Search

This systematic research aims to provide a comprehensive overview of the development of analysis related to the use of programming in the development of Computational Thinking (CT), especially in the context of education from primary to tertiary levels. This study analyzed 88 articles from empirical studies related to the use of programming to develop CT sourced from the Scopus database. The analysis process followed the PRISMA 2020 guidelines and consisted of three stages: search, selection, and data analysis. Descriptive and thematic statistical approaches were used for data analysis. Instruments used in the selection of articles included Rayyan for screening based on inclusion criteria, as well as Microsoft Excel for coding and thematic analysis. The results showed that articles related to the use of programming to promote CT have appeared since 2011 but have increased significantly since 2016, with an annual growth rate of 17.6%. Most studies used quantitative approaches, followed by qualitative and mixed methods. Overall, 270 authors from 27 countries contributed to the study, with the United States having the highest number of publications. A total of 33 programming tools were identified, with Scratch being the most widely used tool, followed by Blockly, LEGO, Scratch Jr., Code.org, Python, Alice, App Inventor, Kodu, R, MakeCode, and Arduino. Scratch Jr. is most commonly used at the early childhood education level, while programming languages such as Python, R, and MATLAB are more commonly used in higher education. The implications of these findings suggest that the trend of using programming tools such as Scratch and Blockly has the potential to influence CT teaching strategies in the classroom, as well as the importance of using varied programming tools in efforts to integrate CT into the education curriculum.

Computational Thinking (CT) has emerged as a crucial and foundational skill in the 21st century, capturing the attention of researchers across various domains, including mathematics education in Indonesia. This research identifies and analyzes trends, themes, focuses, and research findings on CT through mathematics learning in Indonesia. This Systematic Literature Review (SLR) adheres to the PRISMA guidelines, encompassing three stages: search, selection, and data analysis. The search process on the Scopus database, utilizing queries aligned with the research objectives, yielded 31 articles related to CT and mathematics education authored by Indonesian researchers. Subsequent selection involved criteria such as research focus, study type, document availability, and intervention differences, resulting in 14 articles. Data analysis employed Bibliometrix and NVivo 14 Plus software. The findings reveal a significant increase in research on CT in mathematics education from Indonesian researchers over the past two years, with an annual growth rate of 5.74%. This publication landscape involves contributions from 39 authors affiliated with 16 higher education institutions in Indonesia. Researchers have delved into diverse themes, including the analysis of CT skill achievement, the development of CT-oriented instructional models and media, and the creation of assessment tools to measure CT skills. These findings underscore the broad relevance of CT in the realm of mathematics education. As a burgeoning field of study, numerous aspects of CT remain open for further research, development, and exploration, especially those related to mathematics. To enhance the impact and implementation of Computational Thinking (CT) within the context of Indonesian education, future research may consider investigating innovative pedagogical approaches, assessing the long-term impact of CT interventions, and exploring the potential integration of CT across various educational levels.

Several inconsistent research results regarding the effect of Problem-Based Learning (PBL) in enhancing Mathematical Abilities (MA) of primary school students have been reported widely by some researchers. To summarize, estimate, and evaluate the effect of PBL on MA of primary school students and investigate the study characteristics that provoked the heterogeneous effect size, a systematic review and meta-analysis was employed. Hedge's equation was employed to measure effect size using Comprehensive Meta-Analysis (CMA) software as an analysis tool. The synthesis of 16 relevant primary studies revealed that the overall implementation of PBL had a high positive effect (g = 1,050) significantly compared to conventional learning on MA of primary school students based on a random effect model. Moreover, the characteristics of the sampling technique, publication index, and MA significantly caused the heterogeneity of the effect size of PBL towards students' MA. Thus, these results suggest that primary school mathematics teachers should select PBL as one of the best solutions to enhance students' MA and consider the characteristics of students MA that will be improved. Besides, researchers should select random sampling as a sampling technique to select the sample and the literature indexed by Scopus as references to support the quality of research.