The Implementation of Computational Thinking on Mathematics Learning Research: A Systematic Literature Review

The aim of this study is to describe the implementation of computational thinking research results on mathematics learning in Indonesia. The method used in this study is a systematic literature review (SLR) by using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) protocol. The sample consists of 25 results of computational thinking research on mathematics learning, the sample is journal articles and proceedings published in 2019-2023. The description of this research will be reviewed based on the year of research, the level of education, research location, and research methods used. The results of the study show that (1) computational thinking research on mathematics learning has increased every year where 2022 become the year with the most number of publications, there are 15 articles, (2) 32% (8 articles) of computational thinking research on mathematics learning is conducted at the university level, (3) East Java is the province with the most research on computational thinking on mathematics learning, with 7 studies, (4) quantitative method is the method most frequently used in computational thinking research on mathematics learning in Indonesia, there are 12 studies that used quantitative method, and (5) students are able to perform abstraction and algorithm processes. However, they still have difficulty in performing the decomposition and pattern recognition processes.


INTRODUCTION
One of the skills that students must possess in the era of globalization is computational thinking skills. Grover (2018) and Riddell (2018) state that computational thinking skills deserve to be the "5th C" in 21st century skills beside 4Cs (critical thinking, creativity, collaboration, and communication) because in this era students are required to be able to complete computational problems, namely thinking logically, algorithmically, and being able to use computational tools and present data. Wing (2017) defines computational thinking as a person's ability to present problem and its solution in an algorithm so that both computers and other people can use the same steps to solve the same problem. In line with Bocconi et al. (2016) who stated that computational thinking is a thinking process (one's thinking skills) that uses analytical approaches and algorithms to formulate, analyze, and solve a problem. In problem solving, Bocconi et al. (2016) also added that problem solving activities, that involve computational thinking, can be seen from a person's ability to (1) decompose, namely breaking down complex problems into small parts that are easier to understand and solve (Wing, 2011), (2) recognize patterns, (3) perform abstraction, namely the ability to formulate solutions in general terms so that they can be applied to different problems, (4) design a series of operations/actions in a systematic way (step by step) on how to solve a problem (algorithm). Computational thinking ability allows one to be able to solve complex problems (Inganah et al., 2023;Maharani et al., 2019), makes one smarter and easier to understand the technology (Cahdriyana & Richardo, 2020), and is useful for one's education and future (Adler & Kim, 2018).
The use of computational thinking is not only limited to the computer field (Nurwita et al., 2022). But it can also be applied in various scientific fields (Ansori, 2020;S. Maharani et al., 2021). Barr & Stephenson (2011) explain that computational thinking can be integrated into several subjects, such as: Mathematics, Sciences, Social Studies, Languages, and Arts. Mathematics has a close relationship with computational thinking because mathematics involves patterns, problem structures, and variables that can be used with different values . Furthermore, Nurwita et al. (2022) explain that computational thinking can be applied in learning mathematics because computational thinking synthesizes critical thinking skills and creative thinking skills so that it allows students to be able to formulate problems and develop solutions to solve these problems. Teaching computational thinking means that the teacher teaches students how to think and solve problems like as computers (Zahid, 2020).
In Indonesia, computational thinking was introduced in 2018. Through Permendikbud number 37, the government stated that computational thinking is one of the Basic Skills (KD) in informatics subjects so that informatics is an important subject that must be integrated into the curriculum structure in junior and senior high school levels (Permendikbud, 2018). However, in practice, computational thinking has begun to be taught in schools in the 2019/2020 academic year due to consideration of teacher resources and supporting facilities (Zahid, 2020).
Nowadays, research on computational thinking in mathematics learning has been carried out by many researchers in Indonesia, including research conducted by (Apriani et al., 2021;Pratiwi & Akbar, 2022;Wardani et al., 2022). In the Systematic Literature Review research conducted by Marifah et al. (2022)  Systematic literature review, according to Petticrew & Roberts (2006), is a method of making sense of large bodies of information, and a means of contributing to the answers to questions about what works and what does notand many others types of question too. In line, Harris et al. (2014) explain that a systematic review is a comprehensive summary which is carried out by identifying, selecting, and synthesizing to answer certain questions. Using this systematic literature review, researchers can find research gaps for further research purposes (Rum & Juandi, 2022).
This study aims to describe research results related to computational thinking in mathematics learning which are reviewed from the year of publication, the level of education, research location and type of research. Therefore, an important step in this SLR is to collect research results regarding computational thinking in mathematics learning. From the research data, the researchers ask several questions: (1) how is research description result regarding computational thinking in mathematics learning seen from the year of publication? (2) how is research description result regarding computational thinking in mathematics learning seen from the level of education? (3) how is research description result regarding computational thinking in mathematics learning seen from the research location? (4) how is research description result regarding computational thinking in mathematics learning seen from the research method? And (5) how students' computational thinking process in mathematics learning research?

METHODS
This research is a systematic literature review. Systematic literature review is secondary research that uses systematic methodology to identify, analyze and interpret all available evidence to answer a specific research question in a way that is impartial and (to a degree) repeatable (Kitchenham & Charters, 2007). In determining the appropriate data, researchers used PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) protocol. The steps of selection data are identification, screening, eligibility, and inclusion (Juandi & Tamur, 2020). Stapic et al. (2012)  The selection data using PRISMA protocol can ben seen in Figure 1.

Research Instruments
The instrument that is used in this research was an observation sheet or matters that related to the inclusion criteria. The criteria that are used in this study are based on the year of research, education level, research location, and research methods conducted.

Population and Sample
The research population is all researches related to computational thinking in mathematics learning which is published in various media publications. Based on the search, there were 25 articles that met the specified inclusion criteria.

Data Collection Process
The data collection technique that is used in this research is to collect articles that discuss computational thinking. The data collected is taken from the Google Scholar database, Google, direct links, and URL's journals.

Data Analysis Techniques
The technique of data analysis in this research was a descriptive quantitative.  Whereas 2019 was the year with the least amount of computational thinking research in the last 5 years, namely 1 article. This is because computational thinking has just been integrated into the 2013 curriculum in the 2019/2020 academic year, so the number of research conducted is still very limited (Zahid, 2020). Furthermore, Maharani (2020) also revealed that teachers are still in the stage of understanding computational thinking but still have difficulties integrating it in learning. While in 2023 on February there will only be 2 articles and it is possible to add even more.

Study by Education Level
Studies on computational thinking in mathematics learning in Indonesia were carried out at various levels of education. The number of studies seen from education level can be seen in Figure 3. Based on Figure 3, this can be inferred that most research on computational thinking in mathematics learning is carried out at the university level where there are 8 studies conducted at that level. This is because teachers need to master computational thinking, so that prospective teachers also need to be prepared to be competent in teaching computational thinking (Yuntawati et al., 2021). Meanwhile, at the elementary and high school level each has the same number of studies, namely 5 studies.
This number certainly still needs to be increased because computational thinking needs to be introduced since elementary school to deal with developments in information technology (Kuswanto et al., 2020), and at the junior high school level there were 7 studies.   such as mathematical literacy skills which are mostly also conducted on the Java Island (Rum & Juandi, 2022) and students' error due to lack of mathematics skills are mostly conducted in Java Island (Aswin & Juandi, 2022). Therefore, computational thinking research in mathematics education must be handled in other provinces in Indonesia so that computational thinking skills are evenly distributed to all students in Indonesia and teachers know various ways to improve computational thinking skills.

Study by Research Methods
Furthermore, research on computational thinking in mathematics learning has been carried out using various methods. The number of studies, based on the research method used, can be seen in Figure 5 below: Based on Figure 5, the research method that is often used in researching computational thinking in learning mathematics is the quantitative research method, there are 12 studies using this quantitative method. This is because computational thinking is a thinking skill so that in order to teach it, teachers must carry out certain activities that aim to discuss and improve computational thinking (Zahid, 2020). Meanwhile, there were 9 studies using qualitative methods, 2 studies using the research and development method, and 1 other study using the design research method.

Implementation of Computational Thinking on Mathematics Learning Research
Based on the 25 research articles analyzed, some studies have implemented the aspects of computational thinking completely. the implementation of computational thinking in mathematics learning is presented in Table 1. Based on Table 1, it can be concluded that, in implementing computational thinking in mathematics learning, students are able to carry out the abstraction process and the algorithm process. However, students in performing the algorithm are still not perfect so that students still have difficulty in answering the problems given. In the decomposition process, students have not been able to identify and simplify the information provided. Meanwhile, in the pattern recognition process, students still have difficulty in recognizing various patterns.
Therefore, a special stimulus is needed to train students to recognize various patterns. Four aspects of computational thinking, namely: decomposition, pattern recognition, abstraction, and algorithm, have been carried out and the results showed that there was an increase in the ability of computational thinking ability of students who are taught using interactive multimedia-based teaching materials 2 Kadarwati, S., Suparman, and Astutik, K.

2020
The application of the computational thinking and problem based-learning model using four aspects of computational thinking, namely: decomposition, pattern recognition, abstraction, and algorithms effectively improves student creativity and learning outcomes.

2020
This research has applied four aspects of computational thinking, namely: decomposition, pattern recognition, abstraction, and algorithms. The results showed that there is no significant relationship between computational thinking ability and math ability in elementary school-age children.

2022
From the answers of 3 students presented, only one answer of the third student was able to fulfill the computational thinking indicator. Meanwhile, the first student's answer has not been able to identify the information asked (decomposition) and the second student's answer has been able to

No Researcher(s) Year Implementation
identify information and draw conclusions from the patterns found in the given problem (abstraction) but the algorithm used is not perfect so that students have not been able to recognize the characteristics of problem solving which causes the problem to not be answered (resolved).
5 Nurmuslimah, Hilda 2019 The results of this study show that students overall have the ability to design algorithms and abstraction. However, special stimulus needs to be given to train students in recognizing patterns, processing data, and especially in problem solving.

2022
This study involved 10 students, the use of jungle adventure games in learning computational thinking influenced problem solving skills with an average of 60% of students said to be good at decomposition, 70% said to be good at pattern recognition, 80% said to be good at abstraction, and 90% at algorithm.

CONCLUSION
Based on this systematic literature review, it was found that computational thinking research in mathematics learning in Indonesia has received considerable attention. This was indicated by the increasing number of studies conducted each year. As much as 32% of computational thinking research was carried out at the university level and 48% of it was carried out using quantitative methods. Because of the importance of computational thinking for students' futures, it is suggested for future researchers to carry out mathematics learning that integrates computational thinking starting from the elementary school level. Meanwhile, the implementation of computational thinking research in mathematics learning has not been evenly distributed throughout Indonesia. Dominant computational thinking research was conducted on Java Island. Students are able to perform abstraction and algorithm processes. However, they still have difficulty in performing the decomposition and pattern recognition processes. So that, this can be a consideration for future researchers to try to integrate computational thinking in mathematics learning that is carried out in all regions of Indonesia, not only limited to Java Island, and focuses on improvement of decomposition and pattern recognition process.