Fostering Computational Thinking in Secondary School through Music

An Educational Experience based on Google Blockly

Adriano Barat

e, Andrea Formica, Luca A. Ludovico and Dario Malchiodi

Department of Computer Science, Universit

a degli Studi di Milano, Via Comelico 39, Milan, Italy

Keywords:

Music, Education, Visual Programming.

Abstract:

We propose a methodology especially conceived to exploit the musical media in order to vehiculate some

aspects in the realm of computational thinking to pupils of the lower secondary school (6

to 8

grades). The

related activities are based on a visual programming language whose execution generates a melody shown

using both its traditionally-notated musical score and its audio reproduction. This language provides the basic

programming tools, such as simple and structured variables, iterations and so on. The learning activities are

based on challenging small groups of students to solve programming exercises of increasing difﬁculty.

1 INTRODUCTION

A signiﬁcant effort in educational research has been

recently spent focusing on the study of speciﬁc met-

hodologies for teaching concepts from the informatics

ﬁeld since the early school stages. There are two main

fronts in this research area:

1. Selecting subjects apt to be explained since the lo-

wer secondary or even the primary school levels.

See for instance (Hubwieser, 2006; Hromkovi

and Steffen, 2011; Saeli et al., 2011);

2. Providing technological tools speciﬁcally desig-

ned in order to support teachers in their activities.

Also in this case the literature offers several cues,

such as (Burbait

e et al., 2013; Meerbaum-Salant

et al., 2013; Hadjerrouit, 2015)

In this work, explicitly referring to the algomotri-

city learning methodology (Bellettini et al., 2014a),

we propose both an informatics topic to be inves-

tigated in the lower secondary school, that is from

to 8

grade, and a computer-based tool expres-

sly conceived in order to support pupils in learning

this topic. Namely, we focus on the description of

musical information using a programming language.

More precisely, we consider the possibility of tea-

ching basic computational thinking concepts through

engaging pupils in writing programs whose output is

Note that in this vein we consider only tools expressly

conceived for teaching informatics, and not computer-based

tools which generally support teaching of non-informatics

concepts, for instance in the STEM ﬁeld.

a preﬁxed melody. The tool supporting this process

is a customization of the Blockly visual programming

language, extended in order to deal with the main ob-

jects and operators in the realm of traditional musical

notation.

The paper is structured as follows: Section 2

brieﬂy summarizes the recent research results in the

research area of informatics teaching, with special

emphasis on the proposed methodologies. Section 3

illustrates the proposed approach, both detailing its

musical and computational aspects and describing the

developed software tool to be used in the learning

activities, which in turn are depicted in Section 4.

Some concluding remarks end the paper.

2 RELATED WORKS

After having described the two main educational re-

ferences of computational thinking and algomotricity,

this section highlights the intersection of musical and

computational education characterizing the proposed

learning activity.

2.1 Computational Thinking

Current research is focusing on those psycho-

pedagogic practices that emphasize the potential of

new technologies and their use to motivate young

students through active learning. With reference to

early education stages, some experts underline the

need to deﬁne new and engaging learning experiences

Baratè, A., Formica, A., Ludovico, L. and Malchiodi, D.

Fostering Computational Thinking in Secondary School through Music - An Educational Experience based on Google Blockly.

DOI: 10.5220/0006313001170124

In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 2, pages 117-124

ISBN: 978-989-758-240-0

117

for children, addressing in particular those future job

opportunities related to information technology and

computing. Many scientiﬁc papers that propose co-

ding activities recall the need to develop computatio-

nal thinking even in primary schools (Sabitzer et al.,

2014; Yadav et al., 2014; Kalelio

glu, 2015). An inte-

resting debate on this subject is presented in (Duncan

et al., 2014), a paper that impartially collects argu-

ments in favor and against learning coding at a young

age.

During a coding activity, students are exposed to

computational thinking (Wing, 2006), namely a form

of reasoning oriented to problem solving which in-

volves abstraction, debugging, remixing and iteration

processes (Wing, 2008; Brennan and Resnick, 2012).

Computational thinking is in line with the skills ex-

pected for 21st-century students, such as creativity,

critical thinking and problem solving (Ananiadou and

Claro, 2009; Binkley et al., 2012).

The challenge is that young students - while ap-

proaching the basic concepts and the cognitive strate-

gies related to coding - can develop logical-cognitive

abilities, with positive future effects in terms of meta-

knowledge (self-regulation, peer-seeking, problem

posing and solving, etc.) and digital skills obtained

through playful learning processes (Lillard, 2013).

2.2 Algomotricity

The term algomotricity (Bellettini et al., 2014a) refers

to an active/kinesthetic teaching methodology (Be-

gel et al., 2004) grounded on the experiential lear-

ning theory (Kolb et al., 2001). This methodology

has been introduced with the aim of teaching through

laboratorial activities some core concepts of infor-

matics also at lower educational grades, meanwhile

avoiding common misconceptions identifying the dis-

cipline with operational abilities in using software

tools such as Web browsers or spreadsheets (Bellet-

tini et al., 2014b).

Each laboratory is typically composed by three

phases. In the ﬁrst one, with reference to the PBL

methodology (Hmelo-Silver, 2004), the facilitator

describes a problem which pupils should address to,

working together in small groups. These groups are

engaged in playful activities based for instance on ob-

ject manipulation, in order to informally introduce the

topic under investigation. A second abstract learning

phase gives each pupil the possibility to build its men-

this term typically designates the equivalent of a tea-

cher in an active learning environment, underlining the fact

that learning takes place without direct transmission of kno-

wledge, and pupils should be just oriented in their autono-

mous discovery of concepts.

tal model of that topic. Finally, a computer-based

phase ends the activities, also closing the loop with

the previous acquaintance of students with applicati-

ons, thus meeting at least in part their expectations.

The algomotricity approach has been already ap-

plied to several informatics concepts, including rea-

soning on meta-information (Bellettini et al., 2012),

programming (Lonati et al., 2015), or recursion (Lo-

nati et al., 2016). In Section 4 we will propose a le-

arning activity based on music operators in an algo-

motricity context, in order to develop computational-

thinking skills.

2.3 Music and Computational Thinking

Many experts agree that music can inﬂuence the con-

struction of the young students’ personality since it

promotes the integration of perceptual, motor, af-

fective, social and cognitive dimensions (Willems,

2011) by relating the basic aspects of human life

(e.g., physiological, emotional, and mental spheres)

with the basic elements of music (e.g., rhythm, me-

lody, and harmony). The abilities of listening, ex-

ploration and analysis are fundamental for the deve-

lopment of general meta-cognitive skills, such as at-

tention, concentration, and control. Through music,

young students can develop the aspects of analysis

and synthesis, problem posing, argumentation, eva-

luation, and application of rules (Berkley, 2004; Bur-

nard and Younker, 2004; Kaschub and Smith, 2009;

Major and Cottle, 2010).

In the digital era, new technologies and computer-

based approaches can inﬂuence music learning and te-

aching processes. A recent and comprehensive review

of this subject can be found in (Finney and Burnard,

2010), a work that discusses a range of innovative

practices in order to highlight the changing nature of

schooling and the transformation of music education.

Many researchers, experts and music teachers feel a

pressing need to provide new ways of thinking about

the application of music and technology in schools.

It is necessary to explore teaching strategies and ap-

proaches able to stimulate different forms of musical

experience, meaningful engagement, creativity, and

teacher-learner interactions.

Music can be an effective way to develop com-

putational thinking. A comparison between coding

environments and music-oriented programming fra-

meworks unveils similarities and differences. Accor-

ding to (Lye and Koh, 2014), coding environments for

young students should foster the development of three

dimensions of computational thinking: computational

concepts, practices, and perspectives. Computational

concepts are the conceptual entities that programmers

CSEDU 2017 - 9th International Conference on Computer Supported Education

118

use, such as variables and loops, in order to solve

a problem algorithmically. Computational practices

are problem-solving practices that occur during the

process of programming (e.g., iteration, reuse and re-

mix, abstraction, and modularization). Finally, Com-

putational perspectives refer to students’ understan-

ding of themselves, their relationship to others, and

the world around them.

A suitably designed music-coding environment

can achieve all the mentioned goals. Computatio-

nal concepts – mapped onto musical operators and

constructs to manipulate them – will be discussed in

Section 3. As it regards computational practices, such

an environment allows the exploration of music con-

tents through abstraction processes, as the learning-

activity case study discussed in Section 4 will clarify.

Moreover, the availability of ad-hoc operators (e.g.,

iteration) and storage tools (e.g., memory drawers)

encourages reusing and remixing of music materials.

Finally, as it regards computational perspecti-

ves, an immediate feedback in terms of graphi-

cal rendering and audio reproduction improves self-

consciousness. Besides, the lesson can be structured

to foster cooperative discussion and peer as well as

expert review about the coding activity, in order to

strengthen the relationship among students and with

the teacher. Moreover, the coding environment itself

could implement features to allow self-assessment,

peer seeking (i.e. searching for the help of a colle-

ague), and peer reviewing (i.e. asking for other stu-

dents’ opinions).

A music-based approach to develop computatio-

nal thinking has been already explored in other expe-

rimentations. Concerning the efforts to promote co-

ding to kids, we can mention programmable robots.

In this context we are not generically interested in

Music Information Robotics, a branch that has been

explored in a number of scientiﬁc works, such as (Ka-

pur, 2005), (Solis and Takanishi, 2007) and (Ness

et al., 2011). Rather, we are addressing those initi-

atives aiming to foster the development of computa-

tional thinking through music at an early age. For

instance, Play-I

is a robot that can move around, ex-

press character using sounds and lights, and respond

to a number of external stimuli. Using a tablet with

the app, a child can program the robot to play music

and dance during the performance. Another example

is Wigl

, an interactive educational robot able to hear

notes from any instrument and to respond with real-

world movements and lights.

Another relevant initiative is Note Code, a music

programming puzzle game designed as a tangible de-

https://www.makewonder.com/

http://wiglbot.com/

vice coupled with a graphical user interface (Kumar

et al., 2015). Tapping patterns and placing boxes in

proximity enables programming these entities to store

sets of notes, play them back and activate different

sub-components or neighboring boxes.

Finally, Ruthmann et al. discussed the experience

of an interdisciplinary general education course called

Sound Thinking offered to university students (Ruth-

mann et al., 2010). Even if the recipients and the pe-

dagogical goals are different from our proposal, the

adopted approach and technological tools are quite si-

milar.

Consequently, a music coding framework can im-

prove music skills while fostering, but above all it can

convey computational thinking even better than other

“traditional” coding environments, thanks to the in-

trinsic characteristics of the musical media.

3 THE PROPOSED APPROACH

This section illustrates the approach guiding the pro-

posed learning activity, in terms of its goals, contents,

and also describing an ad hoc software to be used as

a support for pupils.

3.1 Goals and Design Choices

As shown by great composers and theorists of the

past, a formal and algorithmic approach to music

composition and analysis is possible. In recent times,

a milestone is the generative theory of tonal music

proposed in (Jackendoff, 1985), aiming to uncover a

grammar that could explain the human musical mind

in a scientiﬁc manner comparable to Noam Chom-

sky’s transformational or generative grammar (Chom-

sky, 2002).

In our framework we introduced both music ope-

rators – which can be seen as atomic steps of a gene-

rative algorithm to build a music tune – and constructs

to manipulate and combine the result of operators.

Due to the educational goal of this initiative, the

application domain has been intentionally simpliﬁed.

First, the score is considered as composed by a single

melody, presenting no chords. This is sufﬁcient to ca-

tch the melodic and rhythmic aspects of music, not the

harmonic ones. Besides, every note (and rest) is quan-

tized: the quantum represents the smallest rhythmi-

cal value that can be encoded. Longer values can be

obtained by tying notes together, thus joining quanta,

or adding augmentation dots. This approach is valid

for simple tunes, where a coarse quantization is suf-

ﬁcient, but it would show its limits in the representa-

tion of tuplets and other complex rhythmic layouts.

Fostering Computational Thinking in Secondary School through Music - An Educational Experience based on Google Blockly

119

Finally, as a design choice, all supported operators

should be easily understandable and simple to use. In

fact, the goal is not to cover all possible compositio-

nal processes, but to provide also musically-untrained

students with an intuitive tool set to build basic scores.

3.2 Musical Operators

In order to achieve our educational goal, we desig-

ned a basic tool set of music operators which can be

used to create a logic representation of music scores.

Consequently, the purpose of such statements is not

to draw music symbols on a staff or to play notes, but

to generate a sequence of music events which can be

parsed by ad hoc applications in order to produce a

traditional staff notation or an audio performance.

Music operators are organized in the following

categories: 1. Melodic Operators (MOs) working

on pitches; 2. Rhythmic Operators (ROs) acting on

rhythm-related aspects of music events; 3. Iteration

Operators (IOs) implementing loops; 4. List Opera-

tors (LOs) building and manipulating lists of music

events. Operators in the ﬁrst three families are the

following:

• AddElementToScore(m) – A MO that adds music

event m at the end of the score, where m is either

a pitched note or a rest. A pitch can be set in two

ways, i.e. by choosing a note name in the range

[A . . . G] or providing a more accurate deﬁnition

in terms of note name, octave and accidental;

• AddElementToScore(L) – A MO that appends a

list of music events called L, made of any non-

null combination of pitches and rests (see the end

of this section);

• AddTiedElementsToScore(n, m) – A variant of

AddElementToScore(m) aiming to insert n conse-

cutive music events, all set to the same pitch and

tied together. In music notation, a tie is a cur-

ved line connecting the heads of two notes of the

same pitch and name, indicating that they are to be

played as a single note with a duration equal to the

sum of the individual notes’ values. In a quantized

context, this operator is intended to input longer

rhythmical values, and – from this point of view –

it belongs both to MO and to RO families;

• AddAugmentationDot() – A RO that adds an aug-

mentation dot (i.e. half of the current duration) to

the event it is applied to;

• DiatonicallyTranspose(m,g) – A MO that aims to

transpose the pitch of music event m by g grades,

with g ∈ Z. For the sake of simplicity, the refe-

rence key is C major. Consequently, the diato-

nic transposition of a D-pitched note by g = +2

produces an F-pitched note, and the transposition

of a G]-pitched note by g = −2 produces an E]-

pitched note. Please note that many pitched in-

struments for young students are diatonic, such as

most toy xylophones and whistles;

• ChromaticallyTranspose(m,h) – A MO that aims

to transpose the pitch of m by h halftones, with

h ∈ Z. The chromatic transposition of a D-pitched

note by h = +1 produces a D]-pitched note, and

the transposition of a G]-pitched note by h = −3

produces an F-pitched note;

• Repeat(X ,n) – This ﬁrst case of operator belon-

ging to the IO family allows to repeat X for n

times, where X can be a single music event m

(either a note or a rest) or a list L;

• RepeatAndDiatonicallyTranspose(X ,n, g ) – This

variant repeats X for n times, applying a diatonic

transposition by g grades at each iteration;

• RepeatAndChromaticallyTranspose(X , n, h) –

This variant repeats X for n times, applying a

chromatic transposition by h halftones at each

iteration.

Lists and related operators require a more detailed

discussion. In order to be a type of parameter inter-

changeable with respect to the single music event, a

list must be built, manipulated and stored through ad

hoc operators. After saving the list, this object can

be passed to some of the above listed music operators

like a single pitch or a rest. The set of LOs includes,

but is not limited to:

• CreateList(L,X ) – A LO that creates list L and

initialize it to object X , namely a music event m

or an already existing list M ;

• AddElementToList(m,L) – A LO that appends mu-

sic event m to the already existing list L;

• AddTiedElementsToList(n, m, L) – A LO that ap-

pends n occurrences tied together of music event

m to the already existing list L.

The proposed language contains other aggregated

LOs which have not been described here for sake of

brevity, being their meaning either very easy to under-

stand, or similar to one of the three described LOs.

3.3 Music Coding

The proposed framework aims to provide pupils with

the opportunity to learn a variety of computational

concepts, including variables, functions, and itera-

tion, while they engage in analyzing, composing, and

playing music.

CSEDU 2017 - 9th International Conference on Computer Supported Education

120

First, the idea itself of music event – namely an

entity that can be set to a given pitch – recalls the ba-

sic concept of variable (in its simplest form) or struc-

ture/object (in its more detailed form).

The range of expected values that a music event

can take in terms of pitch, octave number and ac-

cidental status can be linked to the concept of data

type. Speciﬁcally, the octave number can be intuiti-

vely mapped onto the range of integers, whereas the

limited set of values available for note name and ac-

cidental status can recall the concept of enumeration.

From this point of view, lists of music events can be

seen as arrays or dynamic lists.

The use of music operators taking [0 . . . n] para-

meters is similar to function calls in traditional pro-

gramming. Please note that the availability of music

operators sharing the same name but taking a different

number or different types of parameters recalls the

concept of function overloading, namely the possibi-

lity to create multiple methods with different imple-

mentations whose call is disambiguated on the base of

the context. It is the case of AddElementToScore(m)

and AddElementToScore(L), acting on a single music

event or a list respectively.

Moreover, in order to foster computational thin-

king, iteration is a fundamental concept to convey. In

this sense, the functions belonging to the IO family

allow to choose a sequence of generative steps to be

repeated for a given number of times. Please note that

in music notation there are symbols that recall this ap-

proach (e.g., n-bar repeat signs, repeat barlines, and

text indications such as “Da capo”).

Finally, the possibility to create, manipulate and

rework entire code sections pushes towards the well-

known practice of code reuse, presenting a positive

impact on time and resources consumption, cost ef-

fectiveness, code length and redundancy reduction

(Goguen, 1986).

3.4 The Software Tool

In the context of the algomotricity approach discussed

in Section 2.2, computer activities can be conducted

through a software tool expressly designed and im-

plemented. This tool is based on Google Blockly

, a

visual programming platform. In this environment,

the programmer is freed from syntax checking: all

language elements are denoted by blocks of diffe-

rent shapes, and the systems allow them to be com-

posed together only if the resulting structure denotes

a well-formed language statement. The execution of

a program gives directions to one or more interacting

sprites living on a stage, thus the overall system is

https://developers.google.com/blockly/

Figure 1: The Blockly-based software tool designed for mu-

sic coding.

not dissimilar in its philosophy to Seymour Papert’s

LOGO (Papert, 1980).

This visual environment has been rethought and

applied to music coding, as shown in Figure 1. Now

the problem to solve is how to reconstruct a given

music tune through a suitable combination of avai-

lable music operators, and success is achieved when

the user’s music score (displayed below) is equal to

the one originally proposed (displayed above). The

blocks presented in the middle column correspond to

music operators described in Section 3.2.

This customized Blockly game is publicly availa-

ble for free. Even if Web technologies should gua-

rantee multi-platform compatibility, audio rendering

of music scores – a fundamental feature to provide

feedback – requires non-trivial software installations,

including a virtual MIDI synthesizer and a browser

compatible with the Web MIDI API (Wilson and Kal-

liokoski, 2015), which is still a working draft by

W3C

. For this reason, the tool will be both pu-

blished as a Web-available framework and distribu-

ted to schools and any other interested subject as a

Docker package

. The latter solution highly simpli-

ﬁes the tool deployment in schools. Indeed, Docker

is an open-source project wrapping up in a so-called

container all the resources needed in order to run one

or more processes. This isolation concerns both har-

dware (CPU, memory, ﬁle system, etc.) and software

(libraries, tools, code, and so on). The deployment

is further simpliﬁed since container descriptions can

be automatically downloaded from publicly available

repositories.

The software is already available for teachers who

request it, and it will be published shortly on a repo-

sitory with GPL-like license.

https://www.w3.org/

https://www.docker.com/

Fostering Computational Thinking in Secondary School through Music - An Educational Experience based on Google Blockly

121

4 AN EXAMPLE

As mentioned above, the ﬁnal goal is to push students

towards computational thinking through a music-

oriented activity. In practical terms, this assignment

is to reconstruct increasingly complex music tunes

through a proper combination of available music ope-

rators. Achieving this goal requires a mix of analy-

tical skills to be applied to music, knowledge of the

available tools (non only their function, but also their

limitations and the possibilities to combine them), and

even a good amount of creativity. All these ingre-

dients are part of computational thinking.

Algomotricity, a methodology introduced in

Section 2.2, fosters multiple skills through an expe-

riential learning approach occurring in three phases:

1) deﬁnition of the problem, 2) self-reﬂection and

construction of a mental map of the solution, and 3)

hands-on experience. Consequently, even if Blockly

games are conceived for self-exploration of program-

ming concepts (and this is virtually possible also for

our proposal), the idea is to build a learning activity

composed by the mentioned stages and supervised by

an expert.

The solutions of the proposed exercises are not

unique: some are easier to ﬁnd (but often require a

greater number of steps), some other are smarter (and

more compact). The advantages of this approach in-

clude:

• The possibility for teachers to modulate the dif-

ﬁculty of an exercise by enabling/disabling some

music operators;

• The reduction of students’ level of frustration,

since a simple solution, roughly based on an im-

perative programming paradigm, always exists;

• The intrinsic push towards peer confrontation and

discussion of different solution strategies, which

is an integral part of the educational process;

• The presence of different (although all correct) so-

lutions is a key aspect in active learning, because

students can explore the solution space without

feeling constrained in following a ﬁxed learning

path.

Now an example of assignment is called for, to-

gether with a discussion of multiple solution strate-

gies. The music tunes to produce is a rigaudon – a

form of French baroque dance – composed by Henry

Purcell, and its score is shown in Figure 2. A ﬁrst

solution consists in explicitly declaring all the notes

to be played. Recalling the music operators listed in

Section 3.2, code listing would be:

AddElementToScore(C)

AddElementToScore(B)

AddElementToScore(A)

AddTiedElementsToScore(2,G)

AddElementToScore(G)

AddElementToScore(A)

AddElementToScore(B)

AddElementToScore(G)

AddTiedElementsToScore(2,C)

AddTiedElementsToScore(2,G)

AddElementToScore(C)

AddElementToScore(B)

AddElementToScore(A)

AddTiedElementsToScore(2,G)

AddElementToScore(G)

AddElementToScore(A)

AddElementToScore(B)

AddElementToScore(G)

AddTiedElementsToScore(4,C)

This strategy, based on invoking as many MOs as

notes, is the simplest one, but it fosters computatio-

nal thinking to a very small extent. For instance, re-

dundancy in program listing is evident, since the ﬁrst

eleven operators are repeated later.

Another way to solve the assignment could be ba-

sed on nested iterations and diatonic transposition.

This proposal requires analytical skills and the deve-

lopment of an algorithmic approach in order to use a

smaller number of operators and to make code more

compact. The resulting listing would be:

AddElementToScore(C)

RepeatAndDiatonicallyTranspose(C,3,-1)

AddTiedElementsToScore(2,G)

RepeatAndDiatonicallyTranspose(

Repeat(G,2),2,1)

AddElementToScore(B)

AddElementToScore(G)

RepeatAndDiatonicallyTranspose(

AddTiedElementsToScore(2,C),2,-3)

AddElementToScore(C)

RepeatAndDiatonicallyTranspose(C,3,-1)

AddTiedElementsToScore(2,G)

RepeatAndDiatonicallyTranspose(

Repeat(G,2),2,1)

AddElementToScore(B)

AddElementToScore(G)

AddTiedElementsToScore(4,C)

Finally, another approach which is based on the

recognition of redundancy and fully exploits code

reuse could employ lists and LOs, as shown in the

following listing:

CreateList(List1,C)

AddElementToList(C,List1)

AddElementToList(B,List1)

AddElementToList(A,List1)

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122

Figure 2: A rigaudon composed by Henry Purcell.

AddTiedElementsToList(2,G,List1)

AddElementToList(G,List1)

AddElementToList(A,List1)

AddElementToList(B,List1)

AddElementToList(G,List1)

AddElementToScore(List1)

AddTiedElementsToScore(2,C)

AddTiedElementsToScore(2,G)

AddElementToScore(List1)

AddTiedElementsToScore(4,C)

These solutions for the same assignment are only

some of the possible alternatives that may emerge.

Please note that peer collaboration during the activity,

peer discussion after the activity and a ﬁnal review

phase supervised by experts may be integral parts of

the educational path.

The functions that the framework offers to graphi-

cally render score symbols on staff and to listen to

notes, also during a step-by-step search for solution,

provide prompt feedback and may represent effective

reinforcement techniques.

5 CONCLUSIONS

In this work we proposed a learning activity for lo-

wer secondary schools with the mixed learning ob-

jective of focusing both on musical and computatio-

nal aspects. The activity, rooting on the algomotricity

learning methodology, has a twofold purpose: it can

be used in order to vehiculate some of the main ingre-

dients of computational thinking (with special emp-

hasis on programming) through the use of traditional

musical notation, or conversely it can focus on lear-

ning the traditional musical notation from the point of

view of formal descriptions. The activity is coupled

with an expressly designed software tool, whose pe-

dagogical value lies in the presence of a modular set

of musical operators (to be enabled in function of the

learning acitivity stage), on immediate visual and au-

dio feedback, and on its customizability (teachers can

easily add new learning stages to the existing ones).

In the next future we plan to start a research-action

experimentation in schools, in order to validate the

methodology and to tune both the learning activities

and the proposed software tool.

Authors would like to thank prof. M. Monga for

his valuable suggestions in the deﬁnition of the vir-

tual architecture used to implement the software tool

described in this work.

REFERENCES

Ananiadou, K. and Claro, M. (2009). 21st century skills and

competences for new millennium learners in OECD

countries. OECD Publishing.

Begel, A., Garcia, D. D., and Wolfman, S. A. (2004).

Kinesthetic learning in the classroom. In Proc. of

the 35th SIGCSE TSCSE, pages 183–184, New York,

USA. ACM.

Bellettini, C., Lonati, V., Malchiodi, D., Monga, M., Mor-

purgo, A., and Torelli, M. (2012). Exploring the pro-

cessing of formatted texts by a kynesthetic approach.

In WiPSCE’12 Proceedings of the 7th Workshop in

Primary and Secondary Computing Education, pages

143–144. ACM.

Bellettini, C., Lonati, V., Malchiodi, D., Monga, M., Mor-

purgo, A., Torelli, M., and Zecca, L. (2014a). Ex-

tracurricular activities for improving the perception of

informatics in secondary schools. In Informatics in

Schools. Teaching and Learning Perspectives 7th Int.

Conf. on Informatics in Schools: Situation, Evolution,

and Perspectives, ISSEP 2014. Proceedings, Lecture

Notes in Computer Science, page 161172. Springer

International Publishing.

Bellettini, C., Lonati, V., Malchiodi, D., Monga, M., Mor-

purgo, A., Torelli, M., and Zecca, L. (2014b). Infor-

matics education in italian secondary school. ACM

Transactions on Computing Education (TOCE) Spe-

cial Issue on Computing Education in (K-12) Schools,

14(2):15.115.6.

Berkley, R. (2004). Teaching composing as creative pro-

blem solving: conceptualising composing pedagogy.

British Journal of Music Education, 21(03):239–263.

Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M.,

Miller-Ricci, M., and Rumble, M. (2012). Deﬁning

twenty-ﬁrst century skills. In Assessment and teaching

of 21st century skills, pages 17–66. Springer.

Brennan, K. and Resnick, M. (2012). New frameworks for

studying and assessing the development of computa-

tional thinking. In Proceedings of the 2012 annual

meeting of the American Educational Research Asso-

ciation, Vancouver, Canada, pages 1–25.

Burbait

e, R., Dama

sevi

cius, R.,

Stuikys, V., et al. (2013).

Teaching of computer science topics using meta-

programming-based glos and lego robots. Informa-

tics in Education-An International Journal, 12 1:125–

142.

Burnard, P. and Younker, B. A. (2004). Problem-solving

and creativity: Insights from students individual com-

Fostering Computational Thinking in Secondary School through Music - An Educational Experience based on Google Blockly

123

posing pathways. International Journal of Music Edu-

cation, 22(1):59–76.

Chomsky, N. (2002). Syntactic structures. Walter de Gruy-

ter.

Duncan, C., Bell, T., and Tanimoto, S. (2014). Should your

8-year-old learn coding? In Proceedings of the 9th

Workshop in Primary and Secondary Computing Edu-

cation, pages 60–69. ACM.

Finney, J. and Burnard, P. (2010). Music education with

digital technology. Bloomsbury Publishing.

Goguen, J. A. (1986). Reusing and interconnecting software

components. Computer, 19(2).

Hadjerrouit, S. (2015). Exploring the effect of teaching

methods on students learning of school informatics.

In Proceedings of Informing Science & IT Education

Conference (InSITE), volume 201, page 219.

Hmelo-Silver, C. E. (2004). Problem-based learning: What

and how do students learn? Educational Psychology

Review, 16(3):235–266.

Hromkovi

c, J. and Steffen, B. (2011). Why Teaching Infor-

matics in Schools Is as Important as Teaching Mathe-

matics and Natural Sciences, pages 21–30. Springer

Berlin Heidelberg, Berlin, Heidelberg.

Hubwieser, P. (2006). Functions, Objects and States: Te-

aching Informatics in Secondary Schools, pages 104–

116. Springer, Berlin, Heidelberg.

Jackendoff, R. (1985). A generative theory of tonal music.

MIT Press.

Kalelio

glu, F. (2015). A new way of teaching programming

skills to K-12 students: Code.org. Computers in Hu-

man Behavior, 52:200–210.

Kapur, A. (2005). A history of robotic musical instruments.

In Proceedings of the International Computer Music

Conference (ICMC 2005), pages 21–28.

Kaschub, M. and Smith, J. (2009). Minds on music: Com-

position for creative and critical thinking. R&L Edu-

cation.

Kolb, D. A., Boyatzis, R. E., and Mainemelis, C. et al..

(2001). Experiential learning theory: Previous rese-

arch and new directions. Perspectives on thinking, le-

arning, and cognitive styles, 1:227–247.

Kumar, V., Dargan, T., Dwivedi, U., and Vijay, P. (2015).

Note code: A tangible music programming puzzle

tool. In Proceedi of the Ninth Int. Conf. on Tangible,

Embedded, and Embodied Interaction, TEI ’15, pages

625–629, New York, USA. ACM.

Lillard, A. S. (2013). Playful learning and montessori edu-

cation. American journal of play, 5(2):157.

Lonati, V., Malchiodi, D., Monga, M., and Morpurgo, A.

(2015). Is coding the way to go? In Informatics

in Schools. Teaching and Learning Perspectives 8th

International Conference on Informatics in Schools:

Situation, Evolution, and Perspectives, ISSEP 2015.

Proceedings.

Lonati, V., Malchiodi, D., Monga, M., Morpurgo, A., and

Previtali, M. (2016). A playful tool to introduce lower

secondary school pupils to recursive thinking. In Pro-

ceedings of 9th International Conference on Informa-

tics in Schools: Situation, Evolution, and Perspecti-

ves, ISSEP 2016, pages 51–52.

Lye, S. Y. and Koh, J. H. L. (2014). Review on teaching and

learning of computational thinking through program-

ming: What is next for k-12? Computers in Human

Behavior, 41:51–61.

Major, A. E. and Cottle, M. (2010). Learning and teaching

through talk: Music composing in the classroom with

children aged six to seven years. British Journal of

Music Education, 27(03):289–304.

Meerbaum-Salant, O., Armoni, M., and Ben-Ari, M.

(2013). Learning computer science concepts with

scratch. Computer Science Education, 23(3):239–

264.

Ness, S. R., Trail, S., Driessen, P. F., Schloss, W. A., and

Tzanetakis, G. (2011). Music information robotics:

Coping strategies for musically challenged robots. In

Proc. of the 12th Int. Society for Music Information

Retrieval Conference (ISMIR 2011), pages 567–572.

Papert, S. (1980). Mindstorms: Children, computers, and

powerful ideas. Basic Books, Inc.

Ruthmann, A., Heines, J. M., Greher, G. R., Laidler, P.,

and Saulters II, C. (2010). Teaching computational

thinking through musical live coding in scratch. In

Proceedings of the 41st ACM technical symposium on

Computer science education, pages 351–355. ACM.

Sabitzer, B., Antonitsch, P. K., and Pasterk, S. (2014). In-

formatics concepts for primary education: preparing

children for computational thinking. In Proceedings

of the 9th Workshop in Primary and Secondary Com-

puting Education, pages 108–111. ACM.

Saeli, M., Perrenet, J., Jochems, W. M., Zwaneveld, B.,

et al. (2011). Teaching programming in secondary

school: a pedagogical content knowledge perspective.

Informatics in Education-An International Journal,

10 1:73–88.

Solis, J. and Takanishi, A. (2007). An overview of the re-

search approaches on musical performance robots. In

International Computer Music Association.

Willems, E. (2011). Las bases psicol

ogicas de la educaci

musical. Grupo Planeta (GBS).

Wilson, C. and Kalliokoski, J. (2015). Web MIDI API. wor-

king draft, W3C. https://www.w3.org/TR/webmidi/.

Wing, J. M. (2006). Computational thinking. Communica-

tions of the ACM, 49(3):33–35.

Wing, J. M. (2008). Computational thinking and thinking

about computing. Philosophical transactions of the

royal society of London A: mathematical, physical and

engineering sciences, 366(1881):3717–3725.

Yadav, A., Mayﬁeld, C., Zhou, N., Hambrusch, S., and

Korb, J. T. (2014). Computational thinking in elemen-

tary and secondary teacher education. ACM Transacti-

ons on Computing Education (TOCE), 14(1):5.

CSEDU 2017 - 9th International Conference on Computer Supported Education

124