Zero Digital Competence: Training Needs through
Data Mining towards an Innovative Digital Training System
Competencia Digital Cero: Necesidades
Formativas vía Minería de Datos hacia un Sistema de Formación Digital Innovador
y Disruptivo
Mgtr. Antonio
González Grez. Living Lab - Collaborations
and Diffusion Coordinator. Instituto
Tecnológico Monterrey. México
Received: 2024-09-15 Received: 2024-10-24 Accepted: 2025-02-19 Online First: 2025-02-28 Published: 2025-05-01
How to cite:
González Grez, A. (2025).
Zero Digital Competence: Training Needs
through Data Mining towards an Innovative Digital
Training System [Competencia Digital Cero:
Necesidades Formativas vía Minería de Datos hacia un Sistema de Formación
Digital Innovador y Disruptivo]. Pixel-Bit. Revista de
Medios y Educación, 73, art.4. https://doi.org/10.12795/pixelbit.108664
ABSTRACT
RESUMEN
Este estudio identifica
necesidades formativas docentes mediante minería de textos para fundamentar un
sistema formativo digital innovador que contribuya a reducir la brecha de
competencias digitales, comparando períodos pre y post-pandemia. Se empleó un
diseño cualitativo secuencial con entrevistas semiestructuradas a 21
especialistas de cinco países hispanoamericanos (fase pre-pandemia, 2018-2020)
y focus groups con 6 especialistas (fase post-pandemia, 2023). Los datos se
analizaron mediante técnicas de minería de textos (análisis de bigramas) y
microanálisis cualitativo, aplicando criterios de saturación teórica y
triangulación metodológica. El análisis pre-pandemia reveló preocupaciones
centradas en aspectos curriculares, evaluación de aprendizajes y estructura
universitaria. El estudio post-pandemia evidenció un desplazamiento hacia
tecnologías como apoyo de datos, enfoque humanizante del aprendizaje y solución
de problemas reales mediante inteligencia artificial. Los bigramas más
significativos ("realidad-virtual", "habilidades-blandas",
"inteligencia-artificial") confirman esta evolución. Los hallazgos
fundamentan un Sistema Formativo Digital basado en comunidades de aprendizaje
mediadas por IA, trayectorias personalizadas y equilibrio técnico-humanístico,
trascendiendo los modelos tradicionales para abordar la brecha de competencias
digitales docentes de manera innovadora y contextualizada.
KEYWORDS · PALABRAS CLAVES
Digital Teaching
Competence; Digital Divide; Teacher
Training; Educational Innovation; Data Mining; Emerging Technologies;
Artificial Intelligence
Competencia
digital; brecha digital; formación docente; innovación educativa; minería de
datos; tecnologías emergentes; Inteligencia Artificial
1. Introduction and Background
1.1. Introduction
The digital transformation of
education has experienced an unprecedented acceleration since the onset of the
COVID-19 pandemic, highlighting and deepening pre-existing gaps in teachers’
digital competencies (Cabero-Almenara & Valencia-Ortiz, 2021; Fernández-Batanero
et al., 2022). This phenomenon has underscored the urgency of rethinking
traditional training systems, which have shown significant limitations in
swiftly responding to the emerging needs of teachers in rapidly changing
contexts (Castañeda et al., 2022).
This study aims to identify
qualitative background on teachers’ training needs through text mining
techniques, analyzing data from participants of the "Competencia Digital
Cero" movement at two key points: pre-pandemic (2018–2020) and post-pandemic
(2023). This comparative analysis allows for an understanding of how the
experience of forced digitalization has transformed teachers’ priorities and
training requirements, thus supporting the proposal of an innovative and
disruptive Digital Training System (DTS).
The digital gap among teachers
remains a persistent challenge in today’s educational landscape. Area-Moreira
et al. (2023) characterize it as a multidimensional phenomenon that goes beyond
access to devices, encompassing instrumental (tool use), pedagogical
(meaningful integration into teaching-learning processes), and
ethical-reflective (critical stance toward digital transformation) dimensions.
This gap, already identified before COVID-19 (Cabero-Almenara &
Ruiz-Palmero, 2018), became dramatically visible during the pandemic and still
persists, with traditional training systems having proven ineffective in
reducing it (European Commission, 2023; Beltrán, 2023).
Recent research on teacher
training in digital competencies shows a progressive shift from instrumental
approaches toward more holistic and integrated perspectives (Garzón-Artacho et
al., 2021; Esteve et al., 2022). However, as Reisoğlu and Çebi (2020)
point out, a significant disconnect persists between conceptual frameworks and
their practical implementation in training programs, which mostly remain
anchored in rigid and decontextualized structures.
In this context, the
"Competencia Digital Cero" initiative emerged in 2018 as a response
to the limitations of traditional training systems in developing teachers’
digital competencies. This movement operates under the principles of
horizontality, contextualization, and collaborative learning—aligned with what
Adell et al. (2018) describe as "emergent pedagogies" in digital
environments. The experience accumulated by this practical community offers a
valuable corpus of data for analyzing the evolution of teachers’ training needs
in the pre- and post-pandemic periods.
Analyzing these needs through
text mining techniques represents an innovative methodological approach in the
educational field. As Escudero et al. (2022) indicate, these techniques enable
the identification of semantic patterns not evident through traditional
analyses, which are especially valuable in understanding changes in perceptions
and priorities. This methodology is complemented by conventional qualitative
analyses that contextualize and interpret the identified patterns.
The general objective of this study
is to identify teachers’ training needs through text mining to support the
design of an innovative and disruptive Digital Training System. The specific
objectives are: 1) to comparatively analyze the training needs expressed by
teachers in pre- and post-pandemic periods; 2) to identify significant semantic
patterns through bigram analysis; and 3) to propose a model for a Digital Training System based on the empirical evidence
gathered.
The relevance of this research
lies in its potential to inform the design of more agile, contextualized, and
effective training systems for the development of teachers’ digital
competencies. In a historical moment of accelerated educational transformation,
understanding the evolution of training needs and proposing disruptive models
represents a significant contribution both for training institutions and
educational policymakers (Portillo et al., 2020; Ramírez-Montoya et al., 2022).
1.2. State of the Art
Teacher training in digital
competencies and innovative training systems are currently vibrant areas of
research, particularly accelerated by the forced digitalization experience
during the pandemic. The main developments are analyzed below in three interconnected
dimensions: conceptual frameworks of teachers’ digital competence, persistent
gaps in its development, and emerging training systems.
1.2.1. Conceptual Frameworks of Teachers’ Digital Competence
The construct of teachers’
digital competence has undergone significant conceptual evolution, with the
European Framework for the Digital Competence of Educators (DigCompEdu)
progressively becoming an international benchmark (Cabero-Almenara & Palacios-Rodríguez,
2020). This framework organizes teachers’ digital competence into six
interrelated areas: professional engagement, digital resources, digital
pedagogy, digital assessment, learner empowerment, and facilitation of
students’ digital competence.
Falloon (2020) proposes the
Teacher Digital Competence (TDC) Framework, which emphasizes the contextual and
situated nature of these competencies, distinguishing between technical,
pedagogical, and evaluative skills. Meanwhile, Mishra and Koehler (2021) update
their influential TPACK model (Technological, Pedagogical, and Content
Knowledge), incorporating ethical and sociocultural dimensions that recognize
the complexity of technology integration in diverse educational settings.
In the Spanish-speaking
context, Pascual et al. (2022) identify the coexistence of multiple reference
frameworks, highlighting the need for contextualization to specific realities.
These authors emphasize that teachers’ digital competencies are not a static
set of skills but dynamic capacities that evolve in response to technological,
pedagogical, and sociocultural transformations.
1.2.2. Persistent Gaps in the Development of Teachers’ Digital
Competence
Despite the consolidation of
robust conceptual frameworks, numerous studies show the persistence of
significant gaps in the effective development of teachers’ digital
competencies. Area-Moreira et al. (2023), analyzing self-perceived competencies
of 1,433 Spanish teachers, found that approximately 40% reported insufficient
levels, particularly in the pedagogical and evaluative dimensions.
Fernández-Batanero et al.
(2022), through a systematic review of 38 studies, confirmed that the greatest
deficiencies are found precisely in the most complex competencies: designing
digital learning experiences, technology-mediated assessment, and creating
digital content. This uneven distribution shapes what Cabero-Almenara and
Llorente-Cejudo (2020) call the "third digital divide" in teaching,
characterized not by access or instrumental use, but by the capacity for
transformative pedagogical integration.
The pandemic served as a
catalyst that made these pre-existing gaps more visible. As Portillo et al.
(2020) demonstrate in a study with 593 teachers from different educational
levels, the emergency remote teaching experience revealed significant disparities
not only between institutions but also among teachers within the same
institution, creating a landscape of "digital inequality" that
continues into the post-pandemic stage (Beltrán, 2023).
A particularly relevant aspect
for our study is the identification of factors that determine teachers’
resistance to technopedagogical innovation. López-Belmonte et al. (2020)
identify the main barriers as: lack of specific training (45.2%), distrust toward
innovative approaches (22.2%), reactive stances toward the use of technologies
(17.2%), lack of resources (12%), and perceived incompatibility with student
characteristics (3.4%). These findings suggest that training needs go beyond
technical instruction, involving attitudinal, emotional, and epistemological
dimensions.
1.2.3. Emerging Training Systems for the Development of Teachers’
Digital Competencies
In light of the limitations of
traditional training models, various researchers have documented the emergence
of alternative systems with disruptive potential. González-Sanmamed et al.
(2022) propose the concept of "digital learning ecologies" as an
interpretive framework for these new training ecosystems. Their empirically
validated model integrates resources, activities, relationships, and contexts
into a dynamic network that transcends conventional institutional boundaries.
Gros and Noguera (2023)
analyze digital learning communities among teachers, identifying them as
particularly effective training spaces due to their ability to provide
situated, collaborative, and contextualized learning. Their longitudinal study
with 42 teachers shows how these communities foster not only technical skills
development but, more importantly, the construction of shared pedagogical
meanings around technology integration.
Artificial intelligence is
emerging as a potentially transformative component of training systems. Fan et
al. (2022) document experiences with adaptive recommendation systems to
personalize learning pathways, demonstrating significant improvements when these
systems address specific individual needs. However, as Williamson (2023) warns,
implementing such systems must be grounded in sound pedagogical frameworks that
avoid technological determinism and prioritize teacher agency.
Particularly relevant to our
study is the experience of the "Competencia Digital Cero" initiative,
launched in 2018 as a response to the limitations of traditional training
systems. This movement operates under the principles of horizontality, contextualization, and collaborative learning,
aligned with what Raffaghelli (2020) describes as "transformative
professional learning" in digital environments. The experience
accumulated by this practical community offers a valuable corpus of data to
analyze the evolution of teachers’ training needs during the pre- and
post-pandemic periods.
1.2.4. Text Mining Applied to the Analysis of Training Needs
The application of text mining
techniques to the analysis of training needs represents an emerging field with
significant potential. Escudero et al. (2022), in their systematic review of 87
studies, identify exponential growth in the use of these techniques in
educational research, emphasizing their ability to process large volumes of
information and detect patterns not evident through manual analysis.
Hidalgo-Ternero and
Pérez-Cordón (2021) specifically examine the application of natural language
processing in education, highlighting bigram analysis as a particularly
valuable method for identifying semantic patterns in educational texts. This
approach allows for the detection of recurring conceptual associations that
reveal concerns, priorities, and underlying interpretive frameworks in teacher
discourse.
Sharma et al. (2020)
demonstrate the effectiveness of text mining in identifying significant communication
patterns in educational contexts, showing how these techniques can complement
traditional qualitative approaches. However, as Marín (2023) cautions, these
methodologies present important limitations that must be acknowledged,
particularly regarding the contextualization and interpretation of identified
patterns.
This study is situated at the
promising intersection of text mining, analysis of training needs, and the
design of disruptive training systems, contributing to a growing field of
research that seeks to empirically support the transformation of teacher training
in the contemporary digital context.
2. Methodology
This study adopts a
qualitative approach with a sequential comparative design, analyzing data
collected in two clearly differentiated time periods: pre-pandemic (2018–2020)
and post-pandemic (2023). This methodological approach enables the
identification of changes in teachers’ training needs resulting from the
disruptive context caused by COVID-19, following the recommendations of
Castañeda et al. (2022) for analyzing educational phenomena during periods of
accelerated transformation.
2.1. Participants and
Selection Criteria
2.1.1. Pre-pandemic Phase
The pre-pandemic phase
involved 21 Spanish-speaking teaching specialists from the following countries:
Argentina (1), Chile (5), Colombia (4), Spain (6), and Mexico (5). This
geographic diversity allowed the study to capture perspectives from various socio-educational
contexts and digital policy frameworks, enriching the comparative analysis.
Regarding their professional
affiliation, six specialists came from education-focused companies, while
fifteen worked directly in public or private universities and national
educational institutions. This heterogeneity enabled the contrast of views from
both academic and educational business sectors.
Participants were selected
using purposive criterion-based sampling (Flick, 2018), applying four
fundamental parameters:
·
Verifiable teaching
experience: Minimum of 5 years in
university or vocational education.
·
Specialization in digital
training: Demonstrated through
publications, projects, or roles in teacher training and/or digital education.
·
Active involvement in
educational innovation: Participation in professional networks
related to digital competencies and pedagogical innovation.
·
Contextual representativeness:
Inclusion of diverse institutional, geographic, and
professional realities within the Spanish-speaking world.
The sample size (n=21) was
determined using the theoretical saturation criterion (Strauss & Corbin,
2002), halting the inclusion of new participants once additional interviews no
longer yielded substantially new categories or properties. This procedure
follows the methodological recommendations of Guest et al. (2020) for
qualitative studies based on semi-structured interviews in specialized fields.
2.1.2. Post-pandemic Phase
The post-pandemic phase
included 6 education specialists from Chile (1), Spain (3), Mexico (1), and
Peru (1), all engaged in academic and educational activities related to teacher
training and digital education. Selection followed the same criteria as in the
pre-pandemic phase, ensuring comparability between the two samples.
The smaller sample size in
this phase (n=6) is explained by two methodological factors: 1) The greater
depth and intensity of the focus group method compared to individual
interviews, following Krueger and Casey’s (2015) recommendations; y 2) Earlier
saturation patterns, likely due to the shared experience of forced
digitalization during the pandemic.
To compensate for this
numerical difference and ensure the validity of findings, methodological
triangulation was implemented by complementing the data with an open-ended
questionnaire applied to participants from the “Mission 3” phase of the
Competencia Digital Cero initiative.
2.2. Data Collection
Techniques and Instruments
2.2.1. Pre-pandemic Semi-structured Interviews
A semi-structured interview protocol
was designed with 12 open-ended questions organized into five thematic blocks:
·
Teacher professional profile
and expected transformations
·
Emerging educational models
·
Evolution of assessment systems
·
Technological transformations
and their impact on training
·
Cultural changes and their
influence on training needs
The protocol was validated by
expert judgment (n=3) from individuals experienced in qualitative research and
teacher training, achieving a content validity index of 0.87, considered
optimal according to Lynn’s (1986) criteria.
Interviews were conducted in
person, recorded with informed consent, and fully transcribed for analysis. The
average duration was 22 minutes, with a range from 9 to 58 minutes depending on
the depth of the responses.
2.2.2. Post-pandemic Focus Groups
A specific protocol was
designed with 13 open-ended questions that corresponded thematically to the
pre-pandemic interviews, allowing comparability between the two phases.
Additionally, questions were included regarding the specific impact of the
pandemic on training needs.
Two focus groups were
conducted, each with 3 participants, facilitated by the same researcher who
conducted the individual interviews. The sessions were held virtually via Zoom,
recorded with informed consent, and fully transcribed. The durations were 130
and 90 minutes, respectively.
2.2.3. Complementary Questionnaire
A questionnaire with three
open-ended questions was administered to participants from the "Mission
3" phase of the Competencia Digital Cero initiative:
How do you imagine the future
of teacher training?
What do you want to learn
today?
Would you like to add any
ideas or suggestions about our Mission 3?
This complementary instrument broadened
the post-pandemic database and triangulated the findings from the focus groups
with a wider sample.
2.3.
Data Analysis
A mixed analytical approach
was implemented, integrating text mining techniques with traditional
qualitative analysis, following the methodological integration recommendations
by Escudero et al. (2022) for educational research.
2.3.1. Text Mining: Bigram Analysis
Text mining analysis followed
a systematic procedure structured in five phases:
1. Text corpus preparation: A corpus was created for each
interview, excluding interviewer interventions. The text was normalized through
cleaning processes (removal of punctuation, conversion to lowercase) and
lemmatization (reducing words to their canonical form).
2. Stopword removal: A Spanish stopword list with
310 terms was applied, consisting of grammatical particles or connectors with
no relevant semantic value.
3. Bigram extraction: Bigrams (pairs of related
words) were identified in each interview, following the methodology proposed by
Chen et al. (2006). Bigrams are language models that help determine
dependencies between terms and enable automatic text categorization.
4. Adaptive bigram filtering: Due to semantic richness
variability across interviews, differentiated frequency thresholds were
applieds:
a.
High semantic richness: minimum frequency of 5
b.
Medium semantic richness: minimum frequency of 3–4
c.
Low semantic richness: minimum frequency of 2
This flexible approach enabled
the identification of relevant semantic patterns across all interviews,
adapting to their natural informational density.
5.
Semantic network visualization: Visual
representations of the most significant bigrams were generated, facilitating
the identification of both central nodes and peripheral relationships in the
specialists' discourse.
The computational analysis was
conducted using the R programming language, version 4.1.1, employing the
"tm" package for text processing, "igraph" for network
visualization, and "tidytext" for structured text analysis.
2.3.2. Qualitative Microanalysis
To complement the
computational analysis, line-by-line microanalysis was applied, as proposed by
Strauss and Corbin (2002), enabling the emergence of categories and
relationships among them. This method was applied to both the focus group
transcripts and the responses from the complementary questionnaire.
The microanalysis process included:
1.
Open coding: identification of
concepts within the data
2.
Axial coding: establishing
relationships among categories
3.
Selective coding: integration and
theoretical refinement
2.4. Validation and
Methodological Rigor
To ensure methodological
rigor, the criteria proposed by Lincoln and Guba (1985) for qualitative
research were applied:
·
Credibility: Implementation of
methodological triangulation (interviews, focus groups, questionnaire) and
analytical triangulation (text mining and traditional qualitative analysis).
·
Transferability: Detailed
description of participants, contexts, and procedures to support judgments
about the applicability of findings to other contexts.
·
Dependability: Maintenance of a
detailed audit trail of all methodological and analytical decisions.
·
Confirmability: Use of textual
citations to support interpretations and validation of preliminary analyses
with a sample of participants.
2.5. Ethical Considerations
The research was conducted
following ethical principles for social science research (AERA, 2011). All
participants signed informed consent forms that explained the study’s
objectives, the voluntary nature of their participation, their right to
withdraw at any time, and confidentiality guarantees. Data were handled in
accordance with personal data protection regulations, using coding systems that
prevented participant identification.
3. Analysis and Results
3.1. Pre- and Post-Pandemic Perspectives on Teacher Training Needs.
3.1.1. Analysis and Results
from Pre-Pandemic Interviews.
Text mining was carried out separately
for each pre-pandemic interview in order to enable semantic pattern comparison.
A corpus was created for each interview, excluding interviewer interventions. A
list of 310 Spanish stop-words was compiled, consisting of grammatical
particles or connectors without semantic meaning.
From the analysis of the 21
interviews, bigrams were obtained with minimum frequencies of two, three, four,
and five, depending on the semantic richness of each interview. Interviews with
greater semantic richness were filtered using a minimum frequency of five,
since non-trivial patterns were consolidated to the point of having high
frequencies such as 12 and 13. In contrast, in interviews with lower semantic
richness, the bigram filter was set at two, to allow more flexibility in
identifying non-trivial semantic patterns. Table 1 shows the distribution of
interviews according to bigram filter criteria.
Table 1
Interview Distribution and
Bigram Filters
|
Bigram Filter |
Number of
Interviews |
Range of
Maximum Frequencies |
Interview
IDs |
|
Minimum frequency
2 |
5 |
3 to
5 |
2, 3, 8, 12 y 21 |
|
Minimum frequency
3 |
7 |
5 to
7 |
13, 14, 15, 16, 17, 18 y 19 |
|
Minimum frequency
4 |
2 |
6 to
7 |
10 y 11 |
|
Minimum frequency 5 |
7 |
8 to
13 |
1, 4, 5, 6, 7, 9 y 20 |
|
Total |
21 |
|
|
According to Table 1, five
interviews showed low semantic richness, requiring the bigram filter threshold
to be lowered to a minimum frequency of two in order to identify non-trivial
semantic patterns. Furthermore, the patterns found were not consolidated, and
within these five interviews, the maximum bigram frequencies ranged only from
three to five. In contrast, seven interviews presented a large number of
consolidated non-trivial patterns, with minimum bigram frequencies of five and
maximums of up to thirteen.
The visual representation of
the bigrams from the seven interviews with the highest semantic richness is
shown in Figures 1 to 7.
In contrast, seven interviews
revealed a large number of consolidated non-trivial patterns, with minimum
bigram frequencies of five and maximums reaching up to thirteen.
The visual representation of
the bigrams from the seven interviews with the highest semantic richness is
shown in Figures 1 to 7.
Figure 1
Bigrams from
Interview 1, with Frequencies Ranging from 5 to 10
Figure 1 shows that the interviewed
specialist formulated their reflections primarily from a personal belief
standpoint (the central node is the word “creo” [I believe]), which makes the
responses somewhat diffuse, as key aspects such as teacher needs are not
clearly addressed. The word “profesor” [teacher] appears isolated and weakly
connected to the central nodes. Additionally, the interviewee refers to key
terms such as “formación” [training], “tecnología” [technology], “competencias”
[competencies], “digitalmente” [digitally], and “universidad” [university],
which contextualizes their prospective outlook toward 2030.
Figure 2
Bigrams from Interview 4, with Frequencies Ranging from 5 to 13
Figure 2 shows that this
interview contained a richer set of relevant patterns, unlike Interview 1, as
it does not reflect a discourse based on personal belief (the term “creo” [I
believe] is absent). The main nodes indicate that the prospective perspective
is closely linked to Mexican educational policy (connection between “política”
[policy], “México,” and “educación” [education]), and to issues related to
“programas” [programs], “proceso(s)” [processes], and the “realidad” [reality]
of using “tecnología” [technology], “computadora” [computer], and
“conectividad” [connectivity].
Less directly connected but
still related to the central nodes is the discussion of the “brecha digital”
[digital divide], “acceso” [access] to “tecnológico” [technological] tools, and
the “estudiante” [student]. Notably, aspects related to “formación docente”
[teacher training] are practically disconnected from the prospective
perspective presented.
Figure 3
Bigrams from
Interview 5, with Frequencies Ranging from 5 to 9
In Interview 5, it is clear
that the focus of the dialogue was the “estudiante” [student], projecting a
perspective in which the most important aspects are the “aprendizajes”
[learning outcomes], the possible forms of “evaluación” [assessment] of those learnings,
and the use of “información” [information] to provide “sentido” [meaning] and
to understand their “manera” [way] of “ver” [viewing] the “universidad”
[university].
Tangentially, a certain role of the “profesor” [teacher] in the “aprendizaje”
[learning] process is also observed, but the main focus remains on the
“estudiantes” [students].
Figure 4
Bigrams from
Interview 6, with Frequencies Ranging from 5 to 9
In Interview 6, a perspective
centered on “evaluación” [assessment] is observed, rather than on the student,
as in Interview 5. In this sense, the interview projects aspects related to the
assessment of “aprendizaje” [learning], the “alumno” [student], the role of the
“profesor” [teacher], and different “modelos” [models] of evaluation.
Figure 5
Bigrams from
Interview 7, with Frequencies Ranging from 5 to 12
In Interview 7 (Figure 5), as
in Interview 1, the prospective perspective is primarily framed from a personal
standpoint, with the term “creo” [I believe] as the most prominent node.
However, this interview addresses a broader range of topics, including “competencias”
[competencies], “formación” [training], “aprendizaje” [learning], “trabajo”
[work], and “cursos” [courses]. Likewise, although indirectly linked to the
main nodes, the topic of “competencia digital” [digital competence],
“tecnologías” [technologies], and “escuela” [school] is repeatedly addressed.
Figure 6
Bigrams from
Interview 9, with Frequencies Ranging from 5 to 8
The significant patterns observed in Figure 6 indicate that, in Interview 9, the center of the
prospective perspective is the “universidad” [university]. Various related
topics are discussed, such as the “profesor(es)”/“docente(s)”
[teacher(s)/educator(s)], “formación” [training], “competencia digital”
[digital competence], and “tecnología” [technology].
Figure 7
Bigrams from Interview
20, with Frequencies Ranging from 5 to 10
Finally, Figure 7 shows that, in
Interview 20, the focus of the dialogue is on the “docente(s)” [teacher(s)],
relating to topics such as “educación” [education] and “formación” [training],
their “trabajo” [work], the “estudiantes” [students], “clases” [classes], and
the “universidad” [university].
3.1.2. Analysis and Results from the Post-Pandemic Focus Group.
Microanalysis
The line-by-line microanalysis
based on Strauss and Corbin (2002) allowed for the identification of six
thematic categories and a series of specific subtopics (see Table 2).
Table 2
Thematic Categories and Subtopics Resulting from the Microanalysis
|
Thematic Category |
Subtopics |
|
1. How digital technologies could support
education. |
Technologies support data collection. Technology helps when integrated, not when it
replaces. Technology won’t help if evaluation doesn’t
improve. Teachers are irreplaceable in assessment. |
|
2. On improving the competency-based approach, or proposing a better model. |
The system continues to demand a numerical
evaluation model, not a competency-based one. Assessment should be more qualitative. Informal assessment is gaining social traction. More learning takes place online than at
university. Growth of online learning. Companies offering online training are
expanding. A transition from in-person to online learning
is underway. Online education is more up to date than
in-person education. |
|
3. On teacher training and technology. |
Teacher training in classroom practice is complex; The shift to digital is necessary but slow. The curriculum must be updated because digital
competencies are not included. Teachers are in a vulnerable position, as their
jobs are at risk if they do not update their skills. Teachers are forced to learn ICT for educational
purposes. Teachers are required to use ICT tools. There is a lack of ability to use technology in
the classroom. Using technology
to teach. |
|
4. Free global education model. |
Prejudices about free offerings: - Free means low quality. - Paying provides a certain
guarantee of service quality. - Free
implies less commitment. Provide personalized feedback (performance
compared to one's own progress). Peer assessment. In some places (Argentina), free education is
highly regarded. Free training is undervalued when it comes to
employability. Assessment is practical. Even in competency-based assessment, a numeric
value is still required. |
|
5. Training needs for the next 5 years |
Current training focuses on understanding
curricular content. In 5 to 10 years, it will be necessary to
understand the learner as a person. We already know what needs to be done—now it’s
time to act. The focus should be on the human being. Training is not addressing the human dimension. Efforts aim to solve problems, but there is no
guidance on identifying what the problems are. |
|
6. What will prevail in 2030 regarding teacher
training? |
Current practices will continue, but informal
education (such as short courses) will gain strength. The year is too near to expect a radical change. Greater attention to the human dimension. Corporate universities. Prepare learners to solve real-world problems. More focus on attitudes and coexistence. |
The themes addressed
post-pandemic are deeply influenced by the experiences lived during the
pandemic. There is greater emphasis on topics related to the use of technology
compared to the pre-pandemic interviews, where—although technology was
discussed—the focus was directed toward other aspects of teacher training, such
as student-centered learning or public education policy.
·
Bigrams from Specific
Post-Pandemic Questions.
The following questions were
included in the short questionnaire:
1. How do you imagine the
future of teacher training?
2. What do you want to learn
today?
3. Would you like to add any
ideas or suggestions about our Mission 3? We’re listening. Now is your moment.
Figure 8
Bigrams from the
question “How do you imagine the future of teacher training
Teachers envision the future
of teacher training as being centered on “continuous” and “ongoing” “training”
for the development of “digital” “competencies.” This vision is reinforced by
references to elements such as “virtual” “reality,” “augmented” “reality,” and
“artificial” “intelligence.” Notably, there is also a general emphasis on the
development of competencies in terms of “soft” “skills.”.
The preferences regarding
content are clearly focused on the “use” of “technological” “digital” “tools,”
such as “ICTs,” “artificial” “intelligence,” and “virtual” “reality.” However,
it is worth noting that there remains a strong preference for “soft” “skills,”
which aligns overall with the future-oriented perspective on teacher training.
Figure 9
Bigrams from the
question “What do you want to learn today?
Figure 10
Bigrams from the
question “Would you like to add any ideas or suggestions about our Mission 3?
We’re listening. Now is your moment.”
The main nodes continue the
trend of focusing on digital tools and competencies, explicitly acknowledging the
importance of a project like CDO, and expressing gratitude and curiosity about
future workshops.
4. Discussion
The results of this study reveal
significant transformations in teacher training needs between the pre- and
post-pandemic periods, providing empirical foundations for the
conceptualization of an innovative Digital Training System (SFD). In this
section, we critically analyze the findings, contrast them with previous
research, assess methodological limitations, and develop an operational model
of the proposed system along with its practical implications.
4.1. Evolution of Teacher
Training Needs: From Structural to Transformational
The bigram analysis highlights
a paradigmatic shift in teacher training needs. During the pre-pandemic period,
semantic networks were predominantly organized around institutional
(university), policy (regulations, programs), and assessment (models, instruments)
dimensions, with technology occupying peripheral positions. This configuration
aligns with what Cabero-Almenara and Palacios-Rodríguez (2020) define as the
instrumental approach to digital competence, in which technology is viewed as a
tool subordinate to pre-existing structures.
In contrast, the post-pandemic
analysis reveals a reconfiguration where bigrams like
“artificial-intelligence,” “virtual-reality,” and “soft-skills” emerge as key
organizing elements of the discourse. This shift goes beyond the mere increase
in the perceived value of digital competencies, as documented by studies such
as Portillo et al. (2020), to reveal a qualitative change in the very
conceptualization of these competencies: from instrumental to transformational.
The emergence of the bigram
“soft-skills” is particularly noteworthy, as it reveals a conceptual
integration of technical and socio-emotional competencies—something absent in
the pre-pandemic discourse. This finding expands on Fernández-Batanero et al.’s
(2022) understanding of the multidimensional nature of digital teaching
competence by empirically identifying the integration of socio-affective
dimensions as a post-pandemic emergent component. The pandemic appears to have
catalyzed not only an intensification in the use of technologies—as documented
by numerous studies (Marín et al., 2021)—but also a deep reconceptualization of
the relationship between technology and humanism in teacher training.
4.2. Epistemic Patterns in the
Conceptualization of Training Needs
A particularly relevant
finding from our analysis concerns the underlying epistemic patterns in the
conceptualization of training needs. The pre-pandemic predominance of bigrams
centered on “I believe” (Figures 1 and 5) suggests an experiential-subjective
anchoring in the identification of needs, whereas the post-pandemic networks
show greater articulation with established theoretical constructs (digital
competencies, soft skills, artificial intelligence).
This epistemic evolution
suggests that the forced digitalization experience during the pandemic not only
changed perceptions of specific technologies but also transformed the
conceptual frameworks through which training needs are interpreted. Teachers appear
to have shifted from primarily experiential positions to more structured and
theorized interpretive frameworks—a phenomenon identified by Area-Moreira et
al. (2023)
as “forced epistemic maturation” in contexts of digital disruption.
This transformation has
fundamental implications for training system design, suggesting a shift from
models based on the transmission of instrumental competencies toward ecosystems
that facilitate the collective construction of interpretive frameworks on the
integration of technology and pedagogy. Our findings thus extend the
conclusions of González-Sanmamed et al. (2022) on digital learning ecologies by
empirically demonstrating that these must respond not only to technical needs
but also to transformations in teachers’ epistemic frameworks.
4.3. Methodological Limitations and Analytical
Complementarity
The bigram analysis has proven
effective in identifying significant semantic patterns in teachers’ discourse
regarding training needs. However, we concur with Escudero et al. (2022) in
acknowledging the limitations inherent to this methodological approach. The
bigram technique, by nature, captures binary lexical associations but not
necessarily complex argumentative structures or contextual nuances.
This limitation is
particularly relevant in our case, where categorizing texts through bigrams
proved insufficient to fully capture the multidimensional complexity of
expressed training needs. As Hidalgo-Ternero and Pérez-Cordón (2021) warned,
text mining techniques must be complemented by interpretative approaches that
contextualize the patterns identified.
Triangulation with qualitative
microanalysis has partially compensated for these limitations, revealing
dimensions that bigrams alone could not capture—such as attitudinal ambivalence
toward emerging technologies or ethical concerns about artificial intelligence
in education. This methodological complementarity reinforces Sharma et al.’s
(2020) argument on the need for hybrid approaches that combine the processing
power of computational techniques with the contextual sensitivity of
qualitative analysis.
Acknowledging these
limitations does not invalidate the findings; rather, it defines their
interpretive scope and supports our proposal for a training system that
integrates both computational analysis and qualitative approaches for the
ongoing identification of teacher training needs.
4.4. Digital Training System:
Operational Model and Practical Implications
Grounded in empirical findings
on the evolution of training needs, we propose a Digital Training System (SFD)
structured around four interrelated components that go beyond traditional
training models. Figure 11 illustrates the conceptual and operational architecture
of the proposed system.
Figure 11
Diagram of the
Digital Training System
Source: Own elaboration
4.4.1. Components of the Digital Training System
1.
Adaptive Artificial Intelligence Core: Directly addresses
the identified need for personalized training by implementing:
o Semantic analysis
algorithms to continuously identify emerging needs
o Recommendation
systems that create personalized learning paths based on teacher profiles
o Matching mechanisms
between teachers with complementary profiles
This component goes beyond mere automation of training
content, addressing what Fan et al. (2022) identify as the main challenge in
digital training systems: contextualized and dynamic personalization.
2.
Interconnected Communities of Practice: Addresses the
post-pandemic emerging need for collaborative learning by means of:
o Self-organized
digital spaces by domains of interest
o Peer mentoring
systems with complementary expertise
o Dynamic
repositories of pedagogical experiences and solutions
This component operationalizes
what Gros and Noguera (2023) conceptualize as "distributed learning
networks," where knowledge emerges from horizontal interactions rather
than vertical transmissions.
3.
Technopedagogical Integration Lab: Responds to the
identified need to link practice with theory through:
o Simulation
environments for pedagogical experimentation
o Co-design systems
for digital learning experiences
o Prototyping and
testing tools for didactic innovations
This component brings to life the
proposal by Mishra and Koehler (2021) regarding learning environments that
integrate technological, pedagogical, and disciplinary knowledge
simultaneously.
4.
Metacognition and Professional Development Platform:
Addresses the emerging need for techno-humanist integration through:
o Reflection tools on
the ethical impacts of educational technologies
o Documentation systems
for professional development pathways
o Certification
mechanisms based on evidence of contextualized performance
This component addresses what
Area-Moreira et al. (2023) identify as the most neglected dimension in digital
teacher training: the reflective capacity regarding the pedagogical and ethical
implications of technology integration.
4.4.2. Practical Implications for
Implementation
The effective implementation
of the proposed Digital Training System (SFD) requires specific actions at
different levels.
At the institutional level:
·
Create specialized technopedagogical
design units that integrate expertise in AI, instructional design, and
subject-specific teaching methods
·
Develop institutional recognition systems for
competencies acquired through the SFD
·
Implement hybrid physical-virtual spaces to facilitate
interaction among communities of practice
At the educational policy
level:
·
Establish regulatory frameworks to facilitate the
certification of competencies acquired through non-traditional training systems
·
Develop targeted funding programs for educational AI
infrastructure
·
Create centralized repositories of anonymized data to
feed adaptive AI systems
At the level of teacher professional development:
·
Implement initial AI literacy programs in education to
overcome entry barriers
·
Develop specific "connector" roles between
communities of practice
·
Establish incentives for teachers who actively
contribute to shared repositories
These concrete practical
implications address the need identified by Ramírez-Montoya et al. (2022) to
transform conceptual models into operational roadmaps that guide effective
transformations in training systems.
4.5. Contribution to the
Existing Literature and Future Projections
This study contributes to the
literature on digital teacher training in three key areas:
1.
Methodologically: It demonstrates the
potential and limitations of text mining for analyzing training needs,
highlighting the need for hybrid methodological approaches that integrate
computational and qualitative techniques.
2.
Conceptually: It empirically
identifies a paradigmatic shift in teacher training needs, from models centered
on institutional structures toward integrated technopedagogical
and humanistic approaches.
3.
Practically: It proposes a
Digital Training System model with specific components and actions that go
beyond conceptual proposals to offer concrete implementation pathways.
Future research projections
include the development of specific prototypes of the proposed Digital Training
System (SFD) components, longitudinal studies on its impact in different
educational contexts, and comparative analyses with other emerging training
models. Particularly promising is the exploration of hybrid systems that
integrate computational and qualitative approaches for the ongoing
identification of training needs in rapidly evolving technological contexts.
5. Conclusions
This research has identified
teacher training needs through text mining in order to support an innovative
Digital Training System (DTS), by comparatively analyzing pre- and
post-pandemic periods. The results allow for drawing significant conclusions
related to the proposed objectives.
The comparative analysis of
training needs between the pre- and post-pandemic periods shows a substantial
transformation. Pre-pandemic semantic patterns revealed five priority axes:
curricular aspects, learning assessment, institutional structure, technology as
a tool, and instrumental digital competence. Teachers' concerns were mainly
articulated from institutional, evaluative, or student-centered perspectives,
with technology occupying peripheral positions in the semantic networks.
In contrast, the post-pandemic
analysis identified a central repositioning of technology—not as a subordinate
tool but as a transformative ecosystem. The most significant bigrams
("virtual-reality", "soft-skills", "artificial-intelligence")
reflect a reconceptualization where previously disconnected technical,
pedagogical, and humanistic dimensions now converge. Particularly noteworthy is
the emergence of the "soft-skills" bigram, absent in pre-pandemic
discourse, reflecting a new prioritization of socioemotional competencies
integrated with technical capabilities.
The complementary qualitative
microanalysis revealed emerging categories that both reinforce and nuance this
transformation: technological resistance as a determining factor, the
perception of professional vulnerability in the face of accelerated digitalization,
the growing appreciation of informal learning over traditional academic
pathways, and an increasing concern for the humanization of digital educational
environments.
Regarding the methodology
used, we conclude that bigram analysis constitutes a valuable yet insufficient
approach on its own to capture the multidimensional complexity of teacher
training needs. Text mining techniques enabled the identification of significant
semantic patterns, particularly useful for detecting terminological and
conceptual evolutions. However, these techniques showed limitations in
capturing contextual nuances, attitudinal ambivalences, and complex
argumentative structures—elements that the qualitative microanalysis was able
to complement. This methodological complementarity stands out as a significant
methodological finding for future studies in this field.
The proposed Digital Training
System, grounded in these empirical findings, transcends traditional models by
integrating four interrelated operational components: a core of adaptive
artificial intelligence, interconnected communities of practice, a technopedagogical
integration lab, and a platform for metacognition and professional development.
This system responds to the detected evolution in training needs by
prioritizing adaptive personalization, situated and collaborative learning,
integrated technopedagogical experimentation, and ethical reflection on
educational digitalization.
Among the most relevant
practical implications derived from this proposal are: the need for specific
institutional units that integrate expertise in AI, technopedagogical design,
and subject-specific didactics; regulatory frameworks that formally recognize
competencies acquired in non-conventional training systems; and specific
initial literacy programs in educational AI to overcome entry barriers.
The limitations of this study
include the exploratory nature of bigram analysis in
the field of training needs, its restriction to Spanish-speaking contexts, and
methodological differences between pre-pandemic (in-person) and post-pandemic
(virtual) data collection, which—although justified by circumstances—introduce
additional contextual variables.
Future lines of research
should delve into the pilot implementation of the components of the proposed
Digital Training System, evaluating their effectiveness in different
institutional contexts; developing more sophisticated hybrid methodological
approaches for the analysis of training needs; and comparatively analyzing the
evolution of teacher training needs across different educational levels and
disciplinary areas.
In summary, this research
significantly contributes to the understanding of how the pandemic not only
accelerated ongoing processes of educational digitalization but also
qualitatively transformed teacher training needs, demanding disruptive systems
that can respond to these new priorities in an agile, contextualized, and
humanizing way. The proposed Digital Training System constitutes an innovative
starting point for rethinking teacher training in the post-pandemic era,
fostering meaningful integration between technology, pedagogy, and humanism.
Author
contributions
Not applicable as it is a single author
Funding
This research has not received
external funding
Data Availability Statement
The data set used in this
study is available at reasonable request to the corresponding author
Ethics approval
Not aplicable
Consent for publication
The author has consented to
the publication of the results obtained by means of the corresponding consent
forms.
Conflicts of interest
The
author declares that they have no conflict of interest
Rights and permissions
Open Access. This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction
in any medium or format, as long as you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
References
Adell, J., Castañeda, L., & Esteve, F. (2018).
¿Hacia la Ubersidad? Conflictos y contradicciones de la universidad digital. RIED.
Revista Iberoamericana de Educación a Distancia, 21(2), 51-68. https://doi.org/10.5944/ried.21.2.20669
AERA (American Educational Research Association).
(2011). Code of Ethics. American Educational Research Association. https://bit.ly/3pyCJmR
Area-Moreira, M., Bethencourt-Aguilar, A., &
Martín-Gómez, S. (2023). Análisis de las competencias digitales autopercibidas del
profesorado de Educación Primaria y Secundaria en España. Comunicar, 31(74),
33-45. https://doi.org/10.3916/C74-2023-03
Beltrán, R. (2023). Brecha digital después de la
pandemia. Indicadores de inclusión digital en el sector educativo. Revista
Innova Educación, 5(2), 29-44. https://doi.org/10.35622/j.rie.2023.02.002
Borden-Lanza, Y.,
Lórez-Gómez, B., Usart-Rodríguez, M., & Colobrans-Delgado, J. (2023).
Competencia digital y formación profesional en España: análisis documental
sobre su regulación, propuestas y recomendaciones. Hachetetepé. Revista
Científica de Educación y Comunicación, (26), 1-14. https://doi.org/10.25267/Hachetetepe.2023.i26.1204
Cabero-Almenara, J.,
Barroso-Osuna, J., & Palacios-Rodríguez, A. (2021). Estudio de la
competencia digital docente en Ciencias de la Salud. Su relación con algunas
variables. Educación Médica, 22(2), 94-98. https://doi.org/10.1016/j.edumed.2020.11.014
Cabero-Almenara, J.,
& Llorente-Cejudo, C. (2020). Covid-19: transformación radical de la
digitalización en las instituciones universitarias. Campus Virtuales, 9(2),
25-34.
Cabero-Almenara, J.,
& Palacios-Rodríguez, A. (2020). Marco Europeo de Competencia Digital
Docente «DigCompEdu» y cuestionario «DigCompEdu Check-In». EDMETIC, Revista
de Educación Mediática y TIC, 9(1), 213-234. https://doi.org/10.21071/edmetic.v9i1.12462
Cabero-Almenara, J., & Ruiz-Palmero, J. (2018). Las Tecnologías de la Información y Comunicación para
la inclusión: reformulando la brecha digital. IJERI International Journal of Educational Research
and Innovation, (9), 16-30. https://upo.es/revistas/index.php/IJERI/article/view/2665
Cabero-Almenara, J.,
& Valencia-Ortiz, R. (2021). Y el COVID-19 transformó al sistema educativo:
reflexiones y experiencias. IJERI:
International Journal of Educational Research and Innovation, (15), 218-228. https://doi.org/10.46661/ijeri.5246
Castañeda, L.,
Salinas, J., & Adell, J. (2022). ¿Qué hicimos y qué deberíamos haber hecho?
¿Qué aprendimos y qué deberíamos haber aprendido? Tecnologías educativas en
tiempos de pandemia. RIED. Revista Iberoamericana de Educación a Distancia,
25(2), 9–28. https://doi.org/10.5944/ried.25.2.33703
Chen, Y., Zhon, M., & Wang, S. (2006). Reranking answers for
definitional QA using language modeling. En Proceedings of the 21st
International Conference on Computational Linguistics and 44th Annual Meeting
of the Association for Computational Linguistics. 1081-1088. https://doi.org/10.3115/1220175.1220311
Comisión Europea.
(2023). Año Europeo de las Competencias 2023. https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/europe-fit-digital-age/european-year-skills-2023_es
Dillenbourg, P., & Jermann, P. (2023). Technology-enhanced
collaborative learning. En N. Rummel, M. Kapur, & T. Koschmann (Eds.), International
Handbook of Computer-Supported Collaborative Learning (pp. 193-212). Springer. https://doi.org/10.1007/978-3-031-04060-4_10
Escudero, C. J.,
González, M. T., & Rodríguez, M. J. (2022). Minería de textos en
investigación educativa: revisión sistemática. Comunicar, 30(72), 16-27.
https://doi.org/10.3916/C72-2022-02
Esteve, F.,
Castañeda, L., & Adell, J. (2022). Cinco años después: una reflexión sobre
las competencias digitales docentes. RED. Revista de Educación a Distancia,
22(70), 1-17. https://doi.org/10.6018/red.505661
Falloon, G.
(2020). From digital literacy to digital competence: the teacher digital
competency (TDC) framework. Educational Technology Research and Development,
68(5), 2449-2472. https://doi.org/10.1007/s11423-020-09767-4
Fan, Y., Bai, X.,
Liu, Y., & Ma, L. (2022). Personalized learning recommendation systems in
education: A review of different techniques and challenges. Journal of
Educational Technology & Society, 25(3), 13-27. https://doi.org/10.2307/27032855
Fernández-Batanero, J. M., Román-Graván,
P., Reyes-Rebollo, M. M., & Montenegro-Rueda, M. (2022). Digital teaching
competence in higher education: A systematic review. Education Sciences, 12(2),
120. https://doi.org/10.3390/educsci12020120
Flick, U. (2018). Designing
qualitative research (2nd ed.). SAGE Publications. https://doi.org/10.4135/9781849208826
García-Peñalvo, F.
J., Corell, A., Abella-García, V., & Grande-de-Prado, M. (2021). Recommendations for mandatory online assessment in
higher education during the COVID-19 pandemic. En D. Burgos et al. (Eds.), Radical
Solutions for Education in a Crisis Context (pp.
85-98). Springer. https://doi.org/10.1007/978-981-15-7869-4_6
Garzón-Artacho, E.,
Sola-Martínez, T., Romero-Rodríguez, J. M., & Gómez-García, G. (2021).
Competencia digital docente en educación de adultos: un estudio en un contexto
español. Pixel-Bit, Revista de Medios y Educación, 62, 209-234. https://doi.org/10.12795/pixelbit.89510
Gewerc, A., Fraga-Varela, F., & Rodés,
V. (2020). Producción científica
sobre minería de textos en la literatura educativa: un análisis de co-palabras
basado en Scopus (2015-2019). Digital Education Review, 38, 116-129. https://doi.org/10.1344/der.2020.38.116-129
González-Sanmamed,
M., Estévez, I., & Souto-Seijo, A. (2022). Ecologías digitales de
aprendizaje y desarrollo profesional del profesorado universitario: Elaboración
y validación de un modelo. Comunicar, 30(70), 9-19. https://doi.org/10.3916/C70-2022-01
Gros, B., &
Noguera, I. (2023). Digital Competence in Teacher Training: A Systematic Review
of Approaches and Frameworks. Teaching Education, 34(2), 157-174. https://doi.org/10.1080/10476210.2022.2033399
Guest, G., Namey, E., & Chen, M. (2020). A simple
method to assess and report thematic saturation in qualitative research. PLoS ONE, 15(5), e0232076. https://doi.org/10.1371/journal.pone.0232076
Hidalgo-Ternero, C.
M., & Pérez-Cordón, L. G. (2021). El procesamiento del lenguaje natural y
la minería de textos en el ámbito educativo: Una revisión sistemática de la
literatura. RED. Revista de Educación a Distancia, 21(67), 1-26. https://doi.org/10.6018/red.463171
Kumar, P.,
Sachdeva, N., Mahajan, D., Pande, N., & Sharma, A. (2023). An overview of
recent trends in n-gram language models and applications. Advances in
Natural Language Processing and Machine Learning, 45(2), 134-149. https://doi.org/10.1016/j.nlp.2023.01.009
Krueger, R. A.,
& Casey, M. A. (2015). Focus groups: A practical guide for applied
research (5th ed.). SAGE Publications.
Lincoln, Y. S.,
& Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications.
López-Belmonte,
J., Moreno-Guerrero, A. J., Pozo-Sánchez, S., & López-Nuñez,
J. A. (2020). Efecto de la
competencia digital docente en el uso del blended learning en formación
profesional. Investigación Bibliotecológica: Archivonomía, Bibliotecología e
Información, 34(83), 187. https://doi.org/10.22201/iibi.24488321xe.2020.83.58147
Lynn, M. R.
(1986). Determination and quantification of content validity. Nursing
Research, 35(6), 382-385.
Marín, V. I.
(2023). Textual analysis in educational research: Opportunities and
limitations. Digital Education Review, 43, 83-99. https://doi.org/10.1344/der.2023.43.83-99
Marín, V. I.,
Zawacki-Richter, O., & Bond, M. (2021). Systematic review of research on
emergency remote teaching during the COVID-19 pandemic. Education Sciences, 11(8), 429. https://doi.org/10.3390/educsci11080429
Mariñelarena-Dondena,
L., Errecalde, M. L., & Castro-Solano, A. (2017). Extracción de
conocimiento con técnicas de minería de textos aplicadas a la psicología. Revista
Argentina de Ciencias del Comportamiento, 9(2), 65-76. https://revistas.unc.edu.ar/index.php/racc/article/view/12701
Mishra, P., &
Koehler, M. J. (2021). What Happens When Teachers Design Educational
Technology? A Story of Teachers as Designers of Technology. Journal of
Digital Learning in Teacher Education, 37(4), 220-235. https://doi.org/10.1080/21532974.2021.1964779
Montenegro, S., Raya,
E., & Navaridas, F. (2020). Percepciones Docentes sobre los Efectos de la
Brecha Digital en la Educación Básica durante el Covid-19. Revista
Internacional de Educación para la Justicia Social, 9(3e), 317-333. https://doi.org/10.15366/riejs2020.9.3.017
Noriega-Rodríguez,
L., Vásquez-Parra, J., & Torres-Garrido, M. (2021). Aplicación de minería
de texto para analizar tendencias en educación: Una revisión sistemática. Pixel-Bit,
Revista de Medios y Educación, 62, 107-134. https://doi.org/10.12795/pixelbit.87126
Pascual, M.A.,
Ortega-Carriedo, L., Pérez-Ferra, M., & Fombona, J. (2022). Competencias
digitales del profesorado universitario y su relación con la modalidad de
enseñanza virtual. Pixel-Bit. Revista de Medios y Educación, 65, 35-58. https://doi.org/10.12795/pixelbit.91830
Portillo, J., Garay,
U., Tejada, E., & Bilbao, N. (2020). Self-perception of the digital competence of educators
during the COVID-19 pandemic: A cross-analysis of different educational stages.
Sustainability, 12(23), 10128. https://doi.org/10.3390/su122310128
Pozo-Sánchez, S.,
López-Belmonte, J., Fernández-Cruz, M., & López-Núñez,
J. A. (2020). Análisis
correlacional de los factores incidentes en el nivel de competencia digital del
profesorado. Revista Electrónica Interuniversitaria de Formación Del
Profesorado, 23(1). https://doi.org/10.6018/reifop.396741
Raffaghelli, J. E. (2020). Transformative Professional Learning
for Digital and Networked Teaching in Higher Education. Italian Journal of
Educational Technology, 28(2), 162-181. https://doi.org/10.17471/2499-4324/1170
Ramírez-Montoya,
M. S., Andrade-Vargas, L., Rivera-Rogel, D., & Portuguez-Castro, M. (2022).
Trends for the future of education programs for professional development. Australasian
Journal of Educational Technology, 38(1), 1-13. https://doi.org/10.14742/ajet.7410
Reisoğlu, İ., & Çebi, A.
(2020). How can the digital competences of pre-service teachers be developed?
Examining a case study through the lens of DigComp
and DigCompEdu. Computers & Education,
156, 103940. https://doi.org/10.1016/j.compedu.2020.103940
Ruan, Y., &
Jin, W. (2023). Comparison of text mining methodologies for educational
research: A comprehensive review. Journal of Educational Technology Systems,
52(1), 5-29. https://doi.org/10.1177/00472395221141158
Sánchez-Caballé,
A., Gisbert-Cervera, M., & Esteve-Mon, F. (2021). La competencia digital de los estudiantes
universitarios de primer curso de grado. Innoeduca. International Journal of Technology and Educational
Innovation, 6(1), 4-13. https://doi.org/10.24310/innoeduca.2020.v6i1.7136
Sharma, A., Goyal,
A., & Kaur, D. (2020). Personality prediction through text mining. International
Conference on Inventive Computation Technologies, 763-768. https://doi.org/10.1109/ICICT48043.2020.9112443
Strauss, A., &
Corbin, J. (2002). Bases de la investigación cualitativa. Técnicas y
procedimientos para desarrollar la teoría fundamentada. Contus.
Viberg, O., Khalil, M., & Baars, M. (2020). Self-regulated learning and learning analytics
in online learning environments: A review of empirical research. Proceedings of
the Tenth International Conference on Learning Analytics & Knowledge,
524-533. https://doi.org/10.1145/3375462.3375483
Williamson, B. (2023). The platform university:
Critical approaches to digital higher education. Policy Futures in
Education, 21(1), 101-118. https://doi.org/10.1177/14782103221086450
Zhang, H., & Chen, L. (2022). A comparative
analysis of n-gram and neural language models for educational text mining. Computers and
Education: Artificial Intelligence, 3, 100056. https://doi.org/10.1016/j.caeai.2022.100056