Students’ Understanding of Computational Thinking with a Focus on

Decomposition in Building Network Simulations

Steve Mvalo and Chris Bates

Department of Computing, Sheffield Hallam University, Faculty of Arts, Computing, Engineering and Sciences,

City Campus, Sheffield, U.K.

Keywords: Computational Thinking, Simulation Software, Problem-solving Experiments.

Abstract: This paper reports a study into students’ understanding of decomposition when building network

simulations. Students were asked to complete three problem-solving tasks involving designing and

troubleshooting computer networks using simulation software. Through online surveys, interviews and

focus groups the students’ understanding of computational thinking was interrogated. The results show that

students were not conscious that they were applying computational thinking concepts when designing and

troubleshooting networks on simulation software. It appears their interest were to simply get problems

solved but not necessarily with the understanding of the application of the concepts of computational

thinking.

1 INTRODUCTION

In this study we investigate students’ understanding

and application of one concept of computational

thinking: decomposition. We examine how students

apply the concept when building network

simulations and how, in turn, those simulations

facilitate students’ ability to decompose a

networking problem into a set of smaller tasks.

Decomposition is one of the core concepts of

computational thinking. When using decomposi-

tion, problems are systematically broken into levels

of abstraction that can be understood and solved

more readily than can the original, complex

problem. Computational thinking brings together a

number of ideas about problem solving and

algorithmic thinking in ways that can be readily

applied to a wide variety of problems across diverse

domains.

Section two introduces the idea of computational

thinking, section three looks at how simulation tools

can facilitate teaching computer networks, section

four looks at experimental design, section five

introduces the tasks that were set for the students

and section six presents the results. We conclude by

providing some emerging ideas and

recommendations for further studies.

2 COMPUTATIONAL THINKING

Computational thinking is an approach to problem-

solving which uses abstraction, decomposition,

generalization and the creation of algorithms to

identify solutions. The approach closely mirrors that

which is used in software development and creates

solutions that can be implemented relatively easily

by people or by machines. Originally coined by

Papert (1980), the term was popularized in (Wing,

2006) where the approach was applied to general

problem-solving rather than being restricted to the

domain of computer science. Wing (2006, 2008)

describes computational thinking as involving

problem-solving encompassing a set of mental tools,

the design of systems and an understanding of

human behaviour and that it represents a universally

applicable attitude and skill set everyone, not just

computer scientists, [could] learn and use. In 2011,

Wing revised her definition of computational

thinking as the “thought processes involved in

formulating problems and their solutions so that the

solutions are represented in a form

that can be

effectively carried out by an information processing

agent” (p. 60).

Computational thinking helps students think

algorithmically, define abstractions, decompose and

reify them in their solutions, (Wing, 2011). The core

Mvalo, S. and Bates, C.

Students’ Understanding of Computational Thinking with a Focus on Decomposition in Building Network Simulations.

DOI: 10.5220/0006693302450252

In Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), pages 245-252

ISBN: 978-989-758-291-2

245

concepts of computational thinking are abstraction;

algorithmic thinking; problem solving; pattern

recognition (generalization); design-based thinking;

conceptualising; decomposition; automation;

analysis; testing and debugging; mathematical

reasoning; implementing solutions; modelling,

(Grover & Pea, 2013; Kalelioglu et al., 2016).

This study uses a working definition of

computational thinking as:

Those thought processes that apply

fundamental ideas and approaches from computer

science including, but not limited to, algorithms,

abstraction, decomposition and generalization to

solving technological problems such as the design of

computer networks.

Within the broad discipline of computer science

computational thinking can easily become entwined

with the use of specific tools such as those used for

software design because when using such tools the

ideas and practices of abstraction, decomposition,

generalization and so on become explicit to students.

Wing (2008) is clear that tools should not get in the

way of understanding and applying the concepts

behind computational thinking but, rather, should

reinforce and facilitate them. It is not sufficient that

a learner be adept in the tool, they must become

adept in using the tool to produce abstractions and

concrete implementations from those abstractions.

3 DECOMPOSITION

The intellectual skill of decomposition is the ability

to breakdown complex problems to a level such that

it can be understood, solved, developed or evaluated

(Csizmadia et al., 2015). Decomposition can involve

looking at similarities within, and patterns of, the

constituent parts of the problem. In so doing they

become easier to understand and work with. The

ability to identify these similarities and patterns

depends on one’s previous knowledge, experiences

and skills (Bocconi, et al., 2016). Decomposing a

problem is one thing but solving the problem is

another matter, albeit one that also requires prior

knowledge, experiences and skills.

Wing (2008) defines decomposition within

Computational Thinking as the process of

unwrapping an abstraction of a complex problem

into a concrete solution. Once students have solved a

complex problem, they should be able to describe

both how they identified the problem and the

strategies they used.

4 SIMULATION TOOLS IN

TEACHING COMPUTER

NETWORK DESIGN

Simulation software provides a platform on which

students can design, build and test networks that

vary in complexity from trivial to complex

simulations of the infrastructure of multi-national

companies. Using such software students are able to

work with systems that are far too complex for them

to be able to build in real-life and to include

technologies that they would otherwise not meet at

University.

Teaching students to build even relatively simple

networks that include a couple of routers and a few

VLANs can require racks of dedicated hardware.

Typical university class sizes mean that significant

quantities of specialized equipment must be

available to the students both within formal teaching

sessions and when they undertake their own project

and assessed work. This hardware is not intended by

its manufacturers for a classroom setting. It has the

robustness necessary to run almost indefinitely in the

controlled environment of a network server room but

is less able to withstand the rigour of constant re-

cabling or power cycling or, indeed, of operating in

warm, dusty classrooms.

Simulation software provides an excellent

alternative to physical infrastructure when teaching

computer networking (Janitor et al., 2010). Network

simulations provide feature-rich, flexible platforms

with a range of devices and software that is far

greater than would be possible when using physical

devices (Ruiz-Martinez et al., 2013). When using

network simulations students have the flexibility to

work on their network designs away from the

classroom, (Zhang et al., 2012).

Many studies including those of (Galan,

Fernandez, Fuertes, Gomez, & de Vergara, 2009;

Hwang et al., 2014; Ruiz-Martinez et al., 2013) have

shown that simulation software provides a highly

realistic way of teaching computer networks,

conducting research and experiment in designing

complex network systems. And because simulations

are inherently flexible, extensible and highly

configurable, students are able to use them to design

network topologies that range from simple to highly

complex. The inherent plasticity of a good

simulation tool means that it provides a platform

upon which students can build almost any structure

and, in so doing, extend their inventiveness and

innovation (Ruiz-Martinez et al., 2013).

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5 DATA COLLECTION

METHODS

This study used mixed methods with dominant

qualitative approaches. Initially two separate surveys

comprising 69 students and 14 lecturers respectively

were conducted to discover participants’

understanding of computational thinking and of the

use of simulation software in network design. Seven

undergraduate students studying for computer

networks were sampled randomly for a focus group

interviews. In this focus group, students were

queried about their understanding of computational

thinking and their experiences of using simulation

software to design networks in their day-to-day lab

activities. A small group of postgraduate students

undertook three consecutive problem-solving tasks

and followed-up with one-to-one and focus group

interviews. The postgraduate students were taught

by one of the authors and the three tasks formed part

of their assessment.

The students were given three different problems

and six weeks to complete each. In total the students

were monitored for almost six months as they

designed and built simulations and undertook

troubleshooting of their simulations. On completing

each problem, the students recorded videos in which

they demonstrated their problem-solving approach,

showed their solutions and reflected on their

learning through the task. In the focus group the

postgraduate students were asked to reflect upon

their understanding of the problems and how they

had solved during the practical tasks.

All participants have been anonymized to

preserve their confidentiality. Undergraduate and

postgraduate students in this study have been

referred to as UGx and PGx respectively, where x

represents a random number. Lecturers have been

referred to as LecX where X represents a random

number too.

In the focus groups the students were prompted

to explain their understanding of the differences

between computational thinking and critical

thinking. Students were asked about the strategies

that they used when designing complex network

simulations to try and reveal whether, and how, they

might apply the core concepts of computational

thinking. Students were further asked to explain

their previous experiences in using simulation

software against physical hardware and finally were

asked to explain their general recommendations on

the use of simulation software in developing their

understanding of computational thinking.

The focus groups investigated the students’

perceptions and reflections on the use of simulations

in network problem solving. These students were

also asked their understanding of computational

thinking and their perceptions on the use of

simulation software in developing their

computational thinking. The intention was to

investigate their ability to apply any of the core

concepts of computational thinking and to further

drill down into their use of decomposition in

building network simulation.

To triangulate the findings of the online survey

and focus groups, three lecturers participated on

one-to-one interviews in which they talked about

their own understanding of computational thinking

with a particular interest in decomposing network

abstraction when building network simulation.

Lecturers further talked about how they apply the

core concepts of computational thinking in their own

teaching practice.

6 THE PROBLEM-SOLVING

TASKS

The students undertook series of increasing

complexity tasks across six months during

laboratory sessions for three of their modules. They

recorded themselves using the Screencastomatic

software desktop capture program,

https://screencast-o-matic.com/. Because the

students were asked to record all of their activities in

each lab session we were able to see exactly how

they used the simulation software including

mistakes, dead-ends and failed approaches. Desktop

capture differs from other approaches because it is

unobtrusive - these students tended to forget that it

was recording them - and so gives the researcher a

raw and unfiltered view of the activity.

When capturing their sessions, the students were

asked to outline the task as they understood it, show

themselves solving the task, demonstrate and discuss

the strategies they used, and demonstrate their

working solutions. On completing each lab task, the

students were questioned about their thinking as they

solved the problem.

6.1 Task One

Students had to reverse engineer an enterprise

network from a list of routing tables. Routers

advertise those networks to which they connect

directly and share those networks advertised by their

Students’ Understanding of Computational Thinking with a Focus on Decomposition in Building Network Simulations

247

neighbours. The set of routes gives the topology of

the enterprise network. In reverse engineering the

networks which are advertised are traced back so

that the topology of the entire network can be re-

built.

In this first task the students had to reverse

engineer the enterprise network infrastructure,

troubleshoot design problems that were embedded in

the routing tables, and implement appropriate

solutions. To verify their designs, students had to

show their routing tables matched those given in the

task description and corrected its embedded errors.

6.2 Task Two

In the second task the students had to design from

scratch an enterprise network infrastructure as

shown in Figure 1 with sites dispersed across five

cities. Specific requirements covering: the provision

of bandwidth; throughput; response time; access by

users to appropriate resources; confidentiality; and

system integrity. The students were told to use IP

addressing schemes that involved IPv4 and IPv6 and

to choose suitable routing protocols to facilitate

communication across the network.

This was a challenging task for these students

because it built on their priori knowledge and skills

in LAN design and implementation to produce a

larger, more functional network infrastructure

incorporating WANs. At the time that they

undertook the task, the students were still becoming

familiar with many aspects of networking

technology.

6.3 Task Three

The third task was a set of activities that combined

the design of LANs and WANs to implement a

secure enterprise infrastructure. The key learning

points for the students were the incorporation of

security into otherwise familiar network

infrastructure.

6.4 Task Four

The final task having attempted all three of the

networking tasks, the students were asked to write

an individual reflective report covering all three

tasks. They had to discuss their thought processes

and the strategies that they followed in creating their

solutions and recommendations. This task was an

important part of the assessment that the students

were undertaking. For the researchers these reports

had the benefit that the students' recollections and

memories could be compared with the video

evidence to show whether they had done the things

as they thought they had.

7 RESULTS

Simulations are not just about designing and making

complex systems, they also allow students to work

with complex ideas. The flexibility and usability of

simulations mean that students can be encouraged to

do more testing and thus be able to critically

evaluate their own work. This was alluded to by a

Figure 1: Sample problem-solving task.

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number of subjects in the focus groups:

I think I will however, test more on simulation

software than on real kit. You would therefore

apply problem solving on simulation software

with more critical because you know you are not

doing it on the real thing (UG6)

It's more efficient and you don’t waste time when

configuring on simulation software; as you have

more and more ideas you can apply and

implement them as you wish hence developing

your computational thinking much better (UG4)

As their learning progressed the students learned to

decompose the complexity of their network

topologies and the security requirements built into

the problems into small, solvable tasks. This is what

students had to say in their reflective reports:

The topology was designed in such a way that

each branch is separate and can be easily

evaluated. The idea is to break down the network

structure to be less complex when sorting out

issues, through computational thinking, it makes

it easier to isolate the problem and solve it in bits

(PG1)

Breaking the task into smaller tasks and

concentrating on the main task helped me

a lot

in solving network problems such as when

designing a WAN, the whole design can be

broken down to smaller task that is into LANs

and the LANs can be breakdown into smaller

branches like creating small networks and

combining them as a LAN (PG4)

By building the simulations the students were able to

view the entire enterprise network and identify areas

of vulnerability, loopholes, bottlenecks and threats.

Once they were able to visualize and experiment

with problems within the network, the students

could begin to develop their ideas about overcoming

them and so secure the system. In their reflections

the students noted that decomposing systems within

the simulation meant that they could better

appreciate the abstraction and operation of routing

tables. This is something that has been shown to be

difficult to achieve when using physical hardware

(Janitor et al., 2010). These are some of their

comments:

Depending on the level of complexity working on

simulation was much more appreciated […] it

was less stressful to work on complex design

than real set [hardware] (PG2)

when you are analysing a network it is pretty

much easier to analyse it through Packet tracer.

It is easy to see things which need to be seen.

You can easily break down problems. Packet

tracer is user-friendly as a software and so it is

easy to apply critical thinking (PG1)

Through the simulation's visual representation of a

network the students were able to work at differing

levels of abstraction. They could think about

hardware, applications or routing tables as

necessary, focusing on important details as they

produced the final concrete design. The topology in

Figure 2 shows a partial output from one of the

students after working out a reverse engineering

problem-solving task.

Figure 2: Student partial topology.

After creating a visual representation of the

enterprise network, students were able to solve

problems that were inherent in the routing table that

they were given. The students managed to

breakdown problems for each router and its switches

to create a correct routing table.

Having learned to break problems down to their

constituent parts, the students were asked to

demonstrate whether they could generalize solutions

from specific instances. In computational thinking,

the concept of generalization is extended from the

concept of decomposition (Bocconi et al., 2016).

Once students have broken the problem down and

begin solving them they must apply their prior

knowledge, experience and skills to identify

Students’ Understanding of Computational Thinking with a Focus on Decomposition in Building Network Simulations

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patterns, similarities and commonalities to come up

with their optimum solutions.

Students’

reflective reports showed that they

were often able to identify similarities and

commonalities from their previous knowledge and

experience but they were not aware that they were

applying computational thinking skills to the

problems. They were not able to demonstrate how

they identified patterns in solving tasks that they

could go on to apply to other tasks. This is what one

of the students commented:

I think the main problem is your knowledge in

solving the problem, because in as much as you

may be able to break down the chunk of a bigger

problem into small manageable problems but if

you don’t know how to solve all those small

problems, it still remains a problem. So your

knowledge to the problem you are solving is

significant. […] Background knowledge helps in

understanding the similarities and differences

which will help in making appropriate decision

in solving that problem (UG6)

Some of the lecturers who were surveyed as part of

the work said that their students were interested in

making sure that the problems were solved but not in

how they were doing so. It became clear through the

study that students were solving problems through a

routine of troubleshooting, configuring and fine-

tuning.

These were some of the comments students made

in their reflective report which were not clearly

demonstrated:

Sometimes viewing the case via a general point

of view can be useful to find out possible

solutions as it helped me to recognise the general

similarities and differences in the whole scenario

so that I could apply the same solution for the

similar parts of the case. For example, in WAN

assignment I found out that some LANs followed

the similar patterns so I applied the same

configuration for each of them based on my

previous knowledge in configuring LAN Student

(PG6)

Analyzing similar patterns (network

requirements, when defined the role of each

branch, specifically we had a plan of setting up

similar configuration on different branches, such

as where it was asking to provide NAT on

LEICESTER and DERBY we had a same

requirement, ACLs on VLANs) (PG2)

The students were not taught an explicit approach to

problem solving and the strategies that they

developed did not necessarily map onto a

computational thinking approach. The students'

design approach was not based on their

understanding and application of computational

thinking but was one of simply making sure through

trial and error that their designs were operational.

This was a lot of trial and error for me. I found it

most difficult to find how to use the redistribute

command correctly. I had to use online resources

to figure out a solution. I am still studying up on

this so I may not have used it in the exactly

correct way, but it did produce an output that

appears to match [routing output] (PG3)

This concurred with what one of the lecturers

commented:

I expect students will largely use trial and error

in the beginning until they understand the

problem. If students knew how to do

computational thinking (or indeed any structured

approach to thinking) they would be more

organised. I guess we have to teach them that

(Lec7)

This point was well encapsulated by one of

undergraduate students during focus group

interviews who alluded to the nature of their course

as being one that gave practical skills rather than

teaching a way of thinking about problems and

systems. This suits these students who are focused

on getting into employment on graduation. They are

more interested in gaining good practical skills that

will immediately help them to get employment in

their field of study than in developing those higher-

level analytical skills that may be used to build a

career. This student thinks computational thinking is

just “an academic buzz word”:

I don’t feel our course teaches us any

computational thinking, I feel our course is

design to incooperate workforce processes. The

course is designed to introduce workplace

processes, best practices from hardware, best

practices from enterprises. It is designed ready

to get you in the workplace; it gets you

understand how the mind of everyone who works

in the industry works; so I can jump into my job

and design my network based on CISCO-based

practice or Juniper-based practice, so you don’t

necessarily approach it from a computational

thinking point of view. Computational thinking is

kind of like an academic buzz word and not a

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250

real while in deploying enterprise architects

(UG4)

The lecturers indicated that they do not focus on

computational thinking. Their focus is to test all of

the concepts that they have taught in class by

making sure that students are able to demonstrate

and apply them in practical tasks. One lecturer said

that he does not influence his students’ choice of the

methods that they adopt when designing solutions

because he believes that every student has his or her

own best way of solving problems. These are some

of the comments which other lecturers made:

I simply give them an assessment that test all the

points of knowledge they should have and not

necessarily from the computational thinking

point of view. I look at can they implement it, can

they look at why am I doing this, […] But I have

never thought on how do I create an assessment

from a computational point of view […] may be

its some of the things we should be thinking

about (Lec2)

I want to see that students can demonstrate that

they can apply what is it they have learnt to

produce a viable solution, for example, and be

able to critically evaluate that solution that they

come up with – so am not thinking down the

levels of how would they break down the problem

and how would they solve each element or how

do they choose a protocol so which in effective is

the algorithm path […], Or abstracting by saying

this protocol functions like this and that – so

that’s not how am thinking about it when I am

designing or assessing students (Lec3)

8 ANALYSIS

From the online survey, one-to-one interviews and

focus groups it became clear that neither the students

nor lecturers in this study were aware what

computational thinking is. Several of them indicated

that they had to use internet searches to understand

what the term computational thinking means. It was,

therefore, not surprising that lecturers said that they

are neither conscious of, nor focus on, computational

thinking when teaching and assessing students.

The results have shown that the use of simulation

software in designing computer networks helps

students breakdown complex problems into smaller,

manageable tasks. This is decomposition. Simulation

software allows visual representation (Janitor et al.,

2010) of the enterprise network infrastructure. The

students found it easier to understand the abstract

concepts of network design once they had this

representation. Working back from their abstractions

students were able to produce a new functional

network infrastructure. The ability of simulation

software to provide visual representation of their

entire design let students focus more closely on the

problem (Zhang et al., 2012) so that new ideas

emerged when solving problems. These results are

consistent with Galan et al., (2009); Hwang et al.,

(2014); and Ruiz-Martinez et al., (2013).

Students reported that they were able to identify

the security vulnerabilities and inherent errors in the

design they were given, and hence, work to solve

those problems. However, students found that they

were unable to apply some solutions because of

limitations with the software. Expósito, Trujillo and

Gamess, (2010) reported that simulation software,

particularly Packet tracer, has limitations compared

to physical devices in that some commands cannot

be applied.

It became clear that although students were able to

explain in their reflective report how they identified

patterns, similarities and commonalities in problems,

they were not aware that in doing so they were

applying the concept of computational thinking.

The results show that participants in this study

have little understanding and application of

computational thinking. The results show that

students consistently applied one aspect of

computational thinking: decomposition. At Sheffield

Hallam University teaching and learning in the area

of networking is skills-based which may explain

why the students are able to decompose problems.

Focus-group responses show that stuednts think

that computational thinking helps them understand

abstract concepts and produce concrete solutions.

During their demonstrations of their problem-

solving tasks they were not clear how they applied

computational thinking. Their interest was to solve

problems in any way that worked, including through

trial and error. This observation is in line with the

comments from some of the lecturers when asked

about the strategies they use when teaching and

assessing students when designing networks.

9 CONCLUSION

This study has demonstrated that students are able to

apply the ideas that together form computational

thinking even when they have not formally been

taught those ideas. The students who participated in

Students’ Understanding of Computational Thinking with a Focus on Decomposition in Building Network Simulations

251

this study were able to break complex problems into

smaller sub-problems, build solutions to those sub-

problems and compose them into simulations that

solved the whole problem. Teaching staff who said

that they were more engaged with the use of

technology than with approaches to problem-solving

were actually giving their students the types of

advanced thinking skill that is usually included in a

definition of computational thinking. The study

shows that simulation software is a very important

tool in the teaching of network design. It provides

visual representations of computer networks that can

be manipulated at different levels. By manipulating

the levels of abstraction in their simulations,

students are able to decompose problems. This helps

them to develop their understanding of both

problems and solutions. The use of simulations

within networking courses is recommended because

not only are students able to solve the immediate

problems that they face, their use of the software

improves their ability to apply some of the principles

of computational thinking. It is also recommendable

that lecturers familarise themselves with the

concepts of computational thnking so that they are

able to consciously teach and assess students when

designing simulation networks. Further work is

needed to investigate whether, and how, other

aspects of computational thinking may be developed

implicitly through this and other aspects of

education in computer networking.

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