Analysis of Social Networks of Students' Learning with a Focus on

Their Performance

Waldir Siqueira Moura

, Mônica Ferreira da Silva

Jonice de Oliveira Sampaio Guimarães de Souza

and Tainá Souza

Computer Science Graduate Program, PPGI, Federal University of Rio de Janeiro, Térreo, Bloco E, CCMN/NCE,

University City, post code 68.530, Rio de Janeiro, Brazil

Keywords: Analysis of Social Networks, School Performance, Method.

Abstract: The low performance of students in high school is a problem that has had considerable growth due to the

constant transformations generated by the current pandemic. The formation of groups to develop collaborative

work in the classroom is a rich tool that can effectively develop Collective Knowledge, provided there is

planning. Furthermore, through the analysis carried out, we realised that the way the learning occurs affects

the students' performance and can be reorganised by the teachers to enable a better group (and even individual)

development within the group.

1 INTRODUCTION

Low school performance is already an old problem

(Moura et al., 2020). Based on this reality and related

articles, we seek to determine how social interactions

and school performance influence the formation of

tribal and selective participation. In this way,

understanding how these social networks are built can

help us to understand and delimit the main difficulties

and individual deficits, which may allow us to

elaborate strategies for an integral development of

individuals within groups since a low level of

collaboration results in a low level of participation

(Delbem et al., 2014).

We observed collaborative working relationships

in creating the metrics to map these social networks'

tangency (Xavier, Jr., 2004). The formation of groups

to develop collaborative work in the classroom is a

rich tool that can effectively develop Collective

Knowledge. Therefore, understanding how social

interactions and school performance influence the

formation of these interactions is relevant. Moreover,

the act of understanding how these social networks

are built can help us to understand and delimit the

https://orcid.org/0000-0003-1545-7487

https://orcid.org/0000-0003-0951-6612

https://orcid.org/0000-0002-2495-1463

https://orcid.org/0000-0002-8311-5043

main difficulties and individual deficits of each

student, which can allow us to elaborate strategies for

the full development of each individual within groups

since a low level of collaboration can result in a low

level of interaction.

Our main goal is to create a method to promote

and operate this integration. For that purpose, we

collected data, 40 social networks were analysed, and

the degree of interrelationship between groups. This

provided us with the means to develop metrification

and implement Collective Knowledge construction in

the classroom.

The applied experiment analysis data found a

strong correlation between networks of friendships

and their grades. This allowed us to verify that

retained students do not fit in with students with

similar grades. We also found that students with good

grades have many bonds of friendship. However,

these bonds are weak because they revolve around

interest in grades.

The Collective Knowledge applied by the teacher

should collaborate with the interrelationship of the

students and, consequently, with the strengthening of

bonds and grades. Although social media increases

Moura, W., Ferreira da Silva, M., de Souza, J. and Souza, T.

Analysis of Social Networks of Students’ Learning with a Focus on Their Performance.

DOI: 10.5220/0011052300003182

In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 257-264

ISBN: 978-989-758-562-3; ISSN: 2184-5026

257

connectivity among students, it is noticeable that the

collective work in person lacks low quality.

Furthermore, participating should promote ways of

integrating – of being close to - the native and non-

native capacities of each student.

Therefore, the solution presented to the problem

of tribal participation aiming to improve the student's

performance is the improvement of the articulation of

native instances, which must be activated through

interrelationship and collaboration. The article's

discussion has organized the propositions to reach our

goal, divided into the following sections. Section 2

and 3 present the conceptual foundations. Section 4

describes the methodology used to conduct the study

presented in this article. Section 5 displays the data

analysis and its results. Section 6 presents the related

articles. Moreover, section 7 presents the conclusions

and future work of this research.

2 PERSONALITY

CLASSIFICATION

The primary literature chosen for this research is the

Psychogenetics by Prof. Xavier (Xavier, Jr., 2004),

which originates in Piagetian psychogenetics,

studying the genesis of intelligence. The study is

adopted with the primary motivation of interest in

deepening human functioning, having as its

imperative's: cognition, metacognition, and

interactions.

Prof. Xavier's Psychogenetics points out that it

studies how the "Person System" develops, studies

the genesis of human functioning and development in

its age groups, and researches the fruition or

intentionality of immaterial operations. It also states

that psychogenetics enters the universe of observable

and measurable sciences. (Delbem et al., 2014). Thus,

a scenario of study and research has been created that

generates words reserved for this area of science with

the meanings that articulate among themselves.

The Psychogenetic Theory states that human

nature is interactive; in his words, interactive

development is a diachronic process. Therefore, its'

understood that diachrony is a fundamental element

in interrelational development. (Xavier, Jr., 2004)

Thus, we found some words reserved for this area of

study in this research. For better understanding, we

elucidated their meanings whenever necessary.

The respect the selective participation we may say

that is analogous to the practical community since its

grouping often takes place informally among the

students; all those involved have a final goal to be

achieved; the members form interaction relationship

mechanisms to achieve the proposed goal and,

through these interactions, create a shared repertoire.

About tribal participation, the constitution of

informal groups occurs similar to the selective

grouping, but the shared practices and their objectives

may vary. This means that although tribal groupings

have objectives, they are exclusively a priority for

that group of young people who group automatically.

As far as the results of the two types of participation

are concerned, we can say that the productivity

considered in the different objectives can differentiate

concerning the collaborative work and its production.

In strictly tribal groupings, we highlight

communication and cooperation. This is because the

members of these groups associate themselves not

with an objective and its development but with

common affinities and tastes. This creates a robust

inter relational bond between the members but

specifically disregards the productive capacity.

Therefore, these groups have inter relational solid and

collaborative bonds as their characteristics.

Although they may occur as practical

communities, different, selective groupings tend to

occur in the face of a specific activity, challenge, or

need. In this case, the communication between the

members already has a character of commitment to

the objective. Coordination is a fundamental

characteristic because, once because of the objective,

the members are divided to solve the problem

according to their primary skills. After the division of

tasks, the group tends to operate to fulfil their role,

and thus, together, they cooperate simultaneously and

synchronously.

Xavier (Xavier, Jr., 2004) explains that

psychogenetics is a structuralist approach. It focuses

on the universe of global and partial human

behaviours observable through the structures of Vital

Energy mobilised in them psychogenetics studies

reality since it approaches the "Human Person

System," contextualised in the ecosystem.

3 COLLABORATIVE

PRODUCTION IN ANALYSIS

OF SOCIAL NETWORKS

One of the aspects that marks the 2.0 generation of the

Internet is the idea of co-authorship, that is,

collaborative production. This is because cyberspace

is an environment of production and consumption in

an expanded way. The collaborative production is

achieved through co-authoring, which we can call

cyberculture (Fuks, Hugo; Pimentel, 2011).

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Collaborative groups are those in which the

components share the decisions made and are equally

responsible for the quality of what is produced

together.

However, the mere existence of collaboration does

not mean that there is, in fact, a culture of

collaboration, says Damiani (Damiani, 2008). That is

because alternative ways of collaboration may not

constitute collaborative cultures, although they

involve working together. After all, they present

competing subgroups or only occasional jointactions.

The model (Fuks, Hugo, Pimentel, 2011)

classifies the systems that support group work into

three dimensions: communication, coordination, and

collaboration. This classification gave rise to the 3C

Collaboration model, which was later formulated. In

this model, cooperation strictly refers to operating

together, while collaboration refers to doing all the

work together, which involves communication,

coordination, and cooperation.

The benefits of collaborative work among

students have been presented by several researchers

(Colaço, 2004). Among the significant gains of the

implementation of these works in the educational

process is the socialisation among students - which

includes the learning of communicational modalities

and coexistence -,the control of aggressive impulses,

the adaptation to the established norms - including the

learning related to the performance of social roles -,

the overcoming of self-centeredness - through the

progressive relativisation of the own point of view -,

the acquisition of aptitudes and abilities - including

improvements in school performance -, and the

increase in the level of school aspiration.

The network concept can be recognised through

its effectiveness, both from the static and dynamic use

points. The fixed point of use exploits the network's

structure, while the dynamic point exploits the system

that constitutes the network. The analysis of social

networks establishes a new paradigm since the study

of the behaviour or opinions of individuals depends

on the structures in which they are inserted. Thus the

unit of analysis is not individual - sex, class, age,

gender, between others. - but the whole is built

through the interpellation ofthen whole. (Deroy-

Pineau, 1994). This structure is illustrated and

apprehended concretely by the network of

relationships and limitations that weigh on

individuals' choices, orientations, behaviours, and

opinions, as (Bezerra et al., 2014) explains.

Social networks are an essential part of humanity

(Delbem et al., 2014) (Moura et al., 2020). These

networks are based on the interrelationships between

humans seeking a common goal, between entities

and can be mediated and metrified using technologies

(Silva et al., 2018). The observation and research to

raise patterns of connection between social groups

and how connections are established between

individuals are already found in current research.

However, no metric can see how social interactions

within specific networks interfere with academic

development.

The basic premise of information technology is

management through the epistemic- ethical posture of

the individual in the exercise of his autonomy in social

media (Delbem et al., 2014). Its starting point is the

'Inter-relationship' as a marker of cognitive

development. Thus, interaction precedes and

determines knowledge. Therefore, it is investigated

what to do, live together, collaborate, produce, know,

get to know each other, and reciprocate. In short, the

interrelationship.

Therefore, the operations of academic subjects

carried out in virtual spaces by adolescents maintain

the concreteness of proprioception coming from the

instantiated functioning in 'somesthesia.' The virtual

space aggregates the conceptual data, but without

losing the corporal reality, it only changes the state

from real to virtual, preserving the experience of

contact with the actual object (Fuks, Hugo; Pimentel,

2011).

4 PROPOSED METHODOLOGY

We analyse the built social networks from the data

collection to identify patterns and characteristics that

can be metric, proven, and altered. From the

verification of these patterns, the computational

ingenuity succeeds, which, assembled on the relevant

topics that were mathematised in the empirical phase,

serve as an instrument for mapping and automatic

analysis of social networks and behavioural patterns

of participation.

In the first instance, the mediators of data

collection and analysis are the researcher and the

teachers trained to monitor and collect data directly.

In the second instance, using experiments for data

collection is done by the teachers of the network and

their respective coordinators. However, the

researcher's sole responsibility is the data analysis and

mapping of social networks. Moreover, in the third

instance - after the development of the computational

engine - the teachers and pedagogical teams of each

school are responsible for the collection of digital data,

which should be placed directly on the developed

platform that should map and make the analysis of

social networks and the patterns of participation

Analysis of Social Networks of Students’ Learning with a Focus on Their Performance

259

found, giving as proposal the best possible selective

participation within that analysis.

The first part of our research was the stage of the

bibliographic survey regarding the topics of interest:

psychogenetics, analysis of social networks, and

collaborative systems. This deepening and theoretical

background stage covered our problem's definition,

the hypothesis's formulation, and the proposed

solution. The systematic bibliographic review was

carried out in the scientific's base (ACM, IEEE and

Springer).

The second stage of the research includes the

metrification survey to analyse social networks

formed by collecting students' data. After identifying

the participation patterns and the topics of social

network analysis that would be considered for the

metrification of these patterns, the validation stages

were followed, such as the pilot experiment and the

elaboration of the collaborative work method. This

stage still verifies the feasibility of creating

participation patterns and their analysis. In

constructing the computational engine, the agile

development process is used. The requirements are

collected as a concept, functionality, user history

(students and researchers), analysis of individual and

group metrifications, and tests with selective

participation proposals.

Based on the analysis of social networks, the

organisation of selective groups is part of the third

stage. It corroborates the enturmation's model, which

potentially includes the best native instances of the

students. All development is based on data analysis

and empirical collaborative work, whose psycho-

pedagogical evaluations constitute a theoretical and

operative educational reference.

5 RESULTS AND ANALYSIS

Through the results gathered in the initial experiment,

it was noted that the main difference highlighted for

the differentiation of these groupings is the planned

coordination. It has interrelation

as a fundamental

premise in human development; interaction precedes

and determines knowledge. Thus, psychogenetics

investigates doing, living together, collaborating,

producing, getting to know, and reciprocating, in

short, interrelationship. Based on the 3C model, we

propose some of the main differences that we believe

are distinct in both groupings, which can be observed

in the following subtopics.

5.1 Mapping of the Self-centred Social

Network

For the metrification of students' social networks, a

questionnaire was first elaborated applied individually

to the previously selected students, considering that

they have studied together since the sixth grade of

elementary school II. This questionnaire considers

the students' preferences in several psychic and motor

activities and a previous spontaneous survey of their

networks of friends. In constructing these networks,

it was possible to show which friends were for

specific activities - in sports, collaborative digital

games - such as RPG, school work, proximity to

residence, between others). Data collection from

egocentric networks was carried out using the

questionnaire below:

Table 1: Questionnaires applied.

Application questionnaire template

Student:

Age:

Friend of:

quest

ons

Do you practice any sport?

Yes

Which

one

Do any

ay on you

team

Do you p

ay v

eo games o

ne games

Do you usually play alone o

group?

Do any c

assmates

ve nea

you

ome

etorea

so, w

genre

Do you do any kind o

artistic activity?

Soc

dia

you use:

Because of this reality, we followed the

participation. We analysed the social networks of 40

students to metrify the degrees of the interrelationship

among the groups to implement collective knowledge

in the classroom. Furthermore, one of the main

challenges found for the development of this

experiment was the participation between the native

psychic and somesthetic students.

5.2 Creation of the Complete Social

Network

After these findings returned from the comparative

data between the interview and observation, the data

were analysed within Cytoscape (2020)

, an open-

source software platform for viewing complex

networks. After constructing the graphs of the social

networks built with the help of the software, the

following metric was analysed and calculated: In-

degree (which is the degree of entry of the vertex). It

was calculated based on the number of friends of each

https://www.answerminer.com/

CSEDU 2022 - 14th International Conference on Computer Supported Education

260

student and based on their relationships. For example,

a student x can have a number y of friends and a

number z of friends of works only, so first, the total

entry-level was raised and then subdivided between

friends of worksand personal friends.

Although this relationship between the number of

friends and grades was evident in the graphs, we

sought a means to prove, based on data, that this

relationship was indeed strong and therefore should

be considered. For this, we used the Pearson

Correlation Coefficient - CCP.

Initially, to calculate the CCP, we used the online

AnswerMiner3 automatic calculation platform.

However, after several test calculations, including the

use of the same data, we noticed that there was a slight

divergence of results between one round and another;

with this, fearing the compromise of the truthfulness

of the research and considering increasing its

reproducibility, we decided to create a specific CCP

calculator for this work.

Therefore, based on the metrics of social network

analysis and the CCP calculation, we propose a

formal system that has ruled for analysing

interactions and their representations. According to

psychogenetics (Delbem et al., 2014), psychism and

somesthesia are propositional concepts. Thus,

psychism groups the competencies for so-called

"superior" activities, while somesthesia groups the

bodily functions correlated to the so-called "human"

activities.

As we have already seen, tangency is the

articulation of these two instances, and it is

understood as an articulation of the evolutionary

structure, measurable by seasonal diachrony. The

articulation is the meeting of two energies, which can

be understood as hybridization4 because the energies

co-exist in complementing one within the other. They

do not merge into one another (Xavier, Jr., 2004).

Therefore, it can be understood that a higher

density in one instance does not require damage to the

other because one instance has no quota to the

detriment of another. Therefore, articulation is

summed up as being the regulation between

somesthesis and psychism, and tangency is the

balance between both. This thing that is understood

as "native" is the predominant energy in the

individual that can also be called a pioneering

competence, which is the individual's strong point.

5.3 Complete Social Network

In this first graphic representing the first high school

grade (Figure 1), we can observe how their networks

are structured. With that, we can conclude that: 1 -

student number 13, located in the right median corner,

has the highest average in the class. We can also

observe a large number of entries he has.

However, although he considers all the entries as

reciprocal friendship, the vast majority of the entries

consider him only as a work friend and not as a

personal friend, as can be observed by the pink arrows.

Another essential piece of data that we can

observe in this graph is the relation of the students

(numbers 05, 06, and 03) located in the upper left

corner, the trio's grades are below average, and they do

not relate to anyone in their class. For this more

profound analysis, the first year of High School was

considered because it is a class where the students

have lived together in class since the sixth year of

Elementary II. That is, they keep the same group as the

previous year and keep the interrelations with the

friends who were approved. This fact leads us to

consider a cause-and-effect relationship between low

performance because they do not interact with their

colleagues in the current grade.

Figure 1: A Detailed analysis of the social networks of first-

grade high school students.

The other two series (Figure 2) are presented here

only in their simplified form to highlight the issues

discussed in this research. After structuring the social

networks of each room and analysing the data

according to the graph theory, the comparative tables

were constructed with the information gathered to

prepare the graph of the Pearson's Correlation

Coefficient.

Figure 2: Social network graph of High School First Grade

students.

Based on the detailed chart, we can consider a way

of approaching to improve student performance, being

the implementation of a model of induced

collaborative work, where the teacher should naturally

Analysis of Social Networks of Students’ Learning with a Focus on Their Performance

261

include student number 13 to work with the students

(numbers 05, 06 and 03); this way the network of

interest for his grade is broken and the failed students

have the opportunity to produce and be approved.

In chart one, we have the number representing the

student, which is a standardised numbering and does

not correspond to the actual call number made to

make it impossible for the student to be recognised by

an external agent. As can be observed, there is a

positive linear correlation between Input Grades and

Notes. In detail, this means that the higher the Entry

Grade number, the higher the student's grade, i.e., the

more friends the student has, the better chance of

achieving a grade.

As can be seen, there is a positive linear

correlation between Input Degrees and Notes. In

detail, this means that the higher the Entry Grade

number, the higher the student's grade, i.e., the more

friends the student has, the better chance of achieving

a grade. Considering that Pearson's correlation

coefficient in the 2nd year MS analysis is 0.8030, we

can conclude that this correlation is strong.

Table 2: Analysis input degree x grade 1st High School and

Pearson correlation coefficient for input degree x grade.

Studen

Input degree Grade

Studen

Input degree Grade

1 6 58,9 9 4 62,6

2 6 70,5 10 5 65,9

3 2 49,0 11 5 58,4

4 4 57,5 12 6 73,9

5 2 56,9 13 9 76,1

6 1 35,0 14 4 49,2

7 4 50,0 15 4 57,3

8 4 64,8 16 5 37,7

17 3 56,6

After the data collection of the experiment, it was

evidenced that there is a strong correlation between

the networks of friendships and the grades, which

allowed us to verify that repeat students do not get in

touch with students of the grade they are in and that

students with good grades have many bonds of

friendship. However, these bonds are weak since they

are only interested in grades. The Collective

Knowledge applied by the teacher should collaborate

for the interrelationship of the students and,

consequently, in the fortification of the bonds and the

grades.

5.4 Validation of the Social Network

As an answer to this problem of low school

performance, we propose a collective work, together

with the teaching staff of the whole school network,

that they work, with the use of a Crowdsource tool,

considering that the school network has several

different cities and states. In this way, crowdsource

enable all teachers to work collaboratively, sharing

their experiences and building workgroups that they

use in their daily classroom. Thus, based on the

exchange of the various and different experiences of

teachers supported by the Crowdsource tool, we can

build a unique method that includes all the benefits of

the various experiences reported.

After using Crowdsource to solve this problem,

we hope to build a method that promotes interaction

between students, enabling the split with the

traditional method, based on the exposure of content

solely from the teacher. After the interview, the

teachers started an observation, considering the data

collected, where everyone was previously instructed.

This observation was intended to confirm or refute

the interview data.

Based on the reports of teachers who have

followed the development of students since the sixth

grade of elementary school II, it was possible to see

how interactions occur. These reports provided data

that confirmed much of the information gathered in

the interview, reinforcing the informed social

networks.

This, considering that the interrelationship

between students is a great facilitator in the

construction of knowledge and cognitive, affective,

and social development, we believe that this shared

method can also improve relational, dialogical, and

argumentative skills, which enable a joint reflection

and favour the creation and strengthening of the

bonds of friendship.

5.5 Validation of the Results

As an answer to this problem of low school

performance, we propose a collective work with the

teaching staff of the whole school. After developing

the method for optimal groupings is completed, we

resume Social Network Analysis based on its

parameters. We choose the most appropriate metrics

for the diagnosis and organisation of students

according to their native instances.

Using the method developed with the teachers

specifically to solve this problem of the students'

academic performance, we created a comparative

graph to demonstrate the effects of the new groups on

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262

the performance of each individual. There was a

considerable improvement when comparing students'

grades before and after applying the method based on

social network analysis. The metrics used to prove

this hypothesis were constructed according to the

realities considered a priority within the educational

environment.

We can see how each metric is viewed

individually theoretically. However, after a detailed

analysis of the data that resulted from applying the

applied methodology, we built a comparative table of

these metrics before and after the new grouping

model.

This table can analyse the groups' gains after

applying the method. The first fact that we can notice

the difference is the absenceof a small component

after applying the method. That means an evident

division among the students before applying the

participation method, which formed closed

relationship groups that made the interaction between

certain groups difficult. With the new method,

everyone becomes part of the Giant Component.

What this means is that the proximity among the

students has been so strengthened that there are no

more excluded groups

Indegree and Outdegree also increased almost

100% concerning previous data. The application of

the methodology which caused this was an increase

in interaction between students in the classroom,

which justifies the disappearance of the Small

Component; Betweenness Centrality decreased

because the importance of some specific nodes also

decreased; that is, the information runs similarly

among all members of the network reducing the

dependence of specific nodes for the dissemination of

information; Network Connectivity has also been

enhanced thanks to the degree of interaction that has

increased and eliminated the Small Components.;

Clustering Coefficient also had a drop due to the

strengthening of the participation. All this data can be

visualised in the table below that provides the average

of the metrics explained above.

6 RELATED WORKS

As we can see today, students have a great tendency

to spend their hours in cinemas andsocial media.

However, schools have great difficulties

implementing technology as a support and incentive

for learning (Moura et al., 2020). The present work

proposes one more possible way to use technology as

a pedagogical aid tool. As with other sectors of

society, education was improved with technology. In

the virtual school, social network environments allow

learning communities to group and exchange

experiences among students (Marinho et al., 2015).

Other authors have already proposed using social

network analysis as a basis for school performance.

In 2011 (Fuks, Hugo, Pimentel, 2011) analysed the

social networks of an undergraduate course and how

the influence of the student reflects on their

performance. The experiment took place in six

months. Students were offered a social networking

site with games and collaborative work as a tool.

After the students' use, the researcher generated a

graph with four measures of the public network and

nine of each student. The individual measurements

obtained were used as predictors for evaluating the

students' performance. All tests and analyses have a

common opinion about the potential of structural

metrics aspredictors of school performance analysis,

and it is a helpful tool. (Fuks, Hugo, Pimentel, 2011).

In the school environment, social networks allow

predicting the success of educational learning with

their tools, offering results that can direct the teacher's

attention to theirs teaching practice. Through the

analysis of the environment, modification tools are

added to the physical environment in which the

research actors are inserted (Sousa-Vieira et al.,

2018). Furthermore, students with better performance

generate greater engagement due to their prestige and

influence. It is also noticeable that the involvement of

students with other people in the platform

(networking) partially predicts its outcome (Liu et al.,

2018).

Table 3: Metrics analysis.

Métrics:

Grade

Alunos:

08 07 08 0

05 08 04 07

6 07 07 06 07 06

8 06

07 07 06 08 08 08

7 06 07 07 07 02

Component

G G G

G G P G G G G G G

G G G G G G G G G G G G G

Indegree

04 06 0

03 05 02 05 05 03 05 04 06 0

02 0

02 05 05 08 04 08 06 03 07 04 08 03

Outdegree

02 04 0

2 4 5 8

02 04 03 05 0

02 0

04 07 05 07 09 12 07 00 05 00 04 08

Betweennes centrality

0.107

0.092 0.189 0.

.072 0.115 0.85 1.64

0.345

0.0 0.027 0.154 0.102 0.5

6 0.0 0.1

.716 0.417 1.998 0.518 2.70 0.893

0.588 0.0 0.147 0.0 0.247 2.414

Network Density

0,276

0,145 0,276 0,1

,145 0,276 0,145 0,276

0,276

0,145 0,276 0,145 0,276 0,1

0,145 0,2

,145 0,276 0,145 0,276 0,145 0,276

0,276 0,145 0,276 0,145 0,276 0,14

Neighborhood connectivity

8,5

6.0 8.7

5.5 8.3 1.4 4.3

7.8

6.33 9.0 5.75 8.6 5.

5.0

4.5 7.4 4.83 7.1 4.77 7.2

8.2 5.0 8.2 5.5 7.7 4.5

Clustiring Coefficient

0,53

0.35 0.47

1.0

0.25 0.43 0.05 0.12

0.46

0.83 0.7 0.66 0.6 0.

0.66 0.

0.25 0.33 0.43 0.33 0.27 0.36

0.37 0.83 0.48 0.25 0.35 0.23

Closeness Centrality

0,527

1.0 0.513

0.5

1.0 0.153 1.0 0.59

0.59

0.371 0.452 0.406 0.475 0.43

5 0.351 0.

0.5 0.558 0.541 0.558 0.764 0.655

0.612 0.0 0.527 0.0 0.452 0.72

Aluno

Nota

Componente

Grau de entrada

Grau de saída

Centralidade de

intermediação

Densidade da

rede

Conectividade

de vizinhança

Coeficiente de

agrupamento

Centralidade de

aproximação

Nota

Componente

Grau de entrada

Grau de saída

Centralidade de

intermediação

Densidade d a

rede

Conectividade

de vizinhança

Coeficient e de

agrupamento

Centralidade de

aproximação

0.107 0,276 8.5 0.53 0.527

Antes

5,47

G e P

3,17

3,23

0,579

0,145

5,510

0,52

0,548

0.189 0,276 8.7 0.47 0.513

Depois

7,29

5.88

6,17

0,404

0,276

7,705

0,42

0,546

0.526 0,276 7.0 0.40 0.558

0.115 0,276 8.3 0.43 0.513

1.64 0,276 4.3 0.12 0.593

0.345 0,276 7.8 0.46 0.590

0.027 0,276 9.0 0.70 0.452

0.102 0,276 8.6 0.60 0.475

0.306 0,276 7.3 0.35 0.575

0.104 0,276 8.6 0.56 0.527

0.417 0,276 7.4 0.33 0.558

0.518 0,276 7.1 0.33 0.558

12 0.893 0,276 7.2 0.36 0.655

0.588 0,276 8.2 0.37 0.612

0.147 0,276 8.2 0.48 0.527

0.267 0,276 7.7 0.35 0.452

10 0.578 0,276 7.1 0.34 0.612

Figure 3: Metrics for group analysis.

Analysis of Social Networks of Students’ Learning with a Focus on Their Performance

263

7 CONCLUSIONAND FUTURE

WORK

In the development of this article, we seek to

understand how the grouping of students occurs

through the method presented, and we can regroup

students so that there is an improvement in the

processes of participation. The analyses were carried

out in high school.

Thus, we found the method presented above as a

solution to the problem of tribal participation aimed

at improving student performance improving the

articulation of native bodies, which should be

triggered through inter-relationship and collaboration.

We seek to develop a method to promote and

operationalise this integration among students to

increase their school performance.

However, our research cannot be continued and

applied in the long term by the pandemic, so we

recommend that we suggest a reproduction of this

research on a larger scale as future work. We also

recommend a reproduction in elementary school in

the early years to note a difference in behaviour in

different age groups and an article that is a manual of

applying the method. We also recommend replicating

this method during the pandemic for online

education.

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