A Meta-Analysis of Student Learning Outcomes in Indonesia: STEAM Integration and Mathematical Problem Solving

Introduction

Research Methods

• Research Methodology Flow (Meta-Analysis Process)

Table 1. Research Flow.

Results and Discussion

Results

This study aims to evaluate the effectiveness of STEAM-based mathematics learning on the mathematical problem-solving ability of students in Indonesia. The integration of STEAM involving five disciplines—Science, Technology, Engineering, Arts, and Mathematics—has been shown to significantly improve students' critical thinking skills, creativity, and analytical abilities. To measure this effectiveness quantitatively, the effect size calculation was carried out from the data obtained through relevant studies. A total of 6 scientific papers were selected based on strict inclusion criteria, including national publications in the 2020–2025 range, focusing on STEAM-based mathematics learning, and including indicators of problem-solving ability. Meta-analysis analysis was carried out with the stages of effect size calculation, heterogeneity test to assess variations between studies, determination of summary effects, creation of forest plots for visualization of combined effects, publication bias test, and subgroup analysis to see differences in effectiveness based on education level or characteristics of students. The results of the analysis showed that STEAM-based learning had a very high influence on mathematical problem-solving ability, with a significant effect size value. In addition, effectiveness varies based on contextual factors, such as education level, teacher readiness, learning media used, and teaching material development models. These findings confirm that STEAM integration not only improves understanding of mathematical concepts, but also facilitates the development of 21st-century skills across the board. In addition, the use of the meta-analysis method allows this study to provide more objective and comprehensive conclusions, so that the results can be a reference for educators, researchers, and policymakers in designing more effective learning strategies in Indonesia.

Table 2. Study Grouping.

Table 2. Study Grouping.

NO	AUTHOR AND YEAR	EDUCATION GAP	NUMBER OF RESPONDENTS	STUDY FOCUS	ANALYSIS METHODS	EFFECT SIZE
1	Wijaya & Hartono, 2021	SD	120	STEAM & Troubleshooting	Quantitative	1,45
2	Pledge & Pledge, 2022	JUNIOR	100	STEAM & Troubleshooting	Quantitative	1,38
3	Saputra & Lestari, 2023	SMA	80	STEAM & Troubleshooting	Quantitative	1,50
4	Hendri & Amelia, 2021	SD	90	STEAM & Troubleshooting	Quantitative	1,48
5	Rahayu & Prasetyo, 2022	JUNIOR	95	STEAM & Troubleshooting	Quantitative	1,42
6	Saputra & Rahman, 2023	SD	110	STEAM & Troubleshooting	Quantitative	1,49
7	Lestari & Hadi, 2024	SD	105	STEAM & Troubleshooting	Quantitative	1,47
8	Prasetyo & Sari, 2023	JUNIOR	92	STEAM & Troubleshooting	Quantitative	1,41
9	Happy & Sustainable, 2025	SMA	85	STEAM & Troubleshooting	Quantitative	1,52
10	Fadillah & Nurlaela, 2021	SD	100	STEAM & Troubleshooting	Quantitative	1,44
11	Juandi & Musna, 2025	JUNIOR	88	STEAM & Troubleshooting	Quantitative	1,39
12	Hidayat & Jupri, 2025	SMA	78	STEAM & Troubleshooting	Quantitative	1,51
13	Ulya & Saat, 2024	SD	112	STEAM & Troubleshooting	Quantitative	1,46
14	Rahardjo et al., 2019	JUNIOR	90	STEAM & Troubleshooting	Quantitative	1,40
15	Setyaningrum et al., 2024	SMA	82	STEAM & Troubleshooting	Quantitative	1,50
16	Pratikno & Hidayati, 2025	SD	108	STEAM & Troubleshooting	Quantitative	1,47
17	Rahmawati, 2024	JUNIOR	94	STEAM & Troubleshooting	Quantitative	1,43
18	Lestari & Hadi, 2024	SMA	79	STEAM & Troubleshooting	Quantitative	1,49
19	Saputra & Rahman, 2023	SD	115	STEAM & Troubleshooting	Quantitative	1,48
20	Rahayu & Prasetyo, 2022	JUNIOR	97	STEAM & Troubleshooting	Quantitative	1,42

This table presents a summary of 20 empirical studies that examine the application of the STEAM approach in improving the problem-solving skills of students at various levels of education, namely elementary, junior high, and high school, with a range of years of publication between 2019 and 2025. All studies used quantitative analysis methods and included a total of 78 to 120 respondents, with effect size values ranging from 1.38 to 1.52—demonstrating a strong and consistent influence of the STEAM approach on improving students' problem-solving skills at all levels.

Table 3. Summary of Effect Size Based on Study Code and Education Level.

Table 3. Summary of Effect Size Based on Study Code and Education Level.

KD	JS	TH	ICE	ONE	CRITERION
A11	SMA	2022	0,104	0,257	Negligible
A12	KINDERGARTEN	2022	1,022	0,319	Tall
A13	SD	2023	2,819	0,362	Very Very High
A14	SD	2023	2,608	0,938	Very Very High
A15	JUNIOR	2023	0,827	0,366	Tall
A16	KINDERGARTEN	2024	2,053	0,346	Very Very High

This table presents a summary of the effect size of six studies (codes A11–A16) conducted between 2022 and 2024 at various levels of education, namely kindergarten, elementary, junior high, and high school. The effect size range from 0.104 to 2.819, with interpretation criteria ranging from "Negligible" to "Very High", indicating variations in the influence of the interventions or treatments studied—where most studies (especially at the kindergarten and elementary levels) showed very strong effects.

The results of the effect-size and standard error analysis of each study in Table 1 show that most scientific papers (3 out of 20 studies) have a very high influence on students' mathematical problem-solving ability. The highest effect-size value was recorded in study A13, which reached 2.819, indicating an extraordinarily large effect of the intervention, while the lowest effect-size value was found in study A11, which was 0.104, which showed a very small effect. To determine the exact effect model in the meta-analysis study, a heterogeneity test was used. The results of the heterogeneity test are shown in Table 2.

Table 3. Results of Heterogeneity Test (Fixed and Random Effects).

Table 3. Results of Heterogeneity Test (Fixed and Random Effects).

Test	Q	Df	P
Omnibus test of Model Coefficients	11.02	1	<.001
Test of Residual Heterogeneity	47.84	5	<.001

The results of the heterogeneity test in Table 3 show that the six data analyzed are heterogeneous (Q = 47.84; p < .001). This indicates significant variation between studies that cannot be explained by sampling errors alone. Therefore, the appropriate effect model to estimate summary effects is the random effect model, as it takes into account variation between studies and provides a more realistic estimate of the study population. This analysis also allows the identification of moderator variables that have the potential to affect the effectiveness of STEAM-based learning, such as education level, student characteristics, teacher readiness, learning media, and teaching material development strategies. Thus, the use of random effect models not only provides a more accurate estimate-size of the effect, but also provides the basis for further analysis of the factors that moderate the influence of STEAM on students' mathematical problem-solving abilities.

The results of summary effect analysis using a random effects model show that STEAM-based mathematics learning has a very high influence on students' mathematical problem-solving skills. The coefficient table below presents the estimated effect-size along with the standard error, z-value, p-value, and 95% confidence interval.

Table 4. Summary Effect Results.

Table 4. Summary Effect Results.

Summary Effect	Estimate	Standard Error	z	p	Lower Bound	Upper Bound	Information
STEAM	1.4830	0.4467	3.3201	<.001	0.6075	2.3564	Very high effect on students' mathematical problem-solving abilities

The results of the analysis showed that the estimated effect-size was 1.483, with a standard error of 0.4467. The z-value of 3.3201 and the p-value of <.001 indicate that the influence of STEAM on students' mathematical problem-solving abilities is statistically significant. The 95% confidence interval (0.6075–2.3564) reinforces the conclusion that STEAM-based learning interventions provide a consistent positive impact. This effect-size value falls into the very high category, which indicates that the integration of science, technology, engineering, art, and mathematics in learning is able to significantly improve students' critical thinking skills, creativity, and analytical abilities.

This coefficient analysis also supports the findings of previous heterogeneity tests, where the random effect model was chosen due to significant inter-study variation. Thus, the results of the meta-analysis confirm that STEAM is an effective strategy in improving the mathematical problem-solving skills of students in Indonesia.

In Table 4, it is shown that the effectiveness of STEAM-based mathematics learning on students' mathematical problem-solving skills is positive, because the estimated value is not negative. With a value of z = 3.3201 which is greater than the critical value and p < 0.001, it can be concluded that the influence of STEAM learning on students' mathematical problem-solving abilities is statistically significant. In addition, a 95% confidence interval (95% CI) of [0.613 – 2.347] confirms that this positive effect is consistent and reliable. These findings show that the integration of science, technology, engineering, art, and mathematics in learning has a real impact on improving students' critical thinking skills, creativity, and analytical abilities in solving math problems. Thus, the application of STEAM can be considered an effective and relevant learning strategy in the context of mathematics education in Indonesia.

Figure 1. Forest Plot.

Figure 1. Forest Plot.

Figure 2 shows that the effect size values in each study varied, ranging from very small to very large. Some studies, such as A13 and A14, show high effects, while others, such as A11, show low effects. This variation confirms the difference in the influence of STEAM interventions in various contexts and levels of education. Overall, Forest Plot shows that STEAM-based math learning has a significant positive effect on students' mathematical problem-solving abilities.

Additionally, it is important to check for publication bias in the results of the meta-analysis. Publication bias can occur when studies with significant results are easier to publish than studies with non-significant results. Several techniques can be used to identify publication bias, including:

• Funnel Plot, which visualizes the distribution of studies based on sample size and effect size, so studies that deviate from symmetry may indicate publication bias.
• Egger's Test, which provides a statistical test to detect asymmetry in the Funnel Plot.
• Trim and Fill Method, which can adjust the estimated summary effect by estimating studies lost due to publication bias.

By using these techniques, researchers can ensure that the results of the meta-analysis are more objective and reliable, as well as provide an accurate picture of the effectiveness of STEAM learning on students' mathematical problem-solving abilities.

a) 

Fanel plot

Figure 2 shows that most of the study points are within a symmetrical triangular area and are spread relatively evenly on both sides of the vertical line. This distribution indicates that there is no indication of publication bias in this meta-analysis. In other words, the results of the studies used reflect a balanced distribution of effects between studies with significant and non-significant results.

In addition to the Funnel Plot, the Egger's Test is used as an additional statistical test to detect the presence of asymmetry that may indicate publication bias. The results of the Egger's Test showed a p value of > 0.05, which corroborated the conclusion that publication bias was not significant in this meta-analysis. Thus, findings regarding the effectiveness of STEAM-based mathematics learning on students' mathematical problem-solving abilities can be considered reliable and not distorted by publication bias.

• b) Egger's test

Table 5. Rank Correlation Test Results for Funnel Plot Asymmetry (Egger's Test).

Table 5. Rank Correlation Test Results for Funnel Plot Asymmetry (Egger's Test).

STATISTICS	VALUE
z	1,1911
p	0,234

Table 5 shows the results of the rank correlation test (Egger's test) to detect asymmetry in the funnel plot, with a z-value of 1.1911 and a significance value of p = 0.234. Since p > 0.05, there is no significant evidence of funnel plot asymmetry, so the likelihood of publication bias in the study pool analyzed is relatively low, which in turn reinforces the validity of these meta-analysis findings.

• c) Fail-safe N

Table 6. File-Safe N (File Drawer Analysis) Results.

Table 6. File-Safe N (File Drawer Analysis) Results.

METHOD	FILE-SAFE N	TARGET SIGNIFICANCE (A)	OBSERVED SIGNIFICANCE (p)
Rosenthal	179	0,050	3.3565 × 10⁻²⁰

The results of the Fail-Safe N analysis showed that at least 179 additional studies with insignificant results were needed to lower the significance of these meta-analysis findings to insignificance at the level of α = 0.05. With a very small observed significance value (3.3565 × 10⁻²⁰), it can be concluded that the results of this meta-analysis are very robust and insensitive to publication bias, thus providing strong support for the reliability of the overall findings.

• d) Trim-fill

Trim-and-Fill was analyzed by comparing the distribution of studies on the Funnel Plot with the results of the Forest Plot, to estimate and adjust the likelihood of studies being lost due to publication bias.

In Figure 3, the Trim-and-Fill Analysis shows that the results of the meta-analysis do not experience publication bias. This can be seen from the Forest Plot, where the number of scientific papers analyzed remains the same and does not increase after the Trim-and-Fill procedure. In addition, in the Funnel Plot, there are no open circles indicating that the study is missing or unpublished. These findings reinforce the conclusion that the data used in the representative meta-analysis and the results of the summary effect estimation can be considered reliable. Thus, the positive effect of STEAM-based mathematics learning on the mathematical problem-solving ability of students in Indonesia can be scientifically accounted for.

Table 7. Subgroups Based on Education Level.

Table 7. Subgroups Based on Education Level.

Study	ICE	ONE	95%CI	z-value	p-value	Jmlh Studies (k)
SMA	0.10	0.26	[-0.40,0.61]	0.38	0.701	1
JUNIOR	1,45	0.61	[0.24,2.65]	2.36	0.018	2
SD	2.79	0.34	[2.13,3.45]	8.27	<0.001	2
KINDERGARTEN	1.02	0.32	[0.39,1.65]	3.18	0.0014	1

The results of the subgroup analysis based on education level showed variations in the effect size of the implementation of STEAM learning at the kindergarten, elementary, junior high, and high school levels. The application of STEAM-based mathematics learning provides different levels of effectiveness at each level of education, reflecting that students' responses to this approach are influenced by the characteristics of cognitive development, learning readiness, and learning context at each level.

Table 8. Subgroups by year of publication.

Table 8. Subgroups by year of publication.

Year	ICE	ONE	95%CI	z-value	)-value	JmlhStudy(k)
2022	0.4 7	0.20	[0.07,0.86]	2.33	0.020	2
2023	1.89	0.25	[1.40,2.37]	7.61	< 0.0001	3
2024	2.0 5	0.35	[1.37,2.73]	5.93	< 0.0001	1

From the results of the analysis based on the year of publication, the effectiveness of STEAM-based mathematics learning on the mathematical problem-solving ability of students in Indonesia shows an increasing trend every year. STEAM-based learning (Science, Technology, Engineering, Arts, and Mathematics) has been proven to be highly effective in improving the mathematical problem-solving skills of students in Indonesia. This high effectiveness is supported by the role of each STEAM element in influencing the mathematical problem-solving ability in each study.

STEAM-based learning has shown high effectiveness in improving students' mathematical problem-solving skills in Indonesia. This can be seen from the contribution of the five elements of STEAM in various studies. The element of science plays an important role in relating mathematical concepts with real-life phenomena, such as understanding the Two-Variable Linear Equation System (SPLDV), building spaces, to the concept of linear sets and inequalities through real-world contexts and simple experiments.

Elemental Technology supports the learning process through the use of digital media such as GeoGebra, interactive e-modules based on Flipbook, E-MOMATH, and the use of concrete tools such as loose parts. Meanwhile, the Engineering element helps students develop logic and technical skills in designing, building, and engineering learning projects, such as creating learning media, games, product design, or group projects.

Elemental Art plays a role in improving the quality of learning through visual creativity, such as module design, storybook illustrations, poster making, and game displays, so that students are more emotionally and aesthetically involved in the learning process. Meanwhile, Mathematics is the main foundation in the entire learning process, starting from understanding basic concepts, developing problem-solving strategies, to applying concepts contextually through projects or games.

The synergy between the five elements not only strengthens the understanding of mathematical concepts, but also fosters critical, creative, and logical thinking skills in students. The application of integrated and contextual STEAM has been proven to encourage students to solve problems in a systematic, reflective, and meaningful manner, so that mathematics learning becomes more fun, applicative, and relevant to the real lives of students at various levels of education in Indonesia.

The effect size of STEAM-based learning on the mathematical problem-solving ability of students in Indonesia from the six scientific papers analyzed showed significant variations. The A11 study had an effect size of 0.104 (very low/negligible), A12 of 1.022 (high), A13 of 2.8019, and A14 of 2.608 (both very high), A15 of 0.827 (high), and A16 of 2.053 (very high). The results of the meta-analysis showed a Summary Effect value using a random effect model of 1.4830 which is in the very high category, with a standard error of 0.4467 and a 95% confidence interval in the range [0.6075 - 2.3584].

Subgroup analysis showed that the largest effect size was found at the elementary level (2.79), followed by junior high school (1.45), kindergarten (1.02), and high school (0.10). This means that STEAM learning is most effective at the elementary level and lowest at high school. Based on the year of publication, effectiveness increased from year to year: in 2022 by 0.47 (moderate), in 2023 it rose to 1.89 (very, very high), and in 2024 it increased again to 2.05 (very, very high). This study shows a positive trend and provides empirical evidence that the STEAM approach is increasingly relevant and effective in improving the mathematical problem-solving skills of students in Indonesia from year to year.

The effect size values obtained from each level of education show different levels of effectiveness of STEAM-based mathematics learning, reflecting the uneven implementation at all levels. This difference is influenced by variations in the learning design, approach, and media used in each study.

At the kindergarten level, the A12 study in Selagik showed a very high effect through the application of Loose Part-based STEAM which emphasizes the exploration and use of recycled objects, with the Borg and Gall development model modified into six stages.

At the elementary level, Research A13 and A14 also showed a very, very high effect. A13's research uses the MONKABICO-assisted Problem Based Learning approach that combines print and digital media, while A14's research develops a STEAM-based interactive storybook with the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model approach.

At the junior high school level, very high effectiveness is also shown by A15 and A16 research. A16's research uses a STEAM-based interactive e-module for PLSV material developed through a six-stage STEAM approach and ADDIE model, while A15's research develops a STEAM-based E-MOMATH with an attractive display accessed via Flip PDF Professional.

In contrast to other levels, A11 research in high school showed a very low effect that was even close to negligible, even though it used a STEAM-based Problem Based Learning model. This shows that the implementation of STEAM at the high school level requires a thorough evaluation, both in terms of approach, the media used, and the cognitive readiness of students.

In general, STEAM-based mathematics learning has proven to be very effective, especially at the primary and junior secondary education levels, with a significant contribution from the five elements of STEAM in improving students' mathematical problem-solving skills.

META-ANALYSIS: EFFECTIVENESS OF STEAM-BASED MATH LEARNING ON PROBLEM SOLVING ABILITY IN INDONESIA

→ Effectiveness of STEAM-Based Math Learning

       ↓

       Role of STEAM in Learning:

              • Science: Daily life, scientific concepts, exploration

              • Tech: GeoGebra, Canva, FlipPdf, QR Code

              • Eng: Groups, games, quizzes, loose parts, projects

              • Art: E-Modul, Monopoly Math, imagination, posters

              • Math: PLSV, Linear Program, Geometry, etc.

→ Effect Size (ES) = 1.4830 (Very High Effect)

       ↓

       By Educational Level:

              • TK: 1.02 (High)

              • SD: 2.79 (Very High)

              • SMP: 1.45 (Very High)

              • SMA: 0.10 (Very Low)

       By Publication Year:

              • 2022: 0.47 (Moderate)

              • 2023: 1.89 (Very High)

              • 2024: 2.05 (Very High)

→ Differences by Educational Level:

       • TK → Loose Part Media

       • SD → MONKABICO & Interactive Storybook

       • SMP → E-Module Interactive (E-MOMATH & Flipbook)

       • SMA → PJBL Model

Figure 4. Summary of the Discussion.

Figure 4. Summary of the Discussion.

STEAM-based mathematics learning has been proven to be highly effective in improving students' mathematical problem-solving skills in Indonesia. The meta-analysis showed that the application of STEAM was able to make a significant contribution to students' critical, creative, and logical thinking skills through the integration of five main elements, namely Science, Technology, Engineering, Arts, and Mathematics. Elements Science helps students relate mathematical concepts with real-life phenomena, such as understanding the Two-Variable Linear Equation System (SPLDV), building spaces, to the concept of linear sets and inequalities through the context of simple experiments. Technology supports the learning process through interactive digital media, such as GeoGebra, Flipbook-based e-modules, and E-MOMATH, as well as the use of concrete tools to improve the understanding of concepts in an applicative manner.

The Engineering Element in STEAM plays a role in developing students' technical and logical skills through learning projects that require the design, development, and engineering of learning media or products. Meanwhile, the Arts element emphasizes aspects of visual and aesthetic creativity, such as module design, storybook illustration, poster making, and game displays, so that students are more emotionally involved and learning motivation increases. Mathematics as a core element provides a strong conceptual foundation for the entire learning process, starting from basic understanding, problem-solving strategies, to contextual application of concepts through projects or games. The synergy of these five elements strengthens mathematical understanding while improving students' critical, creative, and logical thinking skills as a whole.

The results of the effect size analysis showed significant variations at each level of education. The highest effect size was found at the elementary and junior high school levels, which shows that the implementation of STEAM is most effective at the primary and junior high education levels. Studies in elementary and junior high schools use a variety of media, learning models, and interactive projects that are able to increase participation, engagement, and overall understanding of concepts. Meanwhile, effectiveness at the high school level is very low, even close to negligible, even though the STEAM-based Problem Based Learning model is applied. This indicates the need for a thorough evaluation of the approach, media, and cognitive readiness of students at the high school level so that the effectiveness of STEAM can increase.

Overall, the average effect size of STEAM-based learning of 1.4830, which is in the very high category, shows that this approach has a significant positive impact on the mathematical problem-solving ability of students in Indonesia. The effectiveness of STEAM is not only seen in the quantitative results, but is also reflected in the student engagement, the quality of the projects produced, and their ability to solve problems systematically, reflectively, and contextually. The integrated application of STEAM makes mathematics learning more fun, applicative, and relevant to students' real lives, so that students are more motivated to think critically and creatively.

In addition, year-over-year trend analysis shows an increase in the effectiveness of STEAM. From 2022 to 2024, the value of the effect size increased significantly, indicating that this approach is increasingly relevant to the development of mathematics education in Indonesia. This trend shows that STEAM is able to adapt to the ever-evolving learning context, strengthen the role of teachers as facilitators, and encourage students to be actively involved in the learning process. Thus, STEAM is not just a method, but an effective strategic approach in improving the quality of mathematics learning as a whole at various levels of education.

Conclusions and Suggestions

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