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. Author manuscript; available in PMC: 2021 Mar 3.
Published in final edited form as: J Educ Res. 2020 Aug 17;113(4):283–291. doi: 10.1080/00220671.2020.1806016

The Effects of Cooperative Learning on Peer Relations, Academic Support, and Engagement in Learning Among Students of Color

Mark J Van Ryzin 1,*, Cary J Roseth 2, Heather McClure 3
PMCID: PMC7928266  NIHMSID: NIHMS1670980  PMID: 33664528

Abstract

Despite Brown vs. Board of Education, prejudice still exists in the American school system. These attitudes can give rise to negative social experiences for students of color (i.e., discrimination), negatively impacting their mental and physical health and creating disparities in educational outcomes. Rather than seeking to ameliorate these negative experiences, our approach attempts to address the underlying prejudices and, in so doing, reduce these disparities. Using 4 waves of data from a cluster randomized trial (N = 15 middle schools, 1,890 students, 47.1% female, 75.2% White), we hypothesized that cooperative learning, which has been shown to reduce prejudice in previous research, would create positive gains in peer relatedness, perceptions of academic support, and engagement in learning, and that gains would be larger for students of color; our results confirmed these hypotheses. Our findings highlight the potential role of cooperative learning in reducing disparities and creating greater equity in education.

Keywords: engagement, peer relatedness, academic support, educational equity, cooperative learning, middle school

Despite landmark judicial victories such as Brown vs. Board of Education, research finds that our educational system remains racially segregated (Orfield et al., 2016; Reardon & Owens, 2014), even more than would be expected based upon neighborhood segregation (Sohoni & Saporito, 2009). Similarly, school-based friendship networks also tend to be racially segregated (Block & Grund, 2014; Cheng & Xie, 2013; Graham et al., 2009). This segregation can create an incomplete understanding of other ethnic or racial groups among White students, which can then manifest as racial prejudice. Importantly, these prejudicial attitudes become harder to change as students grow older (Stephan & Vogt, 2004), and they can persist into adulthood (McPherson et al., 2001). Indeed, students who are educated in racially segregated schools tend to settle in racially segregated neighborhoods as adults, supporting intergenerational continuity in racial segregation and, potentially, racial prejudice (Braddock et al., 2010; Goldsmith, 2010).

Racial segregation and prejudice in schools gives rise to discrimination and related forms of social exclusion and rejection (Wilson & Rodkin, 2013), and these negative social processes impact students of color in the United States on a regular basis (RWJF, 2017; Sue et al., 2007). For example, in a national survey, over 80% of Latino youth reported that discrimination and related negative social experiences were a chronic problem (Foxen, 2010); more recent evidence suggests that these negative social experiences exist at similar rates on-line (Tynes, 2015). Such experiences can negatively impact mental and physical health (Assari et al., 2017; Gee et al., 2012), depress self-esteem (Greene et al., 2006), and elevate risk for substance use, depression, and violent delinquency (Davis et al., 2016; Gil et al., 2000; Martin et al., 2011).

Negative social experiences can also harm academic performance, including academic curiosity, persistence, engagement, and achievement (Han & Ying, 2007; Huynh & Fuligni, 2010; Neblett Jr. et al., 2006; Smalls et al., 2007). Empirical evidence has established that chronic psychosocial stress exposure, such as that related to discrimination and related forms of social rejection, can challenge students’ abilities to learn, and likely serves as a contributor to chronically low levels of graduation among students of color in US public schools (Brondolo et al., 2009; Mays et al., 2007). For example, in 2015–2016, the graduate rates for Black (76%), Latino (79%), and American Indian/Alaska Native (72%) students were all lower than that for White students (88%; McFarland et al., 2018).

To date, research targeting racial and ethnic disparities has often focused on ameliorating the impact of negative social experiences (i.e., discrimination) through individual coping mechanisms such as self-affirmation (e.g., Umaña-Taylor et al., 2008). In this paper, we present an alternative approach that can address the underlying prejudice that gives rise to these negative social experiences in the first place, and we evaluate the potential for this approach to reduce the subsequent ethnic disparities in educational outcomes and create greater equity in education.

Addressing Prejudice with Contact Theory

A powerful framework for addressing racial prejudice is Contact Theory (Allport, 1954; Pettigrew, 1998), which specifies the conditions under which social contact can lead to true social integration among members of different ethnic groups. These conditions include the following: (a) individuals are brought together as equals, with differences in social status being explicitly minimized; (b) pairs or groups of individuals must be given a common goal to direct their interactions, and must be incentivized to work together to achieve their goal; (c) the social contact must involve an extended amount of face-to-face interaction time, preferably including mutual disclosure to assist in discovering areas of commonality; and (d) those in positions of authority (i.e., teachers) must explicitly encourage and support positive, collaborative interactions and discourage any hints of ingroup vs. outgroup bias or prejudice. When these conditions exist, inter-group contact leads to reduced prejudice, and individuals develop more favorable opinions of members of other groups (Molina & Wittig, 2006; Pettigrew & Tropp, 2006). In contrast, when these conditions do not exist, intergroup contact will increase, rather than reduce, intergroup tensions (Cohen & Lotan, 1995).

Contact Theory and Cooperative Learning

Contact Theory serves as the basis for a small-group instructional technique called cooperative learning (also known as peer learning or collaborative learning). The key design aspects of cooperative learning closely mirror the conditions put forth by Contact Theory. Specifically, cooperative learning brings students together under conditions of positive interdependence, where individual goals are structured such that individual goal attainment promotes the goal attainment of others in the learning group and vice versa. Under conditions of positive interdependence, students are more likely to interact in positive ways, such as providing instrumental and emotional support, and sharing information and resources (Deutsch, 1949, 1962; Johnson, Johnson & Maruyama, 1983).

In addition to positive interdependence, cooperative learning specifies that each group member is held accountable for fulfilling their role (i.e., individual accountability), and teachers are encouraged to form groups at random to ensure that students work with a diverse cross-section of other students in the class. Teachers also designate tasks within groups at random (e.g., the person with the earliest birthday will go first) and distribute responsibility among the various roles within the group. The overall effect of these design considerations is to ensure an equal status among group members and eliminate the ability for one student to exert inordinate control over group processes and decision-making.

Cooperative learning also requires face-to-face interactions with mutual disclosure to encourage group members to get to know one another and find common ground. Finally, cooperative learning asks teachers to observe student interactions during learning activities and recognize students that exhibit particular kinds of positive, helpful behavior (e.g., checking for understanding among group members, encouraging others to participate, summarizing the group’s thinking).

Thus, cooperative learning can provide a mechanism by which youth can have positive social experience with peers and, over time, develop more positive peer relationships (Roseth et al., 2008). There is also evidence that cooperative learning can enhance cross-ethnic peer relations (for review, see Johnson & Johnson, 2000; Slavin & Cooper, 1999). For example, cooperative learning has been found to promote more cross-race interaction and interracial attraction, greater cross-ethnic academic support, and more frequent cross-ethnic friendship choices (Johnson, Johnson, Tiffany, & Zaidman, 1984; Slavin, 2001; Weigel et al., 1975). Such cross-ethnic friendships, in turn, can promote greater academic engagement (Kawabata & Crick, 2015). There is also preliminary evidence that the effects of cooperative learning are stronger for at-risk groups. Specifically, Van Ryzin and Roseth (2018a) found that reductions in victimization and perceived stress were stronger among more socially marginalized students. However, very little research has explored the ability of cooperative learning to reduce ethnic disparities in educational outcomes and create greater equity in education.

Hypotheses

Given the evidence for the social and cross-ethnic benefits of cooperative learning, as well as the links between social experiences and academic performance, we hypothesized that the effects of cooperative learning on social and academic outcomes would be more powerful for students of color than for White students. Specifically, we hypothesized stronger effects for cooperative learning on peer relatedness, perceptions of academic support, and engagement in learning among students of color. To obtain accurate measures of change in these outcomes across multiple waves of data, we modeled them as growth curves. We first evaluated the main effect of cooperative learning on all outcomes simultaneously, then explored moderation by ethnic group for each outcome (effects for White students alongside students of color). Unfortunately, the small size of our non-White subsamples prevented us from exploring ethnic groups individually.

Method

Sample

All aspects of this study were approved by the appropriate Institutional Review Board (IRB). The sample was based upon a small-scale randomized trial of cooperative learning in 15 rural middle schools in the Pacific Northwest. Schools were matched based upon size and demographics (e.g., free/reduced lunch percentage) and randomized to condition (i.e., intervention vs. waitlist control). We were concerned about the likelihood of losing control schools, so we randomized an extra school to this condition (i.e., we had 8 waitlist-control and 7 intervention schools).

Our analytic sample included N = 1,890 students who were enrolled in the project during the 2016–2017 (7th grade) or 2017–2018 (8th grade) school years. Student demographics by school are reported in Table 1. We achieved greater than 80% student participation at each data collection point by providing research staff to oversee the data collection, and compensation to the schools for participating in the project; we also enrolled participating students in a prize raffle. The sample was 47.1% female (N = 890) and 75.2% White (N = 1,421). Other racial/ethnic groups included Hispanic/Latino (13.2%, N = 249), multi-racial (5.3%, N = 100), and American Indian/Alaska Native (3.1%, N = 58); our sample was less than 1% African-American, Asian, and Native Hawaiian/Pacific Islander. Overall, 13.9% (N = 262) were reported as having Special Education status, 78.6% (N = 1486) did not have Special Ed status, and 7.5% (N = 142) were missing Special Ed designation. Student free-and-reduced-price lunch status was not made available by the schools, but school-level figures are presented in Table 1.

Table 1.

Descriptive data by school

School Intervention N % female % White % Special Ed % FRPLa
1 Yes 282 47.9 73.0 11.7 53
2 Yes 61 52.5 75.4 16.4 66
3 Yes 110 40.0 60.9 n/a 62
4 No 114 47.4 93.0 24.6 65
5 Yes 112 50.0 83.0 15.2 72
6 Yes 121 47.1 90.1 19.8 71
7 No 53 41.5 92.5 18.9 33
8 Yes 105 46.7 78.1 10.5 57
9 No 71 45.1 81.7 19.7 45
10 Yes 84 33.3 72.6 4.8 95
11 No 183 44.8 65.0 17.5 61
12 No 239 51.0 48.5 13.0 84
13 No 197 49.2 90.4 11.7 66
14 No 50 48.0 88.0 16.0 39
15 No 108 51.9 80.6 15.7 46
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a

State records.

Note. One school did not provide Special Ed status.

Procedure

Training for intervention school staff began in the fall of 2016 and continued throughout the 2016–2017 school year, consisting of three half-day in-person sessions, periodic check-ins via videoconference, and access to resources (e.g., newsletters). The three in-person training sessions per school were conducted in (1) late September and early October, (2) late October through early December, and (3) late January through late March. Training sessions were conducted by D.W. and R.T. Johnson, supported by the first two authors, and utilized Cooperation in the Classroom, 9th Edition by Johnson, Johnson, and Holubec (2013); each staff member was given a copy of the book. Due to the geographic dispersal of the schools, each school received training individually according to their own schedule for professional development. Finally, we conducted a one-day administrator training during the summer of 2017, and a half-day follow-up training for teachers in the second year.

The training was not provided in a lecture format; rather, teachers were trained in cooperative learning through the use of cooperative learning techniques, giving them an authentic sense of how cooperative learning looks and feels from the point of view of the participant. At the conclusion of each lesson, the trainers discussed how the lesson structure reflected the foundational concepts of cooperative learning, providing teachers with insight into how these concepts could be applied in their own teaching.

Cooperative learning is viewed as a conceptual framework within which teachers can apply the basic concepts to design their own group-based activities using existing curricula. Under the Johnsons’ approach, cooperative learning can include reciprocal teaching (e.g., Jigsaw), peer tutoring, collaborative reading, and other methods in which peers help each other learn in small groups under conditions of positive interdependence. In a jigsaw lesson, for example, each student in a learning group is given responsibility for a portion of the overall content of the lesson (Aronson & Bridgeman, 1979). The student must learn their portion of the content and, in collaboration with other students who have been assigned the same content, the student must prepare materials to use when teaching the content to the other students in their group. Similarly, other students in the group learn other portions of the content and teach it to the group. In this way, all the students in the group are exposed to all of the lesson content.

The Johnsons’ approach offers many opportunities to promote positive interdependence. For example, teachers may require a single finished product from a group (goal interdependence), or may offer a reward to the group if everyone achieves above a certain threshold on an end-of-unit quiz or test (reward interdependence). The lesson plan may require that each member of the group be issued different materials that they must share in order to complete the lesson (resource interdependence), or that each member of the group has a different role to play (role interdependence) or a unique task that must be completed sequentially, like an assembly line, in order for the lesson to be completed successfully (task interdependence). These varied forms of positive interdependence can be layered upon one another in a single lesson, increasing the incentives for students to collaborate.

The Johnsons’ approach also emphasizes individual accountability, which could include an end-of-unit assessment to be taken individually (with the potential for group rewards as discussed above), or something as simple as a random oral quiz by the teacher as he or she supervises the group work during class time. In this scenario, if a randomly chosen member of a group can effectively summarize their work or present their project status, then the group earns credit toward their grade in the lesson or the class (or potentially other rewards, such as snacks, special privileges, etc.). Finally, the Johnsons’ approach includes explicit coaching in collaborative social skills (e.g., checking for understanding among group members, encouraging others to participate, summarizing the group’s thinking), a high degree of face-to-face interaction, and guided processing of group performance (e.g., discussing what the group did well and identifying areas for improvement).

Our training recommended group sizes of 2 to 4, assignment to groups at random, and no limitations on gender (i.e., heterogeneous groups). We also recommended that group membership change frequently to ensure that students worked with a broad cross-section of other students in the classroom.

Measures

Student data collection was conducted in September/October and March/April of the 2016–2017 and 2017–2018 school years (4 waves in total) using on-line surveys (i.e., Qualtrics; https://www.qualtrics.com/). To assess fidelity of implementation, we also conducted teacher observations. To shrink the overall number of items and reduce participant burden, existing data from other studies were used to select the highest-loading items from each scale below (additional information available from the first author).

Peer relatedness.

We used 4 items from the Relatedness Scale, which has been used in previous research as a predictor of positive school adjustment in adolescents (Furrer & Skinner, 2003). Items included “When I’m with my classmates, I feel accepted” and “When I’m with my classmates, I feel unimportant” (reverse scored). Students responded on a 4-point scale from 1 (Not at all true) to 4 (Very true). Items were averaged to arrive at the scale score. Alpha reliability was .71 to .84 across the four waves of measurement.

Academic support.

We used 4 items from the Classroom Life Scale (Johnson & Johnson, 1983), which has been used previously among adolescents to assess perceptions of support in school (Van Ryzin, Gravely, & Roseth, 2009; Van Ryzin, 2011). Items included “In my classes, other students like to help me learn” and “Other students in my classes want me to come to class every day.” Students responded on a 4-point scale from 1 (Completely False) to 5 (Completely True). Items were averaged to arrive at the scale score. Alpha reliability was .87 to .94 across the four waves of measurement.

Engagement.

We used 4 items from the Behavioral Engagement subscale of the Engagement vs. Disaffection with Learning Scale (Skinner & Belmont, 1993), including “I try hard to focus in class” and “In class, I do just enough to get by” (reverse scored). Students responded on a 4-point scale from 1 (Not at all true) to 4 (Very true). Items were averaged to arrive at the scale score. Alpha reliability was .75 to.83 across the four waves of measurement.

Demographics.

Ethnicity was collected from school records and coded as White (0) vs. Student of Color (1).

Observed intervention fidelity.

Research staff blind to intervention assignment observed teaching practices in intervention and control schools. We trained our observers to achieve adequate reliability using simulated data before they were permitted to conduct observations in actual classrooms, and we used an established observation protocol for key aspects of cooperative learning (e.g., positive interdependence; Krol, Sleegers, Veenman, & Voeten, 2008; Veenman et al., 2002). Observations were conducted in the late fall/early winter and again in the spring. Observers remained in a classroom for an entire class period. Classrooms were selected at random for observation.

Analysis Plan

We used all four waves of measurement in a latent growth curve and evaluated intervention effects on the linear slopes (e.g., the change in engagement during the project). For each growth curve, we tested for ethnic differences (i.e., White vs. students of color) using a deviance test, where model fit is compared between: (a) a model where the coefficients for the ethnic groups are constrained to be equal; and, (b) a model where the coefficients are freely estimated. All linear growth curve slopes were regressed on the corresponding intercept terms, and intercept terms (and baseline scores for peer relatedness) were allowed to correlate with each other and with the intervention condition.

We fit these models and calculated the significance of the indirect effect using Mplus 7.4 (Muthén & Muthén, 1998–2012) and Maximum Likelihood (ML) estimation with robust standard errors, which can provide unbiased estimates in the presence of missing data and/or non-normal distributions (Enders & Bandalos, 2001). Mplus also enabled us to account for the nesting in the data and calculate appropriate standard errors; however, sample size limitations prevented us from including random effects in the model, so all effects were fixed. For each model, standard measures of fit are reported, including the chi-square (χ2), comparative fit index (CFI), Tucker-Lewis index (TLI), and root mean square error of approximation (RMSEA). CFI values greater than .95, TLI values greater than .90, and RMSEA values less than .05 indicate good fit (Bentler, 1990; Bentler & Bonett, 1980; Hu & Bentler, 1999).

Results

Descriptive data for all variables and correlations are presented in Table 2; students of color reported lower levels of engagement in learning (correlations with ethnicity were −.05 to −.09, p < .05), but no significant differences in terms of peer relatedness and academic support. ANOVA models indicated that students in intervention and control schools did not differ in terms of baseline levels of peer relatedness [F(1,1445) = .04, ns], academic support [F(1,1445) = 1.93, ns], and engagement in learning [F(1,1453) = .05, ns]. With regards to fidelity observations, ANOVA indicated significantly higher levels of observed positive interdependence in intervention schools as compared to control schools in the spring of the first year, F(1,98) = 10.79, p < .01, R2 = .10. Fidelity observations at baseline demonstrated no differences, F(1,99) = 1.41, ns.

Table 2.

Correlations and descriptive data (Level 1)

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13
1. Peer Relatedness (W1) —
2. Peer Relatedness (W2) .50*** —
3. Peer Relatedness (W3) .42*** .45*** —
4. Peer Relatedness (W4) .35*** .38*** .53*** —
5. Academic Support (W1) .46*** .35*** .30*** .24*** —
6. Academic Support (W2) .31*** .52*** .32*** .26*** .53*** —
7. Academic Support (W3) .26*** .28*** .58*** .38*** .42*** .47*** —
8. Academic Support (W4) .23*** .27*** .37*** .62*** .37*** .43*** .53*** —
9. Engagement (W1) .24*** .14*** .14*** .14*** .28*** .22*** .15*** .16*** —
10. Engagement (W2) .15*** .34*** .14*** .13*** .25*** .35*** .13*** .16*** .53*** —
11. Engagement (W3) .16*** .13** .46*** .25*** .20*** .20*** .43*** .24*** .37*** .34*** —
12. Engagement (W4) .13*** .14*** .28*** .52*** .19*** .17*** .29*** .47*** .34*** .32*** .50*** —
13. Ethnic Group −.01 .00 −.04 .01 .00 .00 −.06* −.05 −.05* −.03 −.08** −.09*** —
N 1447 1513 1562 1481 1447 1531 1561 1468 1455 1531 1570 1490 1857
M 3.07 2.97 2.94 2.84 3.12 2.99 1 2.97 2.90 3.38 3.28 3.05 2.96 .23
SD .68 .76 .79 .84 1.04 1.12 1.11 1.20 .60 .68 .69 .71 −
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*

p < .05.

**

p < .01.

***

p < .001.

We first evaluated the direct effects of cooperative learning on peer relatedness, academic support, and engagement in learning across the entire sample. All outcomes were fit in a single statistical model. Model fit was adequate, χ2(57) = 116.51, p < .001; CFI = .99; TLI = .99; RMSEA = .024 (90% C.I.: .017-.030). Results are provided in Table 3 (i.e., Full sample).

Table 3.

Model effects

Model path Full White SOC Deviance
Cooperative learning → Peer Relatedness (Slope) .34*** .31*** .41*** χ2(1) = 14.58, p < .001
Cooperative learning → Academic Support (Slope) .33*** .30*** .37*** χ2(1) = 8.84, p < .01
Cooperative learning → Engagement (Slope) .38*** .37*** .41*** χ2(1) = 4.04, p < .05
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Note. Results provided for Full sample, White students only, and students of color (SOC) only. Deviance test results indicate significant differences for White vs. students of color.

***

p < .001.

We next evaluated moderation of these effects by ethnicity (White alongside students of color). Intervention effects were stronger for students of color on all outcomes, and the group differences were significant (see Table 3). Inspection of the growth curves (see Figure 1) suggested that students of color did very poorly in the control schools, whereas their trajectories were nearly identical to White students in the intervention schools on all outcomes.

Figure 1.

Figure 1.

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Growth curves across 4 waves for peer relatedness (a), academic support (b), and engagement (c). Solid black = Intervention condition, Students of Color. Solid gray = Intervention condition, White students. Dashed black = Control condition, Students of Color. Dashed gray = Control condition, White students.

Discussion

Banks (2006) and others have argued that an important aim of education should be to provide students with experiences that help them to develop positive attitudes toward individuals from different ethnic groups. Not only can cooperative learning promote these sort of positive attitudes about different ethnic groups (Johnson & Johnson, 2000; Slavin & Cooper, 1999), but our results indicate that cooperative learning will have stronger effects on social and academic outcomes among students of color. This suggests that cooperative learning can begin to address the ethnic disparities in education that often arise when negative attitudes about racial or ethnic groups are allowed to persist.

These results confirm our hypotheses, which were built upon the notion that social interactions during cooperative learning reflect the principles of Contact Theory (Allport, 1954; Pettigrew, 1998). Specifically, our implementation of cooperative learning attempted to reduce differences in social status wherever possible by limiting the degree to which individual students could exert inordinate influence on the activities of the group. We also emphasized the creation of positive interdependence, which provides powerful incentives to cooperate, and encouraged a high degree of face-to-face interaction accompanied by mutual disclosure to break down social barriers. However, our implementation of cooperative learning did not explicitly address the final aspect of Contact Theory, i.e., that those in positions of authority (i.e., teachers) must explicitly discourage any hints of ingroup vs. outgroup bias or prejudice. Teachers may well have done this, but it was not official part of our training protocol. This suggests an avenue for future research, as many programs currently exist that are aimed at assisting adults in reflecting on and addressing their own bias and prejudices. For example, one approach simply has individuals visualize positive social contacts with members of other ethnic groups, and this has been found to reduce prejudice (Crisp & Turner, 2009). Prejudice-reduction programs have even been explored as part of teacher education (Pang & Park, 2003). Such programs may be a useful addition to cooperative learning training, and could potentially amplify the beneficial effects that cooperative learning has on educational equity.

While we have no direct evidence of the mechanisms involved in these findings, we propose that cooperative learning is effective in reducing ethnic disparities because it reduces competition and gives members of different ethnic groups the opportunity to work together constructively to achieve common goals. In so doing, cooperative learning shifts social interactions among students that may have been aversive, particularly for students of color, into interactions marked by curiosity, support, and enthusiasm, which can reduce prejudice and discrimination. Future research should investigate the precise nature of social interactions during cooperative learning lessons.

In previous research, cooperative learning has been found to have a wide range of positive effects. In addition to effects on ethnic disparities in peer relatedness, academic support, and engagement in learning as reported herein, there are many other salutary effects that can arise from cooperative learning. For example, the positive peer relations that arise from cooperative learning have been linked to increases in cognitive and affective empathy and, in turn, to reductions in bullying (Van Ryzin & Roseth, 2019a). The positive peer relations that arise from cooperative learning have also been linked to reductions in substance use, stress, and emotional problems, and higher levels of prosocial behavior and academic achievement (Roseth et al., 2008; Van Ryzin & Roseth, 2019b, c; Van Ryzin et al., 2020). These findings argue convincingly for more widespread adoption of cooperative learning as a key component of both on-going professional development as well as teacher preparation programs.

Taking a broader perspective, our results, and those from previous research, suggest that the principles of Contact Theory can be used as guideposts for the development of prejudice-reduction interventions in a variety of contexts. Indeed, reviews of research on prejudice-reduction interventions highlight the important contributions of both Contact Theory and cooperative learning (Paluck & Green, 2009) as compared to other approaches such as sensitivity training, for which there is little evidence of benefits in domains such as law enforcement (Railey et al., 2020; Viljoen et al., 2017), healthcare (Alberga et al., 2016; Lindsay et al., 2019), or the general workplace (Phillips et al., 2017). Research and theory on cooperative learning suggest that more interactive, collaborative approaches to training, where social interactions are structured along the lines of contact theory, may be usefully applied to domains outside of education as a means to reduce prejudice.

Limitations and Conclusion

Although this research has many strengths, including a cluster randomized design and longitudinal data, it is limited in several ways. First, all student measures were self-report, which limits internal validity. Future research should consider additional data sources, such as teachers and/or parents, and should examine academic outcomes such as grades and test scores. Second, students of color represented less than one-quarter of the sample, suggesting that these results should be replicated with more diverse samples. Third, the small number of schools in our sample (i.e., 15) limited the complexity of the models that we were able to fit to the data and may have prevented us from finding significant effects in some cases. Finally, we did not have access to grades or test scores, and were therefore unable to evaluate ethnic group differences in growth in achievement during the study.

In spite of these weaknesses, this study contributes significantly to the literature on the effects of cooperative learning on ethnic disparities in educational outcomes. Our results, and those of others, argue for the inclusion of cooperative learning as a key component of both pre-service and in-service teacher education. Overall, cooperative learning can be seen as an instructional approach that can not only contribute to significant improvement in student behavior, social-emotional skills, mental health, and academic achievement, but can also create greater equity in education.

Acknowledgements

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) provided financial support this project (R34 AA024275; PI: M. J. Van Ryzin). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of NIAAA or the National Institutes of Health.

Footnotes

The authors declare that they have no conflicts of interest.

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