AUTOMATION OF HETEROGENEOUS GROUPING FORMATION TO SUPPORT COOPERATIVE LEARNING PROCESS
Cooperative learning is learning carried out together in a group to achieve a common goal. Many studies show that cooperative learning has a great importance on the success of one's learning. One popular cooperative learning method is Jigsaw method. The Jigsaw method requires heterogeneous f...
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| Format: | Dissertations |
| Language: | Indonesia |
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| Online Access: | https://digilib.itb.ac.id/gdl/view/47359 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Cooperative learning is learning carried out together in a group to achieve a
common goal. Many studies show that cooperative learning has a great
importance on the success of one's learning. One popular cooperative learning
method is Jigsaw method. The Jigsaw method requires heterogeneous formations
in groups and homogeneous between groups. The traditional grouping process in
class usually determined by the teacher, student or randomly. Determination of
student group formation conducted by students or random will produce an
incorrect formation. Determination of formation by the teacher is difficult
because of several factors, the number of students and the attributes.
The Jigsaw Method divides students into heterogeneous groups based on
attributes of race, ethnicity, ability and gender. However, this attribute is not
relevant to the school situation in Indonesia which has almost the same race,
Malay. Many heterogeneous grouping studies use different attributes, learning
styles, supported subject matter, etc. There are no fixed rules on what student
attributes are used in group formation settings.
There are many and varied types of student attributes that can be used. The more
number of students and attributes used, the more difficult it is for the teacher to
determine the group formation manually. Another obstacle is when cooperative
learning is held at the beginning of class meetings. In addition, the development
of e-learning is also now leading to cooperative learning where it is not possible
to determine the group formation process manually, so that this research is
needed to help education.
The accuracy of formation in a cooperative learning process is very important.
The right group formation is able to encourage cooperative learning optimally
and be able to increase student understanding as much as possible. Therefore,
this study aims to solve the problem of determining optimal group formation for
students based on the formation of the Jigsaw method by building a process model
that is able to classify students automatically. The Jigsaw group formation is
heterogeneous in groups and homogeneous between groups.
Determination of group formation in this dissertation based on dissimilarity
between students which is calculated using the dissimilarity between variables of
mixed type method. The method can be used to calculate the value of inequality
between students based on many attributes possessed by students. This method
also eliminates student attributes that are of equal value. This makes the system
able to accept various types of attributes without limitations, so that this system
can be used in the school environment or online learning environment (elearning)
under any circumstances (without limitation).
The main contribution of this dissertation is the group formation optimization
method with the Fixed Root-Optimization algorithm approach. The optimization
process of determining the proposed formation begins with the process of
determining Fixed Root. Fixed Root is a pair of students who have the highest
inequality between students. Fixed-Root is sought as many as the number of
groups formed. Fixed-Root is not a way to determine the chairman of cooperative
learning in a group. The remaining members of each group were carried out by
an optimization process that kept the heterogeneity level in each group high.
Random, Switching and Fixed Root-Random grouping method were also
performed as comparison algorithms. As a result, the Fixed Root-Optimization
algorithm is able to provide heterogeneous fitness values in a fairly high group
(0.6) and the fastest grouping time (0.4s). Based on Univariate ANOVA analysis,
heterogeneous fitness values are considered very significantly different than other
algorithms. Measuring the value of homogeneous fitness between groups is also
done. Based on Bartlett's homogeneity test each group produced is considered
homogeneous. Complexity of the Fixed Root-Optimization method is O(mn), m is
the number of groups and n is the number of students. Student formation
generated by the Fixed Root-Optimization method increases the cognitive value of
students as well as the cognitive value of students learning in the group formation
produced by the teacher. Based on the experimental results and the analysis of the
Fixed Root-Optimization algorithm, it is able to form the formation needed by the
Jigsaw cooperative learning method. |
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