A meta-analysis to explore the role of design thinking in enhancing creativity as learning outcomes in STEM education

Educational levels were identified as an important moderator variable influencing the effectiveness of informal DT-STEM programs.

“In addition, educational levels were identified as an important moderator variable that influences the effectiveness of informal DT-STEM programs.”

• cites He et al., 2023Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Ellis et al., 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Byrne et al., 2017 sourceCould not verify

  The Methodology describes a workshop for students with a mean age of 16, and the Pre and Post Questionnaire Comparison reports changes after participation. Neither section identifies educational level as a moderator of effectiveness. Searches for educational level, grade level, and moderating effects found no passage addressing that claim. (LLM, conf=0.72)

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Course duration plays a vital role in informal DT models, influencing students' learning outcomes.

“Course duration also plays a vital role in informal DT models, influencing the learning outcomes of the students.”

• cites Kim et al., 2022 sourceNot checked — no full text of the source
• cites Byrne et al., 2017 sourceCould not verify

  The Bridge21 Pedagogy and Model section says activities can be compressed or expanded, and the Limitations section discusses spreading activities across several weeks while maintaining momentum. Neither passage says that course duration plays a vital role in students’ learning outcomes; the paper’s reported outcomes are not linked to a comparison of durations. (LLM, conf=0.73)

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• cites Zulyusri et al., 2023 sourceCould not verify

  The Introduction discusses design-thinking-based STEM learning and student outcomes, but does not address course duration. A search for course duration and informal design-thinking models found no passage linking duration to learning outcomes; the Results instead discuss sample size, research year, research source, and education level as potential moderators. Page numbers were not available in our copy. (LLM, conf=0.76)

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The conceptual framework of this review is based on Santhosh et al. (2023), a 12-year meta-analysis of informal Project-Based Learning in STEM disciplines.

“The conceptual framework of this review is based on research conducted by Santhosh et al. (2023), which involved a 12-year meta-analysis of informal Project-Based Learning (PJBL) in STEM disciplines.”

• cites Santhosh et al., 2023 sourceSupported

There is a lack of studies assessing the impact of DT interventions on creativity on a global scale.

“Furthermore, there is a lack of studies assessing the impact of DT interventions on creativity on a global scale.”

• cites Suprapto, 2023 sourceCould not verify

  The Result and Discussion section reports 13 eligible studies on design-thinking-based inquiry training and students’ creative thinking, but does not assess how many studies of design-thinking interventions exist worldwide. Searches for a global research gap or a shortage of such studies found no passage making that claim; the paper’s analysis concerns students in Indonesia. Page numbers were not available in our copy. (LLM, conf=0.74)

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• cites Zulyusri et al., 2023 sourcePartly supported

  The Introduction says prior studies have not specifically established the effect of design-thinking-based STEM models, which supports a general research-gap claim. It does not identify a global lack of studies assessing creativity as an outcome; the paper’s meta-analysis instead examines critical thinking. (LLM, conf=0.76)

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Moderator variables including assessment methods, grade level, study location, subject area, teaching methodology, course duration, and group size can significantly impact the effectiveness of informal DT-STEM programs.

“These variables include assessment methods, grade level of students, study location, subject area, teaching methodology, course duration and the number of students in the groups, all of which can significantly impact the effectiveness of integrating informal DT models in STEM education (Gao et al., 2020;Li, 2024).”

• cites Gao et al., 2020 sourcePartly supported

  The Monodisciplinary knowledge section (page unavailable) reports that the type of engineering integration was associated with student learning gains, lending limited support to the teaching-methodology part of the claim. The review discusses assessment formats, but does not establish that the listed variables significantly affect the effectiveness of informal DT-STEM programs. (LLM, conf=0.82)

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• cites Li, 2024Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The mean effect size is not precisely zero (Z = 4.496, p < 0.001).

“The Z-value is 4.496 with p < 0.001.Using a criterion alpha of 0.050, we reject the null hypothesis and conclude that in the universe of populations comparable to those in the analysis, the mean ES is not precisely zero.”

• cites Borenstein, 2020 sourceCould not verify

  The Motivating example reports a mean hazard ratio of 0.77 with a confidence interval of 0.75 to 0.79, but it does not report the claimed Z-value or p-value. The section on how much the effect size varies across studies tests a different null hypothesis—whether all studies share a common effect size. Searches for the Z-value and the stated conclusion about a mean of zero found no matching passage in our copy. (LLM, conf=0.73)

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Many prior studies have not conducted moderator variable analysis to understand factors influencing the strength of observed effects.

“Many of these studies have not conducted moderator variable analysis to better understand the influence of various factors on the strength of the observed effects (Suprapto, 2023;Zulyusri et al., 2023).”

• cites Suprapto, 2023 sourceCould not verify

  The Data Analysis and Result and Discussion sections describe this paper’s effect-size and heterogeneity analyses, but do not say that many prior studies omitted moderator-variable analysis. A search for “moderator” found no passage assessing that practice in prior studies. Page numbers were not available in our copy. (LLM, conf=0.72)

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• cites Zulyusri et al., 2023 sourceCould not verify

  The Results and Table 6 report this paper’s own analysis of mediator variables and their relationship to effect size. Our copy does not say that many prior studies omitted moderator-variable analysis; searches for that research-gap claim found no supporting passage. No page numbers were provided for these passages. (LLM, conf=0.72)

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Applying a combination of assessment methods, either direct and indirect methods or triangulation, instead of a single assessment method, can lead to very highly effective learning experiences for students.

“In this result, it was observed that applying a combination of assessment methods, either a combination of direct and indirect methods or the triangulation method, instead of a single assessment method, can lead to very highly effective learning experiences for students (Bellido-García et al., 2022;Maswadi et al., 2022).”

• cites Bellido-García et al., 2022 sourcePartly supported

  The assessment-techniques passage describes a teacher using multiple instruments, including oral and written tests, presentations, and group work. But the Introduction says triangulation was used to validate the researchers’ findings, not as a student-assessment method. Our copy does not show that combining methods produces highly effective learning experiences or works better than a single method. (LLM, conf=0.84)

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• cites Maswadi et al., 2022 sourceCould not verify

  Section 4.4 discusses a statistical test for bias in survey data, and Section 5.1 reports measurement-validity results. Neither addresses student learning experiences or whether combining direct and indirect assessment methods or triangulation improves them. Searches for those assessment terms and for student learning experiences found no relevant passage in our copy. (LLM, conf=0.77)

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When multiple teaching strategies are combined, as in the integrated DT model, cognitive overload can interfere with the depth of engagement required to foster creativity.

“When multiple teaching strategies are combined, as in the integrated DT model, students may struggle to process and retain information effectively, leading to learning challenges (Zhang, 2021).This cognitive overload can interfere with the depth of engagement required to foster creativity.”

• cites Zhang, 2021 sourcePartly supported

  Section 3 reports that students found the combined DBL–OBE approach more difficult and requiring more energy. It does not say they struggled to process or retain information because strategies were combined; instead, it reports improved learning interest and efficiency. The statement about cognitive overload interfering with creativity is an uncited follow-on conclusion, not a finding established by this source. (LLM, conf=0.82)

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The informal DT model, which focuses on targeted processes and deeper engagement, can promote meaningful learning without overwhelming students.

“In contrast, the informal DT model, which focuses on targeted processes and deeper engagement, can promote meaningful learning without overwhelming students (Zhang, 2021).”

• cites Zhang, 2021 sourcePartly supported

  Section 5.1 (Results; page not supplied) reports greater participation, more teacher–student interaction, and improved understanding with design-based learning. Section 2.3.2 says teachers selectively explain material according to students’ needs. The paper does not identify this approach as an informal DT model or establish that it avoids overwhelming students. (LLM, conf=0.84)

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The study's overall effect size of d = 0.876 aligns with previous research focused exclusively on the Indonesian context, which reported an effect size of d = 0.840.

“The study's overall ES of (d = 0.876, p < 0.001) aligns with previous research, which focused exclusively on the Indonesian context with a reported ES of (d = 0.840, p < 0.001) (Suprapto, 2023).”

• cites Suprapto, 2023 sourcePartly supported

  The Result and Discussion section reports a summary effect size of rE = 0.840 (p < 0.001) for students in Indonesia, supporting the cited value, significance, and context. It does not identify that effect size as d, so the claim's metric is not supported. The d = 0.876 result belongs to the citing study, not this source. (LLM, conf=0.88)

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Short-term programs lasting within a week have the potential to inspire students by fostering a mindset to enhance their focus on creativity and innovation.

“Short-term programs lasting 'within a week' have the potential to inspire students by fostering a mindset to enhance their focus on creativity and innovation (Kijima & Sun, 2020, 2021;Mentzer, 2011).”

• cites Kijima & Sun, 2020 sourceNot checked — no full text of the source
• cites Kijima & Sun, 2021 sourceNot checked — no full text of the source
• cites Mentzer, 2011Could not verify

  The Methodology and Summary describe a three-hour design task comparing students with and without internet access, not a short-term program intended to inspire students. The Instrumentation section discusses motivation from open-ended problems, but our copy does not say that programs lasting within a week foster a mindset focused on creativity and innovation. Searched for week-long programs, inspiration, creativity, and innovation; page numbers were not available. (LLM, conf=0.75)

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Informal Design Thinking in STEM education significantly improved students' creativity compared to traditional classroom education (d = 0.876, p < 0.001).

“The meta-analysis, using a random-effects model, assessed the effect size (ES) of informal DT in enhancing creativity in STEM education and revealed a significant improvement compared to traditional classroom education (d = 0.876, p < 0.001).”

• AssumptionRests on an assumption: The 18 included studies are sufficiently comparable in their definitions and measures of creativity to be pooled in a meta-analysis.
• AssumptionRests on an assumption: The effect sizes reported in the primary studies are unbiased estimates of the true effect.

Triangulation assessment methods had a significant effect on creativity (d = 1.009, p < 0.001).

“Moderator analysis revealed that the combination of direct and indirect assessment (d = 1.163, p = 0.010) and triangulation assessment methods (d = 1.009, p < 0.001) had the highest effect size and significance.”

• AssumptionRests on an assumption: The moderator categories (e.g., assessment methods, subject area, group size) are coded consistently and validly across the included studies.

Several moderator variables influenced the impact of informal DT on students' creativity, including teaching methodology, assessment methods, subject area, student group size, course duration, and grade level.

“This study found that several moderator variables influenced the impact of informal DT on students' creativity, including teaching methodology, assessment methods, subject area, student group size, course duration, and grade level.”

• AssumptionRests on an assumption: The moderator categories (e.g., assessment methods, subject area, group size) are coded consistently and validly across the included studies.

Informal DT for STEM as a subject area exhibited the greatest creativity improvements (d = 1.165, p < 0.001).

“Student study sizes of 30 or more (d = 1.143, p < 0.001) were most effective, and informal DT employed for the subject areas like STEM (d = 1.165, p < 0.001) and science/math (d = 0.988, p < 0.001) exhibited the greatest creativity improvements.”

• AssumptionRests on an assumption: The moderator categories (e.g., assessment methods, subject area, group size) are coded consistently and validly across the included studies.

The study location of the students was not a significant moderator of the impact of informal DT on creativity.

“However, the study location of the students was not a significant moderator.”

• AssumptionRests on an assumption: The moderator categories (e.g., assessment methods, subject area, group size) are coded consistently and validly across the included studies.

Informal DT for science/math as a subject area exhibited the greatest creativity improvements (d = 0.988, p < 0.001).

“Student study sizes of 30 or more (d = 1.143, p < 0.001) were most effective, and informal DT employed for the subject areas like STEM (d = 1.165, p < 0.001) and science/math (d = 0.988, p < 0.001) exhibited the greatest creativity improvements.”

• AssumptionRests on an assumption: The moderator categories (e.g., assessment methods, subject area, group size) are coded consistently and validly across the included studies.

The combination of direct and indirect assessment methods had a significant effect on creativity (d = 1.163, p = 0.010).

“Moderator analysis revealed that the combination of direct and indirect assessment (d = 1.163, p = 0.010) and triangulation assessment methods (d = 1.009, p < 0.001) had the highest effect size and significance.”

• AssumptionRests on an assumption: The moderator categories (e.g., assessment methods, subject area, group size) are coded consistently and validly across the included studies.

Student study sizes of 30 or more were most effective (d = 1.143, p < 0.001).

“Student study sizes of 30 or more (d = 1.143, p < 0.001) were most effective, and informal DT employed for the subject areas like STEM (d = 1.165, p < 0.001) and science/math (d = 0.988, p < 0.001) exhibited the greatest creativity improvements.”

• AssumptionRests on an assumption: The moderator categories (e.g., assessment methods, subject area, group size) are coded consistently and validly across the included studies.

The average effect size of the informal design thinking model on students' creativity in STEM education compared with traditional classroom settings is d = 0.876 (95% CI 0.547 to 1.205, p < 0.001, n = 18).

“the average ES of the informal DT model is 0.876 considerably improving students'creativity with a 95% confidence interval of 0.547 to 1.205 [n = 18, d = 0.876, 95% CI {0.547 to 1.205}, p < 0.001].”

• Also stated in: § Results regarding overall learning outcome (creativity) · plain

  “The impact of the informal DT model is significantly more effective compared to the control scenario for augmenting creativity in STEM education ESs value of [n = 18, d = 0.876, 95% CI {0.547 to 1.205}, p < 0.001].”

• Also stated in: § Discussion · boosted

  “The findings indicated an overall high ES of the informal DT model in STEM education (d = 0.876), demonstrating a positive impact on students' creativity compared to traditional classroom settings.”

• AssumptionThe authors state this as an assumption: The true effect sizes in the combined studies are normally distributed around a common mean, with between-study variance estimated using a weighted average of within-study variances.

The direct assessment method has an effect size of d = 0.723 (p < 0.001).

“direct method (d = 0.723, p < 0.001)”

The triangulation assessment method has an effect size of d = 1.009 (95% CI 0.492 to 1.527, n = 8).

“followed by the triangulation method [n = 8, d = 1.009, 95% CI {0.492;1.527,”

There are significant differences among individual studies for educational level (Q_B p < 0.05).

“The Q B value was significant for the educational level with a p-value < 0.05, indicating significant differences among the individual studies (Table 4).”

The true effect size is not the same in all studies (Q = 317.492, df = 17, p < 0.001).

“The Q-value is 317.492 with 17 degrees of freedom and p < 0.001.By implementing an alpha criterion of 0.100, we can reject the null hypothesis that the true effect size is the same in all the studies.”

• Also stated in: § Results regarding overall learning outcome (creativity) · plain

  “Q T = 317.492with 17 degrees of freedom and p < 0.001.”

• AssumptionThe authors state this as an assumption: All studies in the analysis report the same true effect size.

There is a substantial difference between individual studies for assessment methods (Q_B p < 0.05).

“The Q B statistics for assessment methods indicated a substantial difference between the individual studies (p-value < 0.05); see Table 4.”

The combination of direct and indirect assessment methods is the most effective (d = 1.163, 95% CI 0.099 to 2.227, p = 0.010, n = 2).

“the combination of the direct and indirect method was found to be the most effective [n = 2, d =1.163, 95% CI {0.099;2.227},p = 0.010]”

95% of the variation in observed effects is due to variation in true effects rather than sampling error.

“We can conclude that 95% of the variation in observed effects is due to variation in true effects rather than sampling error because of the value of the I-squared statistic.”

Middle school level has an effect size of d = 1.098 (95% CI 0.443 to 1.753, p < 0.001, n = 5).

“the overall ES is higher for middle school [n = 5, d = 1.098,95%CI {0.443;1.753},p < 0.001]”

The indirect assessment method has an effect size of d = 0.620 (p = 0.001).

“indirect method (d = 0.620, p = 0.001)”

There is a substantial difference between the informal DT model and the integrated informal DT model in enhancing students' learning outcomes in STEM education (Q_B p < 0.05).

“The Q B value for the teaching methodologies indicated a substantial difference between the informal DT model and the integrated informal DT model in enhancing students'learning outcomes in STEM education (p-value < 0.05; see Table 4).”

The informal DT model has an effect size of d = 1.018 (95% CI 0.643 to 1.392, p < 0.001, n = 14).

“The ES of the informal DT model is effectively high [n = 14, d = 1.018, 95% CI {0.643; 1.392}, p < 0.001].”

There is a substantial difference between subject areas in the learning outcomes of the informal DT model (Q_B p < 0.05).

“The Q B value for the moderator variable subject area was significant (p-value < 0.05; Table 4), indicating a substantial difference between the subject areas in the learning outcomes of the informal DT model.”

The '6-12 months' course duration subgroup has an effect size of d = 0.807 (95% CI -0.036 to 1.650, p < 0.001, n = 3).

“'6 -12 months'[n = 3, d = 0.807, 95% CI {-0.036;1.650},p < 0.001] with very High ES.”

Out of 18 studies, 13 showed a statistically significant effect (p < 0.05) favoring the informal model.

“Out of 18 studies, 13 showed a statistically significant (p-value < 0.05), favoring the informal model”

The 'more than 30' group size has an effect size of d = 1.143 (95% CI 0.704 to 1.581, p < 0.001, n = 9).

“the 'more than 30'group size has a very high ES [n = 9, d = 1.143, 95% CI {0.704;1.581},p < 0.001]”

Study location did not significantly affect fostering creativity in the informal DT model (Q_B p > 0.05).

“The Q B value for the study place with a p-value > 0.05 indicated that there were no significant differences among the individual studies (see Table 4).So, the subgroup analysis is not relevant.This means that the study location did not affect fostering creativity in the informal DT model.”

Technology subject area has a medium effect size of d = 0.519 (95% CI -0.391 to 1.429, p = 0.365, n = 2).

“technology has a medium ES [n = 2, d = 0.519, 95% CI {-0.391;1.429},p = 0.365).”

The '1-4 weeks' course duration subgroup has an effect size of d = 0.597 (95% CI -0.078 to 1.272, p < 0.001, n = 5).

“'1 -4 weeks'instruction has an ES of 0.597 [n = 5, d = 0.597, 95% CI {-0.078;1.272},p < 0.001].”

Engineering subject area has an effect size of d = 0.562 (95% CI -0.076 to 1.200, p < 0.001, n = 4).

“and then engineering [n = 4, d = 0.562, 95% CI {-0.076;1.200},p < 0.001].”

Science/science & math subject area has an effect size of d = 0.988 (95% CI 0.239 to 1.738, p < 0.001, n = 3).

“followed by science/science &math [n = 3, d = 0.988, 95% CI {0.239;1.738},p < 0.001]”

The '10 to 30' group size has a medium effect size of d = 0.351 (95% CI -0.323 to 1.025, p = 0.032, n = 4).

“The '10 to 30' group size has a medium ES [n = 4, d = 0.351, 95% CI -0.323;1.025},p = 0.032].”

The 'within a week' course duration subgroup has an effect size of d = 0.988 (95% CI 0.097 to 1.879, p = 0.002, n = 3).

“followed by'within a week'[n = 3, d = 0.988, 95% CI {0.097;1.879},p = 0.002]”

The 'less than 10' group size has an effect size of d = 0.786 (95% CI 0.185 to 1.388, p < 0.001, n = 5).

“followed by the'less than 10'group size [n = 5, d = 0.786, 95% CI {0.185;1.388},p < 0.001]”

STEM subject area has an effect size of d = 1.165 (95% CI 0.645 to 1.686, p < 0.001, n = 7).

“The overall ES is highly effective for STEM [n = 7, d = 1.165, 95% CI {0.645;1.686},p < 0.001]”

The '5-15 weeks' course duration subgroup has the highest effect size of d = 1.067 (95% CI 0.495 to 1.639, p < 0.001, n = 7).

“'5 -15 weeks'[n = 7, d = 1.067, 95% CI {0.495;1.639},p < 0.001] has the highest ES”

There is a substantial variation in Q_B for course duration (p < 0.05), indicating significant differences between investigations.

“There was a substantial variation in the Q B for 'course duration' (p-value < 0.05), indicating significant differences between investigations.”

There is a substantial difference for the moderator variable student group size (Q_B p < 0.05).

“The Q B value indicated a substantial difference for the moderator variable, student group size (p-value < 0.05), Table 4.”

High school level has an effect size of d = 0.908 (95% CI 0.425 to 1.391, p < 0.001, n = 9).

“followed by high school [n = 9, d = 0.908, 95% CI {0.425;1.391},p < 0.001]”

The effect sizes ranged from low d = 0.001 to high d = 2.7789.

“The ES values ranged from low (d = 0.001) (Kim et al., 2022) to high (d = 2.7789) (Simeon et al., 2020).”

• cites Kim et al., 2022 sourceNot checked — no full text of the source
• cites Simeon et al., 2020 sourceNot checked — no full text of the source

The ideal course duration to optimize creativity is between 5-15 weeks (d = 1.067, p < 0.001).

“To optimize creativity, the ideal course duration is between'5-15 weeks'(d = 1.067, p < 0.001), though even shorter interventions within a week (d = 0.988, p = 0.002) also yielded significant positive outcomes.”

Informal design thinking models are effective in high school settings (d = 0.908, p < 0.001).

“The findings suggest that the informal DT models are particularly effective in middle school settings (d = 1.098, p < 0.001), followed by high school students (d = 0.908, p < 0.001).”

Informal design thinking might be more effective for STEM subjects (d = 1.165, p < 0.001).

“The results indicate that this informal DT approach might be more effective for STEM subjects (d = 1.165, p < 0.001), followed by Science/ Science &math (d = 0.988, p < 0.001).”

Large groups, particularly those with more than 30 students, may benefit most from informal design thinking (d = 1.143, p < 0.001).

“Large groups, particularly those with more than 30 students (d = 1.143, p < 0.001), may benefit most from this approach.”

The triangulation assessment method is a highly effective assessment method (d = 1.009, p < 0.001).

“The results also highlight that the combination of both direct and indirect approaches (d = 1.163, p = 0.010) and the triangulation method (d = 1.009, p < 0.001) are highly effective assessment methods.”

Even shorter interventions within a week also yielded significant positive outcomes (d = 0.988, p = 0.002).

“To optimize creativity, the ideal course duration is between'5-15 weeks'(d = 1.067, p < 0.001), though even shorter interventions within a week (d = 0.988, p = 0.002) also yielded significant positive outcomes.”

Informal design thinking methodologies have a positive impact on students' creativity within STEM education, with a high effect size (d = 0.876, p < 0.001) compared to traditional classroom settings.

“This meta-analysis provides robust evidence on the positive impact of informal DT methodologies on students' creativity within STEM education, with a high ES (d = 0.876, p < 0.001) than traditional classroom settings.”

Informal design thinking might be more effective for Science/Science & math subjects (d = 0.988, p < 0.001).

“The results indicate that this informal DT approach might be more effective for STEM subjects (d = 1.165, p < 0.001), followed by Science/ Science &math (d = 0.988, p < 0.001).”

The combination of direct and indirect assessment approaches is a highly effective assessment method (d = 1.163, p = 0.010).

“The results also highlight that the combination of both direct and indirect approaches (d = 1.163, p = 0.010) and the triangulation method (d = 1.009, p < 0.001) are highly effective assessment methods.”

Informal design thinking models are particularly effective in middle school settings (d = 1.098, p < 0.001).

“The findings suggest that the informal DT models are particularly effective in middle school settings (d = 1.098, p < 0.001), followed by high school students (d = 0.908, p < 0.001).”

All factors in the moderator analysis impact the learning outcome of informal design thinking.

“The results of the moderator analysis are presented in Table4, indicating that all factors impact the learning outcome of informal DT.”

The study identified 599 documents across Web of Science (n = 114), ERIC (n = 271), and Scopus (n = 214).

“a total of 599 documents were identified across different databases: Web of Science (n = 114), ERIC (n = 271), and Scopus (n = 214).”

• AssumptionRests on an assumption: The search keywords and databases captured the relevant literature on informal DT in STEM K-12.

Ultimately, 18 peer-reviewed empirical publications met the inclusion criteria and were selected for meta-analysis.

“Ultimately, 18 peerreviewed empirical publications that met these criteria were selected for meta-analysis.”

The one-study-removed sensitivity analysis showed no significant inconsistencies affecting the overall average effect size (lowest mean d = 0.761, highest mean d = 0.925).

“According to the data, the random model's lowest Mean for each research that was eliminated was d = 0.761, n = 18, SE = 0.155, and the greatest mean was d = 0.925, n = 18, SE = 0.170.These two newly calculated average ESs indicate that there were no significant inconsistencies that affected the overall average ES”

This meta-analysis synthesizes data from eighteen articles selected from a pool of 595 published between 2005 and 2024.

“This meta-analysis presents a novel contribution to research by synthesizing data from eighteen articles selected from a pool of 595 published between 2005 and 2024.”

DT fosters creativity by blending analytical and imaginative thinking processes.

“Specifically, DT fosters creativity by blending analytical and imaginative thinking processes.”

• cites Guilford, 1950 sourceNot checked — no full text of the source
• cites Guilford, 1957 sourceNot checked — no full text of the source

Simeon et al. (2020) emphasized that DT's constructivist environment enhances understanding of STEM subjects like physics.

“Simeon et al. (2020) further emphasized how DT's constructivist environment, which builds real-life experiences, enhances understanding of STEM subjects like physics (Simeon et al., 2020).”

• cites Simeon et al., 2020 sourceNot checked — no full text of the source

Research by Yalçın (2021) and Simeon et al. (2020) demonstrated that DT positively influences creative thinking among students.

“Research by Yalçın (2021) and Simeon et al. (2020) demonstrated that DT positively influences creative thinking among students (Simeon et al., 2020;Yalçın, 2021).”

• cites Simeon et al., 2020 sourceNot checked — no full text of the source
• cites Yalçın, 2021 sourceNot checked — no full text of the source

Longer-term design thinking interventions help cultivate a maker mindset focused on innovation and creative action.

“These longer-term DT interventions help to cultivate a maker mindset that focuses on innovation and creative action.”

The effectiveness of group size in fostering creativity and the design mindset may depend more on group composition, specifically whether it is a designer group, than on group size.

“Research indicates that the effectiveness of group size in fostering creativity and the design mindset may depend more on the composition of the group, specifically whether it is a designer group or not rather than its size (Kijima & Sun, 2020).”

• cites Kijima & Sun, 2020 sourceNot checked — no full text of the source

Due to insufficient number of studies at the elementary education level, the meta-analysis could not establish statistically significant conclusions for that age group.

“Moreover, due to the insufficient number of studies conducted at the elementary education level, the study could not establish statistically significant conclusions for that age group.”

Only a few articles were included in the meta-analysis because of pre-defined inclusion and exclusion criteria.

“In this meta-analysis paper, only a few articles were included because of the pre-defined inclusion and exclusion criteria.”

The relatively small number of studies that met inclusion criteria constrained the depth and generalizability of the findings.

“Additionally, the relatively small number of studies that met the inclusion criteria constrained the depth and generalizability of the findings.”

Sustained engagement in design thinking activities enhances creativity and other 21st-century skills over long-term durations (6-12 months).

“Long-term studies (6-12 months) by Chiu (2021) and Ellis (2018) indicated that sustained engagement in DT activities enhances creativity, along with other 21st-century skills (Chiu et al., 2021;Ellis et al., 2018).”

• cites Chiu et al., 2021Not checked — no full text of the source
• cites Ellis et al., 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

The integrated DT model may not significantly enhance creativity as a learning outcome.

“The results indicate that the integrated DT model may not significantly enhance creativity as a learning outcome.”

There is heterogeneity in the definitions and implementations of design thinking across the selected studies.

“Another noteworthy limitation is the heterogeneity in the definitions and implementations of DT across the selected studies.”

A study size of more than 30 students appeared to have a stronger impact on enhancing creativity.

“The study size, with more than 30 students, appeared to have a stronger impact on enhancing creativity.”

Informal design thinking is a highly promising pedagogical approach for enhancing creativity and innovation in STEM education.

“Collectively, these findings support the conclusion that informal DT is a highly promising pedagogical approach for enhancing creativity and innovation in STEM education.”