A Quantitative Survey of Digital Competencies of Music Teachers in the

European Union

arthach

O Nuan

ain

1 a

, Esther Vi

nuela Lozano

, Kadri Steinbach

, Yvan Corbat

Themuri Sulamanidze

, Raquel L

opez

and Maria O’Connor

MTU Cork School of Music, Cork, Ireland

Reina Sof

ıa School of Music, Madrid, Spain

University of Tartu, Tartu, Estonia

Grupo DEX, Oviedo, Sapin

ﬁ ﬁ

Keywords:

Music Pegagody, Digital Skills.

Abstract:

In this paper we offer a quantitative survey of the digital skills of music teachers practising in the European

Union. As part of the Erasmus+ Digital Skills for Music Teachers (DISK) project, one of the work packages

is to see how well they are integrated into the everyday teaching of music teachers of all specialisations, from

junior level to conservatoire. To examine this we conducted an online survey that ﬁrst quizzed teachers on

their experiences through a combination of quantitative Likert scale ratings and open-ended feedback. We

present the results and initial analysis of 221 teachers’ responses and conclude with a discussion of the next

steps based on this data.

1 INTRODUCTION

Teachers and educators need a broad toolkit of digital

skills and strategies in the delivery of high quality tu-

ition to their students. In the case of music institutions

and conservatories, the application of digital skills is

particularly challenging due to the specialist, applied

and practical nature of delivery. Instrumental and en-

semble tuition is principally a physical, in-person and

interactive activity that poses major hurdles when at-

tempting to coordinate over video teleconferencing

solutions like Zoom and Microsoft Teams.

These challenges were accelerated and brought to

the fore during the COVID-19 pandemic, which saw

institutions and teachers at all levels forced to adapt

and bring their lessons online within a very short and

intense period of time. Meanwhile, educators across

all disciplines are constantly evolving and adapting

to profound developments and obstacles in areas like

artiﬁcial intelligence (AI) and cybersecurity.

The Erasmus+ project Digital Skills for Music

Teachers (DISK) was initiated in 2024 by three con-

servatories from Spain, Ireland and Estonia to study

the role of digital technologies in music education and

https://orcid.org/0000-0002-3096-3575

prepare frameworks and modules for upskilling its

practitioners. It builds on knowledge gained through

the completion of the previous New Skills for New

Artists (NS4NA) that produced as its primary output

a set of reusable Creative Commons licensed mate-

rials for enhancing the entrepreneurial and technical

skills of recent music graduates (noa, 2022).

The DISK project seeks to extend this work to mu-

sic teachers with the following objectives.

1. To provide a scientiﬁc framework for the devel-

opment of various strategies for the digitisation of

music education.

2. To design and test a training curriculum in digital

competencies for music teachers.

3. To generate a toolbox for scaling up the digitisa-

tion of music education at the European level.

The most noticeable differentiator is its evidence-

based approach at the outset. To provide justiﬁcation

for developing and testing a high quality curriculum

we describe in this paper a large-scale quantitative

survey that examines the extent of digital skills among

221 music teachers.

The European Framework for the Digital Compe-

tence of Educators (DigCompEdu) provides a ”scien-

614

Nuanáin, C., Lozano, E., Steinbach, K., Corbat, Y., Sulamanidze, T., López, R. and O’Connor, M.

A Quantitative Survey of Digital Competencies of Music Teachers in the European Union.

DOI: 10.5220/0012763700003693

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 614-619

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

tiﬁcally sound framework describing what it means

for educators to be digitally competent” (Redecker,

2017). Since then there has been signiﬁcant re-

search investigating and implementing the framework

within European educational institutions (Buckley

and Pears, 2021; Economou, 2023), including several

surveys that evaluate digital competencies using qual-

itative (Ghomi and Redecker, 2019; Rubio-Gragera

et al., 2023) means. In the context of music educa-

tion there have been several surveys published (Pabst-

Krueger and Ziegenmeyer, ), with many focussing

on the Spanish system (Guill

en-G

amez and Ramos,

2021; Garc

ıa et al., 2021; Cuervo et al., 2023)

The paper is structured as follows. Section 2 de-

scribes the methodology from instrument design to.

Section 3 discusses some of the results. Section 4

concludes with some overall remarks and discussion

of the next steps.

2 METHODOLOGY

2.1 Survey Design and Pilot

The survey design phase commenced with a series of

online brainstorming sessions between the 3 partner

conservatoires in an effort to provoke discussion, so-

licit ideas and generate as many possible questions as

possible. These were all collected loosely in shared

documents that allowed further review and discussion

after formal meetings via comments.

Questions were reﬁned based on relevance or re-

dundancy and transferred to a spreadsheet were or-

ganised thematically into sections that align with the

following areas of study of the project (as well as

an introductory ”general proﬁle” category to aid with

subgrouping and data slicing later):

1. General Proﬁle

2. Basic and transversal digital skills

3. Online safety and addressing ethics within the

new technological environment

4. Pedagogical skills

5. Speciﬁc Skills for digital teachers

Question types (e.g. multiple choice versus mul-

tiple answer) and possible options for the questions

were captured. Next the questions were coded into a

Microsoft Forms survey. Using manual and machine-

assisted translation the surveys were localised for

Spanish and Estonian. This pilot survey was then

shared with 5 colleagues from partner institutions but

who were not associated directly with the project for

independent feedback and testing.

2.2 Data Collection

After addressing pilot feedback, the survey was re-

ﬁned and ﬁnalised for collection from the sample pop-

ulation. The survey was shared ﬁrstly amongst col-

leagues within the three conservatory partner institu-

tions, then widened to other institutions in the net-

works of the partners. In addition we targeted spe-

ciﬁc organisations and social media channels related

to music pedagogy, particularly the Association of

European Conservatoires (AEC).

2.3 Data Analysis

Following the closing of the data collection window,

the survey results were downloaded in Excel format

from the Microsoft Forms backend. The data was

ingested into Python using the Pandas data analysis

and manipulation library (pandas development team,

2024; McKinney, 2010). Readable ordinal categories

such as age, years teaching and Likert ratings were

replaced with integers to aid numerical analysis.

Preliminary descriptive statistics were carried out

using pandas and numpy. Inferential statistics were

performed using statsmodel (Seabold and Perktold,

2010). Microsoft Forms helpfully prepares some sim-

ple pie and bar charts, which were supplemented with

more detailed graphs prepared in matplotlib (Hunter,

2007) and seaborn (Waskom, 2021).

3 RESULTS

3.1 Reliability

The Likert ratings were ﬁrst examined to ensure suf-

ﬁcient reliability using Cronbach’s alpha measure as

carried out in the work of. We reported a high alpha

coefﬁcient of 0.964177 within a conﬁdence interval

of 0.957 - 0.971 a which can be interpreted to indicate

that the scale we have used has high internal consis-

tency.

3.2 Descriptive Statistics

3.2.1 General Conﬁdence Proﬁles

Figure 1 shows the distribution of responses for the

general questions gauging participants’ conﬁdence in

the high-level category descriptors. While not as pre-

cise, these broad category questions act as a useful

https://aec-music.eu/news-article/digital-skills-4-

music-teachers-disk/

A Quantitative Survey of Digital Competencies of Music Teachers in the European Union

615

Figure 1: High-level Conﬁdence Categories.

(a) Communication.

(b) Productivity.

Figure 2: Conﬁdence Ratings - Basic Digital Skills.

control and broad indicator for the precise question-

ing that follows in preceding sections.. Most of the

participants indicate that they are broadly conﬁdent in

their use of computers, while the most notable lack of

knowledge lies in the area of online safety, copyright

and intellectual property

3.2.2 Basic Digital Skills (Communication and

Productivity)

In the category of basic digital skills we queried the

abilities of music teachers in general digital compe-

tencies not unique to music pedagogy - such as usage

of common communication and productivity tools.

Figure 2 shows the distribution of scores in these two

categories. Encouragingly, our participants scored

consistently high for tools like email, cloud storage

and telecommunication, indicating a baseline literacy

and understanding. Lower scoring categories such as

forum usage, task organisation and interactive apps

indicate that teachers may not be aware or have the

time to discover more bespoke tools.

(a) Safety.

(b) Ethics and Intellectual Property.

Figure 3: Conﬁdence Ratings - Safety, Ethics and Intellec-

tual Property.

3.2.3 Safety, Ethics and Intellectual Property

A constant challenge and concern for music teach-

ers with digital delivery at all levels - but especially

younger children - is the issue of online safety. Figure

3a shows a fairly balanced distribution of scores with

the lowest weighting in the categories of phishing and

online bullying. This reﬂects the often smaller size

of music teaching schools in many communities who

may not have the resources, budget or knowledge to

develop formal policies and train their staff effectively

in these highly important issues.

Another unique issue in music education deliv-

ery is the issue of copyright and intellectual property.

Music teachers make frequent use of wide-ranging

multimedia resources in addition to sheet music and

recordings. Professional music performers leaving

music institutions also need to understand how mu-

sic royalties and intellectual property is handled in

their regions. Our survey revealed a marked lack of

understanding in the latter. In this section we also

asked about teachers’ knowledge of automatic plagia-

rism detectors, commonplace in other disciplines but

likely less so in music tuition due to the practical na-

ture of delivery. This is reﬂected in the lower distri-

bution of scores for this quality.

3.2.4 Speciﬁc Digital Skills

Our ﬁnal category queried the usage of specialised

tools for music tuition, divided by software and hard-

ware. The software tools examined the teachers’

CSME 2024 - 5th International Special Session on Computer Supported Music Education

616

(a) Software.

(b) Hardware.

Figure 4: Speciﬁc Digital Skills.

awareness or familiarity with music pedagogy spe-

ciﬁc and adjacent software skills such as notation

software, audiovisual editing and theory apps along

with new AI innovations. As expected but still very

skewed, teachers are very unprepared for the impact

of Chat-GPT, but also music speciﬁc generative appli-

cations that can compose or co-create music. Many

teachers are also unaware of the beneﬁt of theory and

ear training apps that allow students to interactively

improve such skills in their own time.

3.3 Inferential Statistics

3.3.1 Overall and Individual Conﬁdence

Relationship

Our ﬁrst and most intuitive hypothesis is that indi-

viduals who rate themselves conﬁdent in the overall

control question should be reﬂected in the categor-

ical conﬁdence ratings. To test this hypothesis we

computed the Spearman’s rank correlation coefﬁcient

between the overall conﬁdence and each individual

facet. We report moderate correlations (0.4 - 0.7) in

all instances and all coefﬁcients are statistically sig-

niﬁcant with (p <0.05) as indicated in the heatmap in

Figure 5.

3.3.2 Effect of Age

We were interested in studying the effect of age and

on the digital expertise and conﬁdence of music teach-

Figure 5: Overall and Individual Conﬁdences - Correlation

Heatmap.

Figure 6: Conﬁdence of Under-45s versus Over-45s.

Table 1: Overall Conﬁdence Correlation Analysis.

General Category Spearman’s rho p-value

Online

collaboration

-0.217865 0.001115

Overall conﬁdence -0.191543 0.004265

Online safety -0.127213 0.059010

Online teaching

strategies

-0.100994 0.134472

Royalties, rights, IP -0.003983 0.953052

ers. A possible hypothesis would suppose that older

teachers who rely on more traditional methods of tu-

ition would be less conﬁdent in terms of digital skills.

To investigate this we ﬁrstly computed Spear-

man’s rank correlation between the ordered age vari-

able and each of the general conﬁdence categories.

Table 1 shows the top 5 correlation coefﬁcients along

with the signiﬁcance levels. As we can see there are

A Quantitative Survey of Digital Competencies of Music Teachers in the European Union

617

Table 2: Speciﬁc Conﬁdence Correlation Analysis.

Digital Competency Spearman’s Rho Signiﬁcance Level

Music streaming (Spotify, SoundCloud, BandCamp, Apple Mu-

sic)

-0.324158 0.000001

Theory/Ear training apps and games (e.g. Auralia, Musition,

Practica Musica, Ear Master)

-0.236085 0.000400

e. Tasks organisation (Notion, Trello, Evernote, etc.) -0.234820 0.000431

Digital Publications (Online books and works, eBooks, digital

workbooks, online repositories)

-0.230316 0.000559

d. Time management (Google Calendar, Notion, Coda, etc.), -0.218825 0.001059

a. Cloud-based ﬁle sharing (Google Drive, Dropbox, Teams,

etc)

-0.218761 0.001063

weak negative correlations but statistically signiﬁcant

effects of age on the overall digital conﬁdence and on-

line collaboration.

In terms of speciﬁc digital skills we drilled down

and performed the same procedure with all the Likert

conﬁdence ratings. Table 2 overleaf shows the sorted

table of the top 5 correlation coefﬁcients and their sig-

niﬁcant levels, indicating a number of statistically sig-

niﬁcant relationships with aspects such as digital pro-

ductivity, music streaming and music theory apps.

Another approach was to compare speciﬁc groups

by partitioning the participants into two age groups

above and below age 45. Figure 6 shows the superim-

posed bar plots of the percentage scores. Performing

this sort of division allowed us to compare the cen-

tral tendencies of both groups, using additional statis-

tical tests. Comparing the medians (under-45 = 4.0,

over-45 = 3.0) we performed a Mann-Whitney U test

to establish whether the medians of the two groups

are statistically different. This was conﬁrmed with a

statistic=5854.0 and a signiﬁcant p-value=0.0121.

4 CONCLUSIONS AND NEXT

STEPS

In this paper we presented initial ﬁndings of a quan-

titative survey of the digital skills of music teachers

practising in the European Union. We introduced the

aims of the survey in the context of the DISK Eras-

mus+ project, which seeks to offer an evidence-based

approach to digital skills enhancement of those teach-

ers.

We produced descriptive and inferential analysis

of the results that revealed important aspects and gaps

in the knowledge of the music teachers under scrutiny.

Many of these details were suspected from the outset

i.e. a lack of awareness around next generation tools

such as AI and immersive technologies. Others were

more surprising, such as the lack of uptake in theory

training apps and games and understanding of music

royalties.

Our next steps are to follow up the quantitative

survey with a qualitative instrument that will give tex-

ture to the numerical data through a series of semi-

structured interviews with selected music teachers.

We are also producing a similar quantitative survey

that gives the perspective of students and their impres-

sions of the extent of digital usage within their class-

rooms. The evidence we are gleaning from these ex-

periments have proved highly informative as we pre-

pare materials for the delivery of training modules in

the next work package, and as we gather more data we

are constantly reﬁning the content to meet the needs

of the music teachers in the EU.

ACKNOWLEDGEMENTS

This publication is part of a project that has received

funding from the European Union’s Erasmus+ pro-

gram.

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