The Web 2.0 has provided a significant increase in the use of online social networks. In this scenario, Twitter has being used for collaborating, communicating and to exchange ideas between users who share common interests. Consequently, it can be observed an increasing adoption of social networks as a resource to support learning outside the classroom. They can provide mechanisms for sharing ideas and discussions about the studying subjects. However, as far as we know, there is no consensus in the literature whether users indeed efficiently employ these resources for such purpose. An important question is: can the online social networks be used as an efficient learning tool? Helping us to find the answer, this paper presents empirical results of an experiment performed to evaluate the effectiveness of Twitter for supporting learning and also to identify the common behavior of its users.

1. Campus SorocabaCampus Sorocaba
Can the Online Social Networks be usedCan the Online Social Networks be used
as a Learning Tool?as a Learning Tool? A Case Study inA Case Study in
TwitterTwitter
Available in:
• Draft in english: http://www.dcomp.sor.ufscar.br/lzaina/papers/CCIS2014_draft.pdf
• Final version in english: Communications in Computer and Information Science (Print)
• In portuguese – RBCA Journal: http://www.upf.br/seer/index.php/rbca/article/view/2944
LERIS-Laboratory of Studies in Networks, Innovation and Software
www.leris.sor. ufscar.br
Federal University of São Carlos - Sorocaba, Brazil
Luciana A M Zaina, Tiago Almeida and Guilherme Torres

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MotivationMotivation
 Communication and collaboration tools such as blogs, wikis and
social networks have attracted billions of users, and online socialonline social
networksnetworks has surpassed the emailemail popularity.
 Online social networks can encourage students interaction.
 Twitter implements the conception of microblogging service.
 Short messages (limited to 140 characters)
 How to analyze the Twitter messages?
 Text mining techniques that allows us to identifyidentify and extractextract patternspatterns
from a set of messages.

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Paper ObjectivePaper Objective
 This work empirically examine the employing of TwitterTwitter
to supportsupport the outsideoutside classroom discussions.
 We have collected datadata through text mining techniques:
 find termsterms commonly usedused by teachersteachers and
studentsstudents
 And comparecompare professor’s messages to the
messages sent by students.
 The experiment was performed with undergraduate
student of a Computer Science course in a Brazilian
University.

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The ExperimentThe Experiment
 Observe the students behavior.
 the students are active agents.
 The course subjects:
 Web Development, Software Engineering and Entrepreneburship
 We collected the messages using the Twitter API
 Participants:
 2 Professors
 52 students
 The steps…

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PreparationPreparation
 We talked with the students about the use of Twitter
but…
 Any kind of recommendation regarding the messages
format were made by the professors.
 After 4 months, 1,794 messages were collected: 118
posted by professors and 1,676 by the students.
 The professors pointed out the most relevant keywords to
the teaching objectives and consequently to guide the
text mining process.

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1st Step – Messages analysis (I)1st Step – Messages analysis (I)
 From the collected messages we applied the text mining
techniques considering the pointed relevant keywords
 tokenized the messages and constructed arrays to
track the occorrenceoccorrence and frequencyfrequency of each relevant
keyword.
 compared two matrices of relevant terms: one from
the messages posted by the teachers and other from
the students ones.
 Almeida and Yamakami

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1st Step – Messages analysis (II)1st Step – Messages analysis (II)
 By the intersection set:
 there was a low frequencylow frequency of messages forward by
the students from the teachers’ original messagesteachers’ original messages
or even postingposting messages that contained the
relevant termsrelevant terms.

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2nd Step – Questionnarie2nd Step – Questionnarie
feedbackfeedback
 To understand our findings:
 a questionnairequestionnaire composed by eight questions was
elaborated and applied to the envolved students.
 From the 52 students who participated of the
experiment, 38 (73%) filled the questionnaire.
 Summarizing the answers...

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Access of Twitter and messagesAccess of Twitter and messages
sending.sending.
 What of the listed terms are you interested in reading
about? (The set of terms considered relevant by
professors was presented).
 the terms differ from the data of the mining process when
since the students retweeted only messages containing the
word "job".

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Access of Twitter and messagesAccess of Twitter and messages
sendingsending
We can observe the
passive behavior of the
students in Twitter,
acting as receivers of
messages.

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Access and relevance of theAccess and relevance of the
linkslinks
the messages they
received helped to
acquire new
information about the
topic of interest

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ConclusionsConclusions
 Comparing the results: text mining x questionnaire
responses:
 the students behaved as receivers of information.
 Although the most of students did not send or forward
messages with the relevant terms, they have agreed that
the posts contributed to the acquisition of new and
interesting information.
 So, we evidenced that despite the messages posted by the
professors have motivated the students, they usually notthey usually not
exchangeexchange the information with their colleagues.

13. Campus SorocabaCampus Sorocaba
AcknowledgmentsAcknowledgments

14. Campus SorocabaCampus Sorocaba
Thanks!Thanks!
lzaina@ufscar.brlzaina@ufscar.br
http://www.dcomp.sor.ufscar.br/lzainahttp://www.dcomp.sor.ufscar.br/lzaina
facebook.com/zaina.lucianafacebook.com/zaina.luciana