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This is How Students Can Learn Problem-Solving Skills in Social Studies

A new study led by a researcher from North Carolina State University offers lessons on how social studies teachers could use computational thinking and computer-based resources to analyze primary source data, such as economic information, maps or historical documents. The findings suggest that these approaches advance not only computational thinking, but also student understanding of social studies concepts.

In the journal Theory & Research in Social Education , researchers reported findings from a case study of a high school social studies class called “Measuring the Past” that was offered in a private school. In the project-centered class, students used statistical software to analyze historical and economic data and identify trends. Researchers found students were able to learn problem-solving skills through the series of structured computer analysis projects.

“The purpose of social studies is to enhance student’s ability to participate in a democratic society,” said Meghan Manfra , associate professor of education at NC State. “Our research indicates computational thinking is a fruitful way to engage students in interdisciplinary investigation and develop the skills and habits they need to be successful.”

There is a growing effort to incorporate computational thinking across subjects in K-12 education, Manfra said, to help prepare students for a technology-driven world. Computers have made new techniques possible for historians and social scientists to analyze and interpret digital data, maps and images. Teachers face a potential “firehose” of primary source data they could bring into the classroom, such as the National Archives’ collection of historical letters, speeches, and maps important to American history .

“There are more efforts to integrate computer science across grade levels and subject areas,” Manfra said. “We take the definition of ‘computational thinking’ to be less computer science specific, and much more about a habit of mind. We see it as a structured problem-solving approach.”

In the high school class under study, researchers offered a phrase for the class to use as a guide for how to think about and structure the class projects: analyze the data, look for patterns, and then develop rules or models based on their analysis to solve a problem. They shortened that phrase to “data-patterns-rules.” The projects were also structured as a series, with each students gaining more independence with each project.

“The teacher had a lot of autonomy to develop a curriculum, and the projects were unique,” Manfra said. “Another important aspect of the structure was the students did three rounds of analyzing data, presenting their findings, and developing a model based on what they found. Each time, the teacher got more general in what he was giving the students so they had to flex more of their own thinking.”

In the first project, students analyzed Dollar Street , a website by GapMinder that has a database of photographs of items in homes around the world. Students posed and answered their own questions about the data. For example, one group analyzed whether the number of books in a home related to a family’s income.

In the second project, students tracked prices of labor or products like wool, grain and livestock in England to understand the bubonic plague’s impact on the economy during the Middle Ages.

In the last project, students found their own data to compare social or economic trends during two American wars, such as the War of 1812 and World War I. For example, one group of students compared numbers of draftees and volunteers in two conflicts and related that to the outcome of the war.

From the students’ work, the researchers saw that students were able to learn problem-solving and apply data analysis skills while looking at differences across cultures, the economic effects of historical events and to how political trends can help shape conflicts.

“Based on what we found, this approach not only enhances students’ computational thinking for STEM fields, but it also improves their social studies understanding and knowledge,” Manfra said. “It’s a fruitful approach to teaching and learning.”

From student essays about computational thinking, the researchers saw many students came away with a stronger understanding of the concept. Some students defined it as thinking “based on computer-generated statistics,” while others defined it as analyzing data so a computer can display it, and others said it meant analyzing information in a “computer-like” logical way. In addition, they also saw that students learned skills important in an age of misinformation – they were able to think deeply about potential limitations of the data and the source it came from.

“We found that students were developing data literacy,” Manfra said “They understood databases as a construction, designed to tell a story. We thought that was pretty sophisticated, and that thinking emerged because of what they were experiencing through this project.”

This story originally appeared on the NC State News site.

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Getting Started With PBL in Social Studies

Implementing project-based learning can lead students to investigate historical movements.

High school student shows his class a project during a web meeting

“I have to go to history class? So boring!”

“Why do we have to learn about revolutions we weren’t even alive for?”

These are just some of the comments that I used to hear from my students, and I remember making the same comments myself in high school. When I became a teacher, I wanted to make my class more fun and engaging, and that’s where project-based learning (PBL) came in—PBL is a learning experience in which students investigate real-world problems that interest them and create solutions that demonstrate their learning for a broader audience than their teacher or their class.

PBL requires intensive planning, but it’s worthwhile in my opinion because it fosters great student engagement and requires students to use crucial skills like critical thinking, creativity, collaboration, and communication.

When I wanted to create a PBL unit for my high school Latin American Studies class, I found few example units for my course—there are some good ones at PBLWorks —so I created one myself. This unit takes place over three to four weeks, with hourlong classes twice a week.

I created this project to bring the Latin American revolutions from the dusty pages of textbooks into the 21st century. What makes it a little different from other projects I’ve seen is that I had students role-play as Latin American revolutionaries to gain a historical perspective.

PBL in Social Studies in 5 Steps

1. Create a question for students to answer in their project: The driving question in my unit was how to solve a current problem affecting a Latin American country of the students’ choosing. I gave them this prompt: “You are a 17-year-old revolutionary in your country. You have been given the ability to change one thing in the future in your country for the better. What would you change? How and why would you change it?”

Examining the political, social, and economic causes of past revolutions helped them identify similar problems in 2020. I used the role-play to create buy-in for the students—the perspective encouraged them to take more ownership of finding a feasible solution to make their country better. Students communicated their solutions in a blog or video diary and could work in pairs or groups.

2. Give students time to research: When students were researching their problems and solutions, I provided scaffolds to support learning. These scaffolds could be mini-lectures or videos. To give students an idea of what to look for in their research, I set up whole-class discussions; in distance learning I used the chat function in our video conferencing application, or posted discussion questions in our learning management system. Students created a rubric to assess their projects and set learning goals to hold themselves accountable.

3. Have students organize and visualize their research: Students could use digital graphic organizers to begin plotting what problem they would focus on and brainstorm solutions. They peer-reviewed rough drafts with project rubrics, which could also be completed asynchronously.

This is the point when teachers should organize an authentic audience for students to share their work with, which can be community leaders, families, or others, depending on the project. I didn’t bring in an outside audience this year; students presented to their families and the class.

4. Have students create their stories: I allowed students to choose the applications they would use to create their final products detailing their solution to the problem they hoped to address. They created either a blog or video diary as their authentic products, creating a day-by-day chronicle identifying their problem along with the solution they would put in place, and what effect they hoped the solution would have on their country.

5. Students share their projects: In PBL units, students present their final project to an authentic audience. In distance learning, I had students present live over Zoom; if they weren’t comfortable with that, they could record their presentation as a Flipgrid video. It’s also important to provide a chance for audience feedback about the projects.

We concluded with self-reflection by the students and me, which helped me gauge my students’ thoughts about the project, workflow, and the effectiveness of the lesson. It’s critical for me as a teacher to reflect on what went well and what can be improved upon. I suggest also critiquing the project with your content team for tweaks, so you can fine-tune the goals that you want to accomplish by the end of the project.

PBL creates an engaging student-centered learning experience that allows students to go beyond just facts and deeply explore real-world issues with a focus on creating solutions.

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Review Article
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Published: 11 January 2023

The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature

Enwei Xu ORCID: orcid.org/0000-0001-6424-8169 1 ,
Wei Wang 1 &
Qingxia Wang 1

Humanities and Social Sciences Communications volume 10 , Article number: 16 ( 2023 ) Cite this article

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Science, technology and society

Collaborative problem-solving has been widely embraced in the classroom instruction of critical thinking, which is regarded as the core of curriculum reform based on key competencies in the field of education as well as a key competence for learners in the 21st century. However, the effectiveness of collaborative problem-solving in promoting students’ critical thinking remains uncertain. This current research presents the major findings of a meta-analysis of 36 pieces of the literature revealed in worldwide educational periodicals during the 21st century to identify the effectiveness of collaborative problem-solving in promoting students’ critical thinking and to determine, based on evidence, whether and to what extent collaborative problem solving can result in a rise or decrease in critical thinking. The findings show that (1) collaborative problem solving is an effective teaching approach to foster students’ critical thinking, with a significant overall effect size (ES = 0.82, z = 12.78, P < 0.01, 95% CI [0.69, 0.95]); (2) in respect to the dimensions of critical thinking, collaborative problem solving can significantly and successfully enhance students’ attitudinal tendencies (ES = 1.17, z = 7.62, P < 0.01, 95% CI[0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z = 11.55, P < 0.01, 95% CI[0.58, 0.82]); and (3) the teaching type (chi 2 = 7.20, P < 0.05), intervention duration (chi 2 = 12.18, P < 0.01), subject area (chi 2 = 13.36, P < 0.05), group size (chi 2 = 8.77, P < 0.05), and learning scaffold (chi 2 = 9.03, P < 0.01) all have an impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. On the basis of these results, recommendations are made for further study and instruction to better support students’ critical thinking in the context of collaborative problem-solving.

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Deborah Martin

Introduction

Although critical thinking has a long history in research, the concept of critical thinking, which is regarded as an essential competence for learners in the 21st century, has recently attracted more attention from researchers and teaching practitioners (National Research Council, 2012 ). Critical thinking should be the core of curriculum reform based on key competencies in the field of education (Peng and Deng, 2017 ) because students with critical thinking can not only understand the meaning of knowledge but also effectively solve practical problems in real life even after knowledge is forgotten (Kek and Huijser, 2011 ). The definition of critical thinking is not universal (Ennis, 1989 ; Castle, 2009 ; Niu et al., 2013 ). In general, the definition of critical thinking is a self-aware and self-regulated thought process (Facione, 1990 ; Niu et al., 2013 ). It refers to the cognitive skills needed to interpret, analyze, synthesize, reason, and evaluate information as well as the attitudinal tendency to apply these abilities (Halpern, 2001 ). The view that critical thinking can be taught and learned through curriculum teaching has been widely supported by many researchers (e.g., Kuncel, 2011 ; Leng and Lu, 2020 ), leading to educators’ efforts to foster it among students. In the field of teaching practice, there are three types of courses for teaching critical thinking (Ennis, 1989 ). The first is an independent curriculum in which critical thinking is taught and cultivated without involving the knowledge of specific disciplines; the second is an integrated curriculum in which critical thinking is integrated into the teaching of other disciplines as a clear teaching goal; and the third is a mixed curriculum in which critical thinking is taught in parallel to the teaching of other disciplines for mixed teaching training. Furthermore, numerous measuring tools have been developed by researchers and educators to measure critical thinking in the context of teaching practice. These include standardized measurement tools, such as WGCTA, CCTST, CCTT, and CCTDI, which have been verified by repeated experiments and are considered effective and reliable by international scholars (Facione and Facione, 1992 ). In short, descriptions of critical thinking, including its two dimensions of attitudinal tendency and cognitive skills, different types of teaching courses, and standardized measurement tools provide a complex normative framework for understanding, teaching, and evaluating critical thinking.

Cultivating critical thinking in curriculum teaching can start with a problem, and one of the most popular critical thinking instructional approaches is problem-based learning (Liu et al., 2020 ). Duch et al. ( 2001 ) noted that problem-based learning in group collaboration is progressive active learning, which can improve students’ critical thinking and problem-solving skills. Collaborative problem-solving is the organic integration of collaborative learning and problem-based learning, which takes learners as the center of the learning process and uses problems with poor structure in real-world situations as the starting point for the learning process (Liang et al., 2017 ). Students learn the knowledge needed to solve problems in a collaborative group, reach a consensus on problems in the field, and form solutions through social cooperation methods, such as dialogue, interpretation, questioning, debate, negotiation, and reflection, thus promoting the development of learners’ domain knowledge and critical thinking (Cindy, 2004 ; Liang et al., 2017 ).

Collaborative problem-solving has been widely used in the teaching practice of critical thinking, and several studies have attempted to conduct a systematic review and meta-analysis of the empirical literature on critical thinking from various perspectives. However, little attention has been paid to the impact of collaborative problem-solving on critical thinking. Therefore, the best approach for developing and enhancing critical thinking throughout collaborative problem-solving is to examine how to implement critical thinking instruction; however, this issue is still unexplored, which means that many teachers are incapable of better instructing critical thinking (Leng and Lu, 2020 ; Niu et al., 2013 ). For example, Huber ( 2016 ) provided the meta-analysis findings of 71 publications on gaining critical thinking over various time frames in college with the aim of determining whether critical thinking was truly teachable. These authors found that learners significantly improve their critical thinking while in college and that critical thinking differs with factors such as teaching strategies, intervention duration, subject area, and teaching type. The usefulness of collaborative problem-solving in fostering students’ critical thinking, however, was not determined by this study, nor did it reveal whether there existed significant variations among the different elements. A meta-analysis of 31 pieces of educational literature was conducted by Liu et al. ( 2020 ) to assess the impact of problem-solving on college students’ critical thinking. These authors found that problem-solving could promote the development of critical thinking among college students and proposed establishing a reasonable group structure for problem-solving in a follow-up study to improve students’ critical thinking. Additionally, previous empirical studies have reached inconclusive and even contradictory conclusions about whether and to what extent collaborative problem-solving increases or decreases critical thinking levels. As an illustration, Yang et al. ( 2008 ) carried out an experiment on the integrated curriculum teaching of college students based on a web bulletin board with the goal of fostering participants’ critical thinking in the context of collaborative problem-solving. These authors’ research revealed that through sharing, debating, examining, and reflecting on various experiences and ideas, collaborative problem-solving can considerably enhance students’ critical thinking in real-life problem situations. In contrast, collaborative problem-solving had a positive impact on learners’ interaction and could improve learning interest and motivation but could not significantly improve students’ critical thinking when compared to traditional classroom teaching, according to research by Naber and Wyatt ( 2014 ) and Sendag and Odabasi ( 2009 ) on undergraduate and high school students, respectively.

The above studies show that there is inconsistency regarding the effectiveness of collaborative problem-solving in promoting students’ critical thinking. Therefore, it is essential to conduct a thorough and trustworthy review to detect and decide whether and to what degree collaborative problem-solving can result in a rise or decrease in critical thinking. Meta-analysis is a quantitative analysis approach that is utilized to examine quantitative data from various separate studies that are all focused on the same research topic. This approach characterizes the effectiveness of its impact by averaging the effect sizes of numerous qualitative studies in an effort to reduce the uncertainty brought on by independent research and produce more conclusive findings (Lipsey and Wilson, 2001 ).

This paper used a meta-analytic approach and carried out a meta-analysis to examine the effectiveness of collaborative problem-solving in promoting students’ critical thinking in order to make a contribution to both research and practice. The following research questions were addressed by this meta-analysis:

What is the overall effect size of collaborative problem-solving in promoting students’ critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills)?

How are the disparities between the study conclusions impacted by various moderating variables if the impacts of various experimental designs in the included studies are heterogeneous?

This research followed the strict procedures (e.g., database searching, identification, screening, eligibility, merging, duplicate removal, and analysis of included studies) of Cooper’s ( 2010 ) proposed meta-analysis approach for examining quantitative data from various separate studies that are all focused on the same research topic. The relevant empirical research that appeared in worldwide educational periodicals within the 21st century was subjected to this meta-analysis using Rev-Man 5.4. The consistency of the data extracted separately by two researchers was tested using Cohen’s kappa coefficient, and a publication bias test and a heterogeneity test were run on the sample data to ascertain the quality of this meta-analysis.

Data sources and search strategies

There were three stages to the data collection process for this meta-analysis, as shown in Fig. 1 , which shows the number of articles included and eliminated during the selection process based on the statement and study eligibility criteria.

This flowchart shows the number of records identified, included and excluded in the article.

First, the databases used to systematically search for relevant articles were the journal papers of the Web of Science Core Collection and the Chinese Core source journal, as well as the Chinese Social Science Citation Index (CSSCI) source journal papers included in CNKI. These databases were selected because they are credible platforms that are sources of scholarly and peer-reviewed information with advanced search tools and contain literature relevant to the subject of our topic from reliable researchers and experts. The search string with the Boolean operator used in the Web of Science was “TS = (((“critical thinking” or “ct” and “pretest” or “posttest”) or (“critical thinking” or “ct” and “control group” or “quasi experiment” or “experiment”)) and (“collaboration” or “collaborative learning” or “CSCL”) and (“problem solving” or “problem-based learning” or “PBL”))”. The research area was “Education Educational Research”, and the search period was “January 1, 2000, to December 30, 2021”. A total of 412 papers were obtained. The search string with the Boolean operator used in the CNKI was “SU = (‘critical thinking’*‘collaboration’ + ‘critical thinking’*‘collaborative learning’ + ‘critical thinking’*‘CSCL’ + ‘critical thinking’*‘problem solving’ + ‘critical thinking’*‘problem-based learning’ + ‘critical thinking’*‘PBL’ + ‘critical thinking’*‘problem oriented’) AND FT = (‘experiment’ + ‘quasi experiment’ + ‘pretest’ + ‘posttest’ + ‘empirical study’)” (translated into Chinese when searching). A total of 56 studies were found throughout the search period of “January 2000 to December 2021”. From the databases, all duplicates and retractions were eliminated before exporting the references into Endnote, a program for managing bibliographic references. In all, 466 studies were found.

Second, the studies that matched the inclusion and exclusion criteria for the meta-analysis were chosen by two researchers after they had reviewed the abstracts and titles of the gathered articles, yielding a total of 126 studies.

Third, two researchers thoroughly reviewed each included article’s whole text in accordance with the inclusion and exclusion criteria. Meanwhile, a snowball search was performed using the references and citations of the included articles to ensure complete coverage of the articles. Ultimately, 36 articles were kept.

Two researchers worked together to carry out this entire process, and a consensus rate of almost 94.7% was reached after discussion and negotiation to clarify any emerging differences.

Eligibility criteria

Since not all the retrieved studies matched the criteria for this meta-analysis, eligibility criteria for both inclusion and exclusion were developed as follows:

The publication language of the included studies was limited to English and Chinese, and the full text could be obtained. Articles that did not meet the publication language and articles not published between 2000 and 2021 were excluded.

The research design of the included studies must be empirical and quantitative studies that can assess the effect of collaborative problem-solving on the development of critical thinking. Articles that could not identify the causal mechanisms by which collaborative problem-solving affects critical thinking, such as review articles and theoretical articles, were excluded.

The research method of the included studies must feature a randomized control experiment or a quasi-experiment, or a natural experiment, which have a higher degree of internal validity with strong experimental designs and can all plausibly provide evidence that critical thinking and collaborative problem-solving are causally related. Articles with non-experimental research methods, such as purely correlational or observational studies, were excluded.

The participants of the included studies were only students in school, including K-12 students and college students. Articles in which the participants were non-school students, such as social workers or adult learners, were excluded.

The research results of the included studies must mention definite signs that may be utilized to gauge critical thinking’s impact (e.g., sample size, mean value, or standard deviation). Articles that lacked specific measurement indicators for critical thinking and could not calculate the effect size were excluded.

Data coding design

In order to perform a meta-analysis, it is necessary to collect the most important information from the articles, codify that information’s properties, and convert descriptive data into quantitative data. Therefore, this study designed a data coding template (see Table 1 ). Ultimately, 16 coding fields were retained.

The designed data-coding template consisted of three pieces of information. Basic information about the papers was included in the descriptive information: the publishing year, author, serial number, and title of the paper.

The variable information for the experimental design had three variables: the independent variable (instruction method), the dependent variable (critical thinking), and the moderating variable (learning stage, teaching type, intervention duration, learning scaffold, group size, measuring tool, and subject area). Depending on the topic of this study, the intervention strategy, as the independent variable, was coded into collaborative and non-collaborative problem-solving. The dependent variable, critical thinking, was coded as a cognitive skill and an attitudinal tendency. And seven moderating variables were created by grouping and combining the experimental design variables discovered within the 36 studies (see Table 1 ), where learning stages were encoded as higher education, high school, middle school, and primary school or lower; teaching types were encoded as mixed courses, integrated courses, and independent courses; intervention durations were encoded as 0–1 weeks, 1–4 weeks, 4–12 weeks, and more than 12 weeks; group sizes were encoded as 2–3 persons, 4–6 persons, 7–10 persons, and more than 10 persons; learning scaffolds were encoded as teacher-supported learning scaffold, technique-supported learning scaffold, and resource-supported learning scaffold; measuring tools were encoded as standardized measurement tools (e.g., WGCTA, CCTT, CCTST, and CCTDI) and self-adapting measurement tools (e.g., modified or made by researchers); and subject areas were encoded according to the specific subjects used in the 36 included studies.

The data information contained three metrics for measuring critical thinking: sample size, average value, and standard deviation. It is vital to remember that studies with various experimental designs frequently adopt various formulas to determine the effect size. And this paper used Morris’ proposed standardized mean difference (SMD) calculation formula ( 2008 , p. 369; see Supplementary Table S3 ).

Procedure for extracting and coding data

According to the data coding template (see Table 1 ), the 36 papers’ information was retrieved by two researchers, who then entered them into Excel (see Supplementary Table S1 ). The results of each study were extracted separately in the data extraction procedure if an article contained numerous studies on critical thinking, or if a study assessed different critical thinking dimensions. For instance, Tiwari et al. ( 2010 ) used four time points, which were viewed as numerous different studies, to examine the outcomes of critical thinking, and Chen ( 2013 ) included the two outcome variables of attitudinal tendency and cognitive skills, which were regarded as two studies. After discussion and negotiation during data extraction, the two researchers’ consistency test coefficients were roughly 93.27%. Supplementary Table S2 details the key characteristics of the 36 included articles with 79 effect quantities, including descriptive information (e.g., the publishing year, author, serial number, and title of the paper), variable information (e.g., independent variables, dependent variables, and moderating variables), and data information (e.g., mean values, standard deviations, and sample size). Following that, testing for publication bias and heterogeneity was done on the sample data using the Rev-Man 5.4 software, and then the test results were used to conduct a meta-analysis.

Publication bias test

When the sample of studies included in a meta-analysis does not accurately reflect the general status of research on the relevant subject, publication bias is said to be exhibited in this research. The reliability and accuracy of the meta-analysis may be impacted by publication bias. Due to this, the meta-analysis needs to check the sample data for publication bias (Stewart et al., 2006 ). A popular method to check for publication bias is the funnel plot; and it is unlikely that there will be publishing bias when the data are equally dispersed on either side of the average effect size and targeted within the higher region. The data are equally dispersed within the higher portion of the efficient zone, consistent with the funnel plot connected with this analysis (see Fig. 2 ), indicating that publication bias is unlikely in this situation.

This funnel plot shows the result of publication bias of 79 effect quantities across 36 studies.

Heterogeneity test

To select the appropriate effect models for the meta-analysis, one might use the results of a heterogeneity test on the data effect sizes. In a meta-analysis, it is common practice to gauge the degree of data heterogeneity using the I 2 value, and I 2 ≥ 50% is typically understood to denote medium-high heterogeneity, which calls for the adoption of a random effect model; if not, a fixed effect model ought to be applied (Lipsey and Wilson, 2001 ). The findings of the heterogeneity test in this paper (see Table 2 ) revealed that I 2 was 86% and displayed significant heterogeneity ( P < 0.01). To ensure accuracy and reliability, the overall effect size ought to be calculated utilizing the random effect model.

The analysis of the overall effect size

This meta-analysis utilized a random effect model to examine 79 effect quantities from 36 studies after eliminating heterogeneity. In accordance with Cohen’s criterion (Cohen, 1992 ), it is abundantly clear from the analysis results, which are shown in the forest plot of the overall effect (see Fig. 3 ), that the cumulative impact size of cooperative problem-solving is 0.82, which is statistically significant ( z = 12.78, P < 0.01, 95% CI [0.69, 0.95]), and can encourage learners to practice critical thinking.

This forest plot shows the analysis result of the overall effect size across 36 studies.

In addition, this study examined two distinct dimensions of critical thinking to better understand the precise contributions that collaborative problem-solving makes to the growth of critical thinking. The findings (see Table 3 ) indicate that collaborative problem-solving improves cognitive skills (ES = 0.70) and attitudinal tendency (ES = 1.17), with significant intergroup differences (chi 2 = 7.95, P < 0.01). Although collaborative problem-solving improves both dimensions of critical thinking, it is essential to point out that the improvements in students’ attitudinal tendency are much more pronounced and have a significant comprehensive effect (ES = 1.17, z = 7.62, P < 0.01, 95% CI [0.87, 1.47]), whereas gains in learners’ cognitive skill are slightly improved and are just above average. (ES = 0.70, z = 11.55, P < 0.01, 95% CI [0.58, 0.82]).

The analysis of moderator effect size

The whole forest plot’s 79 effect quantities underwent a two-tailed test, which revealed significant heterogeneity ( I 2 = 86%, z = 12.78, P < 0.01), indicating differences between various effect sizes that may have been influenced by moderating factors other than sampling error. Therefore, exploring possible moderating factors that might produce considerable heterogeneity was done using subgroup analysis, such as the learning stage, learning scaffold, teaching type, group size, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, in order to further explore the key factors that influence critical thinking. The findings (see Table 4 ) indicate that various moderating factors have advantageous effects on critical thinking. In this situation, the subject area (chi 2 = 13.36, P < 0.05), group size (chi 2 = 8.77, P < 0.05), intervention duration (chi 2 = 12.18, P < 0.01), learning scaffold (chi 2 = 9.03, P < 0.01), and teaching type (chi 2 = 7.20, P < 0.05) are all significant moderators that can be applied to support the cultivation of critical thinking. However, since the learning stage and the measuring tools did not significantly differ among intergroup (chi 2 = 3.15, P = 0.21 > 0.05, and chi 2 = 0.08, P = 0.78 > 0.05), we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving. These are the precise outcomes, as follows:

Various learning stages influenced critical thinking positively, without significant intergroup differences (chi 2 = 3.15, P = 0.21 > 0.05). High school was first on the list of effect sizes (ES = 1.36, P < 0.01), then higher education (ES = 0.78, P < 0.01), and middle school (ES = 0.73, P < 0.01). These results show that, despite the learning stage’s beneficial influence on cultivating learners’ critical thinking, we are unable to explain why it is essential for cultivating critical thinking in the context of collaborative problem-solving.

Different teaching types had varying degrees of positive impact on critical thinking, with significant intergroup differences (chi 2 = 7.20, P < 0.05). The effect size was ranked as follows: mixed courses (ES = 1.34, P < 0.01), integrated courses (ES = 0.81, P < 0.01), and independent courses (ES = 0.27, P < 0.01). These results indicate that the most effective approach to cultivate critical thinking utilizing collaborative problem solving is through the teaching type of mixed courses.

Various intervention durations significantly improved critical thinking, and there were significant intergroup differences (chi 2 = 12.18, P < 0.01). The effect sizes related to this variable showed a tendency to increase with longer intervention durations. The improvement in critical thinking reached a significant level (ES = 0.85, P < 0.01) after more than 12 weeks of training. These findings indicate that the intervention duration and critical thinking’s impact are positively correlated, with a longer intervention duration having a greater effect.

Different learning scaffolds influenced critical thinking positively, with significant intergroup differences (chi 2 = 9.03, P < 0.01). The resource-supported learning scaffold (ES = 0.69, P < 0.01) acquired a medium-to-higher level of impact, the technique-supported learning scaffold (ES = 0.63, P < 0.01) also attained a medium-to-higher level of impact, and the teacher-supported learning scaffold (ES = 0.92, P < 0.01) displayed a high level of significant impact. These results show that the learning scaffold with teacher support has the greatest impact on cultivating critical thinking.

Various group sizes influenced critical thinking positively, and the intergroup differences were statistically significant (chi 2 = 8.77, P < 0.05). Critical thinking showed a general declining trend with increasing group size. The overall effect size of 2–3 people in this situation was the biggest (ES = 0.99, P < 0.01), and when the group size was greater than 7 people, the improvement in critical thinking was at the lower-middle level (ES < 0.5, P < 0.01). These results show that the impact on critical thinking is positively connected with group size, and as group size grows, so does the overall impact.

Various measuring tools influenced critical thinking positively, with significant intergroup differences (chi 2 = 0.08, P = 0.78 > 0.05). In this situation, the self-adapting measurement tools obtained an upper-medium level of effect (ES = 0.78), whereas the complete effect size of the standardized measurement tools was the largest, achieving a significant level of effect (ES = 0.84, P < 0.01). These results show that, despite the beneficial influence of the measuring tool on cultivating critical thinking, we are unable to explain why it is crucial in fostering the growth of critical thinking by utilizing the approach of collaborative problem-solving.

Different subject areas had a greater impact on critical thinking, and the intergroup differences were statistically significant (chi 2 = 13.36, P < 0.05). Mathematics had the greatest overall impact, achieving a significant level of effect (ES = 1.68, P < 0.01), followed by science (ES = 1.25, P < 0.01) and medical science (ES = 0.87, P < 0.01), both of which also achieved a significant level of effect. Programming technology was the least effective (ES = 0.39, P < 0.01), only having a medium-low degree of effect compared to education (ES = 0.72, P < 0.01) and other fields (such as language, art, and social sciences) (ES = 0.58, P < 0.01). These results suggest that scientific fields (e.g., mathematics, science) may be the most effective subject areas for cultivating critical thinking utilizing the approach of collaborative problem-solving.

The effectiveness of collaborative problem solving with regard to teaching critical thinking

According to this meta-analysis, using collaborative problem-solving as an intervention strategy in critical thinking teaching has a considerable amount of impact on cultivating learners’ critical thinking as a whole and has a favorable promotional effect on the two dimensions of critical thinking. According to certain studies, collaborative problem solving, the most frequently used critical thinking teaching strategy in curriculum instruction can considerably enhance students’ critical thinking (e.g., Liang et al., 2017 ; Liu et al., 2020 ; Cindy, 2004 ). This meta-analysis provides convergent data support for the above research views. Thus, the findings of this meta-analysis not only effectively address the first research query regarding the overall effect of cultivating critical thinking and its impact on the two dimensions of critical thinking (i.e., attitudinal tendency and cognitive skills) utilizing the approach of collaborative problem-solving, but also enhance our confidence in cultivating critical thinking by using collaborative problem-solving intervention approach in the context of classroom teaching.

Furthermore, the associated improvements in attitudinal tendency are much stronger, but the corresponding improvements in cognitive skill are only marginally better. According to certain studies, cognitive skill differs from the attitudinal tendency in classroom instruction; the cultivation and development of the former as a key ability is a process of gradual accumulation, while the latter as an attitude is affected by the context of the teaching situation (e.g., a novel and exciting teaching approach, challenging and rewarding tasks) (Halpern, 2001 ; Wei and Hong, 2022 ). Collaborative problem-solving as a teaching approach is exciting and interesting, as well as rewarding and challenging; because it takes the learners as the focus and examines problems with poor structure in real situations, and it can inspire students to fully realize their potential for problem-solving, which will significantly improve their attitudinal tendency toward solving problems (Liu et al., 2020 ). Similar to how collaborative problem-solving influences attitudinal tendency, attitudinal tendency impacts cognitive skill when attempting to solve a problem (Liu et al., 2020 ; Zhang et al., 2022 ), and stronger attitudinal tendencies are associated with improved learning achievement and cognitive ability in students (Sison, 2008 ; Zhang et al., 2022 ). It can be seen that the two specific dimensions of critical thinking as well as critical thinking as a whole are affected by collaborative problem-solving, and this study illuminates the nuanced links between cognitive skills and attitudinal tendencies with regard to these two dimensions of critical thinking. To fully develop students’ capacity for critical thinking, future empirical research should pay closer attention to cognitive skills.

The moderating effects of collaborative problem solving with regard to teaching critical thinking

In order to further explore the key factors that influence critical thinking, exploring possible moderating effects that might produce considerable heterogeneity was done using subgroup analysis. The findings show that the moderating factors, such as the teaching type, learning stage, group size, learning scaffold, duration of the intervention, measuring tool, and the subject area included in the 36 experimental designs, could all support the cultivation of collaborative problem-solving in critical thinking. Among them, the effect size differences between the learning stage and measuring tool are not significant, which does not explain why these two factors are crucial in supporting the cultivation of critical thinking utilizing the approach of collaborative problem-solving.

In terms of the learning stage, various learning stages influenced critical thinking positively without significant intergroup differences, indicating that we are unable to explain why it is crucial in fostering the growth of critical thinking.

Although high education accounts for 70.89% of all empirical studies performed by researchers, high school may be the appropriate learning stage to foster students’ critical thinking by utilizing the approach of collaborative problem-solving since it has the largest overall effect size. This phenomenon may be related to student’s cognitive development, which needs to be further studied in follow-up research.

With regard to teaching type, mixed course teaching may be the best teaching method to cultivate students’ critical thinking. Relevant studies have shown that in the actual teaching process if students are trained in thinking methods alone, the methods they learn are isolated and divorced from subject knowledge, which is not conducive to their transfer of thinking methods; therefore, if students’ thinking is trained only in subject teaching without systematic method training, it is challenging to apply to real-world circumstances (Ruggiero, 2012 ; Hu and Liu, 2015 ). Teaching critical thinking as mixed course teaching in parallel to other subject teachings can achieve the best effect on learners’ critical thinking, and explicit critical thinking instruction is more effective than less explicit critical thinking instruction (Bensley and Spero, 2014 ).

In terms of the intervention duration, with longer intervention times, the overall effect size shows an upward tendency. Thus, the intervention duration and critical thinking’s impact are positively correlated. Critical thinking, as a key competency for students in the 21st century, is difficult to get a meaningful improvement in a brief intervention duration. Instead, it could be developed over a lengthy period of time through consistent teaching and the progressive accumulation of knowledge (Halpern, 2001 ; Hu and Liu, 2015 ). Therefore, future empirical studies ought to take these restrictions into account throughout a longer period of critical thinking instruction.

With regard to group size, a group size of 2–3 persons has the highest effect size, and the comprehensive effect size decreases with increasing group size in general. This outcome is in line with some research findings; as an example, a group composed of two to four members is most appropriate for collaborative learning (Schellens and Valcke, 2006 ). However, the meta-analysis results also indicate that once the group size exceeds 7 people, small groups cannot produce better interaction and performance than large groups. This may be because the learning scaffolds of technique support, resource support, and teacher support improve the frequency and effectiveness of interaction among group members, and a collaborative group with more members may increase the diversity of views, which is helpful to cultivate critical thinking utilizing the approach of collaborative problem-solving.

With regard to the learning scaffold, the three different kinds of learning scaffolds can all enhance critical thinking. Among them, the teacher-supported learning scaffold has the largest overall effect size, demonstrating the interdependence of effective learning scaffolds and collaborative problem-solving. This outcome is in line with some research findings; as an example, a successful strategy is to encourage learners to collaborate, come up with solutions, and develop critical thinking skills by using learning scaffolds (Reiser, 2004 ; Xu et al., 2022 ); learning scaffolds can lower task complexity and unpleasant feelings while also enticing students to engage in learning activities (Wood et al., 2006 ); learning scaffolds are designed to assist students in using learning approaches more successfully to adapt the collaborative problem-solving process, and the teacher-supported learning scaffolds have the greatest influence on critical thinking in this process because they are more targeted, informative, and timely (Xu et al., 2022 ).

With respect to the measuring tool, despite the fact that standardized measurement tools (such as the WGCTA, CCTT, and CCTST) have been acknowledged as trustworthy and effective by worldwide experts, only 54.43% of the research included in this meta-analysis adopted them for assessment, and the results indicated no intergroup differences. These results suggest that not all teaching circumstances are appropriate for measuring critical thinking using standardized measurement tools. “The measuring tools for measuring thinking ability have limits in assessing learners in educational situations and should be adapted appropriately to accurately assess the changes in learners’ critical thinking.”, according to Simpson and Courtney ( 2002 , p. 91). As a result, in order to more fully and precisely gauge how learners’ critical thinking has evolved, we must properly modify standardized measuring tools based on collaborative problem-solving learning contexts.

With regard to the subject area, the comprehensive effect size of science departments (e.g., mathematics, science, medical science) is larger than that of language arts and social sciences. Some recent international education reforms have noted that critical thinking is a basic part of scientific literacy. Students with scientific literacy can prove the rationality of their judgment according to accurate evidence and reasonable standards when they face challenges or poorly structured problems (Kyndt et al., 2013 ), which makes critical thinking crucial for developing scientific understanding and applying this understanding to practical problem solving for problems related to science, technology, and society (Yore et al., 2007 ).

Suggestions for critical thinking teaching

Other than those stated in the discussion above, the following suggestions are offered for critical thinking instruction utilizing the approach of collaborative problem-solving.

First, teachers should put a special emphasis on the two core elements, which are collaboration and problem-solving, to design real problems based on collaborative situations. This meta-analysis provides evidence to support the view that collaborative problem-solving has a strong synergistic effect on promoting students’ critical thinking. Asking questions about real situations and allowing learners to take part in critical discussions on real problems during class instruction are key ways to teach critical thinking rather than simply reading speculative articles without practice (Mulnix, 2012 ). Furthermore, the improvement of students’ critical thinking is realized through cognitive conflict with other learners in the problem situation (Yang et al., 2008 ). Consequently, it is essential for teachers to put a special emphasis on the two core elements, which are collaboration and problem-solving, and design real problems and encourage students to discuss, negotiate, and argue based on collaborative problem-solving situations.

Second, teachers should design and implement mixed courses to cultivate learners’ critical thinking, utilizing the approach of collaborative problem-solving. Critical thinking can be taught through curriculum instruction (Kuncel, 2011 ; Leng and Lu, 2020 ), with the goal of cultivating learners’ critical thinking for flexible transfer and application in real problem-solving situations. This meta-analysis shows that mixed course teaching has a highly substantial impact on the cultivation and promotion of learners’ critical thinking. Therefore, teachers should design and implement mixed course teaching with real collaborative problem-solving situations in combination with the knowledge content of specific disciplines in conventional teaching, teach methods and strategies of critical thinking based on poorly structured problems to help students master critical thinking, and provide practical activities in which students can interact with each other to develop knowledge construction and critical thinking utilizing the approach of collaborative problem-solving.

Third, teachers should be more trained in critical thinking, particularly preservice teachers, and they also should be conscious of the ways in which teachers’ support for learning scaffolds can promote critical thinking. The learning scaffold supported by teachers had the greatest impact on learners’ critical thinking, in addition to being more directive, targeted, and timely (Wood et al., 2006 ). Critical thinking can only be effectively taught when teachers recognize the significance of critical thinking for students’ growth and use the proper approaches while designing instructional activities (Forawi, 2016 ). Therefore, with the intention of enabling teachers to create learning scaffolds to cultivate learners’ critical thinking utilizing the approach of collaborative problem solving, it is essential to concentrate on the teacher-supported learning scaffolds and enhance the instruction for teaching critical thinking to teachers, especially preservice teachers.

Implications and limitations

There are certain limitations in this meta-analysis, but future research can correct them. First, the search languages were restricted to English and Chinese, so it is possible that pertinent studies that were written in other languages were overlooked, resulting in an inadequate number of articles for review. Second, these data provided by the included studies are partially missing, such as whether teachers were trained in the theory and practice of critical thinking, the average age and gender of learners, and the differences in critical thinking among learners of various ages and genders. Third, as is typical for review articles, more studies were released while this meta-analysis was being done; therefore, it had a time limit. With the development of relevant research, future studies focusing on these issues are highly relevant and needed.

Conclusions

The subject of the magnitude of collaborative problem-solving’s impact on fostering students’ critical thinking, which received scant attention from other studies, was successfully addressed by this study. The question of the effectiveness of collaborative problem-solving in promoting students’ critical thinking was addressed in this study, which addressed a topic that had gotten little attention in earlier research. The following conclusions can be made:

Regarding the results obtained, collaborative problem solving is an effective teaching approach to foster learners’ critical thinking, with a significant overall effect size (ES = 0.82, z = 12.78, P < 0.01, 95% CI [0.69, 0.95]). With respect to the dimensions of critical thinking, collaborative problem-solving can significantly and effectively improve students’ attitudinal tendency, and the comprehensive effect is significant (ES = 1.17, z = 7.62, P < 0.01, 95% CI [0.87, 1.47]); nevertheless, it falls short in terms of improving students’ cognitive skills, having only an upper-middle impact (ES = 0.70, z = 11.55, P < 0.01, 95% CI [0.58, 0.82]).

As demonstrated by both the results and the discussion, there are varying degrees of beneficial effects on students’ critical thinking from all seven moderating factors, which were found across 36 studies. In this context, the teaching type (chi 2 = 7.20, P < 0.05), intervention duration (chi 2 = 12.18, P < 0.01), subject area (chi 2 = 13.36, P < 0.05), group size (chi 2 = 8.77, P < 0.05), and learning scaffold (chi 2 = 9.03, P < 0.01) all have a positive impact on critical thinking, and they can be viewed as important moderating factors that affect how critical thinking develops. Since the learning stage (chi 2 = 3.15, P = 0.21 > 0.05) and measuring tools (chi 2 = 0.08, P = 0.78 > 0.05) did not demonstrate any significant intergroup differences, we are unable to explain why these two factors are crucial in supporting the cultivation of critical thinking in the context of collaborative problem-solving.

Data availability

All data generated or analyzed during this study are included within the article and its supplementary information files, and the supplementary information files are available in the Dataverse repository: https://doi.org/10.7910/DVN/IPFJO6 .

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Acknowledgements

This research was supported by the graduate scientific research and innovation project of Xinjiang Uygur Autonomous Region named “Research on in-depth learning of high school information technology courses for the cultivation of computing thinking” (No. XJ2022G190) and the independent innovation fund project for doctoral students of the College of Educational Science of Xinjiang Normal University named “Research on project-based teaching of high school information technology courses from the perspective of discipline core literacy” (No. XJNUJKYA2003).

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Xu, E., Wang, W. & Wang, Q. The effectiveness of collaborative problem solving in promoting students’ critical thinking: A meta-analysis based on empirical literature. Humanit Soc Sci Commun 10 , 16 (2023). https://doi.org/10.1057/s41599-023-01508-1

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Adrian, M., Lyon, A., Oti, R., Tininenko, J. (2011). Social Problem Solving. In: Goldstein, S., Naglieri, J.A. (eds) Encyclopedia of Child Behavior and Development. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79061-9_2703

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6 Skills Students Need to Succeed in Social Studies Classes

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In 2013, the National Council for the Social Studies ( NCSS ), published the College, Career, and Civic Life (C3) Framework for Social Studies State Standards also known as the C3 Framework . The combined goal of implementing the C3 framework is to enhance the rigor of the social studies disciplines using the skills of critical thinking, problem-solving, and participation.

The NCSS has stated that,

"The primary purpose of social studies is to help young people develop the ability to make informed and reasoned decisions for the public good as citizens of a culturally diverse, democratic society in an interdependent world."

In order to meet this purpose, the C3s Frameworks encourage student inquiry. The design of the frameworks is that an "Inquiry Arc" straddles all elements of the C3s. In every dimension, there is an inquiry, a seeking or request for truth, information, or knowledge. In economics, civics, history, and geography, there is required inquiry.

Students must engage in a pursuit of knowledge through questions. They must first prepare their questions and plan their inquiries before they use the traditional tools of research. They must evaluate their sources and evidence before they communicate their conclusions or take informed action. There are specifics skills outlined below that can support the inquiry process.

Critical Analysis of Primary and Secondary Sources

As they have in the past, students need to recognize the difference between primary and secondary sources as evidence. However, a more important skill in this age of partisanship is the ability to evaluate sources.

The proliferation of "fake news" websites and social media "bots" means that students must sharpen their ability to evaluate documents. The Stanford History Education Group (SHEG ) supports teachers with materials to help students "learn to think critically about what sources provide the best evidence to answer historical questions."

SHEG notes the difference between the teaching of social studies in the past compared to the context of today,

"Instead of memorizing historical facts, students evaluate the trustworthiness of multiple perspectives on historical issues and learn to make historical claims backed by documentary evidence."

Students at every grade level should have the critical reasoning skills necessary to understand the role that an author has in each of the sources, primary or secondary, and to recognize bias where it exists in any source.

Interpreting Visual and Audio Sources

Information today is often presented visually in different formats. Digital programs allow visual data to be shared or reconfigured easily.

Students need to have the skills to read and to interpret information in multiple formats since data can be organized in different ways.

Tables use numerals or non-numeral data that is set in vertical columns so that the data may be emphasized, compared, or contrasted.
Graphs or charts are pictures used to make facts easier for a reader to understand. There are different types of graphs: bar graph, the line graph, pie charts, and the pictograph.

The Partnership for 21st Century Learning recognizes that information for tables, graphs and charts can be collected digitally. 21st-century standards outline a series of student learning goals.

"To be effective in the 21st century, citizens and workers must be able to create, evaluate, and effectively utilize information, media, and technology."

This means that students need to develop the skills that allow them to learn in real-world 21st-century contexts. The increase in the amount of digital evidence available means students need to be trained to access and to evaluate this evidence before forming their own conclusions.

For example, access to photographs has expanded. Photographs can be used as evidence , and the National Archives offers a template worksheet to guide students learn in the use of images as evidence. In the same manner, information can also be gathered from audio and video recordings that students must be able to access and to evaluate before taking informed action.

Understanding Timelines

Timelines are a useful tool for students to connect the disparate bits of information that they learn in social studies classes. Sometimes students can lose perspective on how events fit together in history. For example, a student in a world history class needs to be conversant in the use of timelines to understand that the Russian Revolution was occurring at the same time that World War I was being fought.

Having students create timelines is an excellent way for them to apply their understanding. There are a number of educational software programs that are free for teachers to use:

Timeglider : This software allows students the opportunity to create, collaborate, and publish zooming and panning interactive timelines.
Timetoast: This software allows students to make a timeline in horizontal and list modes. Students can design timelines in ancient history to the far-off future.
Sutori : This software allows students to make timelines and to examine sources through contrast and compare.

Comparing and Contrasting Skills

Comparing and contrasting in a response allows students to move beyond facts. Students must use their ability to synthesize information from different sources, so they need to strengthen their own critical judgment in order to determine how groups of ideas, people, texts, and facts are similar or different.

These skills are necessary to meet the critical standards of the C3 Frameworks in civics and history. For example,

D2.Civ.14.6-8. Compare historical and contemporary means of changing societies, and promoting the common good. D2.His.17.6-8. Compare the central arguments in secondary works of history on related topics in multiple media.

In developing their compare and contrasting skills, students need to focus their attention on the critical attributes (features or characteristics) under investigation. For example, in comparing and contrasting the effectiveness of for-profit businesses with nonprofit organizations, students should consider not only the critical attributes (e.g., the sources of funding, expenses for marketing) but also those factors that impact critical attributes such as employees or regulations.

Identifying critical attributes gives students the details needed to support positions. Once students have analyzed, for example, two readings in greater depth, they should be able to draw conclusions and take a position in a response based on the critical attributes.

Cause and Effect

Students need to be able to understand and communicate cause and effect relationships in order to show not only what happened but why it happened in history. Students should understand that as they read a text or learn information they should look for keywords such as "thus", "because", and "therefore".

The C3 Frameworks outline the importance of understanding cause and effect in Dimension 2 stating that,

"No historical event or development occurs in a vacuum; each one has prior conditions and causes, and each one has consequences."

Therefore, students need to have enough background information to be able to make informed guesses (causes) about what could happen in the future (effects).

Maps are used throughout the social studies to help deliver spatial information in the most efficient way possible.

Students need to understand the type of map they are looking at and to be able to use the map conventions like keys, orientation, scale and more as outlined in Basics of Map Reading .

The shift in the C3s, however, is to move students from the low-level tasks of identification and application to the more sophisticated understanding where students “create maps and other graphic representations of both familiar and unfamiliar places.”

In Dimension 2 of the C3s, creating maps is an essential skill.

"Creating maps and other geographical representations is an essential and enduring part of seeking new geographic knowledge that is personally and socially useful and that can be applied in making decisions and solving problems."

Asking students to create maps allows them prompts new inquiries, especially for the patterns portrayed.

National Council for the Social Studies (NCSS), The College, Career, and Civic Life (C3) Framework for Social Studies State Standards: Guidance for Enhancing the Rigor of K-12 Civics, Economics, Geography, and History (Silver Spring, MD: NCSS, 2013).
How Many Electoral Votes Does a Candidate Need to Win?
How John Lewis' "March" Trilogy Can Teach Students About Civil Rights
Sample Report Card Comments for Social Studies
Voting Rights Background for Students
Idioms Used in Elections
10 Test Question Terms and What They Ask Students to Do
Multiple Literacies: Definition, Types, and Classroom Strategies
Higher-Order Thinking Skills (HOTS) in Education
Bloom's Taxonomy in the Classroom
High School English Curricula Explained
How Social Skills Can Lead to Academic Success
How Depth of Knowledge Drives Learning and Assessment
Critical Thinking in Reading and Composition
Typical Course of Study for 9th Grade
Beef Up Critical Thinking and Writing Skills: Comparison Essays
Functional Math Skills That Support Independence

INTERPERSONAL SKILLS
Problem Solving and Decision Making
Social Problem Solving

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Social problem-solving might also be called ‘ problem-solving in real life ’. In other words, it is a rather academic way of describing the systems and processes that we use to solve the problems that we encounter in our everyday lives.

The word ‘ social ’ does not mean that it only applies to problems that we solve with other people, or, indeed, those that we feel are caused by others. The word is simply used to indicate the ‘ real life ’ nature of the problems, and the way that we approach them.

Social problem-solving is generally considered to apply to four different types of problems:

Impersonal problems, for example, shortage of money;
Personal problems, for example, emotional or health problems;
Interpersonal problems, such as disagreements with other people; and
Community and wider societal problems, such as litter or crime rate.

A Model of Social Problem-Solving

One of the main models used in academic studies of social problem-solving was put forward by a group led by Thomas D’Zurilla.

This model includes three basic concepts or elements:

Problem-solving

This is defined as the process used by an individual, pair or group to find an effective solution for a particular problem. It is a self-directed process, meaning simply that the individual or group does not have anyone telling them what to do. Parts of this process include generating lots of possible solutions and selecting the best from among them.

A problem is defined as any situation or task that needs some kind of a response if it is to be managed effectively, but to which no obvious response is available. The demands may be external, from the environment, or internal.

A solution is a response or coping mechanism which is specific to the problem or situation. It is the outcome of the problem-solving process.

Once a solution has been identified, it must then be implemented. D’Zurilla’s model distinguishes between problem-solving (the process that identifies a solution) and solution implementation (the process of putting that solution into practice), and notes that the skills required for the two are not necessarily the same. It also distinguishes between two parts of the problem-solving process: problem orientation and actual problem-solving.

Problem Orientation

Problem orientation is the way that people approach problems, and how they set them into the context of their existing knowledge and ways of looking at the world.

Each of us will see problems in a different way, depending on our experience and skills, and this orientation is key to working out which skills we will need to use to solve the problem.

An Example of Orientation

Most people, on seeing a spout of water coming from a loose joint between a tap and a pipe, will probably reach first for a cloth to put round the joint to catch the water, and then a phone, employing their research skills to find a plumber.

A plumber, however, or someone with some experience of plumbing, is more likely to reach for tools to mend the joint and fix the leak. It’s all a question of orientation.

Problem-Solving

Problem-solving includes four key skills:

Defining the problem,
Coming up with alternative solutions,
Making a decision about which solution to use, and
Implementing that solution.

Based on this split between orientation and problem-solving, D’Zurilla and colleagues defined two scales to measure both abilities.

They defined two orientation dimensions, positive and negative, and three problem-solving styles, rational, impulsive/careless and avoidance.

They noted that people who were good at orientation were not necessarily good at problem-solving and vice versa, although the two might also go together.

It will probably be obvious from these descriptions that the researchers viewed positive orientation and rational problem-solving as functional behaviours, and defined all the others as dysfunctional, leading to psychological distress.

The skills required for positive problem orientation are:

Being able to see problems as ‘challenges’, or opportunities to gain something, rather than insurmountable difficulties at which it is only possible to fail.

For more about this, see our page on The Importance of Mindset ;

Believing that problems are solvable. While this, too, may be considered an aspect of mindset, it is also important to use techniques of Positive Thinking ;

Believing that you personally are able to solve problems successfully, which is at least in part an aspect of self-confidence.

See our page on Building Confidence for more;

Understanding that solving problems successfully will take time and effort, which may require a certain amount of resilience ; and

Motivating yourself to solve problems immediately, rather than putting them off.

See our pages on Self-Motivation and Time Management for more.

Those who find it harder to develop positive problem orientation tend to view problems as insurmountable obstacles, or a threat to their well-being, doubt their own abilities to solve problems, and become frustrated or upset when they encounter problems.

The skills required for rational problem-solving include:

The ability to gather information and facts, through research. There is more about this on our page on defining and identifying problems ;

The ability to set suitable problem-solving goals. You may find our page on personal goal-setting helpful;

The application of rational thinking to generate possible solutions. You may find some of the ideas on our Creative Thinking page helpful, as well as those on investigating ideas and solutions ;

Good decision-making skills to decide which solution is best. See our page on Decision-Making for more; and

Implementation skills, which include the ability to plan, organise and do. You may find our pages on Action Planning , Project Management and Solution Implementation helpful.

There is more about the rational problem-solving process on our page on Problem-Solving .

Potential Difficulties

Those who struggle to manage rational problem-solving tend to either:

Rush things without thinking them through properly (the impulsive/careless approach), or
Avoid them through procrastination, ignoring the problem, or trying to persuade someone else to solve the problem (the avoidance mode).

This ‘ avoidance ’ is not the same as actively and appropriately delegating to someone with the necessary skills (see our page on Delegation Skills for more).

Instead, it is simple ‘buck-passing’, usually characterised by a lack of selection of anyone with the appropriate skills, and/or an attempt to avoid responsibility for the problem.

An Academic Term for a Human Process?

You may be thinking that social problem-solving, and the model described here, sounds like an academic attempt to define very normal human processes. This is probably not an unreasonable summary.

However, breaking a complex process down in this way not only helps academics to study it, but also helps us to develop our skills in a more targeted way. By considering each element of the process separately, we can focus on those that we find most difficult: maximum ‘bang for your buck’, as it were.

Continue to: Decision Making Creative Problem-Solving

See also: What is Empathy? Social Skills

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Problem-Solving: Our Social Studies Standards Call for Deep Engagement

We want Kentucky students to be increasingly able to “Think and solve problems in school situations and in a variety of situations they will encounter in life.” Yesterday’s post looked at how our science standards call for deep work to meet that expectation from our 1990 Kentucky Education Reform Act. Now, let’s turn to social studies, where our standards value problem-solving that includes attention to diverse perspectives and sustained work to develop shared and democratic decisions.

Examples of our social studies standards

Problem-solving matters across all grades in social studies. To give just a small taste:

Kindergartners should be able to “Construct an argument to address a problem in the classroom or school.
Third-graders should be able to “construct an explanation, using relevant information, to address a local, regional or global problem”
Seventh grade students should be able to “analyze a specific problem from the growth and expansion of civilizations using each of the social studies disciplines”

Diverse perspectives get special attention in understanding social studies problems. As they work, students are also expected to discover that people all around them (and in all periods of the past) can see issues quite differently, and the standards expect students to grow steadily more skilled in understanding those varied views, Kentucky is working to equip:

First graders to “identify information from two or more sources to describe multiple perspectives about communities in Kentucky”
Fifth graders to “analyze primary and secondary sources on the same event or topic, noting key similarities and differences in the perspective they represent”
Seventh graders to “Analyze evidence from multiple perspectives and sources to support claims and refute opposing claims, noting evidentiary limitations to answer compelling and supporting questions”

Shared decisions on solutions required intensive work in social studies, where the overall goal is to equip young citizens. Thus, our standards call for:

Second graders who can “use listening and consensus-building procedures to discuss how to take action in the local community or Kentucky”
Fourth graders who can “Use listening and consensus-building to determine ways to support people in transitioning to a new community.”
Eight graders who can “Apply a range of deliberative and democratic procedures to make decisions about ways to take action on current local, regional and global issues”

All three elements –problem solving, understanding perspectives, and building shared decisions—come together at the high school level. There students are asked to “engage in disciplinary thinking and apply appropriate evidence to propose a solution or design an action plan relevant to compelling and/or compelling questions in civics” and do matching work on solutions and action plans in economics, geography, U.S. history and world history.

Working within a cycle of inquiry

Those examples and others like them reflect an “inquiry cycle” built into our social studies approach. As you can see from the graphic illustration below (taken from the standards document itself, Kentucky expects students to deepen their skills each year through work on:

Questioning , which includes both developing major compelling questions that are “open-ended, enduring and centered on significant unresolved issues,” and smaller supporting questions for exploration on the way to answering the compelling ones.
Investigating using the content, concepts and tools of civics, geography, economics, and history to gain insight into the questions they study
Using evidence from their investigations to build sound explanations and arguments to support their claims
Communicating conclusions to a variety of audiences, making their explanations and arguments available in traditional forms like essays, reports, diagrams and discussions and also in newer media forms. The introduction to the social studies standards advises that: “ A student’s ability to effectively communicate their own conclusions and listen carefully to the conclusions of others can be considered a capstone of social studies disciplinary practices.”

Do notice that the second part of the cycle –the disciplinary investigation phase– includes robust detail on working with specific content knowledge each year. These are standards for focused inquiry, organized in thoughtful sequences that connect disciplines and build understanding grade by grade. The problem-solving skills are integrated into that framework of needed knowledge and understanding.

You can get a fuller sense of how these elements work together in each grade from the full standards document , which is definitely worth your close attention.

Problem-solvers now and years from now

Students who take on this kind of problem solving will be participating citizens right here, right now. To learn these capacities, they’ll have to explore varied experiences and understandings, listen to one another, and collaborate to complete big projects and assignments. Done well, those will be challenging, satisfying, memorable parts of each learner’s school years.

Students who solve problems in this inquiry-driven way will also be building skills that can last a lifetime. Imagine communities where many residents are good at this kind of exploring, listening, collaborating, and working toward shared decisions. They’ll be better at tackling local issues than we are today. They’ll be ready to take on bigger problems together and find bolder solutions, with rich results for each of them and all of us.

A note on organization : the science standards and the social studies standards took on a similar design challenge, trying to combine (a) high expectations for students engaging in key practices of scientists, historians, and other practitioners and (b) a lean statement of very important disciplinary content. In the science version, each performance expectation marries a specific practice with a scientific topic. In social studies, there are distinct (though tightly connected) standards for the major inquiry practices and for the investigations into content in each discipline. My take is that the social studies approach has a major benefit in inviting teachers to develop varied ways to apply practices to topics. The science approach could mean that teachers will only feel free to work on the specific practice/content combinations listed as our performance expectations. I support both, but I do think the social studies version invites richer learning opportunities.

Susan Perkins Weston analyzes Kentucky data and policy, and she’s always on the lookout for ways to enrich the instructional core where students and teachers work together on learning content. Susan is an independent consultant who has been taking on Prichard Committee assignments since 1991. She is a Prichard Committee Senior Fellow.

Problem-Solving: Kentucky’s Science Standards Go Deep

Problem-Solving across Many Disciplines

Related posts, national board certification keeps teachers student-centered, focused and effective, we must close the digital divide in kentucky., failing to deliver: kentucky lacks in providing black students with advanced learning opportunities.

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Dev Cogn Neurosci
v.45; 2020 Oct

The building blocks of social competence: Contributions of the Consortium of Individual Development

Caroline junge.

a Departments of Developmental and Experimental Psychology, Utrecht University, Utrecht, the Netherlands

Patti M. Valkenburg

b Amsterdam School of Communication Research ASCoR, University of Amsterdam, Amsterdam, the Netherlands

Maja Deković

c Department of Clinical Child and Family Studies, Utrecht University, Utrecht, the Netherlands

Susan Branje

d Department of Youth and Family, Utrecht University, Utrecht, the Netherlands

Associated Data

Social competence refers to the ability to engage in meaningful interactions with others. It is a crucial skill potentially malleable to interventions. Nevertheless, it remains difficult to select which children, which periods in a child’s life, and which underlying skills form optimal targets for interventions. Development of social competence is complex to characterize because (a) it is by nature context- dependent; (b) it is subserved by multiple relevant processes that develop at different times in a child’s life; and (c) over the years multiple, possibly conflicting, ways have been coined to index a child’s social competence. The current paper elaborates upon a theoretical model of social competence developed by Rose-Krasnor (Rose- Krasnor, 1997; Rose-Krasnor and Denham, 2009 ), and it makes concrete how underlying skills and the variety of contexts of social interaction are both relevant dimensions of social competence that might change over development. It then illustrates how the cohorts and work packages in the Consortium on Individual Development each provide empirical contributions necessary for testing this model on the development of social competence.

1. Introduction

Social competence can be characterized as the effectiveness of a child to engage in social interactions with peers and adults ( Fabes et al., 2006 ; Rubin et al., 1998 ). It is the behavioral manifestation of a child’s emotional and regulatory competencies while interacting with other people. Social competence does not represent a fixed quality but should be viewed as a construct that in itself marks development: Society expects more sophisticated interactions with older children. When children are growing up, interaction contexts beyond the home environment gain importance and become increasingly broader. Moreover, being effective in a variety of social interactions requires children to master many skills that underlie social competence, such as perspective taking, social problem solving, and emotion regulation, which possibly also differ in developmental stadia. Knowledge about (a) these underlying skills, (b) the interaction contexts, and (c) these developmental stadia all contribute to a better understanding of social competence, which is why we consider these three types of knowledge as relevant dimensions, that is, as crucial building blocks of social competence.

Although research on social competence has made great progress in understanding underlying skills and relevant interaction contexts in key periods in children’s lives (see e.g., Rubin et al., 2009 ; Bukowski et al., 2018 ), how these building blocks of social competence connect to each other over the course of development is less well understood: still missing is a detailed model of the development of social competence from infancy to adolescence. The aim of the Consortium on Individual Differences (CID) is to contribute to such a model that captures the development of social competence in a changing society.

In what follows next, we first describe why the field is in need of a developmental model of social competence (Section 2 ). We then give a brief overview of the development of social competence from infancy to adolescence (Section 3 ). In Section 4 , we explain the approach that CID takes towards building a developmental model, which is an elaboration upon a theoretical model of social competence developed by Rose‐Krasnor (1997 ; Rose-Krasnor and Denham, 2009 ). In Section 5 , we show how each of the cohorts and the individual work packages from CID are contributing pieces of evidence to steer the theoretical model. Finally, in Section 6 , we conclude by suggesting how the cohorts and work packages in CID can complement each other in building a developmental model.

2. Why it is crucial to have a better understanding of the development of social competence

Developing social competence is essential for future functioning in society and for reducing risk of behavioral and emotional problems. Indeed, there is ample evidence that variation in social competence in childhood is linked to prowess in other domains in present and later life. For instance, people who as children easily develop good relationships with others are more likely to grow into adults with better health (they live longer; are more resilient to mental health problems, and function better in society; Luthar, 2006 ; Masten and Coatsworth, 1995 ). Socially competent children are more likely to advance in academics ( Caprara et al., 2000 ; Denham, 2006 ; Wentzel, 1991 ), or rate themselves as happier ( Ryan and Deci, 2001 ). Reversely, deviances in social competence can be a symptom for many forms of psychopathology emerging in child development. If social competence appears deviant, many other problems are typically observed, such as peer rejection (in ADHD; Larson et al., 2011 ), social anxiety ( La Greca and Lopez, 1998 ), bullying and aggression ( Warden, and Mackinnon, 2003 ; for overviews, see Happé and Frith, 2014 ; Trentacosta and Fine, 2010 ). Together, this suggests that the construct of social competence is a key factor in explaining individual variation, both in typical and atypical child populations.

The construct of social competence is a developmental construct: it emerges from meaningful interactions with various others in a variety of contexts ( Rose‐Krasnor, 1997 ). Such interactions shape children’s competence: children learn how to behave in their social worlds both through direct instruction as well as by observing others in interactions. As a result, the type and quality of interactions children experience become increasingly more varied and complex over time. Moreover, children’s concepts of the relevance of interactions mark clear progression. Clearly, the construct of social competence changes over time, but a unified model of how social competence emerges from infancy to adolescence remains missing.

There are several reasons why we need a better understanding how social competence unfolds. First, indexes of social competence from early childhood have been shown to be predictive of social competence later in life (e.g., Howes, 1987 ; Monahan and Steinberg, 2011 ; Rubin et al., 1998 ; but see Masten et al., 1995 ). In fact, there appears to be a Matthew effect for social competence: for example, those competent in making friends early in life are becoming more competent in forming friendships, while the less-competent ones are becoming even less competent in forming friendships ( Flannery and Smith, 2017 ; Ladd, 1999 ; Monahan and Steinberg, 2011 ). Research further documents reciprocal links across various underlying skills of social competence. For example, positive experiences in building friendships early in life foster the development of prosocial behavior, which in turn increases the chance to form friendships later in life ( Flannery and Smith, 2017 ; Ladd, 1999 ). Such self-reinforcing links between the underlying skills of social competence underscore the need to view the development of social competence as a dynamic, complex process in which children are actively regulating their own experiences and creating their own contexts ( Sameroff, 2010 ). Yet to fully grasp the complexity of the development of social competence we need to better understand how and when social competence becomes self-reinforcing along development. Researchers should therefore start building and testing more elaborate models of social competence that take into account the interplay between development, the complexity of different underlying skills, and the variety of social contexts that together shape social competence.

A second reason why it is crucial to develop a clearer picture of how social competence unfolds is that social competence can be malleable, and open to interventions. Yet optimization of interventions in childhood requires not only identifying which underlying skills of social competence are well-suited targets, but also selecting optimal periods to administer such interventions, and should be tailored to a child’s stage of social competence. Knowledge on when to start an intervention is essential since developmental models such as the developmental cascades models assume that adaptive and maladaptive behaviors can result in spreading effects over time across various levels ( Cicchetti, 2002 ). Optimal interventions should ideally result in the interruption of negative cascades and the promotion of positive cascades ( Masten and Cicchetti, 2010 ). Thus, it is essential to develop a model of social competence that makes explicit not only how different underlying skills connect with different stages of social competence (the ‘hows’), but also how social competence changes over development (the ‘whens’).

The third and final reason why it is important to develop get a better picture on how social competence unfolds is that children’s social contexts (the ‘wheres’) have changed dramatically in the past two decades. One key change is that most Western infants and toddlers now have extensive experiences with peers and other adults prior to school entry. In fact, unlike earlier generations, most of today’s infants are in some form of day care away from their primary caregiver(s). How does this change affect the formation of peer relations and social competence ( Hay et al., 2018 )?

Another key change involves the rapid changes in children’s and adolescents’ media environments. In the 1970s the average age that a child started watching television was at 4 years of age. But due to the rise of prosocial and educational baby TV and apps (and parents’ tendencies to embrace such media), the onset of media exposure is now dropped to three and five months of age ( Valkenburg and Piotrowski, 2017 ). Developmentally appropriate educational media may support cognitive learning (e.g., numeracy, literacy), but could also improve underlying skills of social competence (e.g., prosocial behaviour), particularly when adults are involved with the content their children consume ( Courage and Howe, 2010 ). Furthermore, increasingly more interactions in childhood and adolescence take place online. What are the consequences of this? Do skills in social competence generalize easily to those required in online social interaction or does effectively communicating in digital interactions require an additional set of skills? Or does the larger amount of online interaction hamper development of complex underlying skills of social competence, such as emotion recognition and perspective taking? This is something research only starts exploring ( Blumberg et al., 2019 ).

3. Sketching the development of social competence

Before we can explain how CID aims to build theory on the development of social competence, it is essential to provide an overview of how social competence develops across childhood. In Table 1 we therefore define each period in childhood and list the main characteristics in marking the development of social competence. Please note that this overview is neither inclusive nor complete—it only serves to outline the highlights of each period in relation to social competence.

Each age period comes with its own characteristics of social competence.

Certainly not surprising, it appears that any period in a child’s life is fundamental in contributing to social competence ( Rubin et al., 2009 ), albeit for different reasons. For example, while in infancy social interaction skills typically evolve within the family context (e.g., Jones et al., 2014 ), childhood highlights the dominating force of peers within the classroom ( Masten and Coatsworth, 1995 ), and adolescence is the period in which most relevant social interactions mainly take place in cliques ( Moffitt, 1993 ; Weiss, 1986 ). In addition, a skill such as perspective taking emerges in early childhood but only reaches mature levels in adolescence, when adolescents have learned to appreciate that others can have different opinions ( Selman, 1980 ). Although each period comes with its own developmental tasks, most central issues continue to be of importance throughout development ( Waters and Sroufe, 1983 ). For instance, the significant association between the quality of parent-child relationship and children’s social competence is not moderated by age ( Groh et al., 2014 ). A developmental model of social competence should thus not only view its development as a set of discrete stages, but also consider the factors that continue to bear on its development.

4. Towards a developmental model of social competence

How should we now start building a developmental model of social competence? We propose to build on an existing theoretical model: the prism model of social competence put forward by Linda Rose‐Krasnor (1997 ; Rose-Krasnor and Denham, 2009 ). This model does not focus on the development of social competence, but describes the different elements required for establishing good social interaction. We will first briefly summarize the prism model, before we outline how CID makes the prism model more concrete by adding a developmental framework.

The prism model has three hierarchical layers of analysis of social competence and one depth- dimension (context). The top layer of analysis is the theoretical one, which concerns social competence defined as effectiveness in interaction ( Rose‐Krasnor, 1997 ). This definition allows us to maintain the same definition from infancy to adolescence. The second layer contains the indexical level and relates to the various ways in which social competence can be measured ( Flannery and Smith, 2017 ). The bottom layer of the prism model is the skills- dimension, which lists those underlying skills that are important across the many different contexts in which social interactions take place, such as emotion regulation and perspective taking skills. Finally, the depth- dimension of the prism model reflects the various kinds of contexts (home vs school; parent vs. peers; online vs. offline) in which interaction takes place.

In the next sessions, we explain how CID implements and provides data for the indexical layer as well as the skills- and context-dimensions in more detail. See Fig. 1 for a schematic representation of our proposed model based on Rose‐Krasnor (1997 ; Rose-Krasnor and Denham, 2009 ).

CID’s adaptation from Rose-Krasnor’s model of social competence ( Rose‐Krasnor, 1997 ; Rose-Krasnor and Denham, 2009 ), adding a developmental perspective.

4.1. The indexical layer

The indexical layer encompasses the numerous ways researchers employ to quantify social competence, each of which characterize aspects of social competence or underlying skills of social competence (cf., Fabes et al., 2009 ; Flannery and Smith, 2017 ). The cohort studies in CID mainly rely on questionnaires as these are one of the easiest, fastest and most common ways to collect information about social competence in large groups of children ( El Mallah, 2020 ; Halle and Darling-Churchill, 2016 ). Most questionnaires are standardized, normed and internationally known questionnaires that can be filled in by either parents, teachers or children themselves.

4.2. The skills-dimension

The skills dimension is concerned with the foundational skills and motivations underlying social competence that are primarily individual in nature. It is at the skills level that developmental change might be considered most prominent and open to interventions ( Rose‐Krasnor, 1997 ). However, there is no consensus on what one considers vital skills, partly because it is often difficult to tease apart underlying crucial skills from manifestations of social competence itself. Take for instance social perspective taking, which can be viewed both as an index of social competence, as well as a necessary skill from which social competence thrives. Table 2 lists the skills that various researchers find crucial for social competence ( Crick and Dodge, 1994 ; Halberstadt et al., 2001 ; Hay et al., 2004 ; Raver and Zigler, 1997 ; Rose‐Krasnor, 1997 ; Rose-Krasnor and Denham, 2009 ). Although this list should not be considered as complete, it shows the variety of skills involved in social competence.

An overview of studies that list various skills as relevant processes to social competence.

Crucially, while Table 2 serves to highlight that there is no consensus in what one considers vital skills for social competence, it also reveals points of intersection. By focusing on those skills that are repeatedly listed we assume that these skills reflect the key foundations for social competence. We selected a set of five skills that serve as possible indicators in representing children’s (potential for) social competence. Below we motivate our choice in more detail. We begin with providing a definition and signaling its agreement with other researchers from Table 2 . We then give a brief overview of development, and end with how interventions targeted to this skill are beneficial for social competence.

4.2.1. Social encoding

Social encoding is the skill that requires a child to attend to the social interaction partner and to interpret meaningful cues from this person, such as emotions. We see the relevance of social encoding to social competence also in other researchers’ inventories of necessary skills (albeit phrased somewhat differently): as ‘encoding social situations’ ( Crick and Dodge, 1994 ), as ‘awareness and identification’ ( Halberstadt et al., 2001 ) and as ‘joint attention’ ( Hay et al., 2004 ). Some researchers suggest that newborns’ early interest in faces may be ‘the gateway to social expertise’ ( Jones et al., 2014 ). There is evidence that already seven-month- olds can differentiate between facial expressions ( Leppänen and Nelson, 2009 ), although the decoding of human faces continues to develop into adolescence (e.g., Cohen Kadosh et al., 2013 ; cf. Blakemore, 2008 ). Our proposal that social encoding is one of the key foundations of social competence is supported by interventions demonstrating that social encoding lead to modest improvements in children’s social competence ( Trentacosta and Fine, 2010 ).

4.2.2. Social problem solving

Social problem solving ( Rose‐Krasnor, 1997 ; Rose-Krasnor and Denham, 2009 ) can be considered a logical continuation of the previous skill (social encoding), as it centers on responding in such a way to achieve social goals, such as solving conflicts with peers or gaining access to peer play. This skill is also listed by some as ‘social decision making’ ( Crick and Dodge, 1994 ). From early childhood up to adolescence, as children function increasingly in groups, social decision making assumes importance and often revolve around conflict resolution. One way to end conflicts is to react with anger or aggression, which often links to negative outcomes of social competence such as peer rejection ( Card and Little, 2006 ; Von Salisch and Zeman, 2018 ; Werner and Crick, 1999 ). This is not only true for behavior at the playground, but also holds for on-line behavior: cyber aggression is related to higher rates of loneliness and lower rates of friendships ( Schoffstall and Cohen, 2011 ). There are developmental shifts in the type of aggression that children can show in conflicts ( Laursen and Pursell, 2009 ), and when children use aggression strategically, it might actually be considered beneficial ( Hawley et al., 2007 ). Like social encoding, social problem solving is a skill susceptible to interventions aimed at improving social competence ( Denham and Almeida, 1987 ; for a recent meta-analysis, see Merrill et al., 2017 ).

4.2.3. Emotion regulation

If there is one skill that all researchers included in Table 2 consider vital to social competence, it is emotion regulation ( Hay et al., 2004 ; Raver and Zigler, 1997 ; also referred to as ‘arousal regulation’; Crick and Dodge, 1994 ; as ‘affect regulation’; Rose‐Krasnor, 1997 ; or as ‘self-regulation’; Rose-Krasnor and Denham, 2009 ; Vink et al., 2020 ). Being unable to exert control over one’s emotions, behaviors and arousals while interacting with others is a clear sign of obtrusive, unpleasant behavior that is typically disliked by most people. Indeed, there is ample evidence linking poor regulation skills to negative indices of social competence, in particular peer problems (e.g., Eisenberg et al., 2001 ; Holmes et al., 2016 ; cf. Eisenberg et al., 2010 ). As inter alia Vink and colleagues describe ( Vink et al., 2020 ), emotion regulation is an umbrella term that covers both effortful control as well as executive functions and executive control (see also Nigg, 2017 ). In early infancy, children’s responses are at first mainly reactive rather than pro-active ( Ruff and Rothbart, 1996 ). Processes related to executive functions also come to the scene, mainly in toddlerhood onwards; for instance, inhibitory control emerges around 24–26 months ( Kochanska et al., 2000 ), whereas improvements in executive control appear most pronounced in early childhood ( Carlson, 2005 ). A recent review on the development of emotion regulation ( Eisenberg et al., 2010 ) reveals that children make great advances in their ability to exert control over their emotions in the preschool years while it improves more slowly into adulthood. Importantly, while individual differences in emotion regulatory skills are rather stable, they can serve as a mediator between parenting and children’s problem behaviors ( Belsky et al., 2007 ; Van Dijk et al., 2017 ). Moreover, interventions targeted at promoting self-regulation or regulating emotions result in more socially competent students ( Domitrovich et al., 2007 ; Low et al., 2015 ).

4.2.4. Communication

Communicative competence refers to the ability to use language effectively and appropriately in different social situations ( Hymes, 1979 ). Developing good communication skills ( Raver and Zigler, 1997 ; Rose‐Krasnor, 1997 ; Rose-Krasnor and Denham, 2009 ) is of course also essential in ‘competent responding’ required for sustaining positive engagement in interactions ( Crick and Dodge, 1994 ). Although communication involves also nonverbal understanding ( Raver and Zigler, 1997 ), it is language that is indispensable to communication. Although language shows marked improvements in all aspects from infancy till early childhood (e.g., Clark, 2003 ), it is in particular a child’s pragmatic abilities (e.g., concerned with how children use language in interactions) that prove most relevant to social competence. For instance, children who scored low in pragmatic abilities (e.g., offered fewer requests for explanations or clarifications, initiated fewer conversations, and showed inappropriate turn-taking behaviors) were more likely to be rejected by their peers ( van der Wilt et al., 2018 ). In addition, children with developmental language disorder often experience peer problems or display problem behaviors (e.g., Curtis et al., 2018 ; Forrest et al., 2018 ; Van den Bedem et al., 2018 ), but this is related to pragmatic rather than structural problems with language ( St. Clair et al., 2011 ; Van den Bedem et al., 2019 ). Interventions aimed at improving pragmatic skills prove beneficial in promoting social competence and reducing peer problems ( Adams et al., 2012 ; Bierman et al., 2013 ; Coplan and Weeks, 2009 ).

4.2.5. Empathy

Empathy is a broad concept which generally entails the skill of identifying with another by taking another person’s perspective (cognitive empathy) as well as sharing the emotions of others (affective empathy). Empathy thus acknowledges the awareness that other people may have different emotions and feelings, but also allows for responding appreciatively, both of which are important prerequisites for maintaining social interactions (Eisenberg Fabes, & Spinrad, 2006). Because empathy is a highly valued trait in interactions, it is relevant for a myriad of social competence indices such as sustaining relationships, forming friendships, and peer popularity ( Eisenberg et al., 2015 , 2006 ; Spinrad and Gal, 2018 ).

Both affective empathy (responding to other person’s emotions, for instance via imitation) and cognitive empathy (‘social perspective taking’; Rose‐Krasnor, 1997 ) are considered vital skills for social competence ( Rose‐Krasnor, 1997 ). We see the relevance of empathy to social competence also acknowledged by other researchers in Table 2 : Hay and colleagues list it as ‘imitation’ as well as ‘causal understanding’ ( Hay et al., 2004 ); Rose‐Krasnor (1997 ) as ‘empathy’ and ‘perspective taking’.

It is possible that affective and cognitive empathy have different developmental paths. For affective empathy, it appears that even neonates can already imitate other’s facial expression (e.g., contagious crying, Simner, 1971 ). There is evidence that affective empathy in childhood and adolescence is an important underlying skill of social competence (e.g., Van der Graaff et al., 2014 ; van Hoorn et al., 2016 ). There is also protracted development in social perspective taking, which starts at a later age ( Rose‐Krasnor, 1997 ; Selman, 1980 ). Toddlers begin social perspective taking by recognizing the separation between self and others. That is, they are developing a theory mind, which is the awareness that others can hold different feelings or opinions from themselves ( Wellman, 1992 ; Wellman et al., 2011 ). Across childhood (2–12 years) children who possess an advanced theory of mind often display higher levels of social competence ( Imuta et al., 2016 ). Yet preschoolers might still find it difficult to act upon it as their own feelings might be a more dominating force. It is only by late childhood that children learn to view oneself from another person’s perspective. Early adolescence sees the development of mutual and third- person perspective, and late adolescence is characterized by taking into account perspectives beyond the immediate interaction as it considers the relevance of one’s current interaction to social norms.

Interventions targeting the skill of empathy often start with improving in social-emotional understanding and prosocial behaviors in class-room settings (that is, in early and late childhood). Such interventions reveal small but positive effects for fostering social competence, visible in indices such as peer nominations and teacher ratings ( Durlak et al., 2011 ; Malti et al., 2016 ).

To conclude, for the skills-dimension CID identifies five skills underlying social competence, each of which are complex constructs of themselves.

4.3. The context-dimension

The context dimension stresses the variety of relevant contexts in which interactions usually can take place in Western society. Such contexts do not only concern the setting of an interaction (“a situation in time and place”; Hartup, 2009 , p.8), but also with whom a child is interacting. While researchers acknowledge the variety in the skills contributing to social competence, few make explicit the variety of contexts that shape social competence. To demonstrate the richness of contexts of these interactions it is helpful to characterize them using pairs of dichotomies. Below we give four useful dichotomies, and sketch development.

4.3.1. Home versus school

It is at home that children will build the first set of meaningful interactions, with their caretaker(s) and with the other members of the household (e.g., siblings). Consequently, the home provides the foundation for social interactions. Nevertheless, this is not the only context in which some infants learn to interact with others. In Western societies such as the Netherlands, the majority of infants and toddlers regularly experience a form of daycare or play groups, which provides opportunities to learn to interact within small and stable groups of age mates ( Hay et al., 2018 ). Most people agree that providing such additional contexts can be beneficial for a child’s development of social competence, but how or when to cater for this is poorly understood. Next, while infants and toddlers differ in how much of the home context provides the dominant social context relative to other contexts, it is in early and late childhood that for all children the classroom setting gradually becomes a dominant social context. This is why indices on social competence collected in childhood often revolve around group dynamics in the classroom setting, such as (perceived) peer popularity and peer rejection ( Asher and McDonald, 2009 ). In adolescence, the major social context is still dominated by peers, but this time from their own cliques and clubs rather than the classroom.

4.3.2. Offline versus online

Early in infancy most of the interactions take place offline, in close proximity of other persons. With the rise of social media, children come into contact with multiple forms of online interactions from an early age. Indeed, there is evidence that even infants can also learn from persons via on-line interactions, as long as there is social contingency between the child and the other (i.e., when turn-taking occurs naturally, and not artificially; Roseberry et al., 2014 ). Children in middle childhood and adolescents increasingly use social media tools to communicate or play with their friends, peers, or partners they meet on game or other types of platforms ( Valkenburg and Piotrowski, 2017 ). A recent survey from the Netherlands has shown the majority of adolescents use two or three social media platforms, such as Instagram, YouTube, and Snapchat, in a complementary way ( van Driel et al., 2019v ). Today’s adolescents are amongst the first cohorts of young individuals who have grown up using mobile devices and social media; unlimited access to digital technologies enables them to be in constant contact with their peers and to engage in various social activities, such as playing games, creating audiovisual content, and sharing knowledge ( Salmela-Aro et al., 2017 ). However, about ten percent of current social media users has been identified as compulsive social media users ( van den Eijnden et al., 2016v ). Furthermore, positive correlations between compulsive use of technology and comorbid psychiatric disorders have been reported ( Andreassen, 2015 ). Since social media are a relatively new phenomenon, many questions regarding their potential impact on social competence and mental health remain unanswered ( Pantic, 2014 ). Therefore, more research in this field is required, and we hope that CID may provide some initial answers.

4.3.3. With adults versus peers

In a child’s life the most important adults are the caretakers (usually, the parents), and from childhood onwards, the teachers as well. (In both cases the child typically cannot control these social interaction partners). There is ample evidence that both parents’ ( Feldman et al., 2013 ; Groh et al., 2014 ) as well as teachers’ characteristics ( Wentzel, 2009 ) provide opportunities of interactions that contribute to a child’s social competence. From early childhood onwards, age-matched peers become increasingly the favored choice of interaction partners, as children learn to play and interact with peers. Although children with good social competence can interact easily in both contexts, children with poorer social competence (e.g., shy, withdrawn) find it often easier to interact with adults than with age-matched peers. Therefore, whereas adults might judge a child to be socially competent, one might reach different conclusions when observing a child interacting in situations with other peers.

4.3.4. With friends versus nonfriends

Already preschoolers can distinguish between friends and nonfriends ( Howes, 2009 ). Friendships centers around concepts of similarity: children like to play with others who are like themselves. However, our definition of social competence also requires that good social competence skills may come to the surface in contexts when the interaction partner is not familiar to the child. That is, how does the child interact when the other is not a friend, and the child therefore may not feel at ease with? Children who are shy in talking to others, or even experience social phobia, are at increased risk of developing poor social competence. It is therefore also important to consider social competence in the context of interaction partners the child is not friends with ( Asher and McDonald, 2009 ).

5. CID contributions to the developmental model for social competence

In the current paper we set out explaining that one of the aims of CID is to grasp the development of social competence. CID is a consortium of Dutch researchers aiming to understand the extent and relevance of individual differences in development. There are four main themes of research in CID, grouped into 4 work packages accordingly. Each work package focuses on different aspects of explaining individual differences across development. Two longitudinal cohorts were set up: the YOUth cohorts to sample neurocognitive development (Work Package 1;Onland- Moret et al., 2020), and the Leiden-CID cohorts (‘L-CID’) to test interventions in twins (Work Package 2; Crone et al., 2020 ). Work Package 3 unites four current cohorts established prior to CID ( Branje et al., 2020 ): Generation R (‘Gen-R’, Kooijman et al., 2016 ); Netherlands Twin Register (‘NTR’, Boomsma et al., 2006 ); RADAR (e.g., Branje and Meeus, 2018 ; Crocetti et al., 2017 ) and TRAILS ( Ormel et al., 2012 ). Finally, Work Package 4 focuses on advanced statistical modelling and animal models. CID thus encompasses six Dutch large-scale longitudinal cohort studies capturing child development through repeated measurements while it also houses the tools and methods required to address the complexity in developmental research. In what follows next, we provide more information how each of the cohorts (Section 5.1 ) and work packages (Section 5.2 ) provide building blocks towards building this model.

5.1. Contributions from the cohorts in CID

The cohorts participating in CID aim to help building a developmental model of social competence that integrates the ‘whens’ (age periods), ‘whats’ (indexical layer), ‘hows’ (skills-dimension) and ‘wheres’ (context- dimension) of social competence (See Fig. 1 ). More specifically, all cohorts in CID provide information about the ‘whens’ and ‘whats’ as all sample the development of social competence, albeit they differ in how exactly. It is one of the strengths of CID that all cohorts employ multiple indices collected at various moments in a child’s life to capture a child’s current stage of social competence. Table 3 lists for all cohorts which questionnaires they include to index social competence and it provides information about the age ranges that are covered; the frequency of administration, and the respondent filling in the questionnaires (amongst others children, parents, teachers).

An overview of the questionnaires that tap social competence and skills underlying social competence, for each of the cohorts involved in CID, with ages in years sampled in brackets.

Cohorts : Gen R = Generation R; l -CID = leiden Consortium on Individual Development; NTR = Netherlands Twin Register; RADAR = Research on Adolescent Development and Relationships; TRAILS = Tracking Adolescents’ Individual Lives’ Survey; YOU-th = Youth of Utrecht.

Indices : ASQ = Ages & Stages Questionnaire – Social Emotional -2 ( Squires et al., 2002 ); CBCL: = Child Behavior CheckList ( Achenbach, 1991 ; Achenbach and Rescorla, 2001 ); IRI = Interpersonal Reactivity Index ( Davis, 1983 ); ITSEA = Infant-Toddler Social and Emotional Assessment ( Briggs-Gowan et al., 2004 ); NRI = Network Relationships Inventory ( Furman and Buhrmester, 1985 ); SDQ = Strengths and Difficulties Questionnaire ( Goodman, 1997 , 2001 ); SSRS = Social Skills Rating System ( Gresham and Elliott, 1990 ).

* Prosocial subscale from the Revised Self-Report of Aggression and Social Behavior Measure ( Morales and Crick, 1998 ).

** – only the prosocial scale from the SDQ.

c self-report; p parent report; t teacher report; f friend report; s sibling report; i partner report.

1 = collected every year; 2 = collected every two years; 3 = collected every three years.

Indeed, one of the strengths of CID is that its variety in questionnaires allows us to sample every period of child development, starting with infancy (YOUth cohort) and toddlerhood (L-CID cohort). This is in contrast with most studies that only begin measuring social competence once children go to school ( Parker et al., 2006 ). Because the CID cohorts cover each period in child development, we can examine not only direct and long-term outcome measures of social competence, but also precursors to social competence in younger children.

Another strength of CID is that these questionnaires are filled in by a variety of raters (children themselves, parents, teachers, or others), as the source of ratings might be prone to rater bias ( Jones and Yudron, 2016 ). It is important to consider the source of ratings (for example, parent-report vs. self-report), as the source often makes a difference on the factor loadings of the assumed underlying construct (e.g., Goodman, 2001 ; Van Roy et al., 2008 ).

Although the cohort studies in CID use a variety of indices of social competence, two are used in virtually all cohorts: the Strengths and Difficulties questionnaire (SDQ: Goodman, 1997 ; for Dutch: Van Widenfelt et al., 2003 ) and the Child Behavior Checklist (ASEBA CBCL; Achenbach, 1991 ; Achenbach and Rescorla, 2001 ; for Dutch: Verhulst et al., 1996 ). Both questionnaires are relevant indices of social competence; they measure different underlying skills and complement each other. Whereas the SDQ measures key underlying skills such as prosocial behavior and friendship behaviors, the CBCL focuses on atypical (problematic) behavior in social interactions. Because all our cohorts employ at least one of these two questionnaires, CID is eventually able to collapse indices of social competence across cohorts that share the same indices (see also Zondervan-Zwijnenburg et al., 2020 for a similar approach to combining multiple cohort data on questionnaires related to behavioral control).

Besides the indexical layer, the cohorts participating in CID also address each of the five identified skills from the skills dimension, via repeated measurements collected in multiple ways spanning development from infancy into adulthood: through questionnaires, experimentally, and in parent-child interaction tasks. In Table 1 from the supplementary information we further delineate how each of the CID cohorts captures these five skills we consider relevant building blocks of social competence. Other articles in this special issue discuss some of the tasks and questionnaires in more detail.

With information adding to the development of skills we can ultimately understand the interplay between different indices, different subserving skills, and different contexts. This is crucial as social competence is a complex developmental construct. Take for instance the development of the underlying skills. While each of these skills show development, they often differ in their trajectories, and operate at different time scales at which they are more influential for social competence than others (e.g., Happé and Frith, 2014 ). We therefore do not assume that development in each skill proportionally continues to shape the development of social competence but rather that weights will change over time.

We illustrate the different time courses by comparing the communication versus empathy skills. Each of these skills have shown to be crucial, but how do they compare to each other in their relevance to social competence? Communication requires in particular a good command of pragmatics in order to confer meaning appropriately for social interactions. For communication skills we assume that pragmatic development has profound influences on social competence in childhood ( van der Wilt et al., 2019 ), but that the additional relevance of language development for social competence might reach a plateau over the following years, before it again assumes importance when friendships in adolescence center on intimacy & self-disclosure ( Troesch et al., 2016 , but see Curtis et al., 2018 ). Nevertheless, our changing society might also add further relevance to communication skills, as interactions increasingly take place online. It is unclear for instance how children with or without developmental language delays fare in digital media contexts that does not require immediate responses ( Drago, 2015 ).

In contrast to the relevance of communication skills to social competence in the early years, cognitive empathy is a skill that shows marked development in the adolescent years ( van der Graaff et al., 2014 ). We therefore expect this skill to continue to grow in importance to social competence, possibly peaking in adolescence, as this is the period when social perspective taking becomes sophisticated ( Selman, 1980 ) and when peer influence becomes a major force in social decision making (e.g., Crone and Dahl, 2012 ).

The above illustrations are mainly speculations. With the evidence gathered so far, we can only isolate the time course of the skills to underlying social competence and provide estimates how their relevance changes over time. What is still missing is evidence that reveals how a range of underlying skills across development together shape the development of social competence. Moreover, given that there is development both in the skills underlying social competence as well as in the different aspects characterizing social competence, such data will also unravel whether there are bidirectional relationships between skills and outcome measures. To illustrate, a recent study shows that while empathy predicts development in friendship quality, the reverse also holds: friendship quality drives empathy development ( Van den Bedem et al., 2019 ). Because CID repeatedly collects information on a wide range of skills (Table 1 from S.I.) concerning the same children from whom we also collect indices of their social competence ( Table 3 ), we aim to eventually contribute the evidence required for a better understanding how these skills work in tandem towards the development of social competence.

5.2. Contributions from the work packages in CID

Above we listed how the cohorts within CID examine the building blocks of social competence, as we make concrete how social competence emerges out of a variety in skills and contexts. Even so, fully capturing (the range in) the development of social competence requires integrating biological, psychological, and environmental factors, as well as insights into how these processes influence one another over time ( Beauchamp and Anderson, 2010 ; Karmiloff-Smith, 2017 ; Karmiloff-Smith et al., 2014 ). Further, in-depth understanding of individual differences in social competence begs a more detailed understanding of each of the descriptive levels of analysis, ranging from the molecular to the behavioral level, and how these levels link to each other both at the same time and across development. However, to date it has been virtually impossible to predict which combinations of factors at which times explain individual variability in the development of social competence.

One of the main reasons why there is not yet such a detailed account is that while different strands of research provide relevant blocks of knowledge, these remain limited as they typically do not cross beyond the boundaries of their own scholarly discipline. To illustrate, developmental studies often rely on longitudinal studies to investigate how psychological child characteristics and environmental factors contribute to a child’s well-being in real-life ( Bronfenbrenner, 2005 ), but these studies often do not include a biological or neurocognitive levels through which factors affect social competence (but see Crone et al., 2020 ). In contrast, biologically-oriented models provide us with a detailed mechanistic understanding of genes, neural function, or brain maturation relevant to the development of social cognition (e.g., Bakermans‐Kranenburg and Van IJzendoorn, 2007 ; Blakemore, 2008 ; Happé and Frith, 2014 ; Johnson et al., 2015 ; Robinson et al., 2008 ; Werker and Hensch, 2015 ) but they do not take into account child characteristics such as emotion regulation. It is here that the CID proves instrumental to building a developmental model of social competence as it accommodates the various disciplines of research that examine the development of social competence in both online and offline interactions as well as possess the statistical knowledge to integrate these findings.

As noted above, social competence is a developmental outcome measure that is reciprocal in the long-term. This makes social competence an example of a developmental cascade as it reflects behavior that can prove adaptive for some while having maladaptive consequences for others. Masten and Cicchetti (2010) identify five strands of research that would inform and optimize interventions required to promote positive cascades but to interrupt negative cascades: all of which are available in the work packages in CID.

One of the proposed strands is that research should determine when the cascade of social competence begins and accelerates to optimize the timing of interventions ( Masten et al., 2009 ). As laid out in this paper, data collected in our work packages 1–3 together provides an overview of social competence spanning from 20 weeks’ pregnancy (YOUth cohort) to far into adulthood (e.g., RADAR, TRAILS). Consequently, we cover development of social competence completely; that is, we can observe precursors in pregnancy, infancy and toddlerhood as well as its long-term consequences from conception and infancy onwards.

Second, cascade models would benefit from repeated measurements of social competence collected at various overlapping time scales. The choice of a lag is often chosen arbitrarily, while there must be adequate time for the cascading effects of factors leading to social competence to be manifested (e.g., Cole, 2006 ). With ample variation in time lags we can measure effects of time continuously; this allows us to reach a better understanding of how effects manifest themselves over time, as we can disentangle direct from indirect pathways in which the various variables of interest contribute to social competence ( Masten et al., 2006 ). Indeed, we are collecting longitudinal human data indexing social competence ranging from days (WP3: RADAR cohort) to yearly measurements (WP2: l -CID; WP3: Generation R, NTR, RADAR) to three-year intervals (WP1: YOUth cohorts) to generations (WP3: RADAR; TRAILS cohort). The RADAR cohort is of especial interest here as it is one of the few existing cohorts that even combines various lags within their data collection.

Then there are three remaining strands of research that according to Masten and Cicchetti (2010) are also instrumental in informing interventions, but which have not received as much attention in this paper. In all three cases, CID is able to contribute missing information. One line of research should be demonstrating the necessity of testing intervention designs that target mediating processes for change in social competence, which we cover in Work Package 2. A second strand of research should address how the interplay between genes, brain and environment affects social competence, which we address in our multi- method cohorts: YOUth-cohorts, l -CID and NTR all collect genes and multiple indices of environment (YOUth cohorts in WP1: Onland-Moret et al., 2020 twin cohorts L-CID in WP2; cf. Crone et al., 2020 ; NTR cohort in WP3: Boomsma et al., 2006 ; cf. Branje et al., 2020 ). We also have access to rodent models that allow for a level of control at the level of genes, brain, or environment that cannot be achieved in humans (WP4; cf., van der Veen et al., 2020 ). Finally, Masten and Cicchetti (2010) stress the need of well-designed experiments to further bolster our model that go beyond longitudinal cohorts to demonstrate causal directions between variables of interest and the outcome measure of social competence. With the help of animal models (WP4) and neurocognitive testing (WP1, WP2) we can achieve this. For instance, while Bierman et al. (2008 ) suggest that enriched environments foster social competence, we now test its specificity and generalizability of this in rodent models, which allows for hypothesis-testing in more stringent conditions as the contribution of other factors such as socio-emotional and genes are controlled for ( van der Veen et al., 2020 ). All in all, the CID unites various strands of research that together centers on achieving a better understanding of the development of social competence.

6. Conclusions

To conclude, the literature is still missing a unified approach that integrates how a range of underlying skills together shapes the development of social competence in a range of contexts. The cohorts in CID collect information on different indices of social competence as well as on a wide range of underlying skills concerning the same children in a range of contexts repeatedly across various lags. The work packages in CID each provide unique additional information in testing our model. Putting these pieces together, CID aims to provide the evidence required for such theory-building and bridge these gaps in the literature.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The Consortium on Individual Development (CID) is funded through the Gravitation program of the Dutch Ministry of Education, Culture, and Science, and the Dutch Research Council (NWO) (Grant No. 024.001.003). The authors thank Chantal Kemner and Carlijn van den Boomen for helpful comments on earlier versions of this article, and Lotte Houtepen for assistance. The authors also would like to thank representatives of the cohorts involved in CID for their assistance: Eveline Crone and Bianca van Bulk from the Leiden-CID cohort; Dorret Boomsma and Eveline de Zeeuw from the Netherlands Twin Register; Tineke Oldehinkel from the TRAILS cohort; Manon Hillegers and Elize Koopman Verhoef from the Generation-R cohort; and Juliëtte van der Wal from the YOUth cohorts.

Appendix A Supplementary material related to this article can be found, in the online version, at doi: https://doi.org/10.1016/j.dcn.2020.100861 .

Appendix A. Supplementary data

The following is Supplementary data to this article:

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Key facts about Americans and guns

A customer shops for a handgun at a gun store in Florida.

Guns are deeply ingrained in American society and the nation’s political debates.

The Second Amendment to the United States Constitution guarantees the right to bear arms, and about a third of U.S. adults say they personally own a gun. At the same time, in response to concerns such as rising gun death rates and mass shootings , President Joe Biden has proposed gun policy legislation that would expand on the bipartisan gun safety bill Congress passed last year.

Here are some key findings about Americans’ views of gun ownership, gun policy and other subjects, drawn primarily from a Pew Research Center survey conducted in June 2023 .

Pew Research Center conducted this analysis to summarize key facts about Americans and guns. We used data from recent Center surveys to provide insights into Americans’ views on gun policy and how those views have changed over time, as well as to examine the proportion of adults who own guns and their reasons for doing so.

The analysis draws primarily from a survey of 5,115 U.S. adults conducted from June 5 to June 11, 2023. Everyone who took part in the surveys cited is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the analysis on gun ownership , the questions used for the analysis on gun policy , and the survey’s methodology .

Additional information about the fall 2022 survey of parents and its methodology can be found at the link in the text of this post.

Measuring gun ownership in the United States comes with unique challenges. Unlike many demographic measures, there is not a definitive data source from the government or elsewhere on how many American adults own guns.

The Pew Research Center survey conducted June 5-11, 2023, on the Center’s American Trends Panel, asks about gun ownership using two separate questions to measure personal and household ownership. About a third of adults (32%) say they own a gun, while another 10% say they do not personally own a gun but someone else in their household does. These shares have changed little from surveys conducted in 2021 and 2017 . In each of those surveys, 30% reported they owned a gun.

These numbers are largely consistent with rates of gun ownership reported by Gallup , but somewhat higher than those reported by NORC’s General Social Survey . Those surveys also find only modest changes in recent years.

The FBI maintains data on background checks on individuals attempting to purchase firearms in the United States. The FBI reported a surge in background checks in 2020 and 2021, during the coronavirus pandemic. The number of federal background checks declined in 2022 and through the first half of this year, according to FBI statistics .

About four-in-ten U.S. adults say they live in a household with a gun, including 32% who say they personally own one, according to an August report based on our June survey. These numbers are virtually unchanged since the last time we asked this question in 2021.

There are differences in gun ownership rates by political affiliation, gender, community type and other factors.

Republicans and Republican-leaning independents are more than twice as likely as Democrats and Democratic leaners to say they personally own a gun (45% vs. 20%).
40% of men say they own a gun, compared with 25% of women.
47% of adults living in rural areas report personally owning a firearm, as do smaller shares of those who live in suburbs (30%) or urban areas (20%).
38% of White Americans own a gun, compared with smaller shares of Black (24%), Hispanic (20%) and Asian (10%) Americans.

A bar chart showing that nearly a third of U.S. adults say they personally own a gun.

Personal protection tops the list of reasons gun owners give for owning a firearm. About three-quarters (72%) of gun owners say that protection is a major reason they own a gun. Considerably smaller shares say that a major reason they own a gun is for hunting (32%), for sport shooting (30%), as part of a gun collection (15%) or for their job (7%).

The reasons behind gun ownership have changed only modestly since our 2017 survey of attitudes toward gun ownership and gun policies. At that time, 67% of gun owners cited protection as a major reason they owned a firearm.

A bar chart showing that nearly three-quarters of U.S. gun owners cite protection as a major reason they own a gun.

Gun owners tend to have much more positive feelings about having a gun in the house than non-owners who live with them. For instance, 71% of gun owners say they enjoy owning a gun – but far fewer non-gun owners in gun-owning households (31%) say they enjoy having one in the home. And while 81% of gun owners say owning a gun makes them feel safer, a narrower majority (57%) of non-owners in gun households say the same about having a firearm at home. Non-owners are also more likely than owners to worry about having a gun in the home (27% vs. 12%, respectively).

Feelings about gun ownership also differ by political affiliation, even among those who personally own firearms. Republican gun owners are more likely than Democratic owners to say owning a gun gives them feelings of safety and enjoyment, while Democratic owners are more likely to say they worry about having a gun in the home.

A chart showing the differences in feelings about guns between gun owners and non-owners in gun households.

Non-gun owners are split on whether they see themselves owning a firearm in the future. About half (52%) of Americans who don’t own a gun say they could never see themselves owning one, while nearly as many (47%) could imagine themselves as gun owners in the future.

Among those who currently do not own a gun:

A bar chart that shows non-gun owners are divided on whether they could see themselves owning a gun in the future.

61% of Republicans and 40% of Democrats who don’t own a gun say they would consider owning one in the future.
56% of Black non-owners say they could see themselves owning a gun one day, compared with smaller shares of White (48%), Hispanic (40%) and Asian (38%) non-owners.

Americans are evenly split over whether gun ownership does more to increase or decrease safety. About half (49%) say it does more to increase safety by allowing law-abiding citizens to protect themselves, but an equal share say gun ownership does more to reduce safety by giving too many people access to firearms and increasing misuse.

A bar chart that shows stark differences in views on whether gun ownership does more to increase or decrease safety in the U.S.

Republicans and Democrats differ on this question: 79% of Republicans say that gun ownership does more to increase safety, while a nearly identical share of Democrats (78%) say that it does more to reduce safety.

Urban and rural Americans also have starkly different views. Among adults who live in urban areas, 64% say gun ownership reduces safety, while 34% say it does more to increase safety. Among those who live in rural areas, 65% say gun ownership increases safety, compared with 33% who say it does more to reduce safety. Those living in the suburbs are about evenly split.

Americans increasingly say that gun violence is a major problem. Six-in-ten U.S. adults say gun violence is a very big problem in the country today, up 9 percentage points from spring 2022. In the survey conducted this June, 23% say gun violence is a moderately big problem, and about two-in-ten say it is either a small problem (13%) or not a problem at all (4%).

Looking ahead, 62% of Americans say they expect the level of gun violence to increase over the next five years. This is double the share who expect it to stay the same (31%). Just 7% expect the level of gun violence to decrease.

A line chart that shows a growing share of Americans say gun violence is a 'very big national problem.

A majority of Americans (61%) say it is too easy to legally obtain a gun in this country. Another 30% say the ease of legally obtaining a gun is about right, and 9% say it is too hard to get a gun. Non-gun owners are nearly twice as likely as gun owners to say it is too easy to legally obtain a gun (73% vs. 38%). Meanwhile, gun owners are more than twice as likely as non-owners to say the ease of obtaining a gun is about right (48% vs. 20%).

Partisan and demographic differences also exist on this question. While 86% of Democrats say it is too easy to obtain a gun legally, 34% of Republicans say the same. Most urban (72%) and suburban (63%) dwellers say it’s too easy to legally obtain a gun. Rural residents are more divided: 47% say it is too easy, 41% say it is about right and 11% say it is too hard.

A bar chart showing that about 6 in 10 Americans say it is too easy to legally obtain a gun in this country.

About six-in-ten U.S. adults (58%) favor stricter gun laws. Another 26% say that U.S. gun laws are about right, and 15% favor less strict gun laws. The percentage who say these laws should be stricter has fluctuated a bit in recent years. In 2021, 53% favored stricter gun laws, and in 2019, 60% said laws should be stricter.

A bar chart that shows women are more likely than men to favor stricter gun laws in the U.S.

About a third (32%) of parents with K-12 students say they are very or extremely worried about a shooting ever happening at their children’s school, according to a fall 2022 Center survey of parents with at least one child younger than 18. A similar share of K-12 parents (31%) say they are not too or not at all worried about a shooting ever happening at their children’s school, while 37% of parents say they are somewhat worried.

Among all parents with children under 18, including those who are not in school, 63% see improving mental health screening and treatment as a very or extremely effective way to prevent school shootings. This is larger than the shares who say the same about having police officers or armed security in schools (49%), banning assault-style weapons (45%), or having metal detectors in schools (41%). Just 24% of parents say allowing teachers and school administrators to carry guns in school would be a very or extremely effective approach, while half say this would be not too or not at all effective.

A pie chart that showing that 19% of K-12 parents are extremely worried about a shooting happening at their children's school.

There is broad partisan agreement on some gun policy proposals, but most are politically divisive, the June 2023 survey found . Majorities of U.S. adults in both partisan coalitions somewhat or strongly favor two policies that would restrict gun access: preventing those with mental illnesses from purchasing guns (88% of Republicans and 89% of Democrats support this) and increasing the minimum age for buying guns to 21 years old (69% of Republicans, 90% of Democrats). Majorities in both parties also oppose allowing people to carry concealed firearms without a permit (60% of Republicans and 91% of Democrats oppose this).

A dot plot showing bipartisan support for preventing people with mental illnesses from purchasing guns, but wide differences on other policies.

Republicans and Democrats differ on several other proposals. While 85% of Democrats favor banning both assault-style weapons and high-capacity ammunition magazines that hold more than 10 rounds, majorities of Republicans oppose these proposals (57% and 54%, respectively).

Most Republicans, on the other hand, support allowing teachers and school officials to carry guns in K-12 schools (74%) and allowing people to carry concealed guns in more places (71%). These proposals are supported by just 27% and 19% of Democrats, respectively.

Gun ownership is linked with views on gun policies. Americans who own guns are less likely than non-owners to favor restrictions on gun ownership, with a notable exception. Nearly identical majorities of gun owners (87%) and non-owners (89%) favor preventing mentally ill people from buying guns.

A dot plot that shows, within each party, gun owners are more likely than non-owners to favor expanded access to guns.

Within both parties, differences between gun owners and non-owners are evident – but they are especially stark among Republicans. For example, majorities of Republicans who do not own guns support banning high-capacity ammunition magazines and assault-style weapons, compared with about three-in-ten Republican gun owners.

Among Democrats, majorities of both gun owners and non-owners favor these two proposals, though support is greater among non-owners.

Note: This is an update of a post originally published on Jan. 5, 2016 .

Partisanship & Issues
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About 1 in 4 U.S. teachers say their school went into a gun-related lockdown in the last school year

Striking findings from 2023, for most u.s. gun owners, protection is the main reason they own a gun, gun violence widely viewed as a major – and growing – national problem, what the data says about gun deaths in the u.s., most popular.

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6 Skills Students Need to Succeed in Social Studies Classes

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Comparing and Contrasting Skills

Cause and Effect

Social Problem-Solving

A Model of Social Problem-Solving

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Patti M. Valkenburg

Maja Deković

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2. Why it is crucial to have a better understanding of the development of social competence

3. Sketching the development of social competence

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