Post on 24-Feb-2017
Using Twitter as a Diagnostic Teaching and Learning
Assessment Tool Bahar Karaoğlan
#, Cemre Candemir
#, Elif Haytaoğlu
#,
Gül Boztok Algın#, Sercan Demirci
#
#International Computer Institute, Ege University
Izmir, Turkey, 35100
{bahar.karaoglan, cemre.candemir, elif.acar, gul.boztok, sercan.demirci}@ege.edu.tr
Abstract— Higher education students coming from different
regions and schools have different interests and knowledge levels.
These differences can be exploited by teachers to improve the
course efficiency. Knowing beforehand the misconceptions and
the prior knowledge of the students, the teacher can tune the
content of the lecture accordingly. In traditional systems, short
essay, multiple choice or true-false diagnostic quizzes that
include several potential misconceptions related to the targeted
learning, are often practiced for this purpose. This approach
reveals the differences in prior knowledge, misconceptions and
deficiencies in prerequisite skills amongst the students. The
teacher armed with this information can organize both the
content and the structure of his/her teaching more efficiently.
In this paper, we propose using Twitter as a diagnostic
teaching and learning assessment tool. In this scenario the
teacher tweets hashtags related to key concepts or
misconceptions. The comments of the students are retrieved
using Twitter APIs and stored in a local database. The teacher
views and analyzes the retrieved data to tune her/his instruction.
After lecturing, the same hashtags are sent and responses are
collected. Analysis of the data before and after will reveal how
much learning is achieved. Besides, this tool will enable
instructors to provide some hints to students about the topic of
the lecture and engage students more through the use of social
media.
Keywords— twitter, learning assessment, diagnostic assessment,
lecture tuning, microblogging
I. INTRODUCTION
For higher education courses, the engaged students may
have different levels of prior knowledge and also they may
have various interests which may affect their personal learning.
Since students come from diverse regions and schools, their
learning style preferences also may be different. These
differences decrease the chance of success of a teaching
system which is treating unique to all students. In classical
classroom, summative assessments are often used as a
technique to measure the level of student’s learning after the
completion of a unit or a module. While this is a good way of
checking the success of the teaching period, post assessments
occur too late to influence the period itself. Recent studies [1]-
[3], show that regular use of ongoing assessments, with the
aim to improve the course’s efficiency to make it intelligently
cover all level of students, gives brilliant contributions for
student’s learning results.
Diagnostic assessments enable instructors to have
feedback about the student’s knowledge and needs. Tuning
the course content by considering these data will improve the
course efficiency by shortening the already known parts and
by emphasizing the deficiencies and misconceptions. Besides,
pre-assessments are good means to determine how to teach the
planned content according to the student’s learning skills and
interests. This timesaving strategy also provides a means to
compare the pre and post knowledge of the students at the end
of the teaching process. From the student’s perspective,
diagnostic assessments provide a way to have information and
to think about the incoming topic. It makes students to focus
on what the instructors expect them to learn, whilst they create
a learning goal for themselves before instructors assess them
finally with grades [3].
The classical assessments as well as the whole teaching
and learning approaches change with the innovative
technologies. According to [4], with the information
revolution following the changes in communication
technologies, the importance of what an individual knows is
shifted to what an individual knows how to find out.
Connectivist perspective supports this idea by indicating that
the process of learning is enhanced by anything that increases
the connections among students and instructors and online
resources [5]. Therefore, connectivism theory is somehow
aligned with the learning mechanisms using social media [5].
In such learning environments students can have many
communication abilities with other students which may lead
informal learning by information sharing. Actually, the social
media is already one of the daily habits of young pupils, and
they commonly use it to communicate easily with a
widespread user group and to share and gain information,
media and ideas. Twitter and Facebook are amongst the
mostly used microblogging social media environments [6].
Recently, this easily accessible platform is considered as a
good candidate for a stress-free non-threatening sharing
medium for learning assessments. Since new generation
students are tightly connected to social media, this can be an
opportunity to transfer this habit to the process of learning and
teaching in order to enhance the learning process and increase
the engagement of students [5]. This opportunity triggers a
question arisen in the literature: How can these
communication abilities are exploited as a learning
environment? Although there are many studies conducted,
which aspects of the learning can be improved by social media
remains a significant research area [5].
978-1-4799-4205-3/14/$31.00 ©2014 IEEE
According to the recent studies, students prefer using
twitter because of its anonymity, accessibility and
connectivity [7]. Also students report that twitter provides
enhanced learning about the subject, greater enjoyment of the
module, concise and useful communication, timeliness and
greater realism [8]. On the other hand, the students also value
that the accessibilities of the instructors become more
approachable through social media [5].
Moreover, [9] addresses the usage of microblogging to
encourage informal learning. [9] and [10] listed the
functionalities presented by microblogging in the educational
concepts as “asking questions, giving opinions, changing
ideas, sharing resources and reflection”. The instructors have
used these functionalities in a learning environment to draw
the attention of the students on the subject and encourage
them to participate and articulate their own ideas [11].
The functionalities mentioned above may offer more than
being just an informal teaching platform. Some analytic data
can be extracted from the related communications which
contains clues for directing the lecture for increasing the
efficiency of the learning. This data can be exploited to assess
students’ learning level via pre and post-tests which are
conducted on Twitter. Using these pre-tests, the instructor can
tune her/his lecture content by focusing mainly on the
misconcepted and unknown subjects. The pre-tests can be
conducted through sending hashtags before the lecture.
Moreover, the post-tests provide a learning level of the
students. These post-tests can also be employed on the Twitter
using hashtags.
In this study, twitter is proposed as a diagnostic teaching
and learning assessment tool to improve the course efficiency
in higher education. It is hoped that this tool serves (1) the
assessment of learning via pre and post-hashtags (2) the
opportunity for students to have some hints about the
upcoming lecture; (3) a feedback to instructor for tuning the
content of the lecture; (4) motivation of students via using
social media for learning. The sections of the paper are as
follows: in Section 2, a survey of related work is given; in
Section 3, the framework of the developed tool is presented
and finally, in Section 4 we give concluding remarks.
II. RELATED WORK
As the smart phones and tablets become more accessible,
interest in the usage of social media in many different ways
arouse. Because the idea of using social media for educational
purposes goes very well with both in school and lifelong
learning, many researches and experiments are done on the
subject. Here, we will give brief overview of the current use
of Twitter which is a widely used social media by both the
instructors and the students. From the work done in the
literature we can see that twitter
has shifted teaching and learning techniques to
microblogging applications.
has been used as a tool to engage the students
has been used as a tool to improve language learning
and communicative competence
The studies in [9], [10] and [12] mention the use of
microblogging applications in teaching and learning. In [9] the
usage of Twitter on the area of process-oriented learning in
higher education is investigated. The study points out that
microblogging can be used as a new form of communication
in informal learning. The authors of [10] summarized benefits
and drawbacks about Twitter as an educational tool. From the
point view of students, exploiting Twitter for classroom
activities to get helpful information and for instructors posting
lecture notes via Twitter is amongst the benefits of Twitter.
However, some drawbacks of using Twitter are also noted as
that, it is a time consuming task and it has limited number of
characters. In [12] improving the face-to-face lectures using
Twitter is proposed. During lecture presentation with Twitter,
students ask questions by sending tweets. Also additional
lecture material can be added or students share their ideas
using Twitter while presenting the lecture.
Some of the studies in the literature are focused in using
Twitter as a tool to engage the students like [5], [13] and [14].
Twitter is used for communications amongst undergraduate
students and instructor for 12-week course in [5]. These
communications are analyzed and the results of these
evaluations emphasize that the increase in the level of the
usage of Twitter effects the student engagement positively.
Moreover, according to the study the class attendance is not
related with the usage of Twitter. To understand the practices
of scholars’ on the Twitter network is investigated in [13].
One of the findings of this study indicates that students use
twitter to engage in social network make connections with
other. The study in [14] states that Twitter can impact student
grades and engagement. This study has two groups. The first
group used Twitter and the second group did not. The final
grades for the first group were significantly improved
compared to the second group.
In [15]-[18], improving language learning and enhancing
students’ communication skills are referred. In [15], authors
propose an experiment to analyze the benefit of Twitter to
learn a new language. This study shows that Twitter can help
students to make language communication practice instead of
only oral communication in class environment. In addition, it
is implied that Twitter can be used to form collaborative
community for language learners that share their ideas by
using Twitter in [16]. The study in [17] explores how to utilize
Twitter as a teaching method to learn basic history course with
sharing some readings in Twitter. Students follow each other
and discuss about readings using Twitter. With predefined
hashtags, instructor follows discussions of the students about
readings. During the class, verbal discussions are conducted in
detail after one week of the twitter meetings. The study
indicates that the usage of social media can facilitate the
discourse skills of students. The authors in [18] points out that
the usage of Web 2.0 applications can encourage students to
in terms of communication.
Apart from the studies aforementioned, the results of the
survey from [19] report that most of the academicians do not
even use Twitter since they don’t see the means of it in
education. They also expressed the limited character size in
tweets may limit the expression of ideas. A recent surveys
[20], [21] shows that a very small amount of academicians
used Twitter in class. A larger but yet still small amount of
academicians believe that Twitter is a valuable tool for
classroom use.
III. FRAMEWORK
Our study considers using Twitter as a diagnostic teaching
and learning assessment tool. In particular, the aim is to
investigate tuning content of the lecture by using Twitter
before lecturing. On the other hand, by analyzing the tweets, it
can be revealed how much learning is achieved by twitting
before and after the lecture.
In Twitter, the ‘#’ symbol denotes a hashtag. Hashtag is
used to mark a certain keyword or a topic in tweets [22].
Hashtags categorize the tweets and help to find easily as a part
of specific topic in Twitter search. When a hashtag is searched,
every tweet can be accessed including that hashtag. So, as the
first step instructor must define a good hashtag. A good
hashtag must be quiet short, without using too much of the
140 character limit, logical, which makes it easy to remember,
and unique, which keeps it confined to the class only [23]. As
can be seen in Fig. 1, after defining the hashtag(s), related to
the content of the lecture, the instructor tweets them. Also
he/she make sure to tweet the hashtags quite early and remind
all the students that they are going to use that hashtag in their
responses. Students tweet their comments, questions, videos,
web links or images including the hashtag and these tweets are
stored in a local database with the hashtag. The instructor can
evaluate the students’ interests, prior knowledge,
misconceptions and level of knowledge before the lecture and
according to this evaluation; he/she can tune the content of the
lecture. Also, hashtag and related tweets can be used for the
discussions during the session. Such organized discussions
may be helpful for enhancing the students’ knowledge with
different levels. Besides these, learning can be assessed by
twitting the same hashtag after the lecture. The same hashtag
can be sent with short or long delays depending on the
lecturer’s demand.
Fig. 1 Pre-processing for the lecture through sending hashtags related the keynotes and receiving tweets
Students at different ages, from different regions or
graduated from different kind of high-schools have different
knowledge, thoughts and experience. These data are
significant and valuable; so, storing some demographic data in
a database, such as their high school, age, country or region
may be useful for the instructor to estimate the prior
knowledge of the students while tuning the content of the
lecture.
Since all sent hashtags related to topics of the lecture are
stored in the database, the instructor can perform some
analysis such as which hashtags are sent in which topic, how
many hashtag and tweets are sent and replied in each topic by
querying from the database. A sample query output asking for
the complete list of all tweets that were sent before the lecture
about a certain hashtag could be applied similar to the one in
Table 1.
TABLE I
EXAMPLE OUTPUT OF A SAMPLE QUERY
Tweet-id Tweet Twitter
username
Tweet
date
774873xxx
can we use the database of a
#ubi_rulebasedsystems to make a
decision tree?
student1 18.4.2014
673592xxx #ubi_rulebasedsystems why do we start
at the 3rd tile in path smoothing alg.? student2 17.4.2014
635434xxx There is a relationship between artificial
intelligence and #ubi_rulebasedsytems. student3 17.4.2014
495873xxx #ubi_rulebasedsystems consist of a list of
rules student4 16.4.2014
239789xxx we cannot run in parallel the
#ubi_rulebasedsystems student5 16.4.2014
These analyses provide a general perspective to the teacher
about the lecture and the students. Furthermore, it gives an
idea about common misconceptions of students, and that
guides to the instructor to form his/her lecture.
The local database is needed to store some information
mentioned above. The structure of the database is composed
of three tables: (1) student-table, storing students’ information,
(2) tweet-table, storing hashtags and tweet information, (3)
course-table, storing the topic of the course and hashtags
about the topics. Organization of the database is depicted in
Fig. 2.
Fig. 2 Database design for a single course
Full framework for diagnostic teaching and learning
assessment tool is illustrated in Fig. 3 below.
Fig. 3 Framework for diagnostic teaching and learning tool
IV. CONCLUSIONS
Diagnostic assessments are widely used to collect data
about students including their prior knowledge, skills, and
abilities, and also their interests and misconceptions before
lecturing the course. Using this data, instructors become able
to intelligently tune their course content according to the
needs and skills of the targeted students. In this study, one of
the widely used social media tools, Twitter, is used as a
diagnostic teaching and learning assessment tool. With this
tool, the instructors can draw the attention of the students on
the important points and encourage them to participate and to
articulate their own ideas. While they can collect some data
about the pre-knowledge, they can also assess the learning
level of the students by comparing pre and post-hashtags. As
for students, using this tool, learning goals can be constructed
by motivating on what the instructors expect them. Moreover,
students may have an opportunity to express themselves in a
stress-free platform.
For larger classrooms and distance learning programs,
automated clustering of the tweets of a related hashtag may be
performed using text mining techniques. The collected data
may be clustered according to important keywords. With this
clustering mechanism, the instructor may figure out which
subjects are mostly misconcepted or already known or easy to
comprehend. Such a clustering solution is considered as a
future work.
This work, by introducing Twitter as a tool for learning
assessment and diagnostic teaching, relates well with the
objectives of WP4 of SALEIE Project (Project Ref. 225997-
CP-1-2005-1-FR-ERASMUS-TNPP, October 2005 to 2008)
funded by the European Union. The objective of this work
package is to enhance the competitiveness of EIE education
within Europe, especially in relation to modern global
technical challenges.
ACKNOWLEDGMENT
The authors wish to thank the European Commission for
the grant to SALEIE (Strategic Alignment of Electrical and
Information Engineering in European Higher Education
Institutions) Project: 527877-LLP-1-2012-1-UK-ERASMUS-
ENW.
The authors also thank to Lecturer Kaya Oğuz for sharing
his Twitter data.
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