three dimensions, namely: classroom practice,
cognitive development and learning attitudes.
Correspondingly, Ali Gunay Balim (2009) revealed
that GeoGebra is able to present an overview so that
students can understand the material. Use of
GeoGebra is very easy. Given interface makes the
students more interested in the subject presented by
the teacher.
2 DISCOVERY LEARNING
MODEL
Discovery learning model is defined as a learning
process that occurs when students are not presented
with a lesson in its final form, but is expected to
organize themselves. It is more emphasis on the
discovery of concepts or principles that were
previously unknown.
In applying the discovery learning model,
teachers act as mentors by providing opportunities
for students to learn actively, the teacher should be
able to guide directly the learning activities of
students in accordance with the purpose. Conditions
such as these will change the teaching and learning
activities from teacher-oriented to student oriented.
The following are the phases in the discovery
learning model:
1. Stimulation. Teachers raise the question or ask
the students to read or hear a description that
includes the issue.
2. Problem Statement. The students were given the
opportunity to identify problems and formulated
in the form of a question or hypothesis.
3. Data Collection. To answer a question or to
prove the hypothesis, the students were given the
opportunity to collect data and information
needed.
4. Data Processing. Event processing data and
information has been obtained by the students,
and then interpreted.
5. Verification. Based on the results of processing
and Opera-existing hypotheses formulated
question should be checked beforehand. Can it
be missed or well proven that the results are
satisfactory.
6. Generalization. In this last phase the students
learn to draw certain conclusions and
generalizations.
Illahi (2012).
A basic concept of discovery learning is that
teachers should facilitate instruction that allows
students to discover predetermined outcomes
according to the level of learning required by the
curriculum 2013, Mandrin and Preckel (2009).
Hopefully, students will pose relevant questions
such as "what if the variables is fewer than the
system?" or "what if the coefficient is the multiple of
other systems?" Discovery learning allows for
deeper thought into the subject.
As an introductory activity, the teacher, acting as
facilitator, should prompt students to recall
knowledge and experiences from previous lessons,
and encourage student participation. The teacher
should then guide students in applying already
existing knowledge to new information to construct
deeper levels of meaning and understanding. This
gives students an active opportunity to apply what
they already know about the topic to the new
situation, (Schunk, 2008).
After introducing the purpose of the lesson, the
teacher describes the materials that will be used in
the experiment and then models the actions and
procedures for the students, GTC (2006). Students
begin the actual lesson by asking questions, guided
by the teacher prompts, and then try to guess at
possible right answers.
3 SYSTEM OF LINEAR
EQUATIONS
In mathematics, a system of linear equations is a
group of two or more linear equations that involving
the same set of variables. For an example, The
following is a linear system of three equations
consisting of three variables
4𝑥 − 2𝑦 − 3𝑧 = 6
5𝑥 + 3𝑦 − 4𝑧 = 2
−𝑥 − 𝑦 + 2𝑧 = 0
A unique solution to that linear system is an
assignment of values to the variables such that all
the equations are simultaneously satisfied. A
solution to the linear system above is given by 𝑥 =
1, 𝑦 = −1, 𝑧 = 0. since there’s no other solution,
the solution is said to be unique solution. Since the
solution set value of (x, y and z) of this problem is
satisfy the equation, the word system indicates that
all the three equations are to be considered
collectively, rather than individually indeed.
The role of technology will be needed in solving
the problem of linear systems that have many
equations. The theory of linear systems is the basis
and a fundamental part of linear algebra.
Computational algorithms for finding the solutions
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