Collaborative learning with think pair -

Computer Applications: An International Journal (CAIJ), Vol.2, No.1, February 2015
DOI : 10.5121/caij.2015.2101 1
COLLABORATIVE LEARNING WITH THINK -PAIR -
SHARE TECHNIQUE
San San Tint1
and Ei Ei Nyunt2
1
Department of Research and Development II, University of Computer Studies,
Mandalay, Myanmar
2
Master of Computer Science, University of computer Studies Mandalay, Myanmar
Abstract
Today is a knowledge age so that world needs to become a more richer palace for everyone. Students can
learn their lectures and students can do their exercises on the web as individually or collaboratively with
their peers like directed by the teacher by using the think-pair-share technique. The system provides the
ability to clear to decide on their choices about the questions. The K-means clustering method is used to
modify the pair state and support for determining students’ grade of classes. The main objective of this
study is to design a model for java programming learning system that facilitates the collaborative learning
activities in a virtual classroom.
Keywords
Cooperative, Education, K-means, Learning, Teaching
1. Introduction
The usage of computers becomes a portal for variety of educational activities in which
collaboration among the lecturers and students. Communication deals with communities of
education which involves students, teachers. The learning method, Collaborative Learning is an
essential method that has facilitated the students to work in group with each other to have their
common academic goal.
The K-means method is evaluated the number of students with their related groups to participate
the collaborative learning of the courses.
Think, Pair and Share is the activity prompts pupils to reflect on an issue or problem and then to
share that thinking with others. Pupils are encouraged to justify their stance using clear examples
and clarity of thought and expression. Pupils extend their conceptual understanding of a topic and
gain practice in using other people’s opinions to develop their own. Therefore, the idea of the
system is to get collaborative learning java course by using the strategy of (TPS) and K-means
clustering methods is help the system to get the automated students groups. And then the student
marks will be shared within their groups by using their basic marks levels.

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2. Background Theory
2.1. Collaborative Learning
Collaborative learning (CL) provides an environment to enliven and enrich the learning process
[1].
Figure 1. Collaborative Learning Architecture
During the collaborative learning, proper communication and interaction among peers allow CL
features that must focus on the synchronous and asynchronous tools. In addition, the document
management should be considered as well. With the above discussion, the following Table 1
describes the features of collaborative learning [2].
Table 1. Collaborative Learning Features
CL Features Supporting Tools
Synchronous
Tools
- Audio Conferencing - Web Conferencing
- Video Conferencing - Chat
- Instant Messaging - Whiteboards
Asynchronous
Tools
- Discussion boards - Calendar
- Links - Group Announcements
- Email - Survey and Polls
Document
Management
- Resource Library - UpLoad/ DownLoad
2.1.1. Computer Support Collaborative Learning (CSCL)
Collaborative learning should be supported by a specific tool that is closely related to Computer-
Supported Collaborative Learning (CSCL). A CSCL tool which supports the collaborative
activities among teachers and students were developed. And it is named as CETLs; Collaborative
Environment for Teaching and Learning (system). CETLs applied the Think-Pair-Share technique
which allows the users (both teacher and students) to communicate and collaborate together,
using the three stages of the selected collaborative technique; think, pair and share [2].

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2.2. Collaborative Techniques for Learners
Collaborative Learning makes students to learn more intensely their education and to think about
their interest fields and to apply variety of settings.
There are many techniques available for collaboration. Some of the collaborative techniques are:
• Fishbowl
• Jigsaw
• Paired Annotations
• Think-Pair-Share
2.2.1. Fishbowl Technique
The first technique is Fishbowl which is also known a strategy in somewhere such as classrooms
and business meetings because of providing for not only a richer discussion but also community
to focus on the ways in which particular groups participate with their groups. Fishbowl is one of
the collaborative learning strategies [3].
The Fishbowl offers the class an opportunity to closely observe and learn about social
interactions. You can use it in any content area. This is a cooperative-learning structure for a
small-group discussion or a partner discussion [4].
2.2.2. Jigsaw Technique
Each small group works on some aspect of the same problem, question, or issue. Jigsaws
facilitate the group like the subgroups related with overall. It is needed to define to contribution
of topic if a Jigsaw has been applied [5].
Jigsaw is used as an efficient means to learn new materials. This process encourages listening,
engagement, and understanding by allowing each member of the group a critical part to play in
the academic process [3]. The jigsaw strategy also makes people who administrate a system to
develop the goal how to divide and shuffle students' group dynamically.
2.2.3. Paired Annotations
In Paired Annotations, two students compare their personal impression or commentary on an
article, story, or chapter. Students may be pair again and again to answer the same article, chapter
or content area so that students explore important facts and search for similarities and
dissimilarities about them [2].
2.2.4. Think-Pair-Share
This is a four-step discussion strategy which incorporates wait time and aspects of cooperative
learning [ 3]. Group members think about a question/topic individually, and then share their
thoughts with a partner. Large group summarized sharing also occurs[4]. This technique will be
describe next section.

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2.3. Think-Pair-Share Techniques for Learning
The technique provides to make discussion and sharing of individual's opinions and ideas. The
Think-Pair-Share method may take some practice [1].
CETLs stands for Collaborative Environment for Teaching and Learning (system). It is an
educational system which is purposely developed for schools to support collaborative activities
among teacher and students. In order to realize the collaborative process, CETLs applies Think-
Pair-Share technique for the teaching and learning process [2].
The ideas of Think-Pair-Share technique are concluded based on the study made from [6] and [7],
which is summarized in the following Table 2.
Table 2. Summary of the Think-Pair-Share
Description
What? Think-Pair-Share; a collaborative learning technique
Why?
To increase participation by allowing a group of collaborators to interact and share
ideas, which can lead to the knowledge building among them.
How?
Consist of three stages:
Think – Individually
Each participant thinks about the given task. They will be given time to jot down
their own ideasor response before discussing it with their pair. Then, the response
should be submitted to the
supervisor/ teacher before continue working with their pair on the next (Pair)
stage.
Pair – With partner
The learners need to form pairs. The supervisor / teacher need to cue students to
share their response with their partner. Each pair of students will then discuss their
ideas about the task, and their previous ideas. According to their discussion, each
pair will conclude and produce the final answer. Then they need to move to the
next (Share) stage.
Share – To all learners / collaborators
The learners pair to share their results with the rest of the class. Here, the large
discussion will happen, where each pair will facilitate class discussion in order to
find similarities or differences towards the response or opinions from various
pairs.
Think-Pair-Share technique is chosen to be applied in CETLs due to some reasons [6]. It is a
learning technique that provides processing time and builds in wait-time which enhances the
depth and breadth of thinking [7]. Using a Think-Pair-Share technique, students think of rules that
they share with partners and then with classmates in a group [6]. Therefore, it is pertinent to apply
this collaborative technique in CETLs.
2.4. Clustering
Clustering is a division of data into groups of similar objects. It models data by its clusters. Data
modeling puts clustering in a historical perspective rooted in mathematics, statistics, and
numerical analysis. Many types of clustering used in data mining as shown in the following:

5
• Hierarchical Clustering
• Linkage Metrics
• Hierarchical Clusters of Arbitrary Shapes
• Binary Divisive Partitioning
• Other Developments
• Partitioning Relocation Clustering
• Probabilistic Clustering
• K-Medoids Methods
• K-Means Methods
• Density-Based Partitioning
• Density-Based Connectivity
• Density Functions
• Grid-Based Clustering
• Co-Occurrence of Categorical Data
• Other Clustering Techniques
• Constraint-Based Clustering
• Relation to Supervised Learning
• Gradient Descent and Artificial Neural Networks
• Evolutionary Methods
Clustering is a division of data into groups of similar objects. Data modeling puts clustering in a
historical perspective rooted in mathematics, statistics, and numerical analysis [8].
2.5. Selection of Initial Means
Typically improvement of clustering is upgraded for user how to select in terms of selection of
initial means. Because these initial means are inputs of K-means algorithm, there are not
independent of K-means clustering,. Some people want to select initial means randomly from the
given dataset but others are not. The selection of initial means affects the execution time of the
algorithm as well as the success of K-means algorithm. Certain strategies make to gather better
results with the initial means.
In the simplest form of these strategies, K-means algorithm with different sets of initial means is
planned and then taking and choosing the best results deriving from the initial mean. When
dataset is considerable large and especially for serial K-means, this strategy is severe feasible.
Another strategy gathering better clustering results is to revise initial points method. There are
number of iterations to be closer to final K- mean from begin mean in possible case.
2.6. K-Means Clustering Algorithm
The idea, K-Means Clustering algorithm needs to divide in to different groups such as K clusters
within the given data set by defining the fixed- value of K.
There are four steps:
1. Initialization for algorithm
Define number of clusters and the centroid for each cluster.
2. Classification the Group
Calculate the distance for each data point with minimum distance from the centroid.

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3. Centroid Recalculation
The centriod will be repeatedly calculated.
4. Convergence Condition
Stopwhen a threshold value is achieved.
5. If all of the above conditions do not meet, then go to step 2 and the whole process repeat again,
while the given conditions meet [9].
Figure 2. Flow Chart Diagram of K-mean
In Figure 2, According to the algorithm k objects are selected as initial cluster centres, then the
distance between each cluster centre and each object are needed to calculate and to assign it to the
nearest cluster, to update the averages of all clusters, to repeat this process until the criterion
function converged. We define Square error criterion for clustering xij , the sample j of i-class,
the center of i-class, and the number of samples i-class, in fig. 1 [10].
(1)
We define K-means clustering algorithm as follows:
Step 1: Input: N = objects
cluster ={x1, x2 ,...,xn};
k= the number of clusters.
Step 2: Output: k = clusters;
with the sum of dissimilarity between each object;
its nearest cluster center is the smallest.
Step 3: Arbitrarily select

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k objects as initial cluster centers with m1, m2,...,mk;
Step 4: Calculate the distance between each object xi;
Calculate each cluster center;
then assign each object to the nearest cluster, formula for calculating distance as:
(2)
i = 1,2,...,N
j = 1,2,...,k
d (xi, mj) is the distance between data i and cluster j;
Step 5: Calculate the mean of objects in each cluster as the new cluster centers,
(3)
i=l, 2,...,k; Ni is the number of samples of current cluster i;
K-mean clustering is simple and flexible. And also K-mean clustering algorithm is easy to
understand and implements. Here the user needs to specify the number of cluster in advanced.
Because of K-mean clustering algorithm's performance depending on a initial centroids, the
algorithm provides no guarantee for optimal solution [11].
3.Design and Implementation
3.1. System Flow Diagram for Admin
In Figure 3, the admin or head are checked their validation such as name and password by
system. If the admin can pass the checking process of the system, he/she can make many
processes for the collaborative learning. After preparing exam, the admin makes the process of
specify exam date. Admin allows students to answer to the questions and group the students with
their education in their profile by using K- Means algorithm.
The admin can also view the students’ exam information. The pairing stage has two steps. In the
first step, admin chooses a number of students' group to answer them. The second step calculates
the grade with results of students’ examinations. According to Think- Pair- Share technique,
admin shares the students’ marks or grades for their group to know their conditions and what are
needed to study about Java programming. This section provides students how to learn and how to
promote their knowledge related Java Programming language. Admin needs to insert the
questions for lessons whatever he/she let to learn to students. In this system, we describe Java
programming as a example.
In short, there are four functions in admin section:
1. View Student Information
2. Specify Exam Date
3. Insert Questions
4. Group Students.
Our system aims at learning environment to be easy to learn about many fields. A person who has
responsibilities for teaching can change to any educational fields like Medicine, Engineering,
Economics and others. Admin always stores students' information in database to specify the
∑=
=
iN
j
ij
i
i x
N
m
1
1

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group and evaluate the performance of the students. And then he/she shows the results of students
after they answered questions. Also admin needs to insert the questions periodically.
Figure 3. System Flow Diagram for Admin/ Head
3.2. System Flow Diagram for Student
In Figure 4, the student is needed to check their validation such as name and password. If the
student can pass the checking process of the system, he/she can answer the exams. But the
examination date has already specified by the admin. The student is needed to fill his/her profile.
On the specify exam date the student can answer the examinations.
If there is no any exam date, the student cannot answer the questions. After the student finished
the basic exam, he/she cannot continue to advance level without passing the basic exam. The

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student can answer advance level questions when he/she passes basic level exam. Finally the
students can see their group's information and grades from share student’s information.
Figure 4. System Flow Diagram for Students

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3.3. Database Diagram for Collaborative Learning
Figure 5. Database diagram for Collaborative Learning
4. Conclusion
This system aids the students in order to promote active learning in computer based learning
environment. Our system can be a more simplicity and more suitability by using well-known
collaborative learning technique, the “Think-Pair-Share”. This system can provide the benefits to
specify the grades and group of the students by using K-mean clustering algorithm. The goal is to
support as a learning tool by using computer-based systems.
Acknowledgements
Our heartfelt thanks go to all people, who support us at the University of Computer Studies,
Mandalay, Myanmar. This paper is dedicated to our parents. Our special thanks go to all
respectable persons who support for valuable suggestion in this paper.

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References
[1]Schreyer Institute for Teaching Excellence, Penn State, 301 Rider Building II, University Park, PA
16802, www.schreyerinstitute.psu.edu, 2007.
[2] N. A. N. Azlina, "CETLs : Supporting Collaborative Activities Among Students and Teachers
Through the Use of Think-Pair-Share Techniques", IJCSI International Journal of Computer Science
Issues, Vol. 7, Issue 5, September 2010, ISSN (Online): 1694-0814, www. IJCSI. org.
[3] Grand Rapids Community College Center, "Ten Techniques For Energizing Your Classroom
Discussions for Teaching and Learning ", [On-line] http://web.grcc.cc.mi.us.
[4] SI Showcase, "The Basic Collaborative Learning Techniques", Supplemental Instruction Iowa State
University, 1060 Hixson-Lied Student Success Center, 294-6624, www.si.iastate.edu.
[5] E. Barkley, K. P. Cross and C. Major, "Collaboration learning techniques", San Francisco, CA:
Jossey-Bass, 2005.
[6] C. Opitz, and W. L. Bowman, " ", Elementary School, Anchorage School District, 2008.
[7] J. M. Tighe and F. T. Lyman, “Cueing Thinking in the Classroom: The Promise of Theory-
Embedded Tools”, Educational Leadership, 1988, Vol. 45, pp. 18-24.
[8] T. Yerigan, “Getting Active In The Classroom.”, Journal of College Teaching and Learning, Vol. 5,
Issue 6, 2008, pp. 19-24.
[9] P. Berkhin, Knoll and S. Jose, "Survey of Clustering Data Mining Techniques", Pavel Berkhin
Accrue Software, Inc.
[10] N. Kaur, J. K. Sahiwal and N. Kaur, “Efficient K-MEANS Clustering Algorithm using Ranking
Method in Data Mining”, ISSN: 2278 – 1323, International Journal of Advanced Research in
Computer Engineering & Technology, Volume 1, Issue 3, May 2012.
[11] P. Vora and B. Oza, “A Survey on K-mean Clustering and Particle Swarm Optimization",
International Journal of Science and Modern Engineering (IJISME) ISSN: 2319-6386, Volume-1,
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Authors
She is Associate Professor, Head of Department of Research and Development II in University
of Computer Studies, Mandalay, Myanmar. Her research areas include Information Retrieval,
Cryptography and Network Security, Web Mining and Networking. She received her B.Sc.
(Physics), M.Sc.(Physics) from Yangon University, Myanmar and M.A.Sc.(Computer
Engineering) and Ph.D.( Information Technology) from University of Computer Studies,
Yangon, Myanmar.
Author studied computer science at the University of Computer Studies, Lashio, Myanmar
where she received her B.C.Sc Degree in 2011. She received B.C.Sc(Hons:) in computer
science from the University of Computer Studies at Lashio, Myanmar in 2012. Since 2012,
Author has studied computer science at the University of Computer Studies, Mandalay,
Myanmar where her primary interests include web mining, graph clustering, grouping and web
log analysis.

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