A Social Network Approach for Student’s School Performance

Measurement

Waldir Siqueira Moura

, Mônica Ferreira da Silva

, Jonice de Oliveira Sampaio

Tainá Guimarães de Souza

, Elton Carneiro Marinho

and Victor Prado

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

vhdoprado@hotmail.com

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

Abstract: This article brings to light analyses of student’s social networks in order to comprehend how social

interactions occur in schools. Furthermore, the present research intents on verifying if social groups affect

student’s performance. In view of this reality, this work presents the analysis of 40 social network students

with the objective of measuring the degrees of interrelationship between groups to provide means of

implementing the process of building Collective Knowledge in the classroom. One of the main challenges

encountered at work is the understanding between native psychic and somesthetic students. From the data

collection of the experiment, it was evidenced that there is a strong correlation between the networks of

friendships and grades, which allowed to verify that repeating students do not fit in with students of the

grade in which they are and that students with good grades have many friendship bonds, however these bonds

are weak since they are only bonds with interest in the notes. The Collective Knowledge applied by the

teacher must collaborate for the interrelation of the students and, consequently, in the strengthening of bonds

and grades.

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

https://orcid.org/0000-0003-0117-0610

https://orcid.org/0000-0003-3960-7195

1 INTRODUCTION

This article brings to light analyses of student’s

social networks in order to comprehend how social

interactions occur in schools. Furthermore, the

present research intents on verifying if social groups

affect student’s performance. Its goal is to help

teachers better comprehend different student’s groups

and support the development of cooperative work

inside the classroom.

The formation of groups to develop collaborative

work within the classroom is a rich tool that can

provide the development of Collective Knowledge

effectively, provided there is planning. For this

reason, understanding how social interactions and

school performance influence the formation of these

interactions is something relevant.

The difficulties of inter-relationship between

students and problems of implementation and use of

technology as pedagogical and social tools, is the

reason behind the current research. The literature

chosen for this research, comes from Professor

Xavier’s psychogenetics, with origin in Piaget’s

psychogenetic study which is directed at the

fundamentals of intelligence. The main focus of the

chosen research, is the thorough comprehension of

the human behaviour, cognition and the interactions

of metacognition.

Inter-relationship is fundamental, as explained by

Delbem (2014), because within it, students are able to

develop connections, have existential exchanges,

establish dialogue, be interested in cooperative work

and feel rewarded by reciprocity.

Moura, W., Silva, M., Sampaio, J., Souza, T., Marinho, E. and Prado, V.

A Social Networ k Approach for Student’s School Performance Measurement.

DOI: 10.5220/0010480003110318

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 2, pages 311-318

ISBN: 978-989-758-502-9

 2021 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

311

Our proposal, as a possible solution towards this

research problem, is to, it is the tangent between

students' native and non-native competences for

integral development that occurs through the

awareness of the individual to the collective, that is,

the awareness that not only should personal and

comfortable factors be considered, but also external

objective characters, common objectives that require

the collaboration of two or more individuals with

divergent native capacities.

As a main result we found after the experiment

that the higher the number of the entry grade, the

higher the student's grade. That is, in this experiment

we can see that the more friends the student has, the

greater his chance of getting a grade. We also noticed

that there is a strong correlation between the networks

of friendships and grades, which allowed to verify

that repeating students do not fit in with students of

the grade in which they are and that students with

good grades have many friendship bonds, however

these bonds are weak since they are only bonds with

interest in the notes

In order to organize the discussion and proposals

described in this article, it is divided into the

following sections: section 2 presents the conceptual

foundations (metrification of complex networks, and

theoretical framework of the research). Section 3

presents the related papers. Section 4 describes the

methodology used to conduct the study presented in

this article. Section 5 displays the data analysis and

its results. And finally, section 6 presents the

conclusions and future work of this research.

2 CONCEPTUAL FOUNDATIONS

2.1 Collaborative Work

For the development of this article, we utilized the

following concept of Social Network. Social

networks are an essential part of humanity (Bezerra

2014). These networks are based on the

interrelationships between humans seeking a

common goal, between entities and can be mediated

and metrified using technologies, as we can see in

Bastos (Bastos, Queiroz 2015). The observation and

research to raise patterns of connection between

social groups and how connections are established

between individuals is something already found in

current research, however there is no metric that

makes it possible to see how social interactions within

certain networks interfere in academic development.

The formation of networks in school, operate in

the same manner as other networks, as explained by

the concept cited above. This means that, the results

of the two enturmentations, we can say that the

productivity considered in the different objectives can

differentiate in relation to collaborative work and its

production. Based on the 3C model

(Fuks; Pimentel,

2011) we proposed some of the main differences

that we believe to be distinct in both groupings:

In strictly hedonistic groups we emphasize

communication and cooperation, because the

members of this group are associated not only with a

view of an objective and its development, but

especially by common affinities and tastes, which

creates a strong interactional bond between the

members, but disregards productive capacity

specifically. Therefore, these groups have strong

interactional and collaborative bonds, but with

exceptions such as a football match, for example, they

do not tend to be organized by skills, and there are

exclusions of weaker members.

Conversely, utilitarian groupings - although they

may occur as practical communities - 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, the coordination is a fundamental

characteristic, because the objective is the focus

through which the groups will organize and divide

and, therefore, the skills of each member are

considered.

It is the model that classifies into three dimensions the

systems that support group work: communication,

coordination and collaboration. This classification gave

rise to the 3C Collaboration model, later formulated. In

this model, cooperation strictly refers to the action of

operating together, while collaboration refers to the action

of doing all the work together, which involves

communication, coordination and cooperation.

The main difference highlighted for the differentiation

of these groupings is the planned coordination, that is,

while in the hedonite grouping this coordination takes

place naturally or not, in the utility grouping it is

fundamental, because the primary objective of this

grouping is to achieve a pre- established objective that

requires the action of ordering the grouping in tasks and

resources according to the capacities of each member.

To understand the scenario of the problem proposed in

this research, we start from the hypothesis that its solution

is to create a means for mapping students' social networks

to formulate a metrification that enables an intervention

that potentializes the tangent of young people's interrelated

capabilities through collective knowledge. We consider

that the methods of work and of potentiation of collective

knowledge are sufficient, but we also recognize that their

application does not favour the means for mapping the

social networks of the participants nor potentiates the ideal

enturmation that aims at the real inter-relational tangency.

CSEDU 2021 - 13th International Conference on Computer Supported Education

312

Even though social networks are apparently

similar to others. When it comes to complex social

network analysis among students, the concerns

become greater, since metrifying their personal

interrelationships and their individual performance is

not a trivial task. Thus, this metrification should

develop sufficient data for the verification of

utilitarian and hedonistic relationships, so that

through this data, teachers can intervene and form

groups in which students with low school

performance and more difficulty in interacting feel

involved and motivated in the learning process.

2.2 Psychogenetics

In creating the metrics for mapping the tangency

(Xavier 2004) of these social networks, our attention

was focused on observing the relationships of students

within the school, both in class and at intervals, for

analyses, Xavier’s psychogenetics was used.

According to psychogenetics (Xavier 2004),

psychism and somesthesis are propositional concepts.

Thus, psychism groups the competencies for the so-

called "superior" activities, while somesthesis groups

the bodily functions correlated to the so-called

"human" activities. Both definitions refer, according

to Delbem (Delbem 2014), to the difference of energy

to action, that is, psychism is the pattern of innovation

dosed with somesthesis, and somesthesis is the

pattern of somatic repetition accelerated by the

psychism.

The tangency is the articulation of these two

instances, and it is understood as articulation, the

evolutionary structure, measurable by seasonal

diachrony. As Xavier explains (Xavier 2004)

articulation is the meeting of these two energies

which, according to him, can be understood as

hybridization

, this is because the energies coexist in

a process of complementing one with the other, they

do not merge into another.

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 the regulation between somesthesis,

psychism and tangency as the balance between all.

This is understood as "native", the predominant

energy in the individual that can also be called as a

pioneer competence, that is, it is the individual's

strong point.

Hybridization is the acquisition of new properties by

combination with the other energy (somesthesis or

psychism).

3 RELATED WORKS

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, as

cyberspace is an environment of production and

consumption in an expanded way, collaborative

production is achieved through co-authoring, which

we can call cyber culture (Fuks; Pimentel, 2011).

This incorporation of the digital into our daily

lives has resulted in what the artist and researcher

Domingues calls biohybrids, that is, a biological,

cybernetic and hybrid subject; and this reality allows

the generation of biohybrid narratives of life in a

mixed way (Domingues, 2004).

The concept of network, according to Deroy-

Pineau (Deroy 1994) can be recognized through its

effectiveness, both as the static point of use and the

dynamic point of use. The static point of use exploits

the structure of the network, 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, etc. - but the

whole built through the interpellation of the whole.

This structure is illustrated and apprehended

concretely by the network of relationships and

limitations that weighs on the choices, orientations,

behaviours and opinions of individuals as Bastos

explains (Bastos et al. 2014).

The analysis of networks is not an end in itself. It

is the means to carry out a structural analysis whose

objective is to show in which form the network

explains the analysed phenomena. The objective is to

demonstrate that the analysis of a diode (interaction

between two people) only makes sense in relation to

the set of other diodes of the network, because its

structural position necessarily has an effect on its

form, its content and its function (Marteleto 2001).

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 as Delbem states (Delbem 2014). Its

starting point is the 'Inter-relationship' as a marker of

cognitive development. Thus, interaction precedes

and determines knowledge. Therefore, one

investigates doing, living together, collaborating,

producing, knowing, reciprocating, finally, inter-

relationship.

Marinho also used the Technology Acceptance

Model (TAM) to identify different motivational

factors of the use of a Virtual Teaching Platform

A Social Network Approach for Student’s School Performance Measurement

313

(Marinho et al., 2015).

One important characteristic of social network

analysis is the capacity of describing mathematically

the characteristics of a node in a network. The

positions of notes in networks are frequently

described in terms of centrality. The three main points

of centrality are the degree of centrality,

intermediation centrality and proximity centrality

(Degenne e Forse 1999).

For example, Liu et al., Use these concepts and

analyze the dynamic characteristics of the network

structures and, in fact, found that the structure of the

students' social network varies dynamically with the

progress of the course. In the same article, they

concluded that "in the interactions of the course

forum, the positions of students in the network are

partially correlated to learning results" (Liu et al.,

2018).

Still in a school environment, social networks

allow the prediction of learning success in education

with their tools offering results that can direct the

attention of teachers to their teaching practice, that is,

through analysis in this environment, tools for

modification are added in the physical environment in

which the research actors are inserted (Souza et. al,

2018).

So the operations of the academic subjects carried

out in virtual spaces by adolescents maintain the

concreteness of proprioception coming from the

instantiated functioning in 'somesthesis'. The virtual

space aggregates the conceptual data, but without

losing the bodily reality. It only changes the state

from real to virtual, preserving the experience of

contact with the real object (Delbem 2014).

4 METHODOLOGY USED

The problem that permeates this research is

understanding how school social interactions are

formed. As well as verify how school performance is

influenced by social groups. To understand how these

networks are developed, might help teachers to map

and comprehend the main difficulties students face

during group work. With this, a strategy can be

crafted for the development of the student within

groups, since low levels of cooperation results in

lower rates of interactions (relationships).

The formations of these networks work in the

same way as the networks of practical communities,

that is, they are made up of informal groups that have

a shared practice and a clear and defined objective.

Therefore, for the metrification of the students' social

networks a questionnaire, which is found in table 1,

was first elaborated, individually made for 40

previously selected students (considering that all of

them studied together since the sixth grade of

elementary school II). In this questionnaire the

preferences of the students in various psychic and

motor activities were considered, as well as a

previous spontaneous survey of their networks of

friends, where they highlighted the friends they have

for specific activities (in sports, collaborative digital

games - like RPG, for example -, school work,

proximity to home, etc). As seen in table 1.

Table 1: Questionnaire applied during the survey.

Application questionnaire template

Student:

Age:

Friend of:

Applied questions

Do you practice any sport?

Yes No Which one

Do any friends play on your team?

Do you play video games or online games?

Do you usually play alone or in a group?

Do any classmates live near your home?

Like to read? If so, what genre?

Do you do any kind of artistic activity?

Social media you use:

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

grade high school students.

Figure 2: Social network graph of High School 1

Grade

students.

After the interview, an observation was initiated

with the teachers, where some of them were

previously instructed (those who considered the data

collected in the interview). This observation was

intended to confirm, or refute, the data previously

provided and tabulated by the interview.

CSEDU 2021 - 13th International Conference on Computer Supported Education

314

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 between them take place. These

reports provided data that confirm much of the

information gathered in the interview, reinforcing the

informed social networks.

After this return of comparative data between the

interview and the observation, the data was analysed

within Cytoscape

, which is an open-source software

platform for viewing complex networks. After the

construction of 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

student and based on their relationships. For

example, a student ‘x’ can have a number ‘y’ of

friends and a number ‘z’ of work friends only. So,

first the total entry grade was raised and then

subdivided between work friends and personal

friends.

To establish a correlation between the in-degree

metric and the average score (which corresponds to

the student's performance in the subjects), we use the

CCP which measures the degree of linear correlation

between these two quantitative variables. The CCP is

a dimensional index with values between -1.0 and 1.0

that reflects the intensity of a linear relationship

between two data sets.

In our analysis, this coefficient is represented

below the graphs by the letter "r" and assumes only

values between -1 and 1, being that 1 means a perfect

positive correlation between the two variables and -1

a perfect negative correlation between the two

variables - that is, if one increases, the other always

decreases. This way, we consider that r=0 means

that the two variables do not depend linearly on each

other. Therefore, based on the metrics of social

network analysis and the CCP calculation, we

propose a formal system that has rules for the analysis

of interactions and their representations.

After the interrelationships were found, the data

collected was strengthened. The notes of each student

were collected from the coordination of the College,

thus generating a general individual performance

average, and with this data, cross-references of

information between the analysis of the social

network and the averages were made.

https://cytoscape.org/ - Accessed 20/09/2019

5 DATA AND ANALYSIS

RESULTS

The experiment was developed on April 2, 2019

targeting students in 1st, 2nd and 3rd grades. The age

of the participants was between 15 and 17 years, and

the number of participants was 40 students. The

method used to collect and analyze the data was

through the empirical sieve, that is, a prior interview

was developed with 40 students regularly enrolled in

the first to third grades of a high school in a private

network and of teacher’s reports.

In this first graph that represents the

interrelationships of the first-grade students (Figure

2), we can see how the networks between them are

structured and with this we can conclude that I - the

student number 13, located in the right median corner,

has the highest average in the class. We can also

observe the 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

important data that we can observe in this graph is the

list of student’s numbers 05, 06 and 03 that are

located in the upper left corner. As can be seen, the

grades of the trio are below average and they do not

relate to anyone in their class. After these findings,

we sought more information about these students and

found that the three are repeat students.

We highlight here, that as mentioned earlier, these

were considered for this deeper analysis in the first

year of high school because it is a class where

students have lived together since the sixth year of

elementary school II. In other words, they have

maintained the same group as the previous year and

have maintained the interrelationships with the

friends who were approved. This leads us to

consider that there may be a cause-and-effect

relationship between poor performance because they

do not interact with peers in the current grade.

In this detailed chart we can consider a way of

approach to improve student performance, being the

implementation of a model of induced collaborative

work, where the teacher should naturally include

student number 13 to work with students’ number 05,

06 and 03. This way, the network of interest for his

grade is broken and failed students have the

opportunity to produce and be approved. The other

two series (Figure 5 and Figure 6) are presented here

only in their simplified form to highlight the issues

discussed in this article.

A Social Network Approach for Student’s School Performance Measurement

315

6 8

Table 2: Analysis input degree x grade 1

series High

School.

Student

Input degree Grade

Student

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

Figure 3: Pearson correlation coefficient for input degree x

grade 1

High School.

Figure 4: Social network graph of High School Third

Grade students.

Figure 5: Social network graph of Secondary School

students.

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 surveyed in order to prepare the

Pearson's Correlation Coefficient chart.

In Table 2 we have the number that represents the

student, which is a standardized numbering and does

not correspond to the actual call number made to

make it impossible to recognize the student by an

external agent. In Figure 3 we find the Pearson

Correlation Coefficient graph, which was calculated

by this data.

Table 3: Analysis input degree x grade 1

series High

School.

Student

Input degree Grade

Student

Input degree Grade

18 4 80,0 26 4 82,8

19 2 77,2 27 2 77,5

20 4 82,7 28 2 73,0

21 1 69,8

22 3 81,0

23 3 76,4

24 2 76,4

25 1 68,6

Figure 6: Pearson correlation coefficient for input degree x

grade 2

High School.

As can be seen (Table 3 and Figure 6), there is a

positive linear correlation between Input Grades and

Grades. In detail, this means that the higher the input

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 High School

analysis is 0.8030 we can conclude that this

correlation is strong.

Table 4: Analysis input degree x grade 3

series High

School.

Student

Input degree

Grade

Student

Input degree Grade

29 2 42,5 37 3 59,9

30 6 70,0 38 6 80,0

31 5 66,3 39 3 54,3

32 5 70,0 40 6 70,9

33 6 75,8

34 4 61,6

35 4 62,2

36 6 75,3

Pearson correlation coefficient’s:

r = 0.8030

Pearson correlation coefficient’s:

r = 0.8419

0,8 1.6 2.4

3.2

CSEDU 2021 - 13th International Conference on Computer Supported Education

316

Figure 7: Pearson correlation coefficient for input degree x

grade 3

High School.

As can be seen (Table 4 and Figure 7), there is a

positive linear correlation between the Input Grades

and Grades. In detail, this means that the higher the

number of input grades, the higher the student's score.

Considering that Pearson's correlation coefficient in

the analysis of the 3rd year of MS is 0.5686 we can

conclude that this correlation is strong.

Table 5: Analysis Input degree x 1

, 2

and 3

grade.

Student

Input degree Grade

Student

Input degree Grade

1 6 58,9 37 3 59,9

2 6 70,5 38 6 80,0

3 2 49,0 39 3 54,3

4 4 57,5 40 6 70,9

5 2 56,9

6 1 35,0

7 4 50,0

8 4 64,8

9 4 62,6

10 5 65,9

11 5 58,4

12 6 73,9

76,1

49,2

15 4 57,3

16 5 37,7

17 3 56,6

18 4 80,0

77,2

20 4 82,7

21 1 69,8

22 3 81,0

23 3 76,4

24 2 76,4

25 1 68,6

26 4 82,8

27 2 77,5

28 2 73,0

29 2 42,5

30 6 70,0

31 5 66,3

32 5 70,0

33 6 75,8

34 4 61,6

35 4 62,2

36 6 75,3

As can be seen (Table 5 and Figure 7), there is a

positive linear correlation between the Input Grade

and the Grades of the 3 high school series when they

are compared with each other. This may mean that the

higher the input grade number the higher the student's

grade. That is, in this experiment we can notice that

the more friends the student has, the higher is their

chance to achieve a grade. Considering that Pearson's

correlation coefficient in this case is 0.5000 we can

conclude that this correlation is strong.

However, the entry note and the note have a

strong relationship repeatedly, as we can observe in

the analysis, which indicates that the research can be

reproduced by other researchers through the

questionnaire applied to the students found in table 1.

As the results of this analysis show, that students with

more friends have a higher chance of obtaining a

grade. Also noted was that there is a strong correlation

between the networks of friendships and grades,

which allowed to verify that repeating students do not

fit in with students of the grade in which they are and

that students with good grades have many friendship

bonds, however these bonds are weak since they are

only bonds with interest in the notes.

6 COMPLETION AND FURTHER

WORK

The difficulties of interrelationship between students,

and the problems of implementing and using

technologies as pedagogical and inter-relational

tools is what motivated us to develop this research.

Technology can be a facilitator in this process of

implementing educational technology in schools, but,

according to Sancho (8), for this to happen, it needs

to be inserted together with a reflection and action

project that uses it in a meaningful way.

What is expected, is that the school is favoured

with tools, for the development of a method that

enables the creation of group works in class, it means

offering collaborative and inter-relational work

among students by developing processes that allow

the control of technologies and their effects, so that,

in this way, students can fully develop through the

interrelation with the aid of technologies.

We believe that a quality human structure should

be tangentiated between psychic (groups the

competencies for the so-called "superior" activities)

and somesthesis (groups the bodily functions

correlated to the so-called "human" activities), and

this tangentiously may be fostered through the

interrelationship between individuals instantiated in

Pearson correlation coefficient’s:

r = 0.5686

6 9

A Social Network Approach for Student’s School Performance Measurement

317

the opposing capacities through an intervention in the

process of diagnosed enturmation.

This research’s goal is to understand the results of

student’s social network analyses to help teachers

better understand groups and develop better

cooperative works., using a method to measure the

weight of social networks built among students and

diagnosing the processes of enturmatization in

evidence: hedonistic and utilitarian.

However, this action may favor new inter-

relational processes, and it is for this reason that we

propose the development of this method which allows

the measurement and diagnosis of the types of

enturmations which occur within the classroom. From

this method we may identify hedonistic and utilitarian

enturmentations and, consequently, an

operationalization of the tangent through collective

knowledge applications.

We applied this method associated with

storytelling technique and we realized that the

techniques of regrouping encouraged the teachers as

we could see in the comments made by them: “I

thought it was very special. These activities cause a

good movement in the College, they win and so do

we ... "and they also commented" It was a rewarding

experience, this type of activity is always something

enriching for both sides, but especially for

students..." (Siqueira et al., 2020).

As future work we indicate the reproduction of the

method in other schools mainly in public schools, the

increase of the method with the insertion of more

metrics and a next research with the comparative of

the evolution of the class after the implementation of

the method.

REFERENCES

Bastos, Cecilio Ricardo de C; Queiroz, Márcio Pedro C. P.

de. 2015. Use of educational social network in research

project at the university. Brazilian Symposium on

Informatics in Education (SBIE): 1270-1273.

Bezerra, Y.M.S., Correia-Neto, J.S., Santos, R.E.S., Vilar,

G., Magalhães, C.V.C., Queiros, L.M. 2014.Virtual

learning environments and virtual social networks:

preliminary results of a systematic mapping in the

context of information technology in education. In

Nuevas Ideas en Informática Educativa, TISE.

Degenne, Alain, and Michel Forse. 1999. Introducing

Social Networks.

Delbem, Edgar, sjr. (2014) Modelo computacional

colaborativo para mensurar o desenvolvimento inter-

relacional em alunos do Ensino Médio. Dissertação

(Mestrad oem Informática) – Universidade Federal do

Rio de Janeiro, Instituto de Matemática, Instituto Tércio

Pacitti Gessinger, R.M. (2008) “Atividade em grupo,

In: A gestão da aula universitáriana PUCRS”, editado

por Grillo, M.C., Freitas, A.L.S., Gessinger, R.M.,

Lima, V.M.R., EdiPUCRS, Porto Alegre.

Delbem, Edgar. 2014. Collaborative computational model

to measure interrelated development in high school

students. Dissertation (MSc in Informatics) -

Universidade Federal do Rio de Janeiro, Instituto de

Matemática, Instituto Tércio Pacitti.

Deroy-Pineau, Françoise. 1994 Reseauxsociaux:

bibliographie commenté. Montréal: Université de

Montréal.

Domingues, Diana Maria Gallicchio. (2004). Ciberespaço e

rituais: tecnologia, antropologia e criatividade.

Horizontes Antropológicos, 10(21), 181-197. DOI:

10.1590/S0104-71832004000100008.

Fuks, Hugo; Pimentel, Mariano. Sistemas Colaborativos.

Rio de Janeiro, Elsevier, 2011.

Liu, Z.; Kang, L..; Domanska, M.; Liu, S.; Sun, J. and Fang,

C. (2018). Social Network Characteristics of Learners

in a Course Forum and Their Relationship to Learning

Outcomes. In Proceedings of the 10th International

Conference on Computer Supported Education:

CSEDU, ISBN 978-989-758-291-2, pages 15-21.

DOI:10.5220/0006647600150021.

Marinho, É. C.; Ferreira da Silva, M.; de Souza Dias, D.;

Schmitz, E. and Alencar, A. (2015). Motivational

Factors and the Intention of Use of a Virtual Learning

Environment - A Preliminary Study about an e-

Learning Application Developed by the Brazilian

Ministry of Education in Proceedings of the 7th

International Conference on Computer Supported

Education - Volume 2: CSEDU, ISBN 978-989-758-

108-3, pages 477-482. DOI: 10.5220/0005429104

770482.

Marteleto, Regina Maria. 2001. Analysis of social networks

- application in studies of information transfer. In Ci.

Inf., Brasília, v. 30, n. 1, p. 71-81, jan./abr.

Sancho, J. M.; HERNANDEZ, F. et al. (Org). (2006).

Tecnologias para transformar a educação. Revista

Educar. Nº 28, p. 279-282. UFPR, Porto Alegre:

Artmed.

Souza Vieira, M. E.; López-Ardao, J. C.; Fernández-Veiga,

M.; Ferreira-Pires and Rodrígues-Péres (2018).

Prediction of Learning Success Via Rate of Events in

Social Networks for Education. In Proceedings of the

10th International Conference on Computer Supported

Education: CSEDU, ISBN 978-989-758-291-2, pages

374-382. DOI:10.5220/0006780703740382.

W. S. Moura, J. de O. Sampaio, J. B. S. França, A. Dias,

and M. R. S. Borges, “Colabsaber: a Pedagogical

Support Framework in the Collaborative Construction

of Knowledge / Colabsaber: Uma Estrutura Pedagógica

De Apoio Na Construção Colaborativa Do

Conhecimento,” Brazilian Journal of Development,

vol. 6, no. 12, pp. 103018–103031, 2020, doi:

10.34117/bjdv6n12-705.

Xavier, Jr., Joaquim Ferreira. A psicogenética –

demarcando os processos da vida. Tremembé: VespeR

Editora, 2004, p. 40-43.

CSEDU 2021 - 13th International Conference on Computer Supported Education

318