A Proposal for Assessing Digital Maturity in French Primary

Education: Design of Tools and Methods

Christine Michel

and Laëtitia Pierrot

Techné, University of Poitiers, 1 rue Raymond Cantel, Poitiers, France

Cren, Avenue Olivier Messiaen, Le Mans University, Le Mans, France

Keywords: Digital Maturity, Technology Integration, Teachers’ Digital Practices.

Abstract: The aim of our work is to re-evaluate the concept of teacher digital maturity to make it more operational for

diagnosing technology adoption in education and supporting teachers' professional development in the use of

technology. To this end, our research adopted a three-pronged approach: 1) establishment of a theoretical

framework based on a critical analysis of existing digital maturity models, 2) development of MUME, a

unified model of teacher digital maturity based on professional development frameworks, and 3) design and

evaluation of a standardized self-report questionnaire to measure teacher digital maturity. This article presents

work in progress on the third axis. Using data from a survey of French primary school teachers in 2023, we

are comparing various measurement scales to come up with a new approach to diagnosing maturity, including

a new scale and new data analysis techniques. The validated questionnaire offers valuable insights into the

diversity and progression of uses, contributing to a better understanding of digital maturity and providing a

practical tool for assessing contemporary teaching practices.

1 INTRODUCTION

The growing digital transformation within education

has propelled it to the forefront of critical educational

issues (Antonietti et al., 2023). This is particularly

salient in the French context, driven by two key

factors: (1) the limited technology integration within

primary and secondary teacher practices (Tondeur et

al., 2008) and (2) the rapid development and societal

implementation of new technologies, posing

challenges for teacher appropriation.

Numerous initiatives, encompassing both initial

and in-service training and supported by institutional

actors, aim to empower teachers to achieve digital

maturity (defined as the ability to seamlessly integrate

technology into their practices) (Michel & Pierrot,

2023). However, both teachers and stakeholders

lament a lack of coordination between these efforts,

customization to individual needs, and transparency

regarding their impact on digital maturity.

To effectively address this lack of information

regarding teachers’ actual practices and foster

technological integration, European and international

https://orcid.org/0000-0003-3123-913X

https://orcid.org/0000-0003-1701-3783

educational institutions explore the utility of skills’

frameworks. DigCompEdu (Redecker, 2017) in

Europe and NETS-T (ISTE, 2017) in the Americas

enable the design of diagnostic tools and training

structures (Kimmons et al., 2020). This strategy

strives to establish a unified approach to directing

both initial and in-service teacher training.

A critical analysis of existing frameworks

(Michel & Pierrot, 2023) reveals their commendable

scope and inclusivity towards various usage

scenarios. However, substantial adaptation remains

necessary, particularly to incorporate the rapid

advance of emerging technologies. Notably, within

the K-12 context, the exploration of digital maturity

remains underdeveloped. While attempts to address

this gap exist (Francom, 2019), they primarily focus

on identifying hindering factors and levers, rarely

translating into concrete support guidelines for

teachers. The absence of a robust conceptual

framework within existing research further hinders

the identification of effective intervention strategies

for teacher development.

Michel, C. and Pierrot, L.

A Proposal for Assessing Digital Maturity in French Primary Education: Design of Tools and Methods.

DOI: 10.5220/0012740200003693

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 1, pages 569-577

ISBN: 978-989-758-697-2; ISSN: 2184-5026

569

In a prior study (Michel & Pierrot, 2023), we

conducted a hermeneutic analysis of existing models

for technology integration and teacher digital

maturity in K-12 education. This analysis informed

the development of the MUME model, a unified

framework encompassing the individual,

organizational, and contextual dimensions

influencing technology adoption (Harrison et al.,

2014). The MUME model, with its non-use to

transformation maturity levels, is designed to assess

technology integration initiatives across various

scales, from large-scale district or regional efforts to

targeted support for smaller teacher groups. This

paper focuses on the latter application.

To evaluate our model, we investigated the digital

practices of K-12 teachers, specifically their use of

virtual learning environments (VLEs) and other

available digital tools. The subsequent section delves

into the literature on teacher digital uses and on the

models and tools used to analyze their maturity.

2 LITERATURE REVIEW

2.1 Teaching Practices and Technology

Existing research on technology integration

predominantly focuses on tool-driven impacts on

learning. However, a gap exists in the investigation of

actual teaching practices themselves. While studies

investigating into this domain often analyze the

pedagogical strategies employed (Lai & Bower,

2019), others emphasize the importance of

understanding how teachers leverage technology in

innovative combinations (Antonietti et al., 2023).

These novel uses are touted to exert a long-term

influence on teacher efficacy and responsibility

(Griful-Freixenet et al., 2021), while fostering

practices that promote student motivation and

success.

Virtual learning environments (VLEs) are a prime

area of exploration for teachers. In France, despite

VLEs deployment since 2006, research suggests use

remains confined to basic functionalities (Michel et

al., 2021). Among other things, teachers' negative

perceptions, such as feelings of inadequacy, lack of

professional meaning and time constraints,

particularly limit the use of these technologies.

Additionally, insufficient support and limited

visibility of usage further limit their adoption (Abel et

al., 2022).

2.2 Tools for Measuring Digital

Maturity

The investigation of teachers' technology use

encompasses various objectives, ranging from

generating descriptive accounts of their practices to

identifying and explaining the factors influencing

adoption, associated effects to usage, and even

modelling the dynamics of appropriation

(Taherdoost, 2018). While large-scale surveys

employing questionnaires provide representative

insights into population-level trends, their utility

primarily lies in generating descriptive studies or

models of appropriation (Schmidt et al., 2009).

Complementing these quantitative approaches,

qualitative case studies offer in-depth explorations of

specific technology usage (Hilton, 2016). However,

such singular perspectives inherently limit the scope

of inquiry, failing to fully capture the multifaceted

nature of technology integration in educational

contexts. This necessitates the development of

multidimensional classification methods that can

encompass a broader range of factors, including the

intended purpose of technology use, users’ skill

levels, perceived benefits and value, and the dynamic

evolution of usage over time.

While predominantly focusing on observational

studies of technology use, the educational research

literature offers several promising tools for measuring

digital maturity. The European Commission's

"SELFIE for Teachers" project (Redecker, 2017)

provides an online self-assessment tool

encompassing six key dimensions: pedagogy,

resources, assessment, collaboration, professional

development, and leadership. Powered by the

DigCompEdu skills repository, it guides schools in

crafting improvement roadmaps based on their self-

assessment results. Antonietti et al.'s ICAP-TS scale

(2023) focuses on evaluating teachers' technology

integration in the classroom, encompassing twelve

items that measure student and teacher digital

engagement across four cognitive levels. Drawing

upon the TPACK framework by Mishra and Koehler

(2006), the TPACK.xs scale (Schmid et al., 2020)

incorporates the contextual dimension of technology

use.

Despite their grounding in validated conceptual

models and the resulting ease of data interpretation,

the identified scales, like their underlying models,

exhibit limitations. They remain fragmented, failing

to encompass the full spectrum of teacher

professional activity, thereby hindering efforts to

provide generalized support (Michel & Pierrot,

2023). Additionally, their context-specific nature

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570

raises concerns regarding subjectivity and the

comparability of results across diverse settings

(Voogt et al., 2013).

3 METHODS

3.1 Dimensions and Items

To comprehensively assess the integration of

technology within teaching practices, we constructed

a multifaceted questionnaire (Table 1) drawing upon

two key sources: (1) validated measurement scales of

technology integration in teaching practices:

ICAP.TS (Antonietti et al., 2023); SELFIE (SELFIE,

2022) and TPACK.x (Schmid et al., 2020), (2)

targeted questions from previous research:

FreqNume, FreqENT (Michel & Pierrot, 2022).

Table 1: Questionnaire structure.

Question category Items

Socio-demographics

*Gender

*Age

*Seniority

*Level of Education

*Initial and Technology Training

VLE usage experience

*Technology use before the pandemic

*VLE use during the pandemic

Technology

knowledge and

representations

*General representations of the

usefulness of technology for education

(TPACK-XK and XK2)

*Knowledge of how technology is used

in educational contexts (TPACK-XK4)

*Digital Competences (DigCompEdu)

Uses

*Technology use frequency (FreqNume)

* VLE service frequency (FreqENT)

*Classroom usage (ICAP-UseEnclasse),

made up of two parts: ICAP-UseEns for

teacher usage and ICAP-UseElev for

student usage.

To ensure clarity and efficiency, we meticulously

translated and streamlined the initial scales while

eliminating redundancy across questions. This

resulted in a concise questionnaire of manageable

length. Additionally, the initial section gathers socio-

demographic data and inquiries about the VLE usage

experience, providing valuable contextual

information for analysis.

3.2 Validation Process

The validation process adhered to the established

guidelines outlined by Taherdoost (2016). Content

validation commenced with the evaluation of the

questionnaire by five subject-matter experts. Their

feedback served to refine the content and enhance its

overall validity. Additionally, Cronbach's alpha was

employed to gauge the questionnaire's internal

reliability, specifically assessing the inter-item

correlation. An alpha coefficient exceeding 0.70 was

targeted to ensure a robust level of internal

consistency.

3.3 Data Analysis Method

The core questionnaire, assessing technology

knowledge/representations (TPACK-XK, XK2,

XK4) and use (UseEnclasse, UseElev, UseEns,

FreqEnt, FreqNume), employed 6-point Likert scales

recoded for consistency: 0 (never/strongly disagree)

to 1 (strongly agree/agree almost daily) for Likert

scales and 0 (don't know how) to 4 (regularly

do/advise others) for DigCompEdu. This resulted in

quantitative data ranging from 0 to 1 and 0 to 4.

Subsequently, descriptive and multivariate

statistical techniques were applied to analyze the

collected data. K-means clustering, an unsupervised

learning algorithm, was used to classify responses for

each variable group. This method partitions

individuals into distinct, homogeneous clusters based

on distance to the cluster's centroid (Ahmed et al.,

2020). Smaller distances indicate greater individual

similarity to the cluster. Executed on XLstat, K-

means identified the central objects representing the

individuals closest to the barycenter of each cluster.

In coherence with the MUME model, the number of

clusters was set to 7. Based on this classification, we

characterized each cluster and defined maturity levels

for each variable group.

To explore deeper into the data, we employed

Principal Component Analysis (PCA), a technique

that reduces dimensionality by identifying the least

informative dimensions within the dataset. Reduction

is achieved by analyzing data correlations and

projecting them onto a matrix. This matrix is then

used to visualize axes (components) around which the

data resides. As the matrix is multidimensional,

multiple axes can be extracted, with the most

informative ones explaining up to two thirds of the

total information. By examining the cosine squared of

each variable with respect to each axis, we can

determine its relevance: a high value indicates a

significant contribution of the variable to that specific

axis (Jolliffe & Cadima, 2016).

3.4 Study Context

The CoAI – DATA SIM project seeks to develop

data-driven approaches and methods to empower

teachers in their individual adoption of digital

resources and foster more mature, collaborative

A Proposal for Assessing Digital Maturity in French Primary Education: Design of Tools and Methods

571

practices. This collaborative effort, involving various

educational stakeholders, is being piloted in the

French academic region of Paris.

In June 2023, we distributed an online

questionnaire to all teachers within the academy via

their VLE platform. The final analysis included

responses from 143 participants, comprising 86

primary school teachers and 18 secondary school

teachers. 39 individuals did not respond.

The sample demographics revealed a female

majority (101) compared to 22 male teachers; 20

individuals did not disclose their gender. Regarding

age, the most prominent group consisted of teachers

aged 41-50 (52), followed by those above 51 (43).

Individuals under 30 and between 31-40 represented

6 and 23 teachers, respectively; 13 participants did

not provide their age. Experience-wise, the majority

(88) possessed over 10 years of experience, while 23

had 3-10 years. Five teachers had less than 3 years,

and 8 belonged to the "Other" category; 19

individuals did not share their experience data.

To ensure data homogeneity, we focused on the

55 complete responses from primary school teachers,

the majority group in our survey. Additionally, we

confirm that sub-questionnaires have achieved

internal reliability with Cronbach's Alpha exceeding

0.70 (table 2).

Table 2: Sub-questionnaire reliability.

Variable groups Cronbach's Alpha

DigCompEdu 0,969

UseEnclasse 0,948

UseElev 0,959

UseEns 0,916

0,901

FreqENT 0,924

FreqNume 0,820

XK4 0,741

4 RESULTS

4.1 Different Maturity Level

Classifications

The processing approach for classifying maturity

levels is the same for all questionnaires. It is detailed

in section 4.1.1 only. All other analyses follow the

same methodology.

4.1.1 VLE Usage Questionnaire

Table 3 details the average frequency of VLE service

use as classified by the K-means algorithm. The

questionnaire enquired teachers on the frequency of

using 23 specific VLE services for professional

activities. Details on response methods and

calculations are included in Table 3.

K-means clustering was employed to reduce data

dispersion, resulting in the identification of seven

distinct teacher profiles (classes). The "non-user"

class (Cl0) comprised three teachers with all service

usage values at 0, while the remaining 52 teachers

distributed across classes Cl1 to Cl6 displayed varied

usage patterns. This classification revealed distinct

behavioral trends: Cl1 exhibited infrequent service

use, while Cl6 demonstrated regular use and

exploration of all available services.

Table 3: Primary teacher classes by VLE use.

To investigate deeper into these dynamics, we

examined service adoption levels by teacher class.

Services with values exceeding 0.75 were considered

"adopted," while those below 0.25 were deemed "not

adopted." Intermediate values indicated services

undergoing adoption. This analysis identified three

service groups. 10 services (group G3) have hardly

been adopted by teachers. 8 services (group G2) are

in the process of being adopted. 6 services (group G1)

have been adopted by at least one class of teachers.

Interestingly, adopted services primarily served

communication objectives, while non-adopted

services were more closely aligned with

teaching/learning activities. Services undergoing

adoption tended to cater to mixed objectives.

Further analysis explored how each teacher class

(Cl1 to Cl6) adopts or rejects VLE services. Cl1

exhibited partial adoption of three services

(messaging, blog, and skills), indicating an emergent

stage with no established VLE practices. Conversely,

Cl2 adopted the multimedia notebook and pursued the

adoption of messaging, document space, and news

feed services. Interestingly, classes Cl2, Cl3, and Cl4

each adopted distinct service combinations

(multimedia notebook, document space, and blog).

Finally, Cl6 demonstrated the highest service

adoption (messaging, blog, news feed, and textbook)

with exploration of nine additional services.

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These findings highlight a diverse spectrum of

VLE adoption strategies among the identified teacher

classes. Examining the adopted and non-adopted

services within each class reveals distinct levels of

digital maturity.

4.1.2 DigCompEdu

Questionnaire

Table 4 shows the average skill level according to the

DigCompEdu questionnaire adapted for our study.

The treatments are the same as above.

Most of these skills correspond to regular practice

(level 1) in at least one teacher's class. Group G1

corresponds to skills mastered at expertise level 3 in

at least one class, and at level 1 in many others: self-

training skills (D1), data protection (D2) or student

protection (D6), and the use of technology for certain

forms of pedagogy: collaboration, active learning and

efficiency gains (D3). Group 4 corresponds to the

most difficult skills to acquire, because 4 to 6 classes

have not developed them. They correspond to specific

pedagogies: problem-solving or experimentation

(D3), evaluation (D4), or adaptation/personalization

(D5). This structuring makes it possible to qualify

skills according to their level of difficulty in terms of

expertise: G1 being the simplest to develop, G4 the

most complex. We can assume that, over time, all

teachers will succeed in developing level 1 skills for

groups G1 and G2, but that they will have more

difficulty with groups G3 and G4.

4.1.3 TPACK Questionnaire

The mean values per class of teachers' representations

of the usefulness of technology for professional

practice (XK) are described in Table 5. Class mean

values for teachers' knowledge of technology use in

their professional context (XK4) are described in

Table 6.

4.1.4 ICAP

Questionnaire

Table 7 describes the average frequency of digital

classroom use by teachers, according to the ICAP

scale. The communication uses from the teachers to

the learners are adopted by almost all classes (group

G1). The collaborative learners centered uses (discuss

lessons, working in groups…) are not adopted (group

G3). The individual learners centered uses have been

adopted by one or two classes or are in the process of

being adopted.

4.1.5 Technology Usage Questionnaire

Table 8 shows the average frequency of use of digital

services by teachers according to the K-means

classification based on the FreqNum questionnaire.

The technologies adopted by almost all classes

(group G1) are the classic document production and

communication tools: Word, e-mail, search engines,

VLE (“ENT”) and other design tools. The tools not

adopted are serious games and online quiz design

applications. Other tools have been adopted by one or

two classes or are in the process of being adopted.

Table 4: Classes by DigCompEdu skills.

Table 5: Classes by utility representations (TPACK-XK).

Table 6: Classes by knowledge of the activity context

(TPACK-XK4).

Table 7: Primary teacher classes by ICAP use.

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Table 8: Primary teacher classes by use of digital tools.

4.2 Comparing Classes

To compare all classifications, a Principal

Component Analysis (PCA) was conducted on the

different classifications obtained using the K-means

method for the six groups of variables. Table 9

presents the eigenvalue analysis, Table 10 the squared

cosine analysis, and Table 11 the correlation analysis.

Table 9: Eigenvalue Analysis.

The first two axes explain half of the information

(54%). The first four axes explain 81% of the

information.

The analysis of the squared cosines of the

variables (see Table 10) allows us to identify the

variables that are the most explanatory for the set of

constructed classes.

Table 10: Squared cosine analysis.

The most coherent and explanatory variables for

the classifications are those constructed from the

questionnaires: « DigCompEdu », « technology use

frequency », « ICAP uses » et « VLE use

frequency ». These variables explain 36% of the

information on the classes and are represented by

Axis F1. This axis represents the skills related to the

implementation of technology in classroom teaching

activities.

The second axis (F2) explains the classifications

based on « TPACK question on the perceived

usefulness of technology (XK2) » and to a lesser

extent « general knowledge about technology

(TPACK-XK) ». Axis F2 represents both

representations and general knowledge related to

technology. The knowledge about the general context

of the application of technology suggested in the

TPACK (XK4) contributes to a lesser extent in Axis

F3.

Table 11: Correlation analysis.

Correlation analysis (see Table 11) shows that the

DigCompEdu classification is significantly correlated

with the ICAP classification (0.575), the Frequency

of Use of Digital Tools classification (0.503), and the

Frequency of Use of VLE Services classification

(0.422). General knowledge about technology

(TPACK-XK) is significantly correlated with the

Frequency of Use of Digital Tools classification

(0.362). The Frequency of Use of Digital Tools and

VLE Services classifications are also correlated

(0.448).

A K-means clustering analysis was performed on

the classifications derived from the five methods

representatives of F1 and F2. This analysis allowed

us to classify teachers according to the intensity of

their use of technology (axis F1) and the types of

representations they have of technology.

Figure 1: Clustering of Teachers Based on PCA of K-means

Classes.

Four groups of teachers were identified (see

Figure 1). On the left (respectively right): teachers

who use technology to a limited extent (respectively

intensively). At the top (respectively bottom):

teachers who have positive (respectively negative)

representations of the value of technology in the

context of education.

It is a classic observation that representations

condition uses (for groups R-U- and R+U+), but that

they are not a condition for the realization of uses

(R+U-). On the other hand, we observe a group of

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teachers who have negative representations but

intensive uses (R-U+).

4.2.1 Respondents’ Profiles by Digital

Maturity Levels

An examination of each teacher group's composition

reveals that those with negative perceptions and

limited use (R-U-) demonstrate the lowest awareness

of mastering digital competencies (DigCompEdu).

This occurs despite their (marginal and restricted)

classroom utilization of digital tools (ICAP). Notably,

this group falls within the category of non-users of

digital tools (FreqNum Cl0 ranking). A representative

teacher from this group is a recent hire (less than 10

years' experience) who serves as the digital referent.

However, their work in kindergarten (medium and

large sections) might explain the limited student

exposure to digital tools in their classroom.

Teachers who hold positive views of technology

but exhibit limited use (R+U-) acknowledge its

various applications (variable XK). While they

occasionally employ digital tools beyond the VLE

(FreqNum), their classroom integration remains

restricted (ICAP). Similar to the previous group, these

teachers belong to the non-user’s category (FreqNum

Cl0 classification). A representative teacher from this

group has over 10 years of experience teaching

primary school. Despite participating in digital

education training and previously using digital tools

moderately in the classroom, her pandemic-era use

remained limited.

Teachers with negative perceptions but intensive

use (R-U+) perceive themselves as competent

(DigCompEdu) and recognize the benefits of digital

education (TPACK XK). However, they prioritize

other digital tools over the VLE, preferring to

maintain control over classroom technology use,

potentially hindering student engagement (ICAP).

This group primarily consists of teachers from the

FreqNum Cl2 class. A representative teacher from

this group has over 10 years of experience in

kindergarten. Before the pandemic, she was a regular

user of technology and had participated in relevant

training courses.

Finally, the group with the most positive

perceptions and intensive use (R+U+) considers

themselves digitally literate (TPACK DigCompEdu

and XK variables). They regularly integrate digital

tools into their own practices and those of their

students (ICAP). This group invests less heavily in

the VLE and comprises teachers from the FreqNum

Cl4 class. The representative teacher from this group

has over 10 years of experience working with Cycle

3 pupils. Her pre-pandemic use was moderate, and

she likely doesn't require frequent digital training due

to her high level of digital literacy.

5 DISCUSSIONS

Our study offers a multifaceted perspective on teacher

technology usage and integration. Employing a

multidimensional approach, we enlighten various

usage dynamics linked to teachers' immediate

contexts. Notably, technology use primarily aligns

with student interaction and self-training objectives,

reflecting teachers' prioritization of these purposes.

Further analysis reveals teachers' confidence in their

mastery of skills linked to professional commitment

(D1) and teaching and learning (D3). Comparing this

confidence with technology and VLE use frequency

reinforces this finding, as evidenced by the higher

utilization of general-purpose digital tools (office

automation, search engines, messaging) and

communication-oriented VLE services.

By leveraging corpus-based classifications, we

identified four distinct teacher profiles based on their

technology representations (R) and usage intensity

(U). Consistent with our findings, teacher digital

maturity manifests in more diverse technology use,

with highly engaged teachers exhibiting greater

versatility within the available VLE ecosystem.

Furthermore, our results echo existing research that

links higher self-reported digital competence

(DigCompEdu) and positive technology perceptions

(TPACK XK2) to increased use of available services,

particularly in classroom settings (Abel et al., 2022;

Francom, 2019; Tondeur et al., 2008).

These findings highlight key challenges for

professional development and teacher support

programs. Specialized software remains

underutilized and less prominent in teachers'

representations (Abel et al., 2022). Additionally,

limited confidence exists in D4 skills related to

assessment. These areas represent priorities for

targeted development efforts. Conversely, the

observed adoption of VLE services suggests a

promising maturation process. Specific support

measures should be explored to facilitate the further

integration of these practices.

This study successfully validated our unified

questionnaire-based approach for measuring teacher

digital maturity. Additionally, the proposed

multidimensional analysis approach demonstrated its

effectiveness in addressing the limitations often

associated with usage observation methods.

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The current research acknowledges two

limitations. Firstly, the sample size was relatively

small. To address this, we plan to compare our

findings with a larger dataset of traces of VLE

activity. This will also mitigate the second limitation

concerning the self-reported nature of questionnaire

data.

6 CONCLUSION

This research explores the digital maturity of

teachers, a multidimensional concept encompassing

teachers' knowledge, skills, attitudes, and practices

towards technology. By identifying distinct teacher

profiles based on their perception of technology and

their usage patterns, we have gained new insights into

the importance of considering the immediate context

of teachers, as interaction with students and self-

training are driving forces in their use of technology.

Additionally, teachers' confidence in their digital

skills and their positive perception of technology

significantly influence the integration of technology

in the classroom.

Our results suggest that initial and continuous

training programs should focus on the use of

specialized tools and the strengthening of assessment

practices, while fostering a positive attitude towards

technology.

ACKNOWLEDGEMENTS

This work was done in collaboration with the

company EDIFICE and financed within the

framework of the CoAI-DataStim project (Academy

of Paris).

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