing concrete cases, adaptation processes and classroom experiences predominate. In this way, the graph confirms the existence of two major semantic blocks: one composed of the communities of Ethics, Control, and Creativity, with a clearly institutional and normative orientation, and another formed by Quality and Big Data, with a more technical and measurable profile. However, the true bridge between both blocks is not the Creativity class itself, but the verbs located at the core, «to say» and «to go», which act as semantic connectors. «To say» links the ethical block with the broader discourse, while «to go» connects the technical and measurable narratives of the Quality block with the rest of the graph. This finding shows that these verbal axes are ultimately the elements that weave together and structure the semantic framework of the «Discipline» scenario. Specificities analysis: gender As shown in Figure 18, the residual value of +4.56 for the word system indicates that male participants referred to the systemic and technological dimension of the scenario far more frequently than expected. Likewise, create/believe shows a residual value of +1.91 in favor of men, highlighting their emphasis on teaching autonomy and structural innovation. Overall, these patterns suggest that male participants tend to dominate the discourse surrounding the macro-systemic vision of the «Discipline» scenario. On the other hand, women make notable use of the word issue (residual +7.29), referring to the formulation of conceptual problems and the boundaries of the scenario; say (residual +4.29), reinforcing their use of argumentative verbs; and example (residual +3.56), introducing anecdotes and concrete cases into the discussion. In addition, regulation shows a female-biased residual of +1.91, suggesting a stronger demand for flexible regulatory frameworks. 52 Figure 18. Specificities analysis by the passive variable «gender» The analysis reveals two gender-based lexical emphases: men tend to dominate the macro-systemic and structural innovation discourse, with a marked inclination toward terms such as system and create/believe. In contrast, women introduce with greater intensity the dimension of conceptual problems and experiential narratives, relying more frequently on issue, say and example. The elevated residue for issue in the female group suggests that women articulate more strongly the limits and challenges posed by the scenario. These specificities complement the distribution of genders across the CHD classes: men are concentrated in Class 5 (Big data) and Class 2 (Creativity), while women predominate in Class 1 (Ethics) and Class 3 (Control). Other relevant terms, such as student, regulation and know, do not reach a significant residue, indicating a shared discursive foundation in these aspects. Specificities analysis: teaching and research staff As shown in Figure 19, teaching staff with institutional role display notable positive residues for regulation (+2.18), student (+0.51) and problem (+0.26). This lexical profile denotes a clear micro-normative orientation, in which those holding institutional roles emphasize the construction of regulatory frameworks, the identification of concrete obstacles and the supervision of the student experience as key axes of their discourse on «Discipline». 53 Figure 19. Specificities analysis for the passive variable «teaching staff» (with or without institutional role or responsibilities) In turn, teaching staff with no institutional role show positive residues for control (+0.64), system (+1.45), university (+0.94), create/believe (+0.41) and say (+0.23). This configuration reveals a more macro-institutional and strategic register, in which pedagogical innovation, debates about the university structure, and the use of linking verbs take precedence over the operational focus on norms and immediate problems. Although both groups share key vocabulary, Figure 19 illustrates how the institutional role shapes discourse priorities: teaching staff with an institutional role operate mainly at the level of day-to-day management and problem solving, whereas teaching staff with no institutional role adopt a more reflective and projective stance, oriented towards reforming systems, strengthening teacher autonomy, and articulating forward-looking perspectives. 3.1.4. «Transformation» future scenario The analyzed corpus is composed of 239 texts, segmented into 346 Elementary Context Units (ECUs) with an average of 24.32 words per segment, in line with the parameters recommended by the Reinert Method. The total number of lexical occurrences amounts to 8,417, distributed across 1,092 lemmas, reflecting an adequate level of lexical richness. The descending hierarchical classification grouped 80.64% of the segments into five classes with significant lexical associations, using the Descending Hierarchical Classification (DHC) 54 algorithm and considering 281 active forms with a minimum frequency of three. These results provide a robust statistical basis for interpreting the predominant themes in the corpus and for analyzing their relationship with the passive variables. The vocabulary of each class aligns with the major narrative blocks delivered prior to the discussion. Indeed, the words function as «anchors» that participants mobilized when positioning themselves in relation to the «Transformation» future scenario. Table 9. Lexical classes (DHC) and their meaning in the «Transformation» future scenario Class % of ECUs Core words (χ² ≥ 8) Thematic core 120.8 % Training, university, company, professional,… Training and professional skills development in labor transformation 213.9 % Create/believe, model, reality, faculty, transformation,… Educational transformation: from model to regulated reality 313.3 % Knowledge, interest, knowing, responsibility,… Motivation and interest in learning: instructional approaches and responsibility 420.8 % Knowing/being, working, truth, time, student,… Practical efficiency and time management 531.2 % Year, digital, scenario, person, system,… Digital transformation of education: interaction between people and practices **Passive variables with χ² > 3.89 and p < .05 considered significant As shown in the dendrogram in Figure 20, the descending hierarchical analysis identified five thematic classes organized into two main branches. On the one hand, Class 1 («Development») appears as a distinct axis. On the other hand, Classes 2 to 5 cluster under the broader domain of «Education.» Within this second division, Class 2 («Control») emerges as an autonomous leaf. Classes 3 to 5 form a sub-branch labelled «Learning,» which then splits into Class 3 («Management») and an intermediate node («Execution») that encompasses Classes 4 and 5. Finally, this node divides into Class 4 («Time»), focused on practical efficiency and time management, and Class 5 («Scenario»), centered on the digital environment and interactions within the virtual classroom. Class 1, «Training» (20.8% of the ECUs), revolves around university education and continuous professional development. One participant expresses it as follows: «We must prepare at the university […] good professionals […] who tomorrow will be able to adapt and will not need to undergo training again» (woman, teaching staff, non-institutional role, engineer; χ² = 59.11). She reinforces this idea by stating: «I mean, I think the university must train in something beyond the immediate needs of a company at that moment» (woman, teaching staff, non-institutional role, 55 engineer; χ² = 33.73). In this class, the passive variables *ingen (p < .0001) and *doc_cc (p < .005) emerge as significant, suggesting a specific association between these training concerns and the profile of female engineers as well as the group of professors with academic responsibilities (doc_cc). Figure 20. Dendrogram of the textual corpus for the «Transformation» future scenario The next category, Class 4, comprises 20.8% of the ECUs, emphasizing time management and practical efficiency as key elements for educational transformation. As one teaching staff member explains: «I think that time can be invested in working, and especially in teams, which is how you learn the most» (man, teaching staff without an institutional role, science education; χ² = 25.90). Another participant highlights the risks of an overly specialized training model: «but this is a bit like the United States, isn’t it? Creating such highly specific university degrees that you cannot later work in another field as a specialist» (woman, technical, management, and administrative staff, social area; χ² = 22.37). No passive variables emerge as significantly associated with this class, suggesting that concerns about efficiency and effective time use cut across profiles and roles. 56 Class 5, «Digitization» (31.2% of ECUs), focuses on the digital environment and interactions within the virtual classroom. One member of the teaching staff notes: «Yes, the digital certificate is the issue, but imagine 2030; progress is very slow now, but I can see it coming in the future» (man, teaching staff, non-institutional position, science education; χ² = 20.42). Another participant adds: «Everything has already been digitalized through the mobile phone, hasn’t it? Now it is about human relations, about people […]» (man, teaching staff, non-institutional position, sociology; χ² = 11.51). The core vocabulary of this class also includes terms such as «class» (χ² = 15.89) and «relation» (χ² = 7.77), highlighting the centrality of the learning context and social interaction in digital transformation. No passive variables emerge as significantly associated with this class, suggesting that perceptions of digitization cut across roles and disciplinary profiles. Class 5 completes the «Scenario» node within the intermediate «Execution» layer, which it shares with Class 4. This structure shows how the practical dimension of effectiveness extends toward the digital environment of the classroom. Class 3, «Motivation» (13.3% of ECUs), constitutes the final node within the intermediate level of «Management» and completes the «Learning» sub-branch that groups Classes 4 and 5. This cluster reveals a tension between the interest in knowledge and individual responsibility within the learning process: «I think that the interest in knowledge would be quite diminished because the only motivation would be a financial one…» (man, teaching staff, non-institutional role, sociology; χ² = 53.52). Likewise, another participant explains: «As a student, I have the obligation to choose what really contributes to me and what does not, and what I want to change, because there is an individual responsibility involved» (woman, technical, management and administrative services staff, social; χ² = 26.17). In this class, the variable *sociol emerges significantly (p < 0.001), and the prominence of the terms «knowledge» (χ² = 51.72) and «learn» (χ² = 15.20) underscores the emphasis on reflective and formative processes Finally, Class 2, «Transformation» (13.9% of ECUs), is structured around the «Control» node, where a marked concern for institutional supervision and regulatory mechanisms emerges. As one professor explains: «So self-regulation in the international market, large companies and so on, especially the big ones […] I think self-regulation is never good; there always has to be a minimum level of control» (woman, teaching staff, institutional role, Law; χ² = 24.97). This idea is reinforced by another participant: «This model is based on covert supervisory mechanisms, so that you do not notice them» (man, teaching staff, noninstitutional role, Sociology; χ² = 37.02). In this class, the variable *sociol stands out significantly (p < 0.005), alongside terms such as reality (χ² = 31.33) and political (χ² = 7.00), which highlight the political and contextual dimension of these control practices. This «Control» node also closes the broader «Education» branch, showing how regulatory dynamics delimit the institutional sphere. In Figure 21, the AFC explains 29.24% of the total inertia on Factor 1 (horizontal axis) and 26.69% on Factor 2 (vertical axis), reaching a cumulative 55.94% and 57 more frequently than expected. Similarly, training shows a residual of +1.24 in favor of men, highlighting their emphasis on academic preparation and professional development. By contrast, company presents a residual of –1.55 and interest a residual of –1.18, revealing that female participants place greater emphasis on practical, employment-related perspectives and cognitive motivation. Taken together, these results suggest that men tend to privilege the university-based and formative framework of «Transformation,» whereas women orient their discourse toward practical application and motivational factors. By contrast, women make notable use of the word company (residual +1.55), referring to the connection with professional sectors and employment opportunities; interest (residual +1.18), reinforcing their emphasis on cognitive motivation as a driver of change; and science (residual +1.71), highlighting the centrality of academic knowledge in the transformation process. In addition, year shows a residual of +1.34 among women, suggesting particular attention to temporal pacing and the planning of training pathways. Figure 23. Specificities analysis with the passive variable «gender» The analysis reveals two gender-related lexical emphases: men tend to center their discourse on the institutional dimension, referring more frequently to university and training, whereas women strongly introduce the practical and motivational dimension, emphasizing company, interest, science, and year. The markedly high residual for company among female participants indicates a concern with professional alignment; likewise, the elevated residuals for interest and science highlight their focus on cognitive motivation and the centrality of academic knowledge. 64 These specificities align with how gender is distributed across the CHD classes: men cluster in Class 5 («Digitalization») and Class 2 («Transformation»), whereas women predominate in Class 1 («Training») and Class 4 («Efficiency»). Other key terms such as interest, time, or model do not reach a significant residual, indicating a shared discursive foundation in these aspects. Specificity analysis: teaching staff Teaching staff with an institutional role show notably positive residuals for university (+1.65), company (+1.55), and training (+1.24), as presented in Figure 24. This lexical profile reflects a clear macro-institutional orientation in which those holding institutional responsibilities emphasize university governance, links with the productive sector, temporal planning, and the continuous development of competencies. Conversely, they display negative residuals for science (–1.71), system (–1.39), model (–1.29), interest (–1.18), and digital (–0.31), indicating that they downplay discussions on specialized knowledge, systemic frameworks, cognitive motivation, and digitalization in favor of governance and institutional coordination. Figure 24. Specificities analysis for the passive variable «teaching staff» (with institutional role / without institutional role) In turn, teaching staff without an institutional role show positive residuals for science (+1.71), system (+1.39), model (+1.29) and interest (+1.18). This lexical profile suggests a more conceptual and strategic register, in which they emphasize debates on scientific knowledge, theoretical frameworks and cognitive motivation, in contrast with an operational focus centered on the immediate management of norms and problems. 65 Although both groups share key vocabulary, Figure 24 shows that the institutional position shapes distinct approaches: teaching staff with an institutional role focus their discourse on university operations, emphasizing university, company and training, whereas teaching staff with non-institutional role deploy a more conceptual and strategic perspective, highlighting science, system, model and interest, oriented toward reframing structures, strengthening pedagogical autonomy and projecting educational futures. Conclusions The «Transformation» scenario is built from a corpus of 239 texts and 346 segments, which show high lexical richness and allow the identification of five thematic cores. The first concerns training and the development of professional competences, highlighting the need to prepare students for a changing labor market and emphasizing continuous upskilling and micro-credentials. The second core, educational transformation and institutional control, addresses the tension between theoretical models and practical realities. Third, motivation and interest in learning emerge as decisive factors for sustaining student engagement, countering the risk of reducing education to purely utilitarian incentives. The fourth core revolves around efficiency and time management, with a strong emphasis on teamwork and process optimization. Finally, digitalization occupies the largest share of the discourse, reflecting the centrality of digital transformation in the classroom and the new forms of interaction between people and technology. Factor analysis positions these classes along two major axes. The first contrasts the consolidation of academic and professional competences with the increasing technification of educational processes, reflecting the need to articulate training and digitalization through integrative strategies. The second axis opposes more conceptual discourses, centered on models and regulatory frameworks, to empirical narratives based on classroom experiences and the practical management of time. Within this structure, the «Transformation» class occupies a central position, functioning as a nexus between institutional discourses and lived experiences. Similarity analysis confirms the existence of an articulating core (subject, go, say) that connects five lexical communities corresponding to the main classes. The discourse is organized around two polarizations: the one that separates the macro-institutional from the micro-pedagogical, and the one that distinguishes conceptual reflections from empirical accounts. Creativity and teaching innovation emerge as connecting points that link the different dimensions of the scenario. Profile analysis offers significant results. Men identify with the dimension of digitalization, whereas women are associated with the discourses on efficiency and time management. Undergraduate students orient themselves toward digitalization, while postgraduate students do so toward training. These differences suggest that the perception of transformation depends partly on 66 academic role and level of study, although digitalization and efficiency appear as transversal concerns. Several operational conclusions can be drawn from this scenario. In the first place, it is recommended to strengthen continuous training and professional development programs, including micro-credentials that enable agile and flexible updating. In the second place, pedagogical innovation should be reconciled with institutional control so that regulatory models support rather than limit teaching practices. In the third place, it is advisable to reinforce students’ intrinsic motivation, promoting learning grounded in interest and responsibility beyond purely instrumental incentives. In the fourth place, effective time management and collaborative learning should be central pillars in educational planning. Finally, the urgency of advancing digitalization in an inclusive manner is emphasized, ensuring that technological tools enhance human interaction and integrate naturally into educational processes. 3.1.5. Closing Session The analyzed corpus is composed of 265 texts, segmented into 331 Elementary Context Units (ECUs) with an average of 18.96 words per segment, following the parameters recommended by the Reinert Method. The total number of lexical occurrences amounts to 6,279, distributed across 870 lemmas, reflecting an adequate lexical richness. The Descending Hierarchical Classification grouped 59.21% of the segments into three classes with significant lexical associations, using the Descending Hierarchical Classification (CHD) algorithm and considering 228 active forms with a minimum frequency of 3. These results provide a solid statistical basis for interpreting the predominant themes in the corpus and for analyzing their relationship with the «passive» variables. The vocabulary of each class matches the major narrative blocks submitted prior to the dialogue. Indeed, the words function as «anchors» employed by participants when positioning themselves with respect to the Closing Session. Table 11. Lexical classes (CHD) and their meaning in the Closing Session Class % of ECUs Core words (χ² ≥ 8) Thematic core 1 35.2% idea, value, feasible, original, be able to,… Viable and structured proposals 2 25.0% know/be, video, use, campus,… Emotional reactions and personal experiences 3 39.8% say, professor, know, class, normative, student,… Desired structural transformations **Passive variables with χ² > 3.89 and p < 0.05 considered significant 67 The descending hierarchical analysis identified three lexical classes, as shown in Figure 25, organized into two major conceptual branches. On the one hand, Class 1, «Ideas», appears as an independent axis that shapes the Conceptualization node, capturing a discourse oriented toward feasibility, organization, and the generation of concrete proposals. On the other hand, Classes 2, «Reactions», and 3, «Transformations», cluster together under the broader domain of Transformation. This second major division reflects, first, a subclass marked by immediate judgments, emotions, and personal experiences, while the upper class articulates a projective and normative vision focused on institutional and educational reconfiguration. The relationship between both suggests a progression from the subjective experience of the present toward the ambition of structurally different futures. Figure 25. Dendrogram of the textual corpus of the Closing Session Class 1, «Ideas» (35.2% of ECUs) brings together a type of discourse oriented toward the development of concrete, feasible proposals aligned with the university context. These ideas are valued both for their transformative potential and for their capacity to be implemented in the short or medium term. As one member of the teaching and research staff with an institutional role states: «Easy to carry out, because as soon as you have a budget line that allows it, we change every year» (man, teaching and research staff, institutional role, Social Sciences Education; χ² = 68 15.35). In the same vein, another participant without an institutional role reinforces the cooperative dimension of these proposals by noting: «Maybe the budget line is that the innovation groups […] are multidisciplinary and then […] we learned and could take things from one another» (woman, teaching and research staff, noninstitutional role, Engineering; χ² = 5.60). The passive variable *ccee (p < 0.0001) is significantly associated with this class, suggesting a greater involvement of profiles linked to Education Sciences in the development of organized and functional ideas. Class 1, «Ideas,» closes the Conceptualization branch and opens the Vision branch, whose first class is Class 2. Class 2, «Reactions» (25.0% of ECUs) brings together contributions marked by a critical, experiential, and subjective tone, expressing skepticism about the effectiveness of current measures regarding university digitalization. This type of discourse is characterized by immediate evaluations of the present, emphasizing contradictions between institutional narratives and everyday experience. One participant summarizes this perception by stating: «I think we fill out more paperwork than we used to, but I’m not sure we’ve improved quality, honestly» (woman, teaching and research staff, noninstitutional role, Engineering; χ² = 16.20). This concern intensifies when the minimum regulatory requirements are questioned: «The university is required to ensure that all materials on its campuses are accessible […] and we’re completely ignoring that» (woman, teaching and research staff, non-institutional role, Engineering; χ² = 5.73). The passive variable *doc_sc (p < 0.005) stands out significantly in this class, suggesting a stronger presence of teaching and research staff without an institutional role in critical discourse about current processes and practices. Finally, Class 3, «Transformations» (39.8% of ECUs) closes the hierarchical structure of the dendrogram, functioning as the final leaf of the Vision node. This discursive space articulates a direct critique of the limits of the current educational system, accompanied by proposals aimed at redistributing roles, introducing regulatory flexibility, and incorporating active methodologies. The desire for teaching autonomy is evident in statements such as: «I think I’m breaking the timetable […] but no one tells me I can’t do project-based learning» (woman, teaching and research staff, non-institutional role, Engineering; χ² = 4.31). At the same time, participants express concern about the system’s limited capacity to listen: «They are the ones who should be talking all the time […] and it’s pathetic that, if I want to listen to my 80 students in Health Education, it takes me 4 hours of class, 3 minutes each […] I can only listen to them once a year» (man, teaching and research staff, non-institutional role, Science Education; χ² = 6.61). Although no passive variables emerge with statistically significant associations, this class carries a strong ideological and normative weight, projecting a profound transformation in the ways teaching, learning and classroom organization are conceived within the university. The Correspondence Analysis (CA) visualizes the main semantic oppositions in the discourse generated during the closing session. In Figure 26, Factor 1, which 69 occupies the horizontal axis, accounts for 54.97% of the total inertia, while Factor 2, represented on the vertical axis, contributes an additional 45.02%, thus reaching 100% of the explained variance between both dimensions. This distribution confirms that the two axes adequately synthesize the discursive tensions across the set of lexical classes. In the right-hand quadrants, both upper and lower, Class 1 («Ideas») is positioned, structured around terms such as idea, value and feasible, which underscore its association with well-defined and actionable proposals. In contrast, on the left side of the plane, Classes 2 («Reactions») and 3 («Transformations») cluster together, sharing spatial proximity and forming the conceptual axis of institutional vision. Class 2 appears in the lower-left quadrant with terms such as thing, to know and good, reflecting an experiential orientation, while Class 3 moves upward toward the upper-left quadrant, articulated around the verbs to say, professor and to give, marking a more normative and forwardlooking discursive stance. This configuration reinforces the separation identified in the dendrogram between the conceptualization of ideas (Class 1) and the dynamics of institutional change and response (Classes 2 and 3). In the lower-left quadrant lies Class 2 («Reactions»), composed of subjective and critical interventions that question the actual effectiveness of certain institutional processes. Terms such as thing, to know and good structure an experiential discourse marked by skepticism toward bureaucratic burden and the gap between regulation and everyday practice. In turn, Class 3 («Transformations») occupies the upper-left quadrant, where proposals aimed at redesigning the classroom, redistributing roles and creating space for active methodologies are articulated. This class is characterized by terms such as to say, professor and to give, which underscore the intention to reconfigure teaching dynamics and encourage greater student participation. Together, these two classes constitute the semantic domain of Vision, in direct contrast to Class 1 («Ideas»), located in the right-hand quadrant of the plane. The latter brings together a discourse focused on the feasibility of proposals, strategic organization and the generation of workable solutions, expressed through terms such as idea, to be worth and feasible. The spatial distribution of the classes across the plane thus reinforces the dialectic between the projective and the practical, between lived institutional experience and the structuring of desirable futures. The two axes of the factorial plane organize the discourse around the desired educational transformation, establishing clear oppositions between the discursive approaches identified. Factor 1 (horizontal axis) contrasts, at its right end, a core centered on the conceptualization of viable proposals—characterized by the terms idea, to be worth and feasible (Class 1)—with an opposing dimension on the left, which brings together more critical and forward-looking discourses expressed through to know, thing, to say or professor (Classes 2 and 3). This polarity reveals a tension between the strategic design of proposals and the need for structural and institutional change. Factor 2 (vertical axis), in turn, distinguishes between a lower area dominated by more subjective and reactive registers, such as those present in Class 2 («Reactions»), and an upper area where Class 3 70 («Transformations») is located, defined by a more normative and projective lexicon. This arrangement suggests a progression from immediate lived experience toward the development of proposals for systemic change, with Class 3 acting as the articulating element of a structured vision of the educational future. Figure 26 reveals not only the semantic tensions within the participant group but also the potential complementarities between those who focus on organizing viable ideas (Class 1) and those who insist on rethinking the conditions that make change possible (Classes 2 and 3). Class 1 offers clear solutions grounded in feasibility, yet remains semantically isolated; meanwhile, the block formed by Classes 2 and 3 contributes a critical, institutional, and ethical perspective that can enrich technical proposals with pedagogical meaning, equity, and sustainability. Taken together, the factorial plane allows the closing session to be interpreted as a space in which strategic design and institutional transformation can (and should) converge to collectively construct a preferred educational future. Figure 26. Correspondence Analysis (AFC) map for the «Closing Session» scenario6. In the map of passive variables, a clear alignment can be observed between *ccee and Class 1 «Ideas,» suggesting that profiles linked to the Education Sciences focused particularly on formulating viable, structured, and functional proposals. In contrast, the variables *doc_sc, *didccex, and *ingen cluster around Classes 2 «Reactions» and 3 «Transformations,» reinforcing the association between non-institutional-role teaching staff members (especially those in 6 Visual rule: the larger the word, the greater its contribution (χ²) to defining the axis; the closer two words appear, the more frequently they co-occur within the same text segments (ECUs). 71 technical or scientific-experimental fields) and a more critical, projective, or normative discourse. Notably, *sex_mujer also appears near this axis, particularly close to Class 2, which may indicate greater female presence in experiential and evaluative registers within the discussion. Conversely, *sex_hombre is located in the lower-right quadrant, in closer connection with Class 1, reinforcing its alignment with a pragmatic discourse centered on the feasibility of proposals. Taken together, this map reflects how certain professional and gender profiles tend to gravitate toward differentiated discursive zones: while some promote practical ideas, others formulate structural critiques regarding the transformation of the university system. Table 12. Relationships between the three lexical classes and the «Closing Session» future scenario Class Core feature of the 2030 scenario Lexical Evidence / Representative Excerpt 1. Ideas (35.2%) Structured design of feasible proposals idea, worth, seem, carry out, easy, original, framework. «Easy to carry out, because as soon as you have a budget line that allows it, we change every year.» 2. Reactions (25.0%) Subjective judgment, scepticism, and everyday experiences thing, know, good, video, stop, use. «I think we fill out more paperwork than we used to, but I honestly don’t know if we have improved the quality.» 3. Transformation (39.8%) Institutional redesign and active methodologies say, professor, give, class, regulatory, share, student. «They are the ones who should be talking all the time […] and it is pathetic that if I want to listen to my 80 students in Health Education, I need 4 hours of class, 3 minutes each […].» Similarity analysis Figure 27 presents, as a result of the lexical similarity analysis for the Closing Session, the co-occurrence network and its community structure. In this graph, each node represents an active form (frequency ≥ 3), whose size is proportional to its absolute frequency. The edges connect words that co-occur in at least two contextual units (threshold ≥ 2), and their thickness indicates particularly consolidated links (≥ 5 co-occurrences). Finally, the different colors identify the lexical communities detected through the Louvain algorithm, grouping together those forms that tend to appear jointly in the discourse. The similarity graph reveals a tripolar discursive configuration, organized around a central semantic node, Point 0, articulated through the verb to do, which functions as a shared axis across the different ways of approaching the preferred future. From this node, three clearly differentiated branches emerge. To the north, Point 1 «Transformation», associated with terms such as more, digital, create, and 72 observatory, projects an institutional discourse oriented toward redesigning educational structures and integrating technologies into the system. To the east, Point 2 «Ideas» is structured around idea, value, and seem, concentrating efforts to categorize, assess, and build consensus around viable and structured proposals. Finally, to the south, Point 3 «Reactions», derived from say, see, clear, and group, brings together critical, experiential, and evaluative expressions linked to subjective judgment and personal experience within the educational environment. This tripartite organization suggests a discursive system in which the normative-projective, the practical-constructive, and the experiential dimensions are interrelated through a shared core of action and agenda. The verb to do functions not only as a nuclear element but also as the generative point from which the different visions of the educational future unfold. Figure 27. Results of the lexical similarity analysis by communities for the «Closing Session» scenario From this central Point 0, three peripheral communities emerge, each corresponding to the lexical classes identified by the CHD analysis: (1) Ideas, centered on the formulation of structured, practical, and feasible proposals, with terms such as idea, value, and seem; (2) Transformations, which brings together a projective discourse oriented toward educational innovation and institutional restructuring, articulated through words such as more, create, digital, and observatory; and (3) Reactions, where subjective judgments, immediate experiences, and critical assessments concentrate, with lexical cores such as say, 73 5. Appendices 5.1. Informed Consent Marque las casillas correspondientes 1. Colaboración en el estudio Sí No He leído y entendido la información del estudio con fecha 10/01/2025, o se me ha comunicado. He podido hacer preguntas sobre el estudio y mis preguntas han sido respondidas satisfactoriamente.   Doy mi consentimiento voluntario, como miembro de la Universidad de Extremadura, para colaborar en este estudio mediante la participación en todas las sesiones necesarias para realizar el «Ejercicio de los Cuatro Futuros».   Entiendo que colaborar en el estudio implica participar en la actividad denominada «Taller sobre Estudios de Futuros» (29/01/2025) en la Facultad de Formación del Profesorado, en sesiones de mañana y tarde. El taller está organizado en seis sesiones, que incluyen la visión del pasado-presente, los cuatros escenarios de futuro y, finalmente, la visión del futuro preferido. Las sesiones serán registradas mediante dispositivos de grabación de audio, imagen y/o vídeo.   2. Uso de la información en el estudio Sí No Entiendo que los datos anonimizados que proporcione la Universidad de Extremadura se utilizarán para el desarrollo del proyecto de investigación titulado «La transformación digital de las titulaciones universitarias. Las analíticas académicas, las subjetividades y el rendimiento en tiempos prepandémicos y durante la COVID-19» (UNIDIGIT@L), con referencia TED2021-130743B-I00, financiado por el Ministerio de Ciencia e Innovación a través de la convoctoria «Proyectos Orientados a la Transición Ecológica y a la Transición Digital, del Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023, en el marco del Plan de Recuperación, Transformación y Resiliencia». Como resultado de esta investigación los participantes recibirán un informe sobre los resultados obtenidos y las conclusiones extraídas. Se prevé una difusión de los resultados a través de la participación de los equipos de investigación en Congresos, Jornadas y Seminarios, así como a través de la publicación de artículos en revistas científicas y/o capítulos de libro.   Entiendo que los datos aportados serán usados exclusivamente para este estudio y no se compartirán más allá del equipo de investigación.   3. Firma [Nombre del participante (en mayúsculas)] Firma Fecha 4. Datos de contacto para posterior información Jesús Valverde Berrocoso – [email protected] 80 5.2. Invitation Letter Estimado/a ….. Desde el grupo de investigación «Nodo Educativo» estamos desarrollando un proyecto titulado «La transformación digital de las titulaciones universitarias» (Ministerio de Ciencia e Innovación / Ref. TED2021-130743B-I00), en colaboración con las universidades de La Laguna y Valladolid. Esta investigación consta de 3 estudios que analizan las analíticas académicas a través de la actividad en los campus virtuales (big data) y sus implicaciones en el rendimiento académico, identifican las percepciones del profesorado sobre el uso de las aulas virtuales y, finalmente, elaboran propuestas de política universitaria para los próximos años. Con relación a este último estudio nuestro grupo quiere invitarte a participar, en tu calidad de PDI/estudiante/PTGAS, en un «Taller sobre Estudios de Futuros» que tendrá lugar en la Facultad de Formación del Profesorado (Cáceres) el próximo día 29 de enero de 2025, en sesiones de mañana y tarde (incluye comida/cafés). Esta participación tendrá un reconocimiento como actividad formativa (certificado emitido por la Universidad de Valladolid). El propósito de este taller es doble: por un lado, conocer el potencial de los «estudios de futuros» en la planificación estratégica de grupos e instituciones, familiarizándose con algunas técnicas para su implementación (ejercicio «Cuatro Futuros»); y, por otro, participar en un proceso de identificación y definición de un futuro preferido con relación a la transformación digital de las titulaciones universitarias de nuestra Universidad. La jornada será muy dinámica y participativa. Formarás parte de un grupo de 10-12 personas pertenecientes a nuestra comunidad universitaria que valorará cuatro diferentes escenarios de futuro, con la finalidad de identificar uno que se considere deseable, preferible y alcanzable para la UEx. Todos los participantes recibirán antes del Taller un dosier informativo sobre la técnica y los procesos implicados. Para su desarrollo, el grupo dispondrá de vídeos, paneles gŕaficos, guiones de preguntas y plantillas de diferentes técnicas de design thinking. Todos los datos recabados por el equipo de investigación estarán sujetos a las normativas de protección de datos personales, cumpliendo con el Reglamento General de Protección de Datos (RGPD) de la Unión Europea. Este proyecto de investigación cuenta con la aprobación de la Comisió de Bioética y Bioseguridad de la Universidad de Extremadura (NºRegistro: 113//2024). Espero contar con tu valiosa participación. Por favor, en su caso, confirma tu asistencia a la mayor brevedad. Muchas gracias. Un cordial saludo. 81 5.3. Work Plan for the Sessions Taller «Estudio de Futuros» Proyecto: «La transformación digital de las titulaciones universitarias» Ref.: TED2021-130743B-I00 Grupo de Investigación «Nodo Educativo» (SEJ035) 29 de enero de 2025 Facultad de Formación del Profesorado Sala Joaquín Sama Sesión de mañana 09:00 – 09:30 Sesión 01 – Presentación del ejercicio y explicación de la metodología de trabajo. Breve presentación de los participantes del taller. Responsable: Jesús Valverde. Lugar: Sala Joaquín Sama. Recursos: Ordenador, proyector, diapositivas. Desarrollo: introducción de la jornada con orientaciones sobre las actividades y criterios fundamentales a considerar en el análisis de los escenarios. 09:30 – 11:00 Sesión 02 – De dónde venimos, dónde estamos. Responsable: Rosa Fernández. Lugar: Aula Versátil. Recursos: Plantilas impresas (A3): Técnica «Análisis de Retrospectiva Organizacional» y DAFO, bolígrafos, grapadora. Desarrollo: 1. Técnica «Análisis de Retrospectiva Organizacional». (09:30 – 10:15) Esta técnica ayuda a reflexionar sobre eventos pasados y a comprender cómo han influido en el desarrollo de la organización. Los participantes conocen de antemano el planteamiento de esta sesión y puede que ya hayan completado, de modo individual, la plantilla. Se reparte a cada participante una plantilla impresa con el fin de que escriba en ella (luego se recoge como datos del estudio). Si 82 alguien la trae completada se grapa junto con la plantilla. PONER EL NOMBRE en cada plantilla. Se anima a que se comiencen a compartir los eventos, logros/desafíos e impacto en la actualidad. En cualquier momento se puede producir debate, realizar preguntas, etc. Pasos: a) Identificación de eventos clave: Lista de eventos importantes en la transformación digital de la universidad, como implantación de nuevas infraestructuras (equipamientos, redes, software, etc.), cambios en los planes de estudio, implementación de proyectos de innovación docente, etcétera. b) Discusión de logros y desafíos: Reflexionar sobre cada evento identificando los éxitos alcanzados, los obstáculos enfrentados y las lecciones aprendidas. c) Impacto en la actualidad: Conectar los eventos pasados con el presente; por ejemplo, cómo una transformación pasada impacta el sistema educativo universitario o la cultura organizacional. 2. DAFO (10:15 – 11:00) Su objetivo es pronosticar aspectos de los futuros. Análisis de los posibles desafíos y oportunidades del futuro, en los próximos 10 años. Los participantes conocen de antemano el planteamiento de esta sesión y puede que ya hayan completado, de modo individual, la plantilla. Se reparte a cada participante una plantilla impresa con el fin de que escriba en ella (luego se recoge como datos del estudio). Si alguien la trae completada se grapa junto con la plantilla. PONER EL NOMBRE en cada plantilla. Se anima a que se comiencen a compartir las fortalezas, debilidades, oportunidades y amenazas. En cualquier momento se puede producir debate, realizar preguntas, etc. −Identificar fortalezas: factores internos positivos que favorecen el logro de los objetivos, como recursos, habilidades, ventajas competitivas, etc. Las fortalezas son los elementos internos que le dan una ventaja a la organización. −Identificar debilidades: factores internos que pueden obstaculizar el éxito, como la falta de recursos, áreas de mejora en los recusos humanos, procesos deficientes, etc. Las debilidades son los factores internos que limitan la capacidad para alcanzar los objetivos y pueden poner en desventaja frente a otras instituciones universitarias. −Identificar oportunidades: factores externos favorables, como tendencias, cambios legislativos, o nuevas necesidades que pueden beneficiar a la institución universitaria Las oportunidades son factores externos que pueden ser 83 aprovechados para beneficiar a la organización, aumentando su éxito o alcance. −Identificar amenazas: factores externos negativos que podrían representar riesgos, como la competencia, fluctuaciones económicas, o cambios en la legislación. Las amenazas son factores externos que representan riesgos y pueden obstaculizar el logro de los objetivos. 11:00 – 11:30 Pausa - Café 11:30 – 12:30 Sesión 03 – Escenario de futuro 1: Disciplina. Responsable: Rosa Fernández. Lugar: Aula Versátil. Recursos: Rolls up, Vídeo introductorio, Texto-descripción del escenario impreso, Bolígrafos, Folios, Registro de sonido e imagen. Desarrollo: 1. Visionado del vídeo introductorio. 2. Lectura individual del escenario de futuro. 3. Debate en torno a las siguientes preguntas: A. Debate general sobre el futuro ¿Cómo actuaría o se comportaría la mayoría de los miembros de la comunidad universitaria (PDI, estudiantes, PTGAS) en un futuro como éste? ¿Qué problemas educativo-digitales (p.ej., distracción/atención; motivación/intereses por el conocimiento; competencias/habilidades digitales; plagio/ética académica; abusos/cyberbulling; etc.) que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué problemas organizativo-digitales (p.ej., asistencia/participación en actividad académica; calendarios/horarios académicos; estructura/funcionamiento de campus/aula virtual; control de la práctica docente; etc.), que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué otros problemas sobre la transformación digital que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué nuevos problemas sobre la transformación digital, que ahora no existen o carecen de importancia, tendrán que preocupar a las universidades? B. ¿Hasta qué punto es probable el futuro descrito en tu escenario? C. ¿Es preferible el futuro descrito en esta hipótesis? Es decir, ¿hasta qué punto se parece al futuro que tú prefieres? 84 D. En la medida en que tu grupo considere preferible el futuro descrito en esta hipótesis, ¿qué cinco cosas hay que hacer ahora para avanzar hacia los aspectos deseables de ese futuro? E. En la medida en que tu grupo considere que el futuro descrito en tu escenario es indeseable, ¿qué cinco cosas hay que hacer ahora para que esos aspectos indeseables no se produzcan? 12:30 – 12:45 Pequeña pausa 12:45 – 13:45 Sesión 04 – Escenario de futuro 2: Crecimiento. Responsable: Rosa Fernández. Lugar: Aula Versátil. Recursos: Rolls up, Vídeo introductorio, Texto-descripción del escenario impreso, Bolígrafos, Folios, Registro de sonido e imagen. Desarrollo: 1. Visionado del vídeo introductorio. 2. Lectura individual del escenario de futuro. 3. Debate en torno a las siguientes preguntas: A. Debate general sobre el futuro ¿Cómo actuaría o se comportaría la mayoría de los miembros de la comunidad universitaria (PDI, estudiantes, PTGAS) en un futuro como éste? ¿Qué problemas educativo-digitales (p.ej., distracción/atención; motivación/intereses por el conocimiento; competencias/habilidades digitales; plagio/ética académica; abusos/cyberbulling; etc.) que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué problemas organizativo-digitales (p.ej., asistencia/participación en actividad académica; calendarios/horarios académicos; estructura/funcionamiento de campus/aula virtual; control de la práctica docente; etc.), que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué otros problemas sobre la transformación digital que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué nuevos problemas sobre la transformación digital, que ahora no existen o carecen de importancia, tendrán que preocupar a las universidades? B. ¿Hasta qué punto es probable el futuro descrito en tu escenario? C. ¿Es preferible el futuro descrito en esta hipótesis? Es decir, ¿hasta qué punto se parece al futuro que tú prefieres? D. En la medida en que tu grupo considere preferible el futuro 85 descrito en esta hipótesis, ¿qué cinco cosas hay que hacer ahora para avanzar hacia los aspectos deseables de ese futuro? E. En la medida en que tu grupo considere que el futuro descrito en tu escenario es indeseable, ¿qué cinco cosas hay que hacer ahora para que esos aspectos indeseables no se produzcan? 14:00 – 15:00 Pausa – Comida Sesión de tarde 15:00 – 16:00 Sesión 05 - Escenario de futuro 3: Transformación. Responsable: Jesús Valverde. Lugar: Aula Versátil. Recursos: Rolls up, Vídeo introductorio, Texto-descripción del escenario impreso, Bolígrafos, Folios, Registro de sonido e imagen. Desarrollo: 1. Visionado del vídeo introductorio. 2. Lectura individual del escenario de futuro. 3. Debate en torno a las siguientes preguntas: A. Debate general sobre el futuro ¿Cómo actuaría o se comportaría la mayoría de los miembros de la comunidad universitaria (PDI, estudiantes, PTGAS) en un futuro como éste? ¿Qué problemas educativo-digitales (p.ej., distracción/atención; motivación/intereses por el conocimiento; competencias/habilidades digitales; plagio/ética académica; abusos/cyberbulling; etc.) que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué problemas organizativo-digitales (p.ej., asistencia/participación en actividad académica; calendarios/horarios académicos; estructura/funcionamiento de campus/aula virtual; control de la práctica docente; etc.), que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué otros problemas sobre la transformación digital que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué nuevos problemas sobre la transformación digital, que ahora no existen o carecen de importancia, tendrán que preocupar a las universidades? B. ¿Hasta qué punto es probable el futuro descrito en tu escenario? 86 C. ¿Es preferible el futuro descrito en esta hipótesis? Es decir, ¿hasta qué punto se parece al futuro que tú prefieres? D. En la medida en que tu grupo considere preferible el futuro descrito en esta hipótesis, ¿qué cinco cosas hay que hacer ahora para avanzar hacia los aspectos deseables de ese futuro? E. En la medida en que tu grupo considere que el futuro descrito en tu escenario es indeseable, ¿qué cinco cosas hay que hacer ahora para que esos aspectos indeseables no se produzcan? 16:00 – 16:15 Pequeña pausa. 16:15 – 17:15 Sesión 06 - Escenario de futuro 4: Colapso. Responsable: Jesús Valverde. Lugar: Aula Versátil. Recursos: Rolls up, Vídeo introductorio, Texto-descripción del escenario impreso, Bolígrafos, Folios, Registro de sonido e imagen. Desarrollo: 4. Visionado del vídeo introductorio. 5. Lectura individual del escenario de futuro. 6. Debate en torno a las siguientes preguntas: A. Debate general sobre el futuro ¿Cómo actuaría o se comportaría la mayoría de los miembros de la comunidad universitaria (PDI, estudiantes, PTGAS) en un futuro como éste? ¿Qué problemas educativo-digitales (p.ej., distracción/atención; motivación/intereses por el conocimiento; competencias/habilidades digitales; plagio/ética académica; abusos/cyberbulling; etc.) que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué problemas organizativo-digitales (p.ej., asistencia/participación en actividad académica; calendarios/horarios académicos; estructura/funcionamiento de campus/aula virtual; control de la práctica docente; etc.), que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué otros problemas sobre la transformación digital que preocupan a las universidades ahora, habrán desaparecido o serán relativamente menores? ¿Qué nuevos problemas sobre la transformación digital, que ahora no existen o carecen de importancia, tendrán que preocupar a las universidades? B. ¿Hasta qué punto es probable el futuro descrito en tu escenario? C. ¿Es preferible el futuro descrito en esta hipótesis? Es decir, 87 ¿hasta qué punto se parece al futuro que tú prefieres? D. En la medida en que tu grupo considere preferible el futuro descrito en esta hipótesis, ¿qué cinco cosas hay que hacer ahora para avanzar hacia los aspectos deseables de ese futuro? E. En la medida en que tu grupo considere que el futuro descrito en tu escenario es indeseable, ¿qué cinco cosas hay que hacer ahora para que esos aspectos indeseables no se produzcan? 17:15 – 17:45 Pausa - Merienda 17:45 – 18:15 Sesión 07 – Visualización del futuro preferido. Responsable: Jesús Valverde. Lugar: Aula versátil. Recursos: Proyector, Ordenador, Plantilla COCD BOX. Desarrollo: 1. Técnica COCD BOX El método organiza las ideas en cuatro categorías, representadas por tres colores (azul, rojo y amarillo) y una «caja sin color». Cada color indica un nivel de innovación y viabilidad: −Caja azul: ideas realistas y fáciles de implementar en el corto plazo. −Caja roja: ideas realizables pero que requieren más tiempo y recursos. Son ideas que no implican una innovación radical, pero tienen un impacto positivo y viable. −Caja amarilla: ideas innovadoras y ambiciosas, difíciles de implementar pero con alto potencial de transformación. −Caja sin color: ideas que no se consideran viables o útiles en el momento; se descartan. 88 5.4. Information on the Reinert Method: types of data Table 13. General data of a «corpus» Data item Meaning Number of texts These are the documentary units that make up the corpus (participants’ interventions in the focus group). Number of text segments (ECUs) This is the total number of fragments into which IRaMuTeQ divided the texts for analysis. Each ECU typically contains between 30 and 50 words. Number of forms This is the total number of unique words (before lemmatization) found in the corpus. Number of lemmas This is the number of unique lemmatized words (variants such as «works,» «working,» or «worked» are grouped under the lemma «work»). Number of occurrences This is the total number of lexical words used across all texts. Mean number of forms per segment On average, each ECU contains approximately 27 words. This aligns with the Reinert method, which recommends between 20 and 50 words per ECU. Table 14. Active and supplementary forms of a corpus Data item Meaning Active forms Words considered relevant for the Descending Hierarchical Classification (stopwords and irrelevant forms are removed). Supplementary forms Words not used to build the classes but included as contextual information. Active forms with frequency ≥ 3 Active words that appear at least three times in the corpus; these are effectively considered in the analysis, as they carry sufficient statistical weight for the Reinert method. Table 15. Descending hierarchical classification (DHC) of a corpus Data item Meaning Number of classes The Reinert method grouped the segments into four distinct thematic classes, each with its own set of key words and representative ECUs. Classified segments The percentage of segments that could be statistically assigned to classes. The remaining segments did not display significant co-occurrence patterns. A rate above 50% is considered acceptable. 89