Effectiveness of Ethnomathematics-Based Learning on Students' Mathematical Literacy: A Meta-Analysis Study

doi:10.21203/rs.3.rs-4856649/v1

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Effectiveness of Ethnomathematics-Based Learning on Students' Mathematical Literacy: A Meta-Analysis Study

https://doi.org/10.21203/rs.3.rs-4856649/v1

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This study aims to determine the effectiveness of ethnomathematics-based learning on students' mathematical literacy through a meta-analysis study. Primary studies were collected from Google Scholar and ERIC databases based on the inclusion criteria. The results of the literature review obtained 16 studies, either from theses, proceedings, or journal articles. Furthermore, the effect sizes were combined according to the Random Effects Model (REM) and data analysis using CMA v3 and R Studio (Meta Package). The calculation results obtained an overall effect size (1,163; 𝐶𝐼 95% [0,719; 1,607]; 𝑝 < 0,001) which indicates that ethnomathematics-based learning has a “strong” effect on students' mathematical literacy when compared to conventional learning. As a result of the moderator category analysis, it was found that the effect sizes differed significantly according to students' regional demographics (𝑄=33,867; 𝑝=0,000), independent variables (𝑄=24,412; 𝑝=0,002), and subject matter (𝑄=67,754; 𝑝=0,000). However, it does not show significant differences when based on the duration of the meeting (𝑄=1,97, 𝑝=0,1601), education level (𝑄=3,743; 𝑝=0,291), sample size (𝑄=3,62, 𝑝=0,0569), and the type of publication (𝑄=4,381; 𝑝=0,112). Thus, it can be said that ethnomathematics-based learning can be an effective learning model for increasing students' understanding of mathematical literacy, especially in the Kalimantan Island region, recommended to be integrated with the Ethnomathematics Pop Up Book and Visual Thinking learning model, and Probability & Sets and 2D & 3D Geometry materials are recommended to be taught through this learning model.

ethnomathematics

mathematics literacy

meta-analysis

effect size

Some concepts in mathematics learning are abstract, that is the view that emerges and we often hear. Especially if the learning concepts learned are not related to activities in the real world, then the stereotype of math teachers who are less pleasant. These views, many of which arise from students, are not new to us. In its implementation in the classroom, however, students simply answer questions without being curious about the real application of mathematics in everyday life. In fact, mathematics is a human activity, as once expressed by Hans Freudenthal, a Dutch mathematician.

As a professional educator, a teacher must be able to determine the appropriate approach so that learning objectives can be achieved. As we known, mathematics education is an important component of the education curriculum around the world. The ability to understand and apply mathematical concepts is not only important in academic contexts, but also in everyday life, so that later it can benefit for students in living life. In this regard, NCTM (2000) believes that mathematics education is an important aspect of the education system, with mathematical literacy being the key to students’ ability to face complex challenges in this modern era.

Mathematical literacy is one of the indicators of international education standards (Holenstein et al., 2021), as well as one of the objectives of PISA (Wijaya et al., 2015), and mathematical literacy is also a demand of the 21st century (Drew, 2012). Improving students' mathematical literacy is the main goal of mathematics education and one way to achieve mathematical literacy is through culture-based learning. Moreover, according to Fajriyah (Yuliana et al., 2023), the diverse cultures that exist in Indonesia are a very good opportunity in the world of education because this can make learning more meaningful, in this case through ethnomathematics in learning mathematics helps students understand the material. Referring to this, then according to the author, one of the approaches that has been developed to improve mathematical literacy is culture-based learning (in this case ethnomathematics). The integration of culture and mathematics can produce great abilities for students because they get both things together (Indriati et al., 2022). This form of relationship can be shown in applying mathematical concepts in a culture (known as street mathematics) and ways of teaching mathematical concepts adapted to local culture (D’Ambrosio, 1989).

Mathematical Literacy

Mathematical literacy is an individual’s ability to formulate, use, and interpret mathematics in various contexts. Mathematical literacy includes not only understanding mathematical concepts, but also students’ ability to apply mathematical knowledge in various contexts of daily life (OECD, 2019). Mathematical literacy is the ability to formulate, apply or use, and interpret mathematics in various contexts in everyday life (Prihatiningtyas & Buyung, 2023), in solving problems in various real-world contexts, and mathematical literacy is also the ability of a person to reason mathematically (Oktavia, 2023).

Setyawan (Maulidya et al., 2023) explains that an individual's ability to utilize skills and potential in processing and understanding information when doing reading, writing, counting, and solving problems in everyday life is called literacy. Kiptiyah et al. (2021) added that mathematical literacy is the ability that individuals have in reasoning by linking relevant mathematical concepts to real problems encountered. In other words, mathematical literacy is a conscious effort by students to use mathematics to solve problems in their lives. In line with this, Ojose (Qauliyah et al., 2022) expressed a simpler meaning of mathematical literacy. According to him, mathematical literacy is the knowledge to know and apply basic mathematics every day.

The author’s own assumption based on some of the things discussed earlier, that mathematical literacy refers to the use of mathematical knowledge and understanding in real life. Knowledge and understanding of mathematical concepts are important, but even more important is the ability to activate mathematical literacy to solve problems encountered in everyday life. This means that using mathematical literacy can help learners understand the role or usefulness of mathematics in everyday life as well as help them make the right decisions as a constructive, caring and thinking 21st century citizen. This is in line with the goal of learning mathematics that everyone should master mathematics at a certain level so that they can understand the real world to succeed in their lives and careers.

Ethnomatematics-Based Learning

Ethnomathematics is the study of the relationship between mathematics and culture. In an educational context, the ethnomathematical approach can help make mathematics learning more relevant to students by connecting it to their own cultural context. However, the potential of this approach, there are still questions about the extent of its effectiveness in improving students’ mathematical literacy. Although the concept of ethnomathematics has been introduced as an attractive alternative in mathematics learning, more in-depth research is still needed to comprehensively evaluate its effectiveness (D’Ambrosio, 1985). Ethnomathematics is a method used in mathematics learning through the introduction of local cultural aspects of certain cultural communities/groups, so that this becomes interesting for students and easy to understand (Indriati et al., 2022).

D’Ambrosio (1985) through his empirical research proved that ethnometamatics can be applied in everyday life. As a result, applying ethnomathematics to the teaching and learning process of mathematics in formal schools can make the subject more real and fun (Sirait et al., 2022). Ethnomathematics in education aims to examine the process of understanding and processing mathematical knowledge, as well as application using mathematical ideas that are expected to help solve problems in everyday life (Putri, 2017). Thus, ethnomathematics is a part that can be utilized to develop contextualized mathematics learning to improve mathematical literacy. Hendrawati et al. (2019) also stated that efforts to improve numeracy literacy can be done by integrating mathematical problems related to everyday life based on ethnomathematics. The author's own assumption based on some of the things discussed earlier, literacy is that students are able to formulate, use, and interpret mathematics in various contexts; this is closely related to the use of mathematics in the natural environment so that contexts that are close to students, such as culture, student environment, social, or certain regional contexts, and ethnomathematics, can be used as a means of student literacy. In other words, if students can make connections between mathematical concepts and the real world through local cultural aspects, they will be greatly helped in carrying out contextual learning processes rather than focusing on the use of formulas and theories alone.

Ethnomathematics is able to express the relationship between culture and mathematical knowledge in the form of ideas and procedures, as well as mathematical practices whose application is in accordance with certain characteristics (Rosa & Orey, 2013; Lisnani et al., 2022; Rozalia et al., 2024). Utama et al. (2023) also explained that the addition of local cultural forms is expected to increase students’ love of culture, because directly love of culture will affect the character education that students have. In his research, ethnomathematics is taken from the geometric shape of batik patterns found in the Pekalongan city. For example, the Jlamprang batik motif can be used as a tool to introduce the concept of geometry in mathematics, thus helping to understand abstract mathematical concepts which include straight and curved lines, symmetry, points, angles, rectangles, squares, triangles, circles, parallelograms, and the notion of congruence. Based on these views, the author can elaborate on the view that strategies, approaches, models, methods, or ethnomathematics-based or nuanced learning are something that must exist in mathematics education. As we know and agree that education is an investment activity, which continues to be inherited from generation to generation, from past to present, and there is no death. Likewise, culture, in this case ethnomathematics, mathematical values found in culture or cultural values themselves need to be explored, understood, and need to be preserved.

Effectiveness of Ethnomathematics-Based Learning on Students’ Mathematical Literacy

Some previous studies include showing results such as the ethnomathematics-based Problem Based Learning model improves students' mathematical literacy skills (Qauliyah et al., 2022; Prihatiningtyas & Buyung, 2023; Simamora et al., 2022; Utama et al., 2023), there is an effect of the Contextual Teaching and Learning (CTL) model based on Ethnomathematics on the numeracy literacy skills of elementary school students (Sirait et al., 2022), the Realistic Mathematic Education (RME) model based on ethnomathematics has an impact on students’ mathematical literacy skills (Witha et al., 2021; Agusdianita et al., 2020; Oktavia, 2023; Rozalia et al., 2024), probing-prompting learning is effective in increasing students' mathematical literacy (Ulya & Rahayu, 2018), students’ learning independence and mathematical literacy skills increased after the implementation of a flipped classroom model with ethnomathematics nuances with E-learning support in online learning (Kiptiyah et al., 2021), and an ethnomathematics-based visual thinking approach was able to improve students' mathematical literacy and cultural motivation (Indriati et al., 2022).

Not only research results that refer to the application of ethnomathematics-based learning methods, models, or strategies, the results of ethnomathematics research related to learning media or learning resources have also provided positive benefits, including there is an increase in mathematical literacy through videos with ethnomathematics characteristics (Susanto et al., 2022), the ethnomathematics pop-up book learning media developed is effectively used to improve the numeracy literacy skills of elementary school students (Maulidya et al., 2023), ethnomathematics-based teaching modules with Realistic Mathematic Education (RME) improve the mathematical literacy of elementary school students (Yuliana et al., 2023), learning tools (RPP, Teaching Materials, Media, LKPD, Mathematical Literacy Questions) developed can increase student literacy (Lestari et al., 2023), as well as the latest from Kusuma et al. (2024) which shows that the application of ethnomathematics using the Trello application can improve numeracy and digital literacy in college students and from Kholifah (2024) who shows that Sundanese ethnomathematics in the Engklek game can increase student literacy.

Ulya (2016) also mentioned that students will more easily understand mathematical concepts through culture-based learning. Prahmana & D’Ambrosio (2020) added that there are changes in students’ views regarding the relationship between mathematics and the surrounding reality and culture after learning mathematics using the context of ethnomathematical exploration. This is evident from increasing students’ understanding and making them feel that the math they learn is meaningful. Fajriyah (2018) also stated that the role of ethnomathematics also supports mathematical literacy by facilitating students in constructing mathematical concepts as part of mathematical literacy based on students’ knowledge of their socio-cultural environment.

Based on several previous research results, this disparity in results indicates the need for a more comprehensive study regarding ethnomathematics-based learning in improving students’ mathematical literacy. In addition, there is an urgent need to conduct a comprehensive synthesis of the literature on the effectiveness of ethnomathematics-based learning on students' mathematical literacy, namely meta-analysis study which is a research method that involves collecting and analyzing data from a number of studies that have been conducted on a particular topic. In this context, a meta-analysis study can be used to evaluate the effectiveness of ethnomathematics-based learning in improving students’ mathematical literacy. By collecting data or combining findings from various existing studies, researchers can get a better overview of what has been researched and what has not. In addition, a meta-analysis study can also help identify factors that might affect the effectiveness of ethnomathematics-based learning (Borenstein et al., 2009).

This research is very important to do because until now in-depth research on the effectiveness of ethnomathematics-based learning on students' mathematical literacy is still limited, so this research can make an important contribution in filling the knowledge gap. In an effort to improve the quality of mathematics education, this research is expected to provide a better understanding of how ethnomathematics-based learning approaches can be effectively implemented in local educational contexts. Thus, it is expected to make a significant contribution in the effort to improve students’ mathematical literacy in Indonesia and other countries (globally). The results of this study can also help shape future mathematics education practices and policies.

Research Questions

Based on the above explanation, this meta-analysis study attempts to answer the following two research questions.

Research Question 1: Does ethnomathematics-based learning positively influence students’ mathematical literacy?

Research Question 2: Which moderator variables might moderate the effect of ethnomathematics-based learning on students' mathematical literacy?

According to Glass (Kanadlı, 2019), meta-analysis is a statistical method used to integrate the results of several analyses obtained from individual studies, namely this method allows a conclusion to be reached by bringing together many major studies on the same subject using statistical methods. The steps of the meta-analysis method used to combine the results obtained from primary research according to Cooper et al. (2009) include: defining the research question, search of literature, collecting the data (determining the inclusion criteria), assessing the quality of studies, analyzing and combining the study data, and interpreting the results.

2.1 Literature Search

Databases such as Google Scholar and the Education Resources Information Center (ERIC) were used to search for relevant literature in this study. The literature search started in February 2024 and ended in April 2024. In this database search, research was conducted between 2020 and 2024. Keywords such as [“ethnomathematics learning” OR “ethnomathematics based learning” AND “mathematical literacy” OR “literacy” OR “literacy ability”] were searched in both Indonesian and English to access the studies. In addition, the researcher also checked whether there were any studies that had not been reached, by checking one by one the reference section of the relevant literature that had been accessed. From the search results, 16 studies on “the effectiveness of ethnomathematics and mathematical literacy” were obtained. Furthermore, the eligibility of the retrieved studies was checked by considering the inclusion criteria defined in the scope of this study.

2.2 Collecting the Data (Determining Inclusion Criteria)

The inclusion criteria set for the research included in this meta-analysis study included:

Published in the last 5 years (between 2020 and 2024)
Published as undergraduate theses, theses, dissertations, proceedings, or research articles in Indonesian or English language
Using an experimental design (pre experiment, quasi experiment, or true experiment)
Contains the effectiveness/influence/impact of ethnomathematics-based learning on students’ mathematical literacy
Study group consisted of students (elementary school, junior high school, high school, and university).
Report sample size (\(\:N\)), mean (\(\:\stackrel{-}{X}\)) and standard deviation (\(\:SD\)), or report parametric test results (t-test or p-value).

Considering the above research inclusion criteria, a summary of the literature search process is presented in Fig. 1 below.

2.3 Assessing Research Quality

To evaluate the quality of the studies included in this meta-analysis, researchers used the quality assessment system developed by Pluye et al. (2009). The criteria include: (a) the study clearly reported on the process of conducting the experiment and randomized sample selection, (b) concealment of group information (participants did not know that they belonged to the experimental or control group), and (c) guaranteed validity and reliability of data, as well as the absence of data loss. Furthermore, Pluye et al. (2009) also formulated that quality assessment is carried out by giving a score of 1 if it meets the specified criteria and a score of 0 if it does not meet the specified criteria.

2.4 Coding the Characteristics of the Study

Next, the characteristics of the selected studies were coded by the researcher using the Research Characteristics Form (Ms.Excel format) prepared by the researcher. In this form, studies were coded in different columns based on the researcher’s name, year of publication, independent and dependent variables, research method, sample size, subject matter (discipline area), data analysis method, experiment duration, publication type, education level, and region where the study was conducted.

The following presents the coding of research characteristics used in this meta-analysis study:

Research report characteristics: the author's surname included in the research report, the type of research, and the date of publication were coded. The type of research is classified into undergraduate theses, theses, dissertations, proceedings, or research journal articles.
Research design: pre-test post-test controlled group study, which examined the effect of ethnomathematics-based learning on students' mathematical literacy was coded.
Sample characteristics: the grade levels of the selected samples have been coded, which include elementary school students in grades II, IV, V, junior high school students in grades VIII, IX, high school students in grade X, and university students.
Period of implementation: The period of application of the experimental process is coded in “meeting”.
Methods and approaches: ethnomathematics-based learning and mathematical literacy have been coded. The ethnomathematics-based learning code includes all learning models applied in research that uses an ethnomathematics approach, for example, Ethnomathematics-Based Problem Based Learning (PBL), Ethnomathematics-Based Realistic Mathematic Education (RME), and Flipped Classroom with Ethnomathematics Nuances.
Quantitative data required for effect size: In this case, the sample size, mean, and standard deviation, which are the posttest scores of the experimental and control groups, t-test values, and p-values have been coded.

2.5 Data Analysis Strategy

In conducting this meta-analysis study, the researcher used Microsoft Excel, CMA v3 software, and R Studio (Meta Package). Sequentially, what the researcher did included: (1) calculating the effect size index (Hedges g or Cohens d), (2) determining the effect model (fixed or random), (3) heterogeneity test, (4) variance, (5) moderator variable analysis, (6) meta-regression, and (7) evaluating publication bias.

The Hedges g effect size index was used to calculate the effect of ethnomathematics-based learning on students' mathematical literacy. As stated by Cooper et al. (2009), considering that there are studies with sample sizes smaller than 20, in this meta-analysis study, the calculation of the effect size in each study is calculated based on the Hedges g index. The calculated effect size can be interpreted as “weak” if between 0 and 0,20, “small” if between 0,21 and 0,50, “medium” if between 0,51 and 1,0, and “strong” if greater than 1,0 (Cohen et al., 2007).

After that, the researcher determines which model to use for the average effect size. The average effect size is calculated by combining the effect sizes determined according to two different models, namely the Fixed Effects Model (FEM) or the Random Effects Model (REM). The random effects model is chosen based on the assumption that the main study population being analyzed is functionally different due to different treatments (experiments) conducted by several people (Borenstein et al., 2009). The differences cause differences in the characteristics of the sample and how the treatment is implemented on the sample. Therefore, in this study, the generalized effect size was calculated based on the random effects model, but the heterogeneity test was also used.

The heterogeneity test uses the \(\:Q\) statistic and \(\:p\) -value to look at the effect size variance of each study. If the result of the variance of the overall effect size of the analyzed studies is heterogeneous, so there is potential to conduct moderator variable analysis to express the heterogeneity of the effect of ethnomathematics-based learning on students’ mathematical literacy. Furthermore, heterogeneity was interpreted according to the \(\:{I}^{2}\) index, where an \(\:{I}^{2}\) value of up to 25% is considered “low heterogeneity”, up to 50% is considered “moderate heterogeneity”, and up to 75% is considered “high heterogeneity” (Higgins et al., 2003; Card, 2012). In this meta-analysis study, the moderator variables were analyzed according to students’ education level, subject matter, student demographics, meeting duration, independent variables, sample size, and publication type. In relation to the duration of the experiment (intervention), the effect of these moderator variables was determined through meta-regression analysis. Meta-regression analysis is used to determine whether a model created to explain the variance between measures of the effect of independent but continuous variables is significant (Kanadlı, 2019).

Finally, the researcher determines or evaluates whether there is publication bias in this meta-analysis study. There are various methods used to evaluate publication bias in this study, including Funnel Plot (Borenstein et al., 2009), Trim and Fill method (Duval & Tweedie, 2000), Egger’s Intercept Test (Egger. et al., 1997), and Rosenthal’s Fail-Safe N (Rosenthal, 1979).

3.1 Results

In this meta-analysis study, 16 articles that met the inclusion criteria were selected and used. All data from each article was extracted and based on calculations using CMA v3 software, of the 16 articles, there was 1 article with a “weak” effect (0–0,20), 1 article with a “small” effect (0,21–0,50), 8 articles with a “medium” effect (0,51–1,00), and 6 articles with a “strong” effect (more than 1,00). Visually, the results of the effect size analysis can be seen in the Forest Plot below.

Based on the Forest Plot in Fig. 2 above, it is known that the effect sizes of the 16 studies or research articles are not same. Through the calculation results obtained, the effect size of each study is in four categories, namely “weak” by 6,25% (\(\:n=1\)), “small” by 6,25% (\(\:n=1\)). “medium” was 50% (\(\:n=8\)), and “strong” was 37.5% 37,5% (\(\:n=6\)).

Based on Fig. 2, also it shows that the effect size of each study is in different intervals. One study that has an effect size categorized as “strong” (Prihatiningtyas & Buyung, 2023) is at 95% [3,208; 5,383]. In addition, based on these confidence intervals, we can also detect that one of the studies analyzed has an effect size that is inconsistent with other studies, namely the study conducted by Simamora et al. (2022) is at 95% [-0,445; 0,643], where statistically, the study is indicated to have an insignificant effect size because it contains a point 0 in its confidence interval (Retnawati et al., 2018). In the Forest Plot above, it can also be seen that in the random effects model, the lower limit of the 95% confidence interval is 0,719 and the upper limit is 1,607 with the overall effect size of this meta-analysis study is 1,163 or can be expressed as: \(\:(\text{1,163};\:CI\:95\%\:\left[\text{0,719};\:\text{1,607}\right];\:p\:<\:\text{0,001})\). When associated with the classification of effect size according to Cohen et al. (2007), it can be stated that the effect size is in the “strong” category, which means that ethnomathematics-based learning has a positive effect on students' mathematical literacy skills.

The Prediction Interval value is [-0.733; 3,060] reflects the range over which we can expect effect values from new individual studies in the future. This interval is wider than the Confidence Interval because it takes into account variations between different studies. Thus, we can predict that the results from individual studies in the future may range from − 0.733 (negative effect) to 3.060 (positive effect), indicating the possibility of significant variation in the results of future studies.

Furthermore, the result of the \(\:Z\) test calculation to determine statistical significance, based on Fig. 2, the \(\:z\) score was found to be5,14. This result can be said to be statistically significant at the \(\:p<\text{0,000}\) level. Then, based on Fig. 2, the \(\:Q\) value obtained is 93,57 and the value of \(\:p=\text{0,000}<\text{0,05}\). Thus, the distribution of effect sizes was found to be heterogeneous at \(\:p<\text{0,05}\), i.e. the actual effect sizes varied from study to study. Similarly, the degree of effect size variation between studies was reflected by the \(\:{I}^{2}\) value of 84%, which means that 84% of the observed effect size variance reflects the percentage of variability caused by true heterogeneity (not due to sampling error). Thus, this study had “high heterogeneity” as the \(\:{I}^{2}\) value of 84% exceeded 75% (Higgins et al., 2003; Card, 2012). Since the homogeneity test result was rejected, the estimation model is a random effects model. Since the \(\:p\)-value is \(\:<\text{0,05}\), it can be said that overall the application of ethnomathematics-based learning has a significant effect on students' mathematical literacy when compared to the application of conventional learning.

Furthermore, of the 16 studies observed in this meta-analysis study, it was confirmed that in the heterogeneity test, there were significant differences in the variance of the effect sizes (\(\:Q=\text{93,57};\:p\:<\:\text{0,000}\)). As stated by (Borenstein et al., 2009), if the \(\:Q\) value is significant (\(\:Q>{\chi\:}^{2},\:p<\text{0,05}\)), then part of the variance of the effect size is explained by the continuous variable, in other words, the variable is related to the effect size and the model is significant. Based on this statement, there is potential to conduct further analysis of moderator variables to state the source of variance in each effect size and also Meta Regression test which aims to test whether the independent variable but continuous (predictor) is significant to changes in the effect size or whether this variable has a relationship with the effect size. It should also be noted that this step was carried out at the same time to answer the second research question, where the researcher needs to analyze the level of research characteristics by testing the effect of moderator variables on the 16 effect sizes of 16 primary studies. The following meta-analysis study results for each study characteristic are presented in Table 1 to Table 5 separately along with their analysis below.

Table 1

Meta-analysis Results on Education Level Characteristics
Moderator Variable	Kelompok	\(\:\varvec{n}\)	Combined Effect Size (Hedges g)	Heterogeneity
				Between Classes Effect (\(\:\varvec{Q}\))	Df	\(\:\varvec{p}\)
Education Level	Elementary School	10	1,084	3,743	3	0,291
	Junior High School	4	1,610
	Senior High School	1	0,671
	University	1	0,718
Overall		16	0,902

Based on Table 1 above, it can be seen that for the moderator variable of education level, the highest study effect size is 1,610 (“strong” category) in the study conducted at Junior High School School and the lowest study effect size is 0,671 (“medium” category) in the study conducted at Senior High School. While the largest effect size was conducted in Elementary School, obtaining an effect size of 1,084, the same as Junior High School, the “strong” category. The heterogeneity test results showed that the average effect sizes between education levels were different (\(\:Q=\text{3,743}\) and \(\:p>\text{0,05}\)). Because the \(\:p\)-value \(\:>\text{0,05}\), the distribution of the effect size for the categories on the characteristics of the education level is homogeneous. Thus, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy is not influenced by education level.

Furthermore, on the moderator variable of subject matter, the material taught to students when providing learning interventions/experiments is presented in the following table.

Table 2

Meta-analysis Results on Subject Matter Characteristics
Moderator Variable	Group	\(\:\varvec{n}\)	Combined Effect Size (Hedges g)	Heterogeneity
Moderator Variable	Group	\(\:\varvec{n}\)	Combined Effect Size (Hedges g)	Between Classes Effect (\(\:\varvec{Q}\))	Df	\(\:\varvec{p}\)
Subject Matter	#N/A	4	1,065	67,754	6	0,000
	2D & 3D Geometry	1	2,461
	2D Geometry	7	0,654
	3D Geometry	1	1,105
	Numbers & Geometry	1	0,718
	Probability & Sets	1	4,296
	Transformation Geometry	1	1,814
Overall		16	0,993

Based on Table 2 above, for the moderator variable subject matter, information was obtained that the lowest study effect size was 0,654 (“medium” category) on 2D Geometry material, followed by Numbers & Geometry material with an effect size of 0,718 (“medium” category). Meanwhile, the other materials are all categorized as “strong” with effect sizes above 1,0, including #N/A where #N/A means it is not explained what material is in the observed study. The results of the heterogeneity test showed that the average effect sizes between subject matter were different (\(\:Q=\text{67,754}\) and \(\:p<\text{0,05}\)). Because the \(\:p\)-value \(\:<\text{0,05}\), the distribution of effect sizes for the categories on the characteristics of the subject matter taught is heterogeneous. Thus, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy is influenced by the subject matter.

Furthermore, on the demographic variable of the region where the learning intervention was carried out, it was generally carried out on 3 different islands located in Indonesia, namely Java Island, Kalimantan Island, and Sumatra Island. Actually, in detail, the provinces are also different, of course, for example on the Java Island, it is carried out in West Java Province, there is also in Central Java Province, and also in East Java Province, and so on in other islands. In this case, the researcher unified into 3 different islands as presented in the following table.

Table 3

Meta-analysis Results on Demographic Characteristics of the Intervention Area
Moderator Variable	Group	\(\:\varvec{n}\)	Combined Effect Size (Hedges g)	Heterogeneity
				Between Classes Effect (\(\:\varvec{Q}\))	Df	\(\:\varvec{p}\)
Demographic	Java Island	10	1,038	33,867	2	0,000
	Kalimantan Island	1	4,296
	Sumatra Island	5	0,836
Overall		16	1,190

For the moderator variable of students’ demographics, information was obtained that the highest effect size was 4,296 (“strong” category) on Kalimantan Island, followed by Java Island with an effect size of 1,038 (“strong” category), and finally Sumatra Island with an effect size of 0,836 (“medium” category). The results of the heterogeneity test showed that the average effect sizes between student area demographics were different (\(\:Q=\text{33,867}\) and \(\:p<\text{0,05}\)). Because the \(\:p\)-value \(\:<\text{0,05}\), the distribution of the category effect size on the demographic characteristics of the area where the learning was conducted is heterogeneous. Thus, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy is influenced by students’ regional demographics.

Furthermore, on the moderator variable of the independent variable, from 16 observed studies, there were 9 models, methods, or learning resources of the independent variable that accompanied ethnomathematics-based learning. We present this in the table below.

Table 4

Meta-analysis Results on Independent Variable Characteristics
Moderator Variable	Group	\(\:\varvec{n}\)	Combined Effect Size (Hedges g)	Heterogeneity
Moderator Variable	Group	\(\:\varvec{n}\)	Combined Effect Size (Hedges g)	Between Classes Effect (\(\:\varvec{Q}\))	Df	\(\:\varvec{p}\)
Independent Variable	Contextual Teaching Learning (CTL)	1	0,986	24,412	8	0,002
	Ethnomathematics Based Learning	2	0,886
	Ethnomathematics Pop Up Book	1	2,461
	Ethno-Module with RME	1	1,105
	Flipped Classroom	1	0,718
	Probing Prompting	1	0,769
	Problem Based Learning	5	1,128
	Realistic Mathematics Education (RME)	3	1,154
	Visual Thinking	1	1,814
Overall		16	1,066

For the moderator variable of the independent variable, it was found that the study with the independent variable of learning media Ethnomathematics Pop Up Book had the largest effect size of 2,461 (“strong” category), and followed by other learning models. Meanwhile, the Flipped Classroom learning model has the lowest effect size, which is 0,718 (“medium” category). None of them obtained “small” or “weak” effect sizes. This means that these learning models are suitable for use in ethnomathematics-based learning. The results of the heterogeneity test showed that the average effect sizes between independent variables were different (\(\:Q=\text{24,412}\) and \(\:p<\text{0,05}\)). Because the \(\:p\)-value \(\:<\text{0,05}\), the distribution of effect sizes for categories on the characteristics of independent variables is heterogeneous. Thus, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy skills is influenced by the independent variables.

Next, on the moderator variable of publication type, the effect size was also calculated. The results of these calculations are presented in the table below

Table 5

Meta Analysis Results on Publication Type Characteristics
Moderator Variable	Group	\(\:\varvec{n}\)	Combined Effect Size (Hedges g)	Heterogeneity
				Between Classes Effect (\(\:\varvec{Q}\))	Df	\(\:\varvec{p}\)
Publication Type	Undergraduate Thesis	2	0,677	4,381	2	0,112
	Proceeding	1	0,797
	Article	13	1,250
Overall		16	0,882

For the moderator variable of publication type, it was found that the magnitude of the effect on research published in Undergraduate Thesis was 0,677 (“medium” category), in Proceedings was 0,797 (“medium” category), and in Article was 1,250 (“strong” category). The heterogeneity test results show that the average effect size between publication types is different (\(\:Q=\text{4,381}\) and \(\:p>\text{0,05}\)). Because the \(\:p\)-value \(\:>\text{0,05}\), the distribution of the effect size for the categories on the characteristics of the type of publication is homogeneous. Thus, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy skills is not influenced by the type or source of publication.

For the moderator variable of meeting duration, the heterogeneity test results show that the average effect sizes between meeting durations are different (\(\:Q=\text{1,97}\) and \(\:p>\text{0,05}\)). Because the \(\:p\)-value \(\:>\text{0,05}\), the distribution of the effect sizes for the categories on the characteristics of the learning meeting duration is homogeneous. So, from this it can be seen that there is no minimum limit to the number of meetings for ethnomathematics-based learning. Thus, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy skills is not influenced by the duration of the learning intervention meeting.

Furthermore, the moderator variable sample size is also taken into account. That is, we can assume whether the condition of too many students also affects the effectiveness of ethnomathematics-based learning on their mathematical literacy? Or is it the small number of students that is ideal for ethnomathematics-based learning? For the moderator variable of sample size, the heterogeneity test results show that the average effect sizes between sample sizes are different (\(\:Q=\text{3,62}\) and \(\:p>\text{0,05}\)). Because the \(\:p\)-value \(\:>\text{0,05}\), the distribution of effect sizes for the categories on the sample size characteristics is homogeneous. Thus, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy is not influenced by the sample size in the class.

The next stage, which is the last stage, is for researchers to examine publication bias. The following is a Funnel Plot of this meta-analysis research in Fig. 3.

Based on the Funnel Plot above, it can be understood that the distribution of effect sizes is quite symmetrical around the vertical line. This means that it can be assumed that this meta-analysis study does not have publication bias. However, it needs to be further tested with other tests such as the Trim and Fill method (Duval & Tweedie, 2000), Egger’s Intercept Test (Egger. et al., 1997), and Rosenthal’s Fail-Safe N (Rosenthal, 1979).

Through the results of the CMA v3 calculation, in the random effects model, the observed and adjusted effect sizes are the same, namely 0,92233 (0,78894; 1,05572). Since the two effect sizes are relatively close (or even the same), it can be assumed that there is no publication bias.

Another test also conducted by researchers is Egger's Intercept Test. If the intercept value obtained from this test is not significantly different from 0 (\(\:p>\text{0,05}\)), it can be concluded that there is no publication bias. Conversely, if it is different, (\(\:p<\text{0,05}\)), it is concluded that publication bias occurs. Based on the calculation results using CMA v3, it is obtained that the intercept value is 9,86712 and the \(\:p\)-value (2-tailed) is 0,00002. Because the significance level is less than 0,05, it can be said that there is publication bias in this meta-analysis study.

Furthermore, in the Rosenthal Fail-Safe N test, based on calculations using CMA v3, an N value of 920 was obtained. The result of the calculation was expressed as \(\:5\times\:16+10=90\). Since the value of 920 is higher than 90, it can be concluded that there is no publication bias in this meta-analysis study. Thus, no studies were missing or needed to be added to the analysis due to publication bias.

So, if we compare all the test results, where some results show that there is publication bias and some others do not show this, it can be concluded that the publication bias in this meta-analysis study is not strong enough against publication bias.

3.2 Discussion

Research on the effectiveness of ethnomathematics-based learning on students’ mathematical literacy has been examined through many research studies, and of course is still ongoing today in other parts of the world. In this meta-analysis study, based on effect size inference using a sample of 16 primary studies on the influence/impact/effectiveness of ethnomathematics-based mathematics learning, the overall effect size was 1,163 or can be expressed as: \(\:(\text{1,163};\:CI\:95\%\:\left[\text{0,719};\:\text{1,607}\right];\:p\:<\:\text{0,001})\). These results indicate that ethnomathematics-based learning has a strong positive effect on students’ mathematical literacy when compared to conventional learning.

However, further investigation was also conducted based on several moderator variables that could potentially or possibly influence the effect of the intervention. These variables include: education level, subject matter, student area demographics, duration of learning meetings, independent variables, sample size, and type of publication. Based on the results of the analysis of the seven moderator variables, when referring to their respective \(\:p\)-values, it can be concluded that the application of ethnomathematics-based learning to improve students’ mathematical literacy skills is influenced by the demographics of the student's region, the independent variable, and the subject matter provided. However, the application of ethnomathematics-based learning to improve students’ mathematical literacy skills is not influenced by the duration of the meeting, education level, sample size, and type of publication.

If we compared the findings of this meta-analysis study with previous studies, several previous meta-analysis studies also showed more or less the same thing, although the dependent variable was different. For example, the results of a meta-analysis study from Sulistyowati & Mawardi (2023) showed that the effectiveness of the application of ethnomathematics-based learning on the mathematical abilities of elementary school students differed significantly based on the year of publication, but there were no significant differences in grade level and sample size. Similarly, the results of a meta-analysis study from Apriatni et al. (2022) showed that ethnomathematics-based learning on students’ problem solving ability was influenced by the level of education and the independent variable of the learning model, but not by the year of publication and sample size. Furthermore, a study conducted by Susanto et al. (2023) showed that the dependent variable and education level significantly influenced the effectiveness of ethnomathematics learning on students’ critical thinking skills.

In summary, of the three previous meta-analysis studies on the topic of ethnomathematics, the findings of the researchers’ meta-analysis studies are quite in line with the existing results although some contradictions also exist. For example, the application of ethnomathematics-based learning is influenced by the independent variable (Apriatni et al., 2022), but no study results were found regarding the demographic variables of the student area and the subject matter provided. This means that the findings of these two moderator variables are new findings that can be useful for further research. Furthermore, the application of ethnomathematics-based learning is not influenced by education level (Sulistyowati & Mawardi, 2023) and sample size (Sulistyowati & Mawardi, 2023; Apriatni et al., 2022), but no study results were found regarding the variables of meeting duration and publication type. This means that the findings of these two moderator variables are also new findings that can be useful for further research. The results of the meta-analysis study conducted by researchers contradict the previous meta-analysis study, namely in the meta-analysis study of Apriatni et al. (2022) and Susanto et al. (2023) which states that ethnomathematics-based learning is influenced by the level of education,

Ethnomathematics allows students to construct mathematical concepts as part of their mathematical literacy based on their knowledge of the socio-cultural field. Furthermore, ethnomathematics is able to create good motivation and provide pleasure in the field of education, so that students are expected to pay great attention to explore mathematics education so that it can affect their mathematical abilities (Surat, 2018). This is of course in accordance with the views of Lev Vygotsky, which in his theory states that socio-cultural activities cannot be separated in the development of students. From Vygotsky's theory, researchers agree that social activities play a crucial role in knowledge construction. This means that through ethnomathematics learning, students can build their own knowledge or collaborate with others, and that knowledge is constructed in a cultural context which is something that is already inherent in students.

Overall, the results of this meta-analysis show that ethnomathematics-based learning has a significant positive effect on students’ mathematical literacy. Thus, the results of this meta-analysis study support the use of ethnomathematics-based learning in mathematics education. Ethnomathematics not only makes mathematics more relevant and interesting for students, but it is also effective in improving their mathematical literacy. Therefore, mathematics education should continue to look for ways to integrate culture or local knowledge into the curriculum, in an effort to improve mathematical literacy, NCTM's (2000) mathematics learning process standards, students’ cognitive, affective and psychomotor abilities, or students’ learning outcomes in general.

However, this meta-analysis study also has limitations, including in the literature search, the primary studies obtained by researchers only come from Indonesia. It is not yet known whether other countries also have the same topic regarding the influence/impact/effectiveness of ethnomathematics-based learning on students' mathematical literacy, but as far as the search is concerned, researchers only found Indonesian studies. Thus, if there are primary studies from other parts of the world, it is hoped that the results of this study can be generalized more broadly. Furthermore, the moderator variables observed in this meta-analysis study were limited to only 7 variables. Researchers still feel insufficient, and it is hoped that further research can complement it with other moderator variables so that it can support the research findings, such as the year of publication, experimental design, data analysis techniques, dependent variables, and so on. In addition, because the meta-analysis study conducted by this researcher is the latest study related to students' mathematical literacy, therefore the researcher also recommends that a meta-analysis study be conducted with other dependent variables, namely the 5 standards of the mathematics learning process recommended by NCTM (2000), including: problem solving, reasoning, connection, communication, and mathematical representation.

Based on the effect size calculation using a sample of 16 primary studies on the influence/impact/effectiveness of ethnomathematics-based mathematics learning on students' mathematical literacy, an overall effect size \(\:(\text{1,163};\:CI\:95\%\:\left[\text{0,719};\:\text{1,607}\right];\:p\:<\:\text{0,001})\) was obtained. This result indicates that ethnomathematics-based learning has a strong positive effect on students’ mathematical literacy when compared to conventional learning.

Furthermore, the application of ethnomathematics-based learning to improve students' mathematical literacy is also influenced by several variables including student area demographics (\(\:Q=\text{33,867}\) and \(\:p=\text{0,000}\)), independent variables (\(\:Q=\text{24,412}\) and \(\:p=\text{0,002}\)), and the subject matter provided (\(\:Q=\text{67,754}\) and \(\:p=\text{0,000}\)). However, the application of ethnomathematics-based learning to improve students’ mathematical literacy was not influenced by variables such as meeting duration (\(\:Q=\text{1,97}\) and \(\:p=\text{0,1601}\)), education level (\(\:Q=\text{3,743}\) and \(\:p=\text{0,291}\)), sample size (\(\:Q=\text{3,62}\) and \(\:p=\text{0,0569}\)), and publication type (\(\:Q=\text{4,381}\) and \(\:p=\text{0,112}\)).

Conflict of Interest Statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed no receipt of any financial support for the research, authorship, and/or publication of this article.

Author Contribution

This meta-analysis study could be carried out well because of collaboration between researchers, namely researcher 1 as a doctoral student from Mersin University who was tasked with collecting primary studies from the Google Scholar and Education Resources Information Center (ERIC) databases, carrying out Reliability Coding, initial stage writing, and analyzing data; and researcher 2 as a Profesor from Mersin University who was tasked with carrying out Coding Reliability and final stage writing. We also thank the Department of Curriculum and Instruction, Mersin University, Türkiye.

Acknowledgement

Data Availability

Data is provided in the form of a manuscript or additional information file in Ms. Excel or CMA v3 format.

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No competing interests reported.

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Effectiveness of Ethnomathematics-Based Learning on Students' Mathematical Literacy: A Meta-Analysis Study

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Abstract

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1 Introduction

2 Method

2.1 Literature Search

2.2 Collecting the Data (Determining Inclusion Criteria)

2.3 Assessing Research Quality

2.4 Coding the Characteristics of the Study

2.5 Data Analysis Strategy

3 Results and Discussion

3.1 Results

3.2 Discussion

4 Conclusions

Declarations

Conflict of Interest Statement

Funding

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

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