Fostering Computational Thinking in Undergraduated Music
Conservatory Students
Marcella Mandanici
Music Conservatory ”L. Marenzio”, p.tta A.B. Michelangeli 1, 25121 Brescia, Italy
Computational Thinking, Music Technology, Programming Skills, The ”Four C’s”.
This paper presents and discusses the syllabus of a music technology course at undergraduate level in a Music
Conservatory. The aim of the course is to introduce the students to computational thinking through the use
of Pure Data, a free visual programming environment for music production. Starting from very basic notions
in music technology and in the use of the program, the students are guided towards the knowledge of the
fundamental steps of computational thinking in music production. Simple musical structures such as pattern
repetition, transposition, polyphony and chords are implemented with the use of loops, abstractions, cycles,
pattern storage systems and timing controls. While students become acquainted with these processes, also
creative group activities are proposed with the aim of exploring the potentialities of the numeric control of
sound events and musical form.
Enhancing digital skills and competences for the dig-
ital transformation is one of the two priority areas of
the Digital Education Action Plan 2021-2027
. Pro-
moted by the European Union, the Digital Education
Action Plan supports the effort of the educational sys-
tems to adapt to the digital age. Digital competences
are considered an essential part of the curriculum of
the XXI century citizen mainly after the experience of
the Covid-19 pandemic, which led teachers and stu-
dents to adopt as soon as possible digital tools to pre-
serve school activities. For instance in the field of mu-
sic education the importance of the use of web plat-
forms, music production software and in general of
digital music education tools emerged with unprece-
dented evidence, showing all their strong potentiali-
ties (Avanzini et al., 2021). This trend is confirmed
by the figures collected in the open public consulta-
tion on the Digital Education Action Plan 2021-2027
which show that:
almost 60% of respondents had not used distance
and online learning before the crisis
95% consider that the Covid-19 pandemic marks
a turning point for how technology is used in ed-
ucation and training
over 60% felt that they had improved their digital
skills during the crisis
Computational thinking is the core of digital skills be-
cause it describes a set of attitudes and ways of think-
ing that allow humans to communicate with machines
(Cuny et al., 2010; Shute et al., 2017). According to
(Selby and Woollard, 2014), these can be represented
by three fundamental ideas:
the thought process
the concept of abstraction
the concept of decomposition.
The though process includes ... formulating prob-
lems so their solutions can be represented as compu-
tational steps and algorithms. (Aho, 2012) and im-
plies abilities in problem solving (Wing, 2011), anal-
ysis and pattern recognition. Abstraction is the pro-
cess of deciding what aspects of the problem to be
solved are essential and need to be included in the
algorithm and what can be neglected (Wing, 2008).
It implies the ability to generalize a problem among
many other similar cases and to write simpler and
more widely reusable code. Finally, decomposi-
tion is the ability of breaking down a big problem
into smaller parts and it is necessary when manag-
ing complex tasks (Selby and Woollard, 2014). Com-
Mandanici, M.
Fostering Computational Thinking in Undergraduated Music Conservatory Students.
DOI: 10.5220/0011134600003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 449-457
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
putational thinking in education is also connected to
the Four C’s” that characterize the ”Learning and In-
novation Skills” for the XXI century learning. The
”Four C’s” stand for Critical thinking, Creativity, Col-
laboration and Communication and embrace a series
of social skills such as performance, simulation, ap-
propriation, multitasking, distributed cognition, col-
lective intelligence, judgement, transmedia naviga-
tion, networking and others (Jenkins, 2009). Critical
thinking refers to the ability of evaluating the relia-
bility of different information sources but also to the
ability of checking the effectiveness of different solu-
tions to solve a problem. Creativity is the final stage
of the learning process in the revised Bloom’s taxon-
omy (Krathwohl, 2002), which considers the ability
of putting the elements together to produce a new co-
herent whole as the maximum level of cognitive de-
velopment. Creativity is also very important, mainly
when computational thinking is applied in the field of
artistic expression.
Figure 1: The SAMR (Substitution, Augmentation, Modi-
fication and Redefinition) model with its fundamental steps
(Enhancement and Transformation).
The SAMR model establishes four lev-
els of technology integration in education
(Hamilton et al., 2016). Starting from the lower
level (simple tool substitution), the model drives the
attention towards the higher one (content redefini-
tion), stressing the cognitive value of discovering
tasks and performance possibilities impossible
without the use of technology (see Figure 1). Here is
where computational thinking in artistic production
hits its final point, realizing artifacts and ways of
expression previously inconceivable. Giving a look
at the history of electronic and computer music
(Copeland and Long, 2017; Collins et al., 2013),
hundreds of examples of music ”redefinition” and
”transformation” can be found in the evolution and in
the artistic use of sound synthesis techniques and al-
gorithmic composition, providing thus the best proof
of the intimate connection between computational
thinking and creativity. Collaboration is the ability
of working together to reach a common goal. It is
particularly important in the process of technology
integration because it helps in overcoming difficulties
and knowledge gaps (consider for instance the role
of blogs and online communities of practitioners
where to share information and solutions to specific
problems). Finally, communication is a fundamental
ability when working with others. It is also the
core of the artistic work and of its propagation
through customized platforms, social media and
video production.
In spite of the long and important tradition
of electronic and computer music - which is in-
timately connected to computational thinking - re-
ally very few examples of application of this kind
of knowledge can be found in music education
(Israel-Fishelson and Hershkovitz, 2022). In Section
2 current approaches, software and tools are presented
with the aim of providing a short overview of compu-
tational thinking in music education. Particularly the
work of V.J. Manzo (Manzo, 2016) has inspired the
design of the course presented in this paper, which
will be examined in detail in Section 3. The character-
istics of computational thinking and the peculiarities
of the ”Four C’s” above discussed will be referenced
in the presentation of the course contents, with a par-
ticular focus on creativity that should be regarded as
one of the most important goals of the course. Assess-
ment methods and tools will be discussed in Section
4, and conclusions drawn in Section 5.
The few experiences of computational thinking in
music education found in literature refer to a limited
set of approaches and software tools. Some of them
address the problem of introducing computational
thinking from the very early stages of school curricu-
lum through various strategies. (Bell and Bell, 2018)
suggest an interesting embodied experience of a phys-
ical parallel sorting network, a game which results
in obtaining elements in ascending order. Extension
of this game beyond simple numerical values can in-
volve various domains such as music (putting sounds
or notes in ascending order) or language (putting
names in alphabetical order, or story elements in
temporal order), and so on. Beyond demonstrating
the power of abstraction in a very simple and effec-
tive way, this game also recalls the concept of al-
CSME 2022 - 3rd International Special Session on Computer Supported Music Education
gomotricity, which is an approach based on kines-
thetic learning activities aimed at highlighting spe-
cific computer science topics (Bellettini et al., 2014).
Another activity proposed by (Bell and Bell, 2018) is
the use of little robots programmed to reach spe-
cific positions on a physical stave as to produce
sound events (melodies or chords). This idea de-
rives directly from the famous Logo turtle by Sey-
mour Papert, the father of computer-based environ-
ments aimed at teaching computation to children
(Resnick et al., 1988). The same authors also em-
ploy Scratch, a very popular visual programming
language, used both at elementary and university
level (Maloney et al., 2010). As Greher and Heines
affirm in their book (Greher and Heines, 2014), al-
though Scratch is designed for children it offers
important tools for teaching computational thinking
such as code blocks, global and local variables, lists,
loops, conditionals, etc. Another popular similar re-
source is Blockly, a visual block language develop-
ment kit that allows the building of programs aimed
at various educational goals (Trower and Gray, 2015).
Blockly has been used by (Barat`e et al., 2017) to
build a platform for melody composition and ma-
nipulation, or by (Gorson et al., 2017) to produce a
sound composition tablet application. Another trend
in the development of computational thinking is the
use of command-line languages for music produc-
tion. These are Sonic Pi (Aaron et al., 2016) and
Ear Sketch (Engelman et al., 2017). While Sonic Pi
is more oriented towards live performance impro-
visations, Ear Sketch aims at engaging students to
learn programming through sound. Both programs
address high school and university students. Vi-
sual programming is very popular among musicians,
as the main software in use belong to this family
of programs (MAX
and Pure Data
). The Pure
Data free programming environment has been used
by (Hancock, 2014) to develop a play-based learn-
ing course to introduce Conservatory students to mu-
sic programming. A step by step approach is fol-
lowed by (Manzo, 2016) who - reflecting the tradi-
tional environment division of Max, MSP (audio) and
Jitter (video) - presents basic programming activities
in three main domains: algorithmic composition and
music training (MIDI); audio playback, processing
and effects (audio); live and recorded video. Partic-
ularly in the first section of the book the author pro-
vides the very basic notions of programming in the
MAX environment to lead the students to the produc-
tion of simple musical structures (scales, intervals and
chords). This is an interesting approach for Conserva-
tory students for at least two reasons: firstly very ba-
sic music features may unveil their algorithmic side,
showing how computational thinking is deeply rooted
in music analysis; secondly the step-by-step method
followed by the author very well shows the process of
abstraction from particular cases towards more tidy,
less redundant and general ways of formulating the
same algorithm. These ideas inspired the methodol-
ogy and contents of the music technology course that
is the focus of this paper.
In the current programs for undergraduate instrumen-
tal studies in Italian Conservatories the technological
courses (Acoustics and Music Technology) weight re-
spectively 2 and 3 ECTS (less than the 3% of the over-
all 180 ECTS). Thus very often they are underesti-
mated by teachers and students as they result com-
pletely disconnected from the other subjects of the
curriculum. Yet, what little technological integration
is entrusted only to these subjects. This is why the
design of the course of Music Technology is so im-
portant and should be regarded as the only occasion
to meet computational thinking in the whole under-
graduate Conservatory curriculum.
3.1 Course Objectives
The course activities aim at the following goals:
1. introducing the students to computational think-
ing by enhancing their abilities in music analysis
and abstraction of musical concepts
2. fostering the potentialities of music creativity
through the manipulation and numeric control of
simple music production algorithms
3. offering them an experience related to the ”Four
C’s” (critical thinking, creativity, collaboration
and communication).
3.2 Material
The course is developed in Pure Data, which is a
free always available software, while MAX requires
to buy a license. MAX is very rich from the graph-
ical point of view and can rely on a lot of support-
ive tools. In spite of this, also if Cycling’74 offers
many facilities (academic licenses, institutional dis-
counts, monthly payments, 30 days free demos), the
advantages of a free software are greater for non pro-
fessional users. Thus, some of the basic algorithms
presented by (Manzo, 2016) have been ported in Pure
Fostering Computational Thinking in Undergraduated Music Conservatory Students
Table 1: Course organization and content.
Week Lesson Workshop Home work
Computational thinking
The ”Four C’s”
Introduction to Pure Data
Controlling numerical flow
Pre-course questionnaires
assignment (logic)
Audio and MIDI
MIDI events
MIDI production
(noteout, makenote)
MIDI controls
(modulation, volume,
pan and sustain)
lab2 assignment
(recursivity, reset, loops)
A pattern of a musical form.
lab3 assignment
(musical form)
4 Scales (midi notes and arrays)
Major and minor scales.
Scale transposition
lab4 assignment
scale fragments
Musical structures
(patterns, polyphony, chords)
Pattern repetition (cycles),
lab5 assignment (chords)
6 Melodies
musical streams
lab6 assignment (creativity)
7 Patch modules
Work group.
Graphical scores
Final group assignment
8 Group assignment discussion Group assignment discussion
Delivery of the final project
Post-course questionnaires
Data and further processed to comply the course pro-
3.3 Organization and Participants
The course is addressed to Music Conservatory stu-
dents at undergraduate level. To access this level a
high school diploma is required, also if exceptions are
allowed in case of particularly gifted students. The
course takes 24 hours, subdivided into 8 weeks start-
ing from February 8, 2022. It is held online through
the institutional platform of the Conservatory
. All
the activities are recorded and made available for the
students until about two weeks after the end of the
course, expected for March 29, 2022. The credits
for each lesson are assigned only after delivery of
the assignments contained in the videos. Currently
take part in the class 55 students (24 females), aged
Mean = 23.43 years, StandardDeviation = 7.75.
3.4 Contents
The program of the course is reported in detail in Ta-
ble 1
. Course activities are subdivided into lessons -
where each topic is presented in detail by the teacher
-, and workshop activities - where each topic is put
in practice through the realization of exercises with
the participation of the whole class -. Each lesson
has its own home assignment, where the students are
The patches used in the course are available at https://
required to develop autonomously the topics experi-
enced during the lesson. The course activities are in-
spired by the ”Four C’s” abilities, whose finalization
is described in the following Sections 3.4.1, 3.4.2, and
3.4.1 Problem Solving and Abstraction
The ability of solving a problem is linked to critical
thinking and to out of the box reasoning. Here the fo-
cus is not only on simple problem solving, but rather
on the ability of formulating the solution in a gen-
eral way, such to be reused in different conditions (ab-
straction). To show an example of this process, let’s
look at the activities of week 4, which focus on the
building of a musical scale. The first step shows an
algorithm for the production of a C major scale, start-
ing from MIDI pitch 60 (see Figure 2).
Figure 2: The basic algorithm for the production of a C
major scale.
To allow the building of the scale on different fun-
damental pitches, the algorithm may be changed as in
Figure 3, where the concept of the use of a variable
(the fundamental pitch of the scale) is introduced.
CSME 2022 - 3rd International Special Session on Computer Supported Music Education
Figure 3: The algorithm for the production of a C major
scale with the use of the fundamental pitch variable.
The final solution proposed is depicted in Figure 4
where the use of an
containing the interval
information related to each scale provides a good de-
gree of flexibility and generalization. The contents of
may be changed with a message; the in-
tervals to be summed to the fundamental of the scale
are available as the output of the
object af-
ter it receives the index number.
The algorithm of Figure 5 produces a scale fol-
lowed by its transpositions one fourth and one fifth up.
The patch is composed by different modules aimed
respectively at:
music information management
time control
musical form management
sound production
The music information module is depicted in Fig-
ure 4. It is put into action by the time control mod-
ule (
). The
the sequence of indices to be sent at timed intervals
to the
object. The
keeps the count inside the size limits of the
producing thus a continuouscycle of scales.
The module that controls the musical form keepstrack
of the output of the
object and through a se-
lector (the
object) provides the transposition of
the scales. This is realized by adding to the funda-
mental the quantity of semitones of the transposition
interval. The same mechanism is used to stop the per-
formance. At least the sound production module re-
ceives the MIDI data (
) and outputs them to
an external virtual synthesizer (
3.4.2 Creativity
During the workshop the students become familiar
with all the modules contained in the patch and then
receive an assignment where the same patch struc-
ture is employed to create something that looks and
sounds very different. The assignment is shown in
Figure 6 in the form of a possible desired output.
Consider the possibility of assigning a random fun-
damental pitch (range 30-80) each time the scale cy-
cle begins and also to assign a random value (range
1-8) to the argument of the
object. This pro-
duces scale fragments of different length starting al-
ways from different fundamental pitches. The aim of
this assignment is to stress the creative possibilities
offered by a simple scale when its production algo-
rithm reaches a level of abstraction that allows nu-
merical manipulation. It also evidences that the tra-
ditional musical scale is only a particular case of a
much wider musical universe, which can be explored
with the use of these very simple programming tools.
Further musical development can be obtained by pro-
gramming musical streams (note bands with common
timbre constrained inside height and duration ranges),
impulsive events (clusters or chords), and polyphony
(i.e. piping events), and so on. Such creative activities
may also be inspired by the use of graphical scores,
where visual elements can be freely interpreted by the
3.4.3 Collaboration and Communication
Weeks 7 and 8 are devoted to group activities. Each
group is formed by 5-6 people. These activities aim
at preparing a creative patch responding to a precise
composition plan (descending from a graphical score
or other organizationcriteria). The patch mustcontain
the instructions for the performance of the composi-
tion, a general description of the composition plan as
well as the comments explaining the functions of the
objects employed. These activities involve group or-
ganization, communication among the members, cre-
ativity and the ability of negotiating with others about
the best solutions to be chosen.
Greher and Heines (Greher and Heines, 2014) have
prepared a pre and post-course questionnaire for the
assessment of course outcomes. It is divided into 4
1. reasons for taking the course
2. expectations for the course
3. self perception and
4. opinions about computing and music
The aim of this questionnaire is to take a picture as
accurate as possible of the ideas of the students and
Fostering Computational Thinking in Undergraduated Music Conservatory Students
Figure 4: The music information module.
Figure 5: The complete algorithm for the production a major scale with transpositions: the fundamental pitch variable, the
timing and cycle module, the control of musical form and transpositions and the sound production module.
Figure 6: A possible desired output example for the home
assignment of lesson 4.
of how these may be changed after course activi-
ties. Many questions concern the ”Four C’s” abilities,
and try to provoke a reflection in the students about
their relationship with computers, music composition
and creativity. For the present course the original
questionnaire has been slightly adapted and integrated
with further questions concerning basic music and
music technology knowledge. The main challenge in
the preparation of this part of the questionnaire has
been how to measure the logical, abstraction and an-
alytical abilities of the students before the beginning
of the teaching activities. As the great majority of the
students (about the 85%) has never used any music
programming language before, the assessment can-
not be made through language specific questions, but
rather involve more general concepts. These are:
music analysis
patch logic and
flow diagram.
In the pre-course questionnaire there are 2 ques-
tions for music analysis and one question for each of
the remaining items. Similar questions will be pre-
pared for the post-course questionnaire.
4.1 Music Analysis
For testing the music analysis abilities a melody is
presented to the students with some examples of
pattern repetition and pattern transposition (see Fig-
ure 7). In the first question the students are required
to identify how many patterns they could find in the
melody. In the second question they must identify
the pattern repetitions and transpositions in the same
melodic excerpt.
4.2 Patch Logic
This test is to check what kind of functions students
consider as the most important for the automatic per-
formance of a melody. The test is presented in the
CSME 2022 - 3rd International Special Session on Computer Supported Music Education
Figure 7: An example of pattern repetition and transposition.
Figure 8: A flow diagram of a music production algorithm.
form of a five point Likert scale matrix (1 not impor-
tant – 5 very important) with the following 15 items:
1. A sound production system
2. Start and stop controls
3. An information storage system
4. A way for controlling the number of voices for the
5. A control for pattern repetition
6. A list of possible note durations
7. A time control mechanism
8. A list of available note timbres
9. A control for pattern transposition
10. An external electronic keyboard
11. A way to express the pitches of the notes
12. A counter
13. A way to introduce a variable
14. A list of possible keys
15. A way to control the speed of the performance
This list has been filled in with modules actually used
in the patches as well as with other musical elements
that are the output of the same modules. At the end
of the course students will be presented with the same
matrix to verify if they can identify such dependen-
4.3 Flow Diagram
The students are presented with the flow diagram de-
picted in Figure 8. They are asked to identify what
can be the output if a short sequence of notes is used
as input. This test checks accurately if the students
understand how the algorithm works and how the sys-
tem output changes after the input has been processed.
A similar question will be inserted in the post-course
questionnaire to see if some improvement has been
4.4 Expected Results
The main expected results for this music technology
course may be grouped into two main areas: improve-
ment of computer literacy/programming skills; en-
hancement of musical creativity.
4.4.1 Improvement of Computer and
Programming Skills
The 6 pre-course questions about computers and pro-
gramming are outlined in Table 2. The answers were
collected as ratings on a Likert scale ranging from 1
(false) to 5 (true). Results are reported in Figure 9.
Although a good number of students seem to be
interested in learning something more about comput-
ers and programming (Q1 and Q2), a smaller number
Fostering Computational Thinking in Undergraduated Music Conservatory Students
Table 2: The 6 pre-course questions about computers and
I want to learn something more about
I want to learn something more about
computers and programming
Q3 I enjoy working with computers
I am confident using a computer
language to accomplish complex tasks
Q5 Computer programming is fun
Q6 Programming a computer is difficult
Figure 9: Results of the 6 pre-course questions about com-
puters and programming, with answers mean and standard
enjoys working with computers (Q3) and a very small
number is confident in dealing with programming lan-
guages (Q4). Moreover computer programming is not
considered to be a very funny activity (Q5), while it is
for sure very difficult for the majority of the students
(Q6). These results depict a not very promising start-
ing point for the course activities, also if Q1 and Q2
seem to show some room for improvement. A great
result should be a higher mean in Q3, 4 and 5, and a
decrease in Q6, showing that actually some students
have earned a major level of computer literacy and
have become more confident in the use of computers.
4.4.2 Enhancement of Musical Creativity
In the pre-course questionnaire there are 6 questions
addressing the topic of musical creativity, particularly
Table 3: The 6 pre-course questions about musical creativ-
ity and composition.
I want to learn more about the integration
of computers and music
I want to learn more about music and
I want to learn to be more creative
Q4 I enjoy creating my own music
I am confident in my ability to express
myself through music
Computers can be used to create cool
Figure 10: Results of the 6 pre-course questions about mu-
sical creativity, with answers mean and standard deviation.
music composition. The questions are outlined in Ta-
ble 3 and results reported in Figure 10. Here the start-
ing situation seems to be more promising, showing
that students feel much more comfortable with cre-
ativity than with computer programming. They seem
to be particularly curious about the relationship be-
tween music and computers (Q1) and show a pos-
itive attitude towards music created with computers
(Q5), also if a lower number enjoys creating music
(Q4). The link between computational thinking and
creativity is one of the main objectives of the course
(see Section 3.1). Hence an increase of the mean of
Q4 and Q6 could indicate a positive evaluation of the
computer–mediated creative activities proposed dur-
ing the course.
In this paper a music technology course program has
been presented. The contents of the course have been
discussed in the light of the enhancement of the ”Four
C’s” abilities (critical thinking, creativity, collabora-
tion and communication). A road from a basic, stiff
and not reusable algorithm (Figure 2) to a more gen-
eral and flexible production module (Figure 4) has
been shown, and the possibilities of creative use of
the same algorithm have been suggested. The aim
of this work is to demonstrate the connection be-
tween computational thinking and creativity as one of
the most powerful expressions of contemporary art.
Finally, some ideas for the assessment of computa-
tional thinking abilities in pre and post-course ques-
tionnaires have been developed and presented. At the
conclusion of the course many important data will be
available for analysis:
quantitative data from pre and post-course ques-
the final course assignments where students will
produce and comment an original music creation
CSME 2022 - 3rd International Special Session on Computer Supported Music Education
the lesson-by-lesson assignments, which will
show the road that each student has followed to
reach the course objectives.
Also if some results have been speculated in Sec-
tion 4.4, only a global analysis of all these data will
provide significant elements for the evaluation of the
course design, if it is successful in reaching the im-
portant goals listed in Section 3.1 and, mainly, if it is
useful in contributing to the progress of the students.
The present research has been funded by Fondazione
, Brescia (Italy).
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