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This study explores schools’ digital maturity self-evaluation reports’ data from Estonia. Based on quantitative data (N = 499) the schools that attempt digital transformation were clustered into three successive digital improvement types. The paper describes 3 main patterns of school improvement in different phases of innovative change: classroom innovation practices’ driven schools, participatory led structural change driven schools; and inclusive and evidence based change management type of schools. The defining variables for digital transformation towards new levels of digital maturity were teachers’ role, digital competences, learning organization culture, participatory management, inclusive leadership, structural changes and network, and IT-manager involvement to structural changes.
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Technology, Knowledge and Learning (2022) 27:823–841
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The Patterns ofSchool Improvement inDigitally Innovative
KaiPata1 · KairitTammets1· TerjeVäljataga1· KülliKori1· MartLaanpere1·
Accepted: 9 April 2021 / Published online: 24 April 2021
© The Author(s) 2021
This study explores schools’ digital maturity self-evaluation reports’ data from Estonia.
Based on quantitative data (N = 499) the schools that attempt digital transformation were
clustered into three successive digital improvement types. The paper describes 3 main pat-
terns of school improvement in different phases of innovative change: classroom innova-
tion practices’ driven schools, participatory led structural change driven schools; and inclu-
sive and evidence based change management type of schools. The defining variables for
digital transformation towards new levels of digital maturity were teachers’ role, digital
competences, learning organization culture, participatory management, inclusive leader-
ship, structural changes and network, and IT-manager involvement to structural changes.
Keywords School’s digital maturity· Digital transformation school improvement patterns
1 Introduction
In the Digital Age schools constantly need to innovate themselves to keep up with the
future requirements such as smart responsive environments (e.g. 2nd survey of schools:
ICT in education, 2019) or opportunities such as personalized learning (Caporarello
etal., 2020). Wong and Li (2011) have asserted that technology-driven organisational and
* Kai Pata
Kairit Tammets
Terje Väljataga
Külli Kori
Mart Laanpere
Romil Rõbtsenkov
1 School ofDigital Technologies, Tallinn University, Narva Road, 25, 10120Tallinn, Estonia
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K.Pata et al.
1 3
pedagogical interventions have the potential to affect the changes in student learning. Mod-
eling technology through administration activities is important for promoting digital teach-
ing and learning (Zhong, 2017).
A majority of studies about the use of digital technologies in school settings focus on
digital competence of the teachers or students and technology acceptance of certain tools in
smart learning places (e.g. 2nd survey of schools: ICT in education, 2019). Only few stud-
ies (e.g. Jeladze & Pata, 2019; Haynes & Shelton, 2018; Tam etal., 2018; Tondeur etal.,
2008; Zhong, 2017) provide systemic understanding of the interplay of different school
improvement factors in the organisational development context. Our study stems from the
limited knowledge of how digitally driven school improvement happens at system level, in
particular what are the potential stages of digital transition in schools and which catalysts
in schoool systems have particular role in triggering transition to the new digital stage.
In this paper we observe the schools from one of the digitally developed countries—
Estonia. In Nordic countries, as well as in Estonia, schools are rather autonomous and
teachers and schools are given the responsibility and leadership for improving organiza-
tional and classroom practices with technologies (Sahlberg, 2011). In innovating learning
and teaching practices different leadership and collaboration patterns of teachers and prin-
cipals have been found (de Jong etal., 2020). Yet it is not clear in this study, whether the
agency of teachers or principals in leading the innovation would lead to successful school
development. Evidence-based school improvement practices are needed to guide these
interwoven processes in digitally innovative schools. Vanari et al. (2020) have demon-
strated that when schools are scaffolded with frameworks and tools to plan, monitor and
evaluate their digital maturity (e.g. Ilomäki & Lakkala, 2018), the evidence-driven prac-
tices are more widely adopted as part of the school improvement process.
In this study we analyze the digital maturity self-assessment reports of Estonian schools
collected with Digital Mirror self-evaluation tool. Our research was driven by two ques-
tions: What characterises improvement stages in digitally innovating schools? What school
improvement patterns appear in digitally innovative schools? The output of our study for
practitioners is an understanding of specific key variables of digital maturity in the school
learning ecosystem that empower digital transition. These digital improvement patterns in
schools at different phases of innovative change could be used for guiding schools’ digital
transformation plans and implementations towards learning environments of the future.
2 Literature Review
2.1 School Improvement andSelf‑Organization inLearning Organizations
As the main goal, school improvement is aimed at improving students’ learning outcomes
(Creemers & Reezigt, 2005). Schools’ digital transformation may be done by restructuring
teaching and learning practices, re-envisioning learning spaces (Sheninger, 2014), leader-
ship practices (Sheninger, 2014; Zhong, 2017) and the ways pedagogical approaches are
organised (Crook etal., 2010; OECD, 2015). The implementation of digital technology
into educational practices contributes to student learning (Wong & Li, 2011) and educa-
tor capacity (Haynes & Shelton, 2018). At system level, a school has to improve itself as a
dynamically responsive sustainable learning organization (Senge etal., 1994) that is able
to maintain the school improvement towards innovative change (Jeladze & Pata, 2018).
Researchers have defined some key characteristics of a learning organization: mutual trust
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and willingness to engage in open communication by the participants (Creemers & Reez-
igt, 2005; Senge et al., 1994); teachers’ shared values with a focus on student learning
(Leclerc etal., 2012); and collaborative knowledge-sharing as a tool for constant growth
of both teachers and schools (Fullan, 2001). Tam et al. (2018) explain school improve-
ment through Hargreaves’ (2001) intellectual, social and organizational capital compo-
nents. Intellectual capital relates school personel’s knowledge, skills, values and disposi-
tions towards ICT with school level goals embedded in curricula, rules, agendas etc. Social
capital is more influential than intellectual capital and it comprises of trust, mutual respect
and reciprocity among teachers, students and parents as well as communication channels
and networks among them. Social capital is considered the lead strategy in enacting change
and improving teaching at schools. Organizational capital associates with school leadership
practices, the knowledge and skills at change management level to efficiently make use of
and guide the intellectual and social capital. Scheninger (2014) suggested that digital lead-
ership requires a shift in leadership style from one of mandates, directives, and buy-in, to
one grounded in empowerment, support, and embracement as keys to sustainable change.
According to him, digital leadership can be defined as establishing direction, influencing
others, and initiating sustainable change through the access of information, and establish-
ing relationships.
To successfully inspire and lead schools’ digital transformation and meet the require-
ment of visionary leadership, awareness of digital management and support from all stake-
holders are two important factors that school principals need to be equipped with (Zhong,
2017). The hierarchical leadership models or distributed leadership models (Spillane,
2006) may be applied and sustained by sociocultural and institutional norms. Distributed
leadership model has been found to coincide with project-based approaches in schools
(Levin & Schrum, 2012) and the increase of organizational capacity for ICT integration
(Tam etal., 2018). Tondeur etal. (2008) created a conceptual model of school-level fac-
tors that can contribute to efficient ICT use for schools’ holistic improvement. These fac-
tors include the provision of appropriate ICT infrastructure, development of positive teach-
ers’ and leaders’ attitudes towards ICT, development of teachers’ ICT skills, use of ICT in
learning and teaching.
School’s capacity could be improved by using ‘guiding instruments’ like D-LIFE
(Haynes & Shelton, 2018) and ‘monitoring instruments’ such as SELFIE (Kampylis etal.,
2016) or Digital Mirror (see below) that contain indicators of digital maturity. For example,
D-LIFE guide addresses the digital infrastructure and resources component with stabilizing
and future-directed sustainability notions; learning and support component to raise educa-
tors’ and students’ intellectual capacity; longitudinal evidence-based feedback loop compo-
nent from learning and practices’ data, and sustainable and proactive policies’ component.
Although the D-LIFE guide incorporates some of the important elements defining digital
transformation, it lacks a comprehensive view on schools, in which for instance organisa-
tional capital at change management level plays an important role for guiding digital trans-
formation. SELFIE instrument contains three self-evaluation questionnaires for leaders,
teachers and students and generates self-reports for schools providing the level of specific
digital development indicators: teaching and learning, professional development, content
and curricula, assessment practices, leadership and governance practices, collaboration and
networking, and infrastructure. In Estonia the Digital Mirror instrument that provides road-
map on three indicator groups (teaching and learning, change management and infrastruc-
ture and services) was nationally implemented for supporting self-evaluations on digital
maturity of schools (see below).
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K.Pata et al.
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We build the theoretical frame of our paper on the model of self-organization processes
in schools (Jeladze & Patta, 2019), which is developed based on the European digital matu-
rity SELFIE study pilot data. This model depicts schools as holistic digital learning ecosys-
tems where different types of components are systemically interconnected. The self-organ-
ization model depicts three loops in a digitally enhanced learning ecosystem: (1) digital
learning loop represents the interaction of classroom level components—digital teaching
strategies, learning practices and assessment approaches; (2) mediating loop represents
digital infrastructure and resources and information provided from socio-technical land-
scape to which human agents interact; (3) transformative loop combines the components
with a transforming agency that are in place on a school level, such as norms and agendas,
support, training, motivation management, analytics and evidence-based decision-making
etc. (Jeladze & Pata, 2019). The self-organization model highlights the need for cyclical
information exchange between digital learning, mediating and transformative loops in
school improvement. The strength of the model of self-organisation processes in schools
is that it takes the holistic view on schools as digital learning ecosystems with a number of
interconnected variables. We explore in this study the variables from digital maturity self-
evaluation instrument Digital Mirror by taking the systemic approach of holistic learning
ecosytems as suggested in self-organization model.
2.2 Approaches ofMeasuring Schools’ Digital Maturity
Maturity concept has been used for evaluating schools and it relates with relative states
of digital innovation. The maturity concept is adopted from natural ecosystems, which
develop successively from early to mature stages (Odum, 1969; Chorley etal., 1971). The
digital maturity evaluation tools enable describing the static states of the learning ecosys-
tems at certain time points. Digital maturity evaluation suggests that a number of succes-
sive stages of digital innovation happen in schools. Several frameworks and tools have been
developed during the last decade to evaluate different aspects of schools’ digital maturity:
whole-school’s use of ICT and digital pedagogical methods (OPEKA, eLEMER, SELFIE),
leadership and governance for the change practices (Microsoft leadership transformation
self-reflection Tool), school’s potential in ICT (Ae-MoYS), ICT-enabled innovations in
different learning settings and implementation strategies, and the effectiveness of learning
(Giovanella, 2016; Galego etal., 2016). Some tools (eLearning roadmap, Future classroom
Maturity Modelling, Digital Mirror) provide the roadmap to expand further the innovative
technology-enhanced practices in schools by postulating the development stages. These
evaluation frameworks examine common educational dimensions for depicting technology
use. They explore pedagogical methods that are enhanced by technology, change manage-
ment and existing infrastructure and access to it. Sergis and Sampson (2014) proposed in
schools’ ICT competence profiling framework the same factors for school profiling and
added the level of ICT use in curriculum and the ways ICT is used in daily teaching and
learning practices. Many of these frameworks monitor only internal factors without explor-
ing the interrelations and interdependences of thise factors and do not embed the schools
into the regional settings or explore the interaction between schools. Some researchers
(Giovanella, 2016; Galego et al., 2016) have also evaluated external components as part
of digital maturity framework. Sergis etal. (2018) developed the concept of school analyt-
ics—school-level educational data that inform schools’ strategic decision-making. Previ-
ously described frameworks tackle important aspects that define and guide digital matu-
rity of schools, however, they fail to recognize that different components of the system are
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coupled together and may influence each others’ functioning. Also Sergis and Sampson
(2016) emphasize the need for a holistic method that supports analysis on three different
layers in order to have an explicit view of school system behavior: (a) microlayer of learn-
ing and assessment practices in school; (b) meso layer of monitoring and assessment of
teaching practices in school; (c) macro level—orchestration of school development as an
organization (Sergis & Sampson, 2016).In this paper we specifically target the schools’
digital maturity as systemic level patterns and search for how different maturity indicators
contribute to it.
2.3 Digital Transformation Stages inSchools
Digital transformation takes place step-by-step because not all the resources and knowl-
edge for a workable solution are available (Teiniker & Seuchter, 2018). Also, organisa-
tions are facing many challenges during the process of digital transformation. Based on a
literature review of digitalization in adult and continuing education, Bernhard-Skala (2019)
found three main challenges in digital transformation: information technology-infrastruc-
ture, staff development and management/leadership.
In the study of Jeladze and Pata (2019) several digital maturity states of schools were
described by using the European’ SELFIE pilot study data and the created paths models
between the studied variables. Here we briefly describe some successive digital maturity
school types from that study that we later intend to compare with our findings in the Esto-
nian context where different set of indicators, but similar indicator groups were used. Digi-
tal teaching strategies centered schools had digital strategies in place that might have sup-
ported digital activities but teachers were seldom involved in designing school vision and
agenda. These schools lacked digital infrastructure and support mechanisms to digital prac-
tices and digital learning activities were not very frequent. There were no loops between
learning at classroom level, infrastructure and change management components. Digital
infrastructure-centered schools had medium level digital infrastructures, but the infrastruc-
ture development was weakly connected with change management. Support mechanisms
were directed to digital infrastructure but not towards students’ digital learning or digi-
tal teaching strategies. The loops were weak between classroom level digital teaching and
learning practices and change management for digital change. However, digital practices,
learning and assessment were well interconnected. Organizational learning-driven schools
were digitally most mature and had strong loops between the interconnected teaching and
learning, infrastructure and change management components, yet the evidence-based deci-
sion-making loop was weak.
In the current study, we aimed at validating these digital maturity states found based
on European schools’ pilot dataset of SELFIE with the different dataset collected from
Estonian schools using the officially collected data from Digital Mirror instrument (https://
digip eegel. ee/) funded by Estonian Ministry of Education and Research.
2.4 Digital Transformation Context inEstonia
Since 1997 dedicated programmes and foundations for supporting the digital turn in
schools have been initiated in Estonia. There is a drive from the industry as well as from the
government to make education more digital and innovative. At first the Tiger Leap Founda-
tion focused on connecting schools, encouraging teachers to develop their own materials
and creating their own training courses. Also, the Tiger Leap Foundation provided teachers
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K.Pata et al.
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with laptops. With respect to technology, the local authority in Estonia is obliged to main-
tain the schools’ technical equipment and network connections as well as provide tools and
services that help to improve education. Additionally, IT companies, such as Microsoft and
Samsung, in collaboration with university trainers and researchers, have contributed to the
digital turn in schools by running several projects. School team training has been used to
empower digital transformation (Lorenz etal., 2016). The Estonian Ministry of Education
and Research Digital Turn Programme 2015–2018 pointed out three directions: digital cul-
ture integration with teaching and learning; development and accessibility of digital teach-
ing materials, and equipping the schools (network and technology). The Estonian Lifelong
Learning Strategy 2020 has provided guidelines for schools to implement digital turn in
terms of digital learning resources, digital infrastructure for learning, development of
digital competences, changed teaching and learning practices. The new Strategy for 2035
sees the main challenge in developing learning analytics enhanced personalized learning
paths. The Education and Youth Administration ( is responsible for envisioning,
developing and maintaining national components of digital learning ecosystem (such as
e-learning environments, e-schoolbag, digital learning resource repository, e-diaries, digi-
tal assessment systems) and digital infrastructures and network services in schools. It leads
the digital skills development providing digital competence frameworks aligned to Dig-
Comp 2.0 and DigCompEdu for students and teachers (including syllaby and job accredita-
tion frameworks), and digital maturity requirements for schools. Free trainings to teach-
ers and school leaders are provided nationally as well as by higher education institutions.
The national curricula have required integrating digital technology use at schools in all
subject lessons. The educational technologists have been trained since 2009 and they have
mediated digital transformation in schools and promoted technology-enhanced learning at
organizational level.
2.5 Sample
The data from schools’ digital maturity evaluation in 2016/2017, 2017/2018 and 2018/2019
with Digital Mirror were used for the analysis. During these years 92 schools evaluated
their digital maturity only once, 363 schools twice and 91 schools three times. The sum-
mative sample of 962 schools (including 499 different schools) was used for cluster and
pattern analysis. Using Digital Mirror for evaluating schools’ digital maturity was set as a
precondition for the schools to receive funding for digital innovations from regional budg-
ets. This sample covers 81% of all schools in the country and is representative to the educa-
tional level and language distribution of schools in Estonia.
2.6 Research Design andProcedure
School teams participating in the study filled in the self-assessment instrument Digital
Mirror to assess digital maturity of their organisation. Self-assessment was organised by
the school teams—in the first phase the team of teachers and educational technologyst/IT
manager filled in the survey based on the framework in Digital Mirror (see details of these
below) (Fig.1) collaboratively. Digital Mirror requires also adding evidences as documents
to support self-assessemnt. In this study we do not study these evidences. In the second
phase school leaders re-assessed or confirmed the assessment results together with the
school team, set the roadmap for the next period and submitted the self-assessment results
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in Digital Mirror. The Digital Mirror framework describes the digitally enhanced school
from three dimensions: Teaching and Learning, Change Management and Infrastructure
and Services.
The Digital Mirror instrument comprises of 15 self-evaluation variables (see Table1
below) that were scaled on 5 levels:
1 Exchange—refers to episodical implementation of digital innovation, rare cases of
using digital technology;
2 Enrich—refers to the coordination within the school, digital technology is used to
experiment new teaching and learning methods; teachers share their experiences;
3 Enhance—refers to the changes in the learning and teaching processes, systematic,
evidence-based changes on a school level;
4 Extend—refers to widening digital culture, combined technologies are normal part
of the school, students are creators of their personal digital spaces;
Fig. 1 An example of one school’s digital maturity level evaluated with the online self-assessment tool
Digital Mirror: teaching and learning (1.1–1.5), change management (2.1–2.5), infrastructure and services
(3.1–3.5); dots demonstrate the planned target of digital maturity
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K.Pata et al.
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Table 1 The ‘Digital mirror’ variables
1.Pedagogical innovation
1.1 Digital age practices (DP) Changing and widening pedagogical repertoire, including
inquiry, discovery, problem- and project-based, self-
directed, creative and collaborative learning practices.
Orchestrating digital-age learning in classroom and
1.2 Digital competences (DC) Redefining and developing digital competence of teachers
and students in the context of teaching and learning;
continuous professional development and organisational
learning on digital competence
1.3 Changing teachers’ role (TR) Enhancing networking and collaboration among teach-
ers to conduct, analyse, share and evaluate innovative
practices. Interdisciplinary peer teaching. Learners
are engaged in self-directed, creative and collabora-
tive learning, they take responsibility for designing
and implementing learning experiences, resources and
environments as well as assessments
1.4 Changing learners’ role (LR) Creative, collaborative, self-directed learning
1.5 Structural changes in curriculum, learning
environment (SC)
Systemic and sustainable structural changes in physical
and digital learning environments, learning resources,
time management, scheduling, workflows
2. Change management
2.1 Strategic planning (SP) Consensus-based, well-defined strategy and action plan
for implementing innovation that guides the decision-
making both in shorter and longer time scale
2.2 Participary management, Partnership (PM) School leaders involve continuously teachers, students,
parents and external partners in decision-making pro-
cesses related to planning, implementing and evaluating
educational change
2.3 Learning organisation (LO) School leaders, teachers, students learn from each other,
they document and disseminate good practice related to
ongoing change process
2.4 Monitoring and analytics (A) School is using a set of valid and reliable indicators, data
collection instruments and methods/practices for con-
tinuous monitoring and analytics of the change process
2.5 Leadership stimulates (L) School administration provides leadership, support and
incentives to facilitate the implementation of change
3. Digital infrastructure
3.1 Networks (N) Well maintained functioning and security of the school’s
network(s), regularly reviewing and enforcing the digital
safety regulations (e.g. Acceptable Use Polic
3.2 Digital Devices (D) One-to-one computing anywhere anytime, ubiquitous
access to digital devices (tablets, laptops, robotics), con-
nected presentation and communication tools
3.3 IT management (ITM) Strategic planning of digital infrastructure, continuous
monitoring and analysis of implementation of the plan
3.4 User support (US) Technical and pedagogical support to all users of digital
technologies provided by school
3.5 Software and services (S) Well-maintained, licensed, up-to-date and interoperable
ecosystem of software, services and information systems
that supports the pedagogical change
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5 Empower—refers to leverage and acting as a regional leader in some certain
aspects of digital innovation.
The items for assessing digital maturity on the 5 level scale are presented in the Table1
and for each item, school team had to also provide evidences to demonstrate their level of
maturity. Links and descriptions of evidences were presented in the Digital Mirror. All
these self-evaluation variables and their explanations were also provided to the school
The results of the internal consistency test on all variables was 0.925 and for compo-
nents ‘Teaching and Learning’ 0.82, ‘Change management’ 0.86 and ‘Infrastructure and
services’ 0.84.
2.7 Analytical Sequences
We analyzed jointly the dataset from two periods 2016/17 and 2018/19 to discover the
patterns of organizational change management in digitaly enhanced schools. This dataset
comprised of two measurments from each school, but we assumed that a larger dataset
would enable us to detect clearer patterns.
The following statistical analyses with IBM SPSS Statistics software were conducted.
Firstly, hierarchical clustering was conducted and the results predicted 3–4 clusters. Sec-
ondly, k-means cluster analysis was run with 3 and 4 clusters to identify the digital matu-
rity clusters of the schools. The 3-cluster model was selected based on the analysis. Next,
testing the mean differences among clusters was performed to extract the most significant
indicators of digital maturity clusters. Then, canonical discriminant analysis using the
“Enter the independents together” method was performed to describe the principal factors
informing the clusters in selected cluster models. Last but not least, principal component
analysis (PCA) was conducted separately with each cluster data to identify the interrela-
tions among the variables in each cluster.
3 Results
3.1 What Characterises Improvement Stages inDigitally Innovating Schools?
K-means cluster analysis differentiated 3 digital maturity clusters where the levels of all
digital maturity variables differed significantly (p < 0.001): Cluster 1 (C1)—181 schools,
Cluster 2 (C2)—357 schools, and Cluster 3 (C3)—424 schools (Table2). We particularly
observed, that Learning Organization variable was among highest in C1, while in C2 and
C3 it was assessed of being on a relatively low level.
For looking the specific variables that contributed to the grouping of schools into dif-
ferent school types we performed canonical discriminant analysis. The discriminant analy-
sis detected two functions (see Fig.2): Function 1 (Df1) described 97.7% of the variance
(λ = 0.168, χ2 = 1699.86, df = 30, p < 0.001), and Function 2 (Df2) only 2.3% of the vari-
ance (λ = 0.905, χ2 = 95.024, df = 14, p < 0.001).
Df1 correlated positively with the ‘Learning and teaching’ component variables
(Digital competence (r=0.414), Teacher’s role (r=0.438), Structural change (r=0.410),
Learner’s role (r=0.347), Digital practices (r=0.291)), ‘Change management variables’
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K.Pata et al.
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Table 2 The mean values and ANOVA results about school clusters
Mean C1 Mean C2 Mean C3 F Sig
Digital practices 3 1.85 2.51 181.987 0.001
Digital competence 3.1 1.44 2.32 363.632 0.001
Teacher’s role 3.1 1.47 2.38 409.045 0.001
Learner’s role 3.12 1.9 2.52 254.209 0.001
Structural change 3.13 1.65 2.51 363.106 0.001
Strategic planning 3.25 1.59 2.29 387.838 0.001
Participatory management 3.45 1.89 2.7 439.704 0.001
Learning organisation 3.09 1.58 2.2 341.838 0.001
Analytics 2.85 1.53 2.08 283.541 0.001
Leadership 2.88 1.75 2.18 296.623 0.001
Network 3.03 1.63 2.24 329.359 0.001
Devices 3.07 1.9 2.47 256.248 0.001
IT-management 3.33 1.61 2.16 430.858 0.001
User support 2.93 1.78 2.25 223.431 0.001
Services 3.38 1.92 2.42 344.273 0.001
Fig. 2 The distribution of schools into digital maturity clusters
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(Participatory management (r=0.456), Learning organization (r=0.401), Strategic planning
(r=0.428), Leadership and Analytics (r=0.366), Leadership (r=0.372)) and ‘Infrastruc-
ture and services’ component variables (IT-management (r=0.443), Services (r=0.398),
Devices (r=0.348), User Support (r=0.325)). Df2 correlated negatively with IT man-
agement (r=−0.591) and Services (r=−0.466), and positively with Structural change
(r=0.409) and Teacher’s role (r=0.323).
The clusters could be separated by Df1 into three progressive stages of schools’ digi-
tal maturity, indicating that the most influential variables according to function coefficents
were: Teacher’s role (0.332), Digital competences (0.253), Participatory management
(0.236) and the IT management (0.253).
Df2 differentiated C3 cluster as being more than C1 and C2 oriented on the Participa-
tory management (0.383), Structural change (0.353) and Teachers’ role (0.202), and
less on IT-management (−0.493), Services (−0.380) and Leadership (−0.299).
3.2 What School Improvement Patterns Appear inDigitally Innovative Schools?
We performed principal component analysis (PCA) separately for each cluster variables to
take an inside look into how the variables that could illustrate the school improvement pat-
terns associated with each other.
The results of Bartlett test of spherity were following:
C1 Chi Sq.= 251.83, df=105, p<0.001, 50.3% of total was variance explained, 4 factors
were described (Factor 1—21.6%, Factor 2—11%, Factor 3—10%, Factor 4—7.5%).
C2 Chi Sq.= 154.85, df=105, p<0.001, 49.9% of total variance was explained, 4 factors
were described (Factor 1—22.5%, Factor 2—11%, Factor 3—9%, Factor 4—6.9%).
C3 Chi Sq.=331.53, df=105, p<0.001, 50.7% of total variance was explained, 5 factors
were described (Factor 1—14%, Factor 2—11.8%, Factor 3—10%, Factor 4—7%, Fac-
tor 5—7%).
Table 3 PCA for cluster
1: Digital transformation
led schools with inclusive
and evidence based change
management practices (f1—most
advanced, f2—low)
Note: The components with highest factors loadings are highlighted
with bold
1 2 3 4
Analytics 0.696 0.156 −0.015 0.081
Participatory management 0.69 −0.073 0.103 0.235
Strategic planning 0.685 0.138 0.206 −0.119
Leadership 0.562 0.298 −0.101 0.383
IT-management 0.395 −0.17 0.322 0.337
Structural change −0.102 0.645 0.162 0.193
Digital practices 0.096 0.575 0.006 −0.198
Learner’s role 0.176 0.517 0.258 0.171
Digital competence 0.37 0.507 −0.385 −0.094
Learning organisation 0.127 0.471 −0.058 0.456
Devices 0.139 0.099 0.748 0.105
User support 0.025 0.056 0.737 0.172
Teacher’s role 0.105 0.446 0.507 −0.227
Network 0.06 0.099 0.193 0.713
Services 0.13 −0.075 0.064 0.663
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K.Pata et al.
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Four factors were differentiated in Cluster 1 with PCA (see Table 3). The cluster
included schools that are digital transformation led and have inclusive and evidence based
change management practices.
Identified patterns in Cluster 1 were following:
Factor 1 Evidence based strategic planning with strong leadership and participatory
management and integrated IT-management that considers the development of digital
Factor 2 Structural changes, changes in digital practices and the teachers’ and learners
roles, as well as in the whole learning organization that considers digital competences of
staff and students.
Factor 3 Digital devices’ focused IT-management and user support that changes the role
of teachers but is not so much focused on digital competences.
Factor 4 Leadership and IT-management for networks and services to support the learn-
ing organization.
The IT-management, Leadership role, Digital competences, Learning Organization
and Teachers’ role are the interconnecting variables between the factor components in this
This digitally most mature Cluster 1 is similar with the Organizational learning-
driven schools’ cluster description found in the European sample (Jeladze & Pata, 2019)
(see above). Based on that study, in this cluster the self-organization loops have emerged
between ‘Learning and teaching’, ‘Infrastructure’ and ‘Change management’ components.
Four factors were also differentiated in Cluster 2 (see Table4). This cluster included
schools where digital transformation is appearing only at some practices and these are dis-
connected from driving organizational goals.
Identified patterns in Cluster 2 were following:
Table 4 PCA for Cluster
2—Schools where digital
transformation is appearing
at some practices level only
disconnected from driving
organizational goals (f1—Least
advanced, f2—low)
Note: The components with highest factors loadings are highlighted
with bold
Strategic planning 0.739 0.125 0.071 −0.153
Participatory management 0.724 0.033 0.107 0.054
Leadership 0.625 0.145 0.135 0.211
Analytics 0.597 0.224 −0.101 0.073
Learning organisation 0.549 0.309 −0.109 0.046
Network 0.097 0.731 0.031 −0.094
User support 0.143 0.671 0.202 0.015
Devices 0.238 0.627 0.006 −0.154
Services 0.09 0.538 0.181 0.12
IT-management 0.242 0.484 −0.045 0.292
Teacher’s role −0.136 0.079 0.755 −0.145
Learner’s role 0.079 0.088 0.694 0.195
Structural change 0.119 0.122 0.651 0.127
Digital practices 0.004 0.113 −0.027 0.783
Digital competence 0.105 −0.152 0.252 0.668
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Factor 1Change management’ component variables (Strategic planning, Participatory
management), Leadership, Learning Organization and Analytics are in a separate factor,
not well connected to the other factors, except with the Learning organization variable.
Factor 2 ‘Infrastructure and services’ component variables (Network, User support,
Devices, Services, IT-management) are in a separate factor, but connected with ‘Change
management’ with the Learning organization variable.
Factor 3 ‘Learning and teaching’ component variables Teacher’s and learner’s role and
Structural change are connected, but not connected with other components and variables
indicating that ‘Change management’ and digital ‘Infrastructure and services’ are not
associated with those changes the school makes to create active learning environments.
Factor 4 ‘Learning and teaching’ component variables Digital practices and Digital
competences form a separate variable and are driven by IT-management but not with
Change management’. These variables are not associated with the structural and role
changes in schools.
The Learning organization and IT-management are connecting variables between fac-
tors in this cluster.
We may compare our least digitally mature Cluster 2 with the Digital teaching strate-
gies centered schools cluster found in the study of Jeladze and Pata (2019). Appearently
the digital practices brought in by innovator-teachers is the first stage in schools’ digital
transformation. Evidences show that the request from teachers-innovators as well as educa-
tional technologists may promote the digital transformation.
Five factors were differentiated in Cluster 3 (see Table5). This cluster included schools
that are in transition showing that digital transformation has started in the structural
changes level but IT management practices are not participative.
Identified patterns in Cluster 3 were following:
Table 5 PCA for Cluster 3—Schools in transition, where digital transformation at the structural changes
level has started, but IT management practices are not participative (Medium by f1, but highest by f2)
Note: The components with highest factors loadings are highlighted with bold
Strategic planning 0.658 −0.006 −0.095 0.032 −0.036
Leadership 0.626 −0.03 0.078 0.058 0.131
Analytics 0.606 0.143 −0.261 0.072 −0.171
Learning organisation 0.551 −0.111 −0.048 −0.153 0.501
Participatory management 0.48 0.078 −0.103 −0.007 −0.479
User support −0.15 0.711 −0.075 0.085 0.009
Devices 0.118 0.618 −0.066 −0.041 −0.026
Network 0.159 0.55 0.298 −0.421 −0.038
Services −0.004 0.545 0.067 0.028 0.216
Learner’s role −0.167 −0.032 0.704 −0.02 0.085
Teacher’s role −0.116 0.077 0.673 0.044 −0.12
Structural change 0.098 −0.082 0.573 0.389 −0.052
Digital practices −0.007 −0.104 0.011 0.79 0.008
Digital competence 0.111 0.147 0.177 0.647 −0.062
IT-management 0.031 0.311 −0.125 −0.013 0.738
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K.Pata et al.
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Factor 1Change management’ component variables (Strategic planning, Participatory
management), Leadership, Learning Organization and Analytics are in a separate factor,
not well connected to the other factors, except with the Participatory management vari-
Factor 2 ‘Infrastructure and services’ component variables Network, User support,
Devices, Services, IT-management are in the separate factor.
Factor 3 ‘Learning and teaching’ component variables Teacher’s and learner’s role and
Structural change are connected, and connected with Structural change variable to Digi-
tal practices and Digital competences (Factor 4).
Factor 4 ‘Teaching and learning’ component variables Structural change, Digital prac-
tices and Digital competences are in the same factor but negatively correlated with Net-
work factor indicating that there are issues with wifi to conduct digital practices exten-
Factor 5 IT management is in a separate factor with negative connections to Participa-
tory management, indicating that teachers have little role in defining what devices and
practices the school obtains.
The Participatory management, Structural change, Network and IT-management are
variables connecting the factors in this cluster.
The medium level digitally mature Cluster 3 in our study resembles the Digital infra-
structure centred schools in the study of Jeladze and Pata (2019), particularly with the sep-
aration of IT management from other change management processes.
4 Discussion
Our analysis stemmed from the idea that according to the system thinking, schools should
successively move from one relatively stabilized digitally enhanced learning system stage
to new stabilized stage that provides increased opportunities and higher productivity for
teaching, learning and school management. This progress, however, requires the transi-
tional stage of restructuring the school towards new opportunities of teaching and learn-
ing, infrastructure and change management.Thus, one of our research questions was to
discover which characteristics of improvement stages could be detected among Estonian
digitally innovating schools. We also intended to discover the ‘catalyst’ type of variables
that have particular role in triggering transition to the new digital stage in schools.
We assumed that within the learning organizations specific configurations of teaching
and learning, change management and infrastructure and services could be detected as sys-
temic states of school improvement towards digitalization. Our second research question
was seeking specific school improvement patterns in digitally innovative schools in order
to illustrate common patterns that could be monitored and used for change management
guidance in digitally advancing schools.
4.1 The Transitional Stages inDigital Innovation atSchool Level
We found three stages of digital transformation in schools:
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Stage 1 Schools, where digital transformation is disconnected from driving organiza-
tional goals and change management and appearing at some practices level only (Clus-
ter 2).
Stage 2 Schools, where digital transformation at the structural changes level has started
at practices level, but IT management practices are not participatively managed and this
is needed to bring on board the digitally enhanced learning practices. Those schools
also have digital infrastructure level issues in wifi access (Cluster 3).
Stage 3 Digital transformation led schools that connect learning and teaching to change
management through Learning organization variable. These schools have inclusive and
evidence-based change management practices (Cluster 1).
Taking a holistic system view on schools’ digital transformation enables to examine
school profiles, their stages of digital transformation as well as understand the interplay
between different variables. Being aware of the level of digital maturity at certain time
point and based on that define its limitations and growth potential, a school can make
development plans and improve itself as a dynamically responsive sustainable learning
organization. Furthermore, such digital maturity profiles are needed on a national policy
level to plan school improvement activities and programs, allowing to target the variables
that need most attention, to support schools moving to the next level of digital maturity.
Earlier research has demonstrated the importance of change management and school-level
knowledge practices in school improvement (Antinluoma etal., 2018) because deliber-
ate efforts are needed to develop high-level pedagogical practices and enhance the digital
4.2 The Defining Variables inDigital Transformation
We found that several variables linked different factors in school clusters and the same
variables were also defining the clusters. We consider these variables as ‘catalysts’ of digi-
tal transformation in schools:
‘Teachers’ role—teachers’ role is changing in digitally transformed schools and they
start providing help to other teachers. Often this is a gradual growth of digitally innovative
teachers towards taking informally or formally the role of educational technologist in the
school. This finding relates to educator capacity development as a result of digital technol-
ogy introduction in schools described by Haynes & Shelton (2018).
‘Digital competence’—the digital competences are not taught separately but become an
intervowen and invisible part of learning competences which will be developed as part of
active learning practices by every subject teacher. It coincides with the digital technology
and learning related results by Wong & Li (2011).
‘Structural change—the changes in learners’ and teachers’ roles towards more active
learning and facilitation models bring along the structural changes in the curriculum, time
management, classroom settings, extention of the learning spaces, usage and authorship
of the learning resources. Sheninger (2014) has suggested the re-envisioning the learning
environment as an important digital transformation component.
‘Participatory management’—it is the change management instrument that creates
shared visions and keeps these dynamically in the active mode at the classroom practices,
school development and external partnership level.
‘Leadership’—it is the change management instrument, that can trigger through
effective motivation management means the development of the learning organization
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K.Pata et al.
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where teachers, school management and IT management could share practices and learn
from each other. Sheninger (2014) and Zhong (2017) have found the visionary and all-
inculsive leadership important in schools’ digital transformation. Tam etal. (2018) and
Spillane (2006) highlighted the role of distributed leadership supported by organiza-
tional sociocultural and institutional norms as the suitable form of organizational capi-
tal that promotes school improvement. Tam etal. (2018) relates distributed leadership
to the increase of organizational capacity of ICT integration. We could observe in our
digitally most mature schools’ Cluster 1 that leadership, and possibly the teachers’ and
learners’ demand for using digital devices more intensively in the subject lessons has
promoted the IT-management for improving networks and services development.
‘Learning organization’—it is the active mutual learning attitude promoted by man-
agement with incentives that transforms the teachers to ‘explorative teachers’ who
make pedagogical innovations by developing themselves, uptaking from other teachers,
accommodating and and testing out new practices, collecting systemically feedback and
reflecting to themselves, to learners and to colleagues about the valueable findings that
should be widely applied in the school. Similarly, Leclerc etal. (2012) have found that
teachers are creating shared values upon students’ learning. The sharing of new digital
practices among teachers is driven by the proactive educational technologist who main-
tains organised regular learning circles in the school, and partnering and network events
among the schools. In the study of Tam etal. (2018) the lead innovative teachers played
similar role in Hongkong schools that were effectively digitally transformed.
‘IT- management’—it is important that IT-management—creating strategies, decid-
ing about digital tools, services and devices—is inclusive to teachers, students and man-
agement, and tightly associated with schools’ strategic plans, agendas and budgets. IT-
manager should drive the infrastructure using the input from teachers’ expectations of
conducting learning practices with digital tools and resources, and considering the digi-
tal competence development needs of the staff and students. In the Cluster 3 we could
also observe that the separation of the IT-management from ‘Change management’ and
‘Teaching and learning practices’ may hinder the transition of schools to the systemi-
cally connected self-organized learning ecosystem stage.
‘Network’—The active learning practices associate with increased usage of internet
in the classrooms with students’ own digital devices and the wifi access in the school
should cover these needs.
Differently from our expectation, ‘Digital practices variable was not among the var-
iables connecting the factors, nor was it one of the school-cluster defining variables.
We may argue that active learning practices with digital tools like co-creative, project-
based and inquiry-based approaches have not yet transformed the Estonian schools to
the new level that requires structural changes. We forsee that there is a potential that
‘Digital practices’ will start to play the leading role when school is able to pick up new
‘Change management’ approaches, and make structural and infrastructural changes as
described above. It is notable, that the ‘Digital infrastructure variables like ‘Devices’
and ‘Services’ were not the drivers of digital innovation in current Estonian schools. We
discovered that in the current period of digital transformation in Estonian schools, the
Analytics’ variable appeared not to be among the ‘catalysts’ of transforming the organi-
zations. However, we predict that organizational changes must be evidence based, and
in the future ‘Analytics’ such as from actual classroom practices, digital resource usage
monitoring, competence gap monitoring, whole school digital maturity monitoring will
be one of the variables that closes the loop of organizational learning and speeds up the
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To summarise, our approach to assess schools’ digital maturity with Digital Mirror pro-
vides an evidence-based view on schools’ current situation and helps them to make plans
for whole-school digital transformation step-by-step. Overview of these aforementioned
‘catalyst’ variables helps school management as well as ministries to direct their attention,
efforts and resources to the most critical aspects and plan supporting activities accordingly.
To become digitally innovative, schools have to promote the development of the catalysts
indicators as the key drivers of digital innovations in schools.
5 Conclusions
Schools have an important role of providing learners with the competences necessary for
the future, and therefore, schools have to improve themselves to keep up with the changes
in the digital age. In comparison to previous studies in the field, this study aimed to
describe digitally innovative schools in Estonia at a system level and three stages of school
improvement in digitally innovative schools were described. The following variables were
found to be important ‘catalysts’ of digital transformation in schools: teachers’ role, digital
competence, structural change, participatory management, leadership, learning organiza-
tion, IT-management and network. These are the key factors that should be developed if
schools want to improve themselves towards digital innovation. Furthermore, our study
demonstrated that for successful digital innovation in schools, detecting and understanding
the interplay between these key factors plays a crucial role. The catalyst factors as interde-
pendent variables help to couple self-organisation loops: digital learning loop, mediating
loop and transformative loop. The self-evaluation tool Digital Mirror used in this study for
data collection enables to capture a holistic view on a school system and identify different
schools’ digital maturity phases as well as the important key factors for moving from one
stage to another one.
Finally we want to highlight some limitations of this study. It must be noted that the
qualitative self-assessment indicators of Digital Mirror are general descriptive variables,
which are justified within the tool with separate surveys and documents as evidences. The
general indicators together with evidences are helpful for schools to understand their digi-
tal maturity, however, from the research perspective, monitoring omainly self-evaluated
indicator levels leaves too much room for interpretation what really is behind each indica-
tor. For example, the within the Digital competence indicator incorporates both students
and teachers’ digital competences, and without seeing the evidences it is impossible for
the outside to suggests improvements for the school. Digital Mirror tool does not provide
for schools the holistic learning ecosystem view with connected indicators, neither does it
group the schools by digital maturity not provide suggestsions for improvement. Our study
is the first step in exploring whether such proactive support could be provided based of
clustering the schools to maturity types, and providing hints where the schools have sys-
temic gaps which could be overcome by improving specific catalyst indicators.
Our future plan is to incorporate to Digital Mirror more automatisized evidences from
different survey data, such as teachers’ and students’ digital competences are self evaluated
and tested, and several reports collect data about infrastructure variables or school leader-
ship. Our findings are useful and transferrable to other digital maturity monitoring systems
for predicting digital maturity levels in schools as well as for comparisons whether specific
catalysts indeed are universal in improving schools towards digital transformation.
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... Relativamente ao perfil dos participantes (n=267 docentes), da informação que recolhemos, verifica-se que 85% são do género feminino e mais da metade tem entre 41 e 50 anos. A Tabela 1 apresenta de forma mais detalhada o perfil socioprofissional dos participantes. 1 O conceito de maturidade digital tem sido usado no contexto da avaliação dos processos e práticas de inovação digital das escolas (Pata et al., 2021). Vários referenciais e ferramentas têm sido desenvolvidos durante a última década para avaliar diferentes aspetos da maturidade digital das escolas (ex. ...
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Introduction: Born from pragmatic demands, arising from the activities that are part of the Amadora Digital Observatory set up under the Digital School Project, this work brings together intellectual interests and is developed to contribute to the construction of a broader perspective on the concept of digitalization in the school context. Objectives: Based on the principles of the phenomenological approach, the aim is to understand and describe the representations of primary education teachers about the phenomenon of digitalization at school. Methods: All elementary school teachers (grades 1-4) from the municipality of Amadora were inquired, asking them to express their opinion, in writing, about what they think a digital school is. For data analysis, inductive techniques were used, benefiting from the intersubjective agreement among researchers. Results: The study highlights three convergent analytical dimensions for understanding the phenomenon under study: strategic dimension (requirements to ensure the path towards digitalization in schools), pedagogical dimension (potential of the digital in teaching and learning), and axiological dimension (values and principles by which any school, more or less digital, should guide its action). Conclusions: From the dialogue between the analysis resulting from the empirical data and the results of previous studies, the conclusions highlight the inclusion of teachers' voices in the understanding of digitalization in the school context, as well as the complex and multifaceted nature of this phenomenon which, while not being restricted to issues of a technical nature, does not ignore them either, but places them in articulation with a set of foundations that emphasize the need to raise education to a qualitatively higher level. Beyond the theoretical contributions, the study adds elements of interest for school leaders and other agents who are involved in the development and implementation of strategies and interventions aimed at advancing digitization and digital transformation in schools.
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School principals and teachers are expected to continuously innovate their practices in changing school environments. These innovation processes can be shared more widely through collaboration between principals and teachers, i.e. collaborative innovation. In order to gain more insight into how school principals enact their leadership practices in leading collaborative innovation, we interviewed 22 school principals of primary, secondary and vocational education in the Netherlands. All participants have implemented the same collaborative innovation programme, aimed at enhancement of collaboration between teachers and school principals within schools, that has already been implemented by 900 Dutch schools. They were interviewed twice during the implementation year. Interview transcripts were analysed using an open coding strategy looking for leadership practices. Based on 11 leadership practices, we described two main leadership patterns: school principals enacting leadership practices as either a team player or as a facilitator. We conclude that our findings suggest a wider repertoire of leadership practices than is reported in previous studies. Future studies would need to address the generalisability of the practices and patterns as found in this specific context of collaborative innovation.
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Abstract The aim of this study was to create a model which describes the main elements for improving schools with digital technology and helps to reveal differences between schools and identify their best practices and challenges. The innovative digital school model (IDI school) offers a framework for research but also a research-based model for schools to examine their own practices with digital technologies. The model combines previous research on school improvement, creation of innovations, and digital technology in education as a special case of innovations and learning as knowledge creation to define six main elements describing an innovative, digital school: visions of the school, leadership, practices of the teaching community, pedagogical practices, school-level knowledge practices and digital resources. The model was applied to investigate three basic education schools. The results indicate that the model worked: we found essential differences between the schools and their best practices and challenges for improvement. It worked particularly well for those elements, which are mainly the responsibility for leadership inside a school. The differences of various elements between schools were not based on socioeconomic background but on the school-level practices. As a conclusion, we suggest that to improve schools with digital technology, all elements of the model should be included in the evaluation and development process.
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This paper stems from the need to identify the sustainability bottlenecks in schools’ digital transformation. We developed the conceptual model of the smart, digitally enhanced learning ecosystem to map transformation processes. We posit that the notion of sustainability is central to conceptualize learning ecosystems’ smartness. The paper presents the mapping results of Georgian public schools’ data using the interviews from 62 schoolteachers, ICT managers, and school principles. The qualitative content analysis revealed that even the schools with comparative digital maturity level could not be considered as smart learning ecosystems that are transforming sustainably. The findings call for the design of technology integration in the school as a dynamic transformation that balances two sustainability intentions—to stabilize the current learning ecosystem with its present needs, while not compromising its pursuit to test out possible future states and development towards them. We suggest schools build on the inclusion of different stakeholders in digital transformation; nourishing their resilience to ruptured situations; widening the development, testing, and uptake of digitally enhanced learning activities; weaving internal networks for sharing new practices; conducting outreach to change the socio-technical landscape; and developing feedback loops from learning, data, and information flows to manage the changes.
To explain how the innovative changes are maintained in digitally enhanced schools this paper proposes the model of self-organization. We examined the school as complex system focusing systemically at digital components in it. The data were collected from 447 schools in 13 European countries in 2017, using SELFIE instrument that measures schools’ digital maturity. Empirical data were used for assessing the goodness of the model and explaining self-organization patterns in different learning ecosystem types. K-Means cluster analysis identified 4 types of schools. In each cluster the regression analysis was conducted to develop the path model between digital components. The findings demonstrated that the proposed model could explain holistically the self-organization in learning ecosystems. The model identified different types of learning ecosystems based on how the innovative changes were maintained in schools: i) organizational learning-driven; ii) digital infrastructure-centered, iii) Mediating loop-centered schools, iv) digital teaching strategies-centered.
The digitalisation of adult and continuing education involves both a societal challenge and a policy demand to harness digital media for effective adult and continuing education provision. Therefore, this article adopts an organisational perspective on adult and continuing education, raising the question of what management knowledge is needed to support the introduction of digital learning formats in public and community-based adult and continuing education organisations. An international literature review is then performed. As a result, the author draws a picture of the current state of adult and continuing education digitalisation in Switzerland and Germany. Based on this ‘state of the German-speaking landscape’, he identifies that information technology-infrastructure, staff development and management/leadership are the most relevant challenges for implementing digital media in adult and continuing education organisations. These are taken as a structure for reviewing international studies and developing a research agenda. The proposed research agenda frames these three fields of investigation by the state of research. The conclusion outlines questions of strategy development and strategy implementation within an organisational theory framework as relevant fields for future research.
Teaching software architecture and design in a part-time bachelor program implies numerous challenges. Part-time students (all already working) start with heterogeneous knowledge, various practical experiences and different ages which leads to diverse learning types. Additionally, the number of students in our bachelor program has been doubled over the past five years. In this paper we present a step-by-step transformation process toward a flipped classroom model based on several digitalization techniques which are also used in industrial practice and open source communities. We recommend such a gradual transformation which is based on evaluation and feedback to reduce risks. Also, efforts needed for this transformation can be distributed throughout many years.