DOI: 10.30738/union.v11i1.13723 © Author (s), 2023. Open Access
This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International
Analysis of STAD model on students with low abilities in learning geometry
Samsul Pahmi 1 *, Nanang Priatna 2, Karina Yulianti 1, Fitria Nurulaeni 1, Akash Satish Kumar 3
1 Facultyof Business and Humanities, Nusa Putra University, Jl. Raya Cibolang No.21, Sukabumi 43152, West Java, Indonesia
2 Department of Mathematics Education, Universitas Pendidikan Indonesia, Jl Dr. Setiabudi No. 229, Bandung 40154, West Java, Indonesia
3 Hindustan Institute of Technology and Science, Rajiv Gandhi Salai (OMR), Padur, Kelambakkam, Tamil Nadu 603103, India
* Corresponding Author. Email: [email protected]
Received: 15 December 2022; Revised: 25 December 2022; Accepted: 28 December 2022 Abstract: This research is based on the mathematics learning outcomes of elementary school students which are still low in terms of student learning outcomes. The current study reveals that as many as 66.67% of students have not been able to achieve the minimum requirements, especially in the section of geometry; reflecting a need to improve mathematics learning outcomes. This gap in learning outcome is due to the fact that a majority of students tended to be passive during learning. The STAD learning model is a learning model that emphasizes student activities and interactions for learning mathematics in the classroom. The research design used in this study is a one group pretest posttest design. The sampling method used in the study was purposive sampling from a class of thirty elementary school students with issues related to mathematical abilities. Data collection techniques consist of primary sources in the form of tests, interviews, and observations, while secondary sources include documentation and literature studies. The data analysis technique used is the t-test (or using the Wilcoxon test if the data is not normally distributed), and the Gain Test. The results showed that there is a positive influence of STAD model on learning outcomes.
Keywords: Geometry; Primary school, STAD learning model
How to cite: Pahmi, S., Priatna, N., Yulianti, K., Nurulaeni, F., Kumar, A. S. (2023). Analysis of STAD model on students with low abilities in learning geometry. Union: Jurnal Ilmiah Pendidikan Matematika, 11(1), 20-28. https://doi.org/10.30738/union.v11i1.13723
INTRODUCTION
Mathematics, a core subject considered as queen of sciences contains abstract concepts.
This makes the topics such as algebra, calculus, and geometry difficult for most of the students, causing a decrease in involvement and skill among students. Experts categorize geometry as a topic requiring basic skill. Several studies reveal difficulties faced by teachers and students in teaching and learning mathematics, especially geometry. Students often fail to develop visualization and exploration skills necessary for geometric concepts, problem-solving skills, and geometric reasoning (Ngirishi & Bansilal, 2019).
In order to describe the knowledge of geometry and the teaching of knowledge of geometry, many studies conducted in several countries have evaluated teachers' knowledge of mathematics subjects based on the general belief that the greater the knowledge of the subjects, the better the teaching (Schmidt et al., 2002; Mapolelo & Akinsola, 2015). In the case of geometry, knowledge of mathematics among teachers appears to be uneven with several gaps especially among elementary school teachers (Kuzniak & Rauscher, 2011; Depaepe et al.,
2015). It has also been observed that relationship between limitations in subject knowledge and teaching quality is not easy to study (Pahmi, 2020). Identifying the effect of difficulties faced by teachers during teaching and its influence on performance of the students is also an issue.
Considering that this problem is often repeated and even in the initial study of the population in this study, the 30 students' understanding of mathematics, especially geometry, was very low with an average of 68.7. This is of course a problem that needs to be solved considering that geometry is a basic skill that must be possessed by students. There are several learning models that have been widely accepted, one of which is the cooperative learning model (Silalahi & Hutauruk, 2020). The cooperative learning may be classified as Teaching Game Team (TGT), Number Head Together (NHT), Student Teams Achievement Division (STAD), Team Accelerated Instruction (TAI), Cooperative Integrated Reading and Composition (CIRC), Group Investigation (Investigation Group), Make a Match models, etc.
The main idea about application of cooperative learning method is the hope that students can be more responsible both for themselves and for others by developing an attitude of teamwork (Slavin et al., 1984; Li & Lam, 2005). In practice, there are several obstacles, but when the cooperative learning method is applied, it has been proven to be effective in improving the level of students’ academic achievements (Abd Algani & Abu Alhaija, 2021). In addition, an important factor that influences the success of cooperative group instruction could be the positive motivational impact of peer support for learning (Alvarez-Bell et al., 2017;
Jeong, 2019). Çapar and Tarım (2015) state that the most effective cooperative learning method is the application of shared, unstructured and STAD learning techniques. In this regard, it is understandable that cooperative learning has the same effect on mathematics learning achievement at the elementary, junior high, high school, and undergraduate levels (Turgut &
Gülşen Turgut, 2018). In other research, it was also found that the STAD learning model could improve students' geometric reasoning (Alfat & Maryanti, 2019). These studies do not provide results on the influence of STAD learning on students with low abilities in geometry.
The main strength (STAD) is cooperative, where students work in teams to achieve common goals. Some of the potential advantages of the STAD model in improving geometry skills and include the following: Collaborative learning: where collaborative can encourage students to work together and share their knowledge and ideas, which helps them learn from each other and deepen their understanding of geometric concepts (Moreno-Guerrero et al., 2020);
Increased engagement: this aspect can increase their sense of ownership and responsibility in the learning process (Gillies, 2016). This is due to the fact that the students work together towards achievement of a common goal, motivating them to learn and participate; Personalized learning: this allows students to learn at their own pace and in a way tailored to their individual needs and abilities (Shemshack & Spector, 2021); and Improved communication skills:
communication itself can require students to communicate and collaborate with one another, which helps them develop important communication and teamwork skills (Ahdhianto & Santi, 2020). Overall, the STAD model can be a useful tool for improving geometry skills by providing a collaborative and personalized learning experience that engages students and helps them develop important skills (Çelik, 2013). This study analyzes the influence of STAD model in improving learning outcomes of students with low abilities in geometry.
METHOD Research design
The research method used in this research is an experimental method with a quantitative approach. The type of experimental research used is a quasi-experimental type with pretest posttest design (Cook et al.,1979). The research is carried out in only one group without a comparison group by giving a pretest or initial test to the research object before the research to obtain student's initial score.
Table 1. Schematic of one group pretest posttest design
O1 X O2
Pretest Treatment Posttest
Population and Sample
The study is based on solving problems in the classroom, and the sampling technique used was purposive sampling with considerations as proposed by Campbell et al., (2020). The students were drawn from elementary schools in Sukabumi Regency and consist of a population involving 451 active students. From the above, a sample of thirty students was selected for the study. The sample of students were identified and selected based on the research objectives (examining the effect of the STAD model on students with low abilities in geometry). The criteria for selection of samples were based on identifying students with low grades in geometry.
Method of Collecting Data
The data collection in the study involves using a test instrument. The tests consisted of six questions related to the shape of a flat side space; and had two easy questions, two medium questions and two difficult questions, all of which were applicable questions. Validation of the test instrument is carried out through expert validation and focuses on aspects of readability, conformity with material indicators and level of difficulty. The test is organized twice, namely pretest and posttest with approximately the same level of difficulty (types of questions use applicative reasoning so that it allows students to understand the meaning of the questions even though they have not received direct learning). The pretest was undertaken prior to treatment by the researcher, while the posttest was undertaken after the research subject was given treatment using the STAD learning model.
Data analysis method
Analysis of the data used in this study is a significance test (paired sample t-test or Wilcoxon test) with a prerequisite test is the data distribution normality test. The gain test is employed to determine the increase in learning outcomes. The data analysis undertaken in the study is described below:
Normality Test
The normality distribution test aims to determine whether the data is obtained is from a normally distributed population or not. The normality test method uses is the Shapiro-Wilk test since the study uses a small sample size of 30 students. The hypotheses used are: H0: Data does not come from a normally distributed population; H1: Data comes from a population that is normally distributed. The basis for making decisions can be made based on probability (asymptotic significance), namely (Rice, 1989): If probability > 0.05 then reject H0; If probability
< 0.05 then accept H0. Hypothesis testing
The hypothesis test is suggested in the study is a paired sample t-test if the data comes from a normally distributed population; else, Wilcoxon test is preferred (Bridge &Sawilowsky, 1999).
The hypotheses used are as follows:
H0: There is no significant difference between students' pretest and posttest results;
H1: There is a significant difference between the students' pretest and posttest results.
Decision making is based on significance. If the value of sig. (2-tailed) < 0.05 then reject H0
suggesting a significant difference in mathematics learning outcomes pretest and posttest; If the value of sig. (2-tailed) > 0.05 then accept H0 suggesting that there is no significant difference in mathematics learning outcomes pretest and posttest.
Gain Test
Gain test in this study aims to determine the effectiveness of the difference between the average pretest and posttest scores (Pahmi, 2020). The formulas and criteria used are as follows (Knapp & Schafer, 2019; Hake, 1999)
𝑁 𝐺𝑎𝑖𝑛 = Posttest score−pretest score
sideal score−pretest score (1) Table 2. Decision making criteria for n-gain test
Percentage (%) Interpretation
< 40 Ineffective
40- 55 Less effective
56 - 75 Effective enough
> 76 Effective
Note. From Hake (1999)
RESULTS AND DISCUSSION Normality test
Based on the results of the normality test of the data gathered from pretest and posttest results, it was found that both the pretest and posttest data were not generated from a normally distributed population. This is obtained based on the output of tests performed on SPSS (see Table 3).
Table 3. Test results of normality
Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig.
Pretest .298 29 .000 .693 29 .000
Postest .177 29 .021 .970 29 .557
a. Lilliefors Significance Correction
From the test results in Table 3, the normality test for the Shapiro-Wilk pretest obtained a sig value of 0.000 for the posttest of 0.557. Upon applying the rules of decision making, it has been found that the results of the pretest and posttest are as follows: pretest (0.000) <0.05 and posttest (0.557)> 0.05. It may be observed from the test results that the distribution of data for the posttest is obtained from a normally distributed population whereas results of the pretest is obtained from a population that is not normally distributed. Based on these results, the hypothesis test in this study will use the Wilcoxon test.
Wilcoxon test
In Table 4 given below, the Asymp. value. Sig. (2-tailed) 0.000 < 0.05, indicating that the null hypothesis (H0) is rejected, confirming that there is a significant difference between the students' pretest and posttest results, so that it can be continued to determine the level of effectiveness of the differences between the two test results.
Table 4. Test Statistics Wilcoxon Test Posttest - Pretest
Z -4.710b
Asymp. Sig. (2-tailed) .000 a. Wilcoxon Signed Ranks Test b. Based on negative ranks.
N-Gain Test
The Gain test in this study was focused on seeing the effectiveness of using STAD as a learning model on geometrical geometry material, namely through the results of the pretest
and posttest of experimental students. From the results of the N Gain test (Table 5), it is known that the N-gain percent has a mean value of 74.74 with a minimum N-gain score of 53.19 and a maximum N-gain score of 91.95. So, based on the category of interpretation of the effectiveness of N-Gain on the mean score, it can be concluded that the use of the STAD model in learning the material for flat-sided geometry is included in the "fairly effective" category. The N-Gain test in this study is displayed in Table 5.
Table 5. N Gain Test Results
Statistic Std. Error
N-gain Percent
Mean 74.7410 1.62723
95% Confidence Interval for Mean Lower Bound 71.4078 Upper Bound 78.0742
5% Trimmed Mean 74.9306
Median 75.2577
Variance 76.788
Std. Deviation 8.76289
Minimum 53.19
Maximum 91.95
Range 38.76
Interquartile Range 11.16
Skewness -.409 .434
Kurtosis .394 .845
STAD Implementation in Learning
The application of the STAD learning model in learning follows 6 main stages, namely: 1) Group division; 2) Submission of Materials; 3) Group Discussion; 4) Presentation of group work results; 5) Delivering Quiz; and 6) Individual Progress Scores; 7) Team Recognition.
Group division
The learning implementation is divided into several groups with a maximum number of 4 people in each group, taking into account the heterogeneity of students based on the results of previous mathematics learning; The 8 students with the highest scores were divided into different groups.
Submission of materials
In the process of delivering the material, students are given a worksheet that is equipped with a summary of material and examples of flat-sided shapes in everyday life. In addition to using student worksheets, the teacher also prepares a Rubik’s cube as a teaching aid in determining the volume based on one volume unit in each section of the Rubik’s box.
Group discussion
Before doing the assignments on the worksheets, students are asked to understand the problem, and to discuss the problem with their group of friends and also with the teacher. In this activity, students looked more serious in trying to solve the problems on the worksheets.
They were also seen discussing the problem with their group friends without involving the teacher (Figure 1). In this activity, the teacher goes to each group and observes the discussion activities among the students. The teacher asked the students about steps used by each group (each group tries to solve the problem without using the standard volume formula).
Presentation of group discussion results
In order to enhance student activity, the activity after group discussion was followed by a presentation of each group regarding the results that had been completed jointly among group members. In this activity, the teacher is also involved in providing a stimulus to each group to
be actively involved in the presentation discussion. This is an effort to improve the mental agility and activeness of students involved in every stage of learning. The group presentation experience is the first experience among the students (source: student interviews. Diring the presentation among the first group of students, both the presenting group and the audience were not actively involved. Therefore, the teacher makes efforts to motivate and appreciate the students who presented. The teacher also stimulates the students by asking simple questions about the material being presented (Figure 2 (b) and (c).
Figure 1. Group discussion activities Quiz or Test
In order to measure the outcome of the STAD learning model, evaluation was carried out by the teacher by providing post-test questions with a level of difficulty that was approximately the same as the pre-test questions. In this process, students were given 50 minutes to complete the post-test questions. The time allocation is determined based on the average time taken in completing the test by the students using a testing instrument. Although there were some students who did not complete all the questions, in general the process of administering the test could be completed well, a significant increase in the amount of time compared to that of the pretest group.
(a) (b) (c)
Figure 2. Student Presentation Activities Individual Progress Score
Upon comparing the scores obtained among pre-test and post-test questions, it may be observed that there are significant differences among the pre-test and post-test groups, especially in the questions in the form of narratives (story). As for the questions in the form of pictures, both during the pre-test and post-test, it was seen that students were able to understand and solve the problems given (Figure 3). Based on Figure 3, it shows that visualization both in the delivery of material and in tests is needed to facilitate understanding and the problem-solving process. As for the story questions in the post-test session, the majority of students have used standard formulas so that reasoning abilities cannot be identified more deeply. However, it may be concluded that students were able to identify questions and develop problem solving strategies well.
(a) (b) Figure 3. Individual Student’s Progress
Team Recognition
In order to avoid social jealousy between groups, the teacher gives recognition to the whole group when each group is able to finish the presentation by giving praise, applause and titles to each group so that no group feels lower or higher than other groups.
(a) (b)
Figure 4. Process of mentoring from peers
Overall, from a total of 30 planned samples, there were 29 students who participated in all stages of the research. Upon comparison between the pre-test and post-test groups, it may be observed that there was an increase in the average score of students by 76.50. Based on the results of observations and interviews during the learning process, the researchers concluded that the increase in student learning outcomes was due to the high level of student involvement (active) both in the discussion process so as to increase motivation in participating in the entire learning process. This is also in line with the research undertaken by Lin and Chen (2017) which reveal that learning motivation appears to have a very positive effect on learning acquisition in learning outcomes. In line with this, Syahidi and Asyikin (2018) also found that application of STAD enabled an increase in learning outcome among students. In addition to active involvement, the placement of students with high mathematical abilities was involved in peer tutoring. In the peer tutoring process, students with high mathematical abilities were tasked with providing assistance to group members who did not understand mathematics.
Although the application of the STAD model was able to improve student learning outcomes in this study, there were still some findings that could not be resolved properly. These include:
1) there were students who were finding difficulty in performing group activities, 2) there were some students who did not contribute much; both in the process of group discussions and presentations, and 3) there were a small number of students who have not reached the standard value (KKM) for learning subjects.
CONCLUSION
After comparing the pre-test and post-test questions, it can be observed that there are significant differences between groups, especially in the narrative (story) questions. As for the questions in the form of pictures, both during the pre-test and post-test, students were able to understand and solve the questions given. This reflects that visualization, both in the delivery of material and in exams, is needed to facilitate understanding and the process of solving problems. Thus, it can be concluded that students have been able to identify problems and determine problem solving strategies properly. In addition to active involvement, the placement of students with high mathematical abilities in each group resulted in a peer tutoring process within each group; wherein students with high mathematical abilities could provide guidance to group members who did not understand mathematics. Although the application of the STAD model was able to improve student learning outcomes in this study, there were still some findings that could not be resolved properly. These include: 1) students who found difficulty in group activities, 2) some students who did not contribute much both in the process of group discussions and presentations, and 3) a small number of students who have not reached the standard value (KKM) for learning subjects.
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