THE EFFECTIVENESS OF GEOGEBRA-BASED VAN HIELE MODEL ON MATHEMATICAL DISPOSITION ASSESSED FROM EARLY MATHEMATICS ABILITY

The aim of this research was to observe the effectiveness of geogebra based van hiele model on mathematical disposition assessed from early mathematics ability. The accessible population of the present study was students of Prof. DR. HAMKA Muhammadiyah University. Samples of this research is semester two students from mathematics education program. Due to the happening Covid-19 pandemic, the present study was conducted online. The method employed was quasi-experiment using factorial treatment by level 2 × 2 design. The data were collected with mathematical disposition questionnaire and early mathematics ability instrument. After the data were collected, they were then analyzed with two-way ANAVA analysis. The result showed that there was no significant difference in the increase mathematical disposition of students who are given the learning model and early mathematics ability. The conclusion obtained is that the geogebra based Van Hiele Model is ineffective on students’ mathematical disposition in terms of their early mathematics abilitiy. This is due to several factors, namely internal factors and external factors. The internal factors are decreas in the score of mathematical disposition from pretest to posttest, lack of understanding applying geogebra software with online learning. External factors are quotas and poor internet network. ARTICLE INFORMATION


INTRODUCTION
We are currently in the beginning of the fourth industrial revolution or simply industry 4.0, in which cyber and digital industry have become a reference for people's lifestyle, especially in education. As science and technology keep developing, mathematics education should also adapt itself to this industry 4.0. Teaching methods from previous era should also be updated and followed by mathematics teachers, that is by making use of ICT and software in the learning process. One of mathematics subjects in college that could use software in learning process is geometry.
Learning geometry is helpful in daily life. As for example, a student is required to make an equilateral triangle, that student has to first understand the concepts of geometry principles in order to avoid mistakes when making the triangle. The student could use a postulate that drawing equilateral triangle could be started from drawing a circle. (S)he could use a compass in the process of making the triangle. An understanding of geometry is required in determining a shape. Most students consider geometry to be a difiicult subject because of its abstract concepts. This makes students less confidents in solving geometry problems. This results in students seeing that mathematics is difficult to understand and students' interest in learning mathematics is reduced (Nurfitriyanti, 2017). This is in line with research by Nopriana (2015) which states that problems that are not understood by students allow students not to have a strong determination to solve math problems. Lack of confidence in learning mathematics, lack of interest in solving math problems ahows that the mathematical position of students is still low. Based on the results of the TIMSS report in 2011 regarding the attitudes of Indonesian students at level 8 towards mathematics compared to Malaysia and Singapore, it can be seen in Based on the 2011 TIMSS report on students attitudes towards mathematics, it can be seen that the attitude of Indonesian students who like learning mathematics as much as 20% has an average below the international average while the attitude of disliking learning mathematics has better results among developing countries. Namely 10% and as many as 70% of Indonesian students who rather like learning mathematics. From the facts above, it shows that the attitude of students in liking mathematics is still low. The low attitude of students in liking to learning mathematics can be the basis for fostering a positive attitude towards mathematics. Positif attitudes that can be developed such as interest, interest, perseverance, confidence and confidence to explore solving mathematical problems. The development of an interest in mathematics will form a strong tendency (Widyasari, Dahlan, & Dewanto, 2016).
The low positive attitude towards mathematics is the low students' disposition. This is in line with (I. Handayani, 2018), disposition is a belief accompanied by positive actions towards mathematics. The low mathematical disposition of students is possible because it does not relate material to uses in everday life, so learning seems boring. This is in accordance with (R. Rahayu, Kartono, & Sulhadi, 2014). Mathematical disposition is defined as the tendency to see mathematics as something that can be understood, something that makes sense and is usefull believing that effort, perseverance in learning mathematics will provide good results and act as an effective student.
One of the students' affective in learning mathematics is known as mathematical disposition (Jatiariska, Sariyasa, & Astawa, 2020). According to Kemendikbud (2016) the affective domain determines a person's learning success. This is in line with Kusmaryono, Suyitno, Dwijanto, & Dwidayati, 2019;Lin & ChunTai (2016) teacher need to focus not just on the development of math knowledge and skills, but on the development of the mathematics disposition and also should optimize the dispositional mental functions of the students by selecting a learning model, affective and conative. Mathematical disposition is, therefore, strong willingness, awareness, dedication, and tendency of a student to think and act mathematically in a positive manner (Akbar, Hamid, Bernard, & Sugandi, 2017). National Council of Teachers of Mathematics stated that mathematics disposition includes the ability to take a risk and explore solutions to various problems, dedication to solve challenging problems, take responsibility to make a job done, respect the power of communication of mathematics language, willingness to ask questions and propose mathematical ideas, have high self-esteem, and treat a problem as a challenge (NCTM, 2000). This all could be seen when a student is in a learning process and solve mathematics problem. Thus, mathematical disposition are abilities that are very important for students, because they can affect many things to learning mathematics and daily life.  Suryawan, & Prabawati, 2020). In the information stage, students ask questions to stop initial skills before learning Euclidean geometry. At the directed orientation stage, students make observations to understand a concept. At the affirmation stage, students can explain both verbally and as a form of communication of their ideas. In the free orientation stage, it allows students to solve more complicated Euclidean geometry problems. The last stage, namely integration, allows students to reflect on the geometric thinking process.Van Hiele's theory is a theory about the level of thinking of students in studying geometry, one of which is a flat shape, where students cannot go up to a higher level without passing the lower level (Musa, 2018). The most prominent learning begins with the "van hiele model" by Dina Van Hiele Geldof and Piere Marie Van Hiele. Van Hiele's theory plays a very important role in the rules of understanding geometry, interpretation and determining the level of development of geometric thinking.The levels are visualisation, analysis, informal deduction, formal deduction, and rigor (Abdullah & Zakaria, 2013). The first level is visualisation level. At this level, students are able to introducing and naming geometric shape.
The second level students are able to identify the properties of certain shapes. The third level students have done sorting or deduction informal. The fourth level students thingking stage has developed, student can appreciate the meaning postulates, theorems, and proofs. Finally, the fifth level in Van Hiele's model is rigor. Students come to thingking complex geometry and understand how to work in an axiomatic system.
To transform high level of spatial imagination to be a real visualization, a software is required. GeoGebra is a mathematical software that combines geometry, algebra, and calculus in which this software could help provide clear information in a form of an image and understanding mathematics concept, therefore GeoGebra is beneficial for both students and lecturers (Siswanto, 2015;Zulnaidi & Zamri, 2017). Attitudes and habits of good thinking will form and develop a good mathematical disposition, in addition to the learning model, student early mathematical ability also affect students' mathematical dispositions. The habituation of the directed Van Hiele Model Learning stage can provide a positive experience and a good impression for students so that learning geometry becomes closer.
According to Akinsola & Odeyemi (2014), early mathematics ability affects students in interpreting new information and decide whether or not the information is relevant. The development of students' knowledge uses their early mathematics ability to connect already understood concepts to understand a new one. Early ability is considered important prerequisite to learn new concept and could form individual knowledge and result of the learning process (F. Handayani & Lestari, 2019;Noviana, Suyono, & Hakim, 2018;Widodo, 2016). Early ability possessed by the students could also provide assessment whether all information and concepts understood are related to new concepts or topics being learned. Given that fact, the students would be motivated to learn topics that are being learned. (I. Handayani, 2018) seconds this with her statement that stated students who have early ability about the concept that is related to new topic would feel enthusiastic in learning the topic, it is due to the fact that they already understand the concepts related to the topic discussed. Based on what has been described above, the purpose of this study is to determine the effectiveness of the geogebra based van hiele model on mathematical dispositions in terms of students' early mathematics ability.

METHOD
The method employed was quasi-experiment using factorial treatment by level 2 × 2 design and research design is non equivalent pre-test and post-test control group design (Ruseffendi, 2010) The present study design is illustrated as follows: Random sampling is used when determining class. Classes were selected by random sampling so that 4 classes, namely class 2B and 2C were treated using the geogebra based van hiele model (as an experimental class) and class 2A and 2E were given conventional learning treatment (as a control class). The independent variable in this study is the Van Hiele Model Based on Geogebra and conventional learning, the dependent variable is mathematical disposition and the control variable is the students early mathematics ability which is divided into two categories, namely high early mathematical ability and low early mathematical ability.
The collecting data in this study used a mathematical disposition questionnaire and an instrument of early mathematics ability test. The early mathematics ability tes was carried out before being given treatment. Early mathematics ability were obtained from the results of the Euclid Geometry mid semester student exam even semester 2019-2020. Furthermore, from each experimental class and control class, a total of 33 1 3 % ( = = 33 1 3 %) where the numbers were quite contrast and reliable (Naga, 2009) As many as 33 1 3 % of the students with the highest score were the sample with high early mathematical ability and 33 1 3 % of the students with the lowest score became the sample of the study with low early mathematical ability. Furthermore, at the begunning of the study students were given a mathematical disposition pretest and athe the end of the study, students were given the same questionnaire, namely the mathematical disposition questionnaire.
Technique of analyzing students mathematical disposition data in term of students; early abilities begins with the prerequisite test, namely the normality and homogeneity test. Then the research hypothesis test used two way ANAVA with help of SPSS 21. Two way ANAVA was used to test the differences in mathematical dispositions between students treated with the Geogebra-based Van Hiele Model and coneventional learning. If there is a difference, the proceed with the t-test to test the difference in the average value of mathematical dispositions based on the sample group.

RESULT AND DISCUSSION
Student pretest data were obtained before getting treatment. This data is used to determine the initial state of students before getting treatment. The following is the data pretest result for the experimental and control classes. Based on Table 2, the average pretest mathematical disposition results for the Geogebrabased Van Hiele Model class at higher early mathematics ability and lower early mathematics ability are higher than conventional learning classes. Furthermore, the data normality test was carried out. The normality test aims to determine the distribution of the selected sample from a normal or abnormal population (Kadir, 2010   Based on Tabel 5, the average results of the mathematical disposition of students treated with the Geogebra-Based Van Hiele Model at high early mathematical ability are lower than students who are given conventional learning treatment at high early mathematics ability. But the average results of the mathematical disposition of students treated with the Geogebra-based Van Hiele Model at low early mathematics ability were higher than students who were treated with conventional learning. Furthermore, the data normality test was carried out. Following are the results of the normality test of the experimental class data. Based on table 6, it can be seen that the significance value for the Shapiro-Wilk test of students who were treated with the Geogebra-based Van Hiele Model and students who were given conventional treatment were 0.926 > 0,05 and 0.503 > 0.05, so that H0 was accepted. So, it can be concluded that the two pre-tests data are mathematically distributed normally. Because the two samples come from normally distributed data, a homogeneity test will be carried out to find out whether the two samples come from populations with the same variance or not. The homogeneity test was carried out using the Levene Statistic test.  In the results of data analysis during the learning during the learning process, it is known that the results of the mathematical disposition questionnaire that were treated with the Geogebra-based Van Hiele Model obtained 47% or as many as 14 students experienced a decrease in scores from pretest to posttest from a total of 30 students treated with the Geogebrabased Van Hiele Model. From the observation in direct experiment through online learning due to Covid-19 pandemic, it is found that many obstacles faced by the students in experiment proses in GeoGebra-based Van Hiele model application were found, namely limited data plan, unstable connection, and the lack of students understanding to operate the GeoGebra software when learning online. In addition, understanding pictures in GeoGebra software is very necessary, meanwhile Perbowo, Hadi, & Haryati (2017) research shows that there are 16.67% of students on low level having a difficulty on representing figures/mathematical object into mathematical model; 70% of students on medium level having a trouble on understanding the information given in the problem and the way they express it into symbols, and 13.33% of students on high level do not provide clarity within every step taken in their answer. This caused the students feel bored when studying which results in their mathematics disposition drop. This is supported by (Widyasari et al., 2016) who stated that mathematics disposition highly depends on intrinsic and extrinsic factors in each individual. Therefore, strong willingness of a student is required to fulfill all disposition indicators. In addition, GeoGebra-based Van Hiele model in online learning encourages students to develop their thinking skills by solving problems, however, many obstacles faced by the students make them less confident in communicating their ideas, and the students tend to not have strong willingness in solving geometry problems if they do not understand how to solve it (Nopriana, 2015).

CONCLUSION
The result showed that there was no significant difference in the increase mathematical disposition of students who are given the learning model and early mathematics ability. The conclusion obtained is that the geogebra based Van Hiele Model is ineffective on students' mathematical disposition in terms of their early mathematics abilitiy. From the results of the research and discussion, it was concluded that there was no interaction between the learning model and early mathematics ability on mathematical dispositions. This is due to several factors, namely internal and external factors. Internal factors are a decrease in the score of mathematical disposition from pretest to posttest, lack of understanding of students in applying geogebra software with online learning and student and student boring in online learning. External factors are quota and bad internet network

ACKNOWLEDGMENTS
The researcher says to thank the Directorate of Research and Community Service,