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Learning together: a meta-analysis of the effect of cooperative learning on achievement among Black and Latinx students

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Learning together: a meta-analysis of the effect of cooperative learning on achievement among Black and Latinx students

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Content Learning Together: A Meta-Analysis of the Effect of Cooperative Learning on
Achievement Among Black and Latinx Students
Ani Amy Aharonian
Rossier School of Education
University of Southern California
A dissertation submitted to the faculty
in partial fulfillment of the requirements for the degree of
Doctor of Education
August 2024

© Copyright by Ani A. Aharonian 2024
All Rights Reserved

The Committee for Ani Amy Aharonian certifies the approval of this Dissertation
Kim Hirabayashi
Adam Kho, Committee Co-chair
Erika A. Patall, Committee Co-chair
Rossier School of Education
University of Southern California
2024

iv
Abstract
This meta-analysis applies interdependence theory from the social science literature to
understand the impact of cooperative learning on students’ achievement among racially diverse
study samples. This study aimed to determine if the effectiveness of cooperative learning
differed based on sample composition. Also, this study sought to determine which cooperative
learning characteristics were most predictive of student achievement. I applied multi-level
mixed effects modeling to meta-analyze secondary data from studies within the meta-analyses
included in John Hattie’s Visible Learning (2023) with samples of at least 40% Black or Latinx
students. Findings from this study indicate that cooperative learning has a significant positive
average effect for diverse groups of learners and there were stronger effects for samples with
larger proportions of Black students. There were larger effects in science, technology,
engineering, and math domains and among elementary school-aged learners. Creating positive
interdependence through group reward structures was an effective essential strategy for
increasing the effectiveness of cooperative learning. Using group rewards to create positive
interdependence, encouraging promotive interaction between students, and ensuring individual
student accountability to groupmates increased the effectiveness of cooperative learning. This
study supports cooperative learning as a culturally relevant and responsive educational approach
and affirms the effectiveness of cooperative learning strategies for diverse student populations
such as those served in many public schools in the United States.
Keywords: cooperative learning, meta-analysis, Visible Learning, racially diverse

v
Acknowledgments
I have no known conflicts of interest to disclose. This dissertation was completed with
the cooperation and assistance of many to whom I am grateful. My dissertation committee Adam
Kho, Erika Patall, and Kim Hirabayashi supported and guided the development of this
dissertation. Amanda Vite diligently reviewed the coded data for accuracy. My dissertation
group, especially Felix Gilewski, Romeo Baldeviso, and Jillian Giese provided support and
encouragement. My colleagues at the Los Angeles County Office of Education and Pasadena
Unified School District provided flexibility to attend class and supported my learning. My family
created space for me to complete this work, forgave my absence at countless events, and
encouraged me every step of the way.

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Table of Contents
Abstract.......................................................................................................................................... iv
Acknowledgments........................................................................................................................... v
List of Tables ............................................................................................................................... viii
List of Figures................................................................................................................................ ix
Review of the Prior Literature ............................................................................................ 4
Defining the Constructs .......................................................................................... 4
Theoretical Foundations for Cooperative Learning................................................ 9
The Role of Race and Ethnicity in Cooperative Learning.................................... 14
Review of Empirical Literature on Cooperative Learning ................................... 17
Characteristics Contributing to Variation in Intervention Effects........................ 20
The Present Synthesis ........................................................................................... 29
Methods............................................................................................................................. 31
Literature Search................................................................................................... 31
Inclusion Criteria .................................................................................................. 34
Data Extraction ..................................................................................................... 35
Interrater Agreement............................................................................................. 38
Computing Effect Sizes ........................................................................................ 39
Data Analysis Strategy.......................................................................................... 39
Results............................................................................................................................... 40
Assessing for Outliers........................................................................................... 41
Overall Effects for Cooperative Learning............................................................. 41
Publication Bias .................................................................................................... 42
Moderator Analyses.............................................................................................. 43
Discussion......................................................................................................................... 50

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The Effect of Cooperative Learning on Achievement.......................................... 51
Practice and Policy Implications........................................................................... 56
Limitations and Recommendations Future Research ........................................... 57
Conclusions........................................................................................................... 58
References..................................................................................................................................... 59
Appendix A: Studies Included in Meta-Analysis ......................................................................... 73
Appendix B: Coding Guide .......................................................................................................... 77

viii
List of Tables
Table 1: Overall Average Effect of Cooperative Learning Interventions on Achievement..........41
Table 2: Results of Moderator Analyses for Study Characteristics...............................................45
Table 3: Results of Moderator Analyses for Sample and Setting Characteristics.........................47
Table 4: Results of Moderator Analyses for Cooperative Learning Characteristics.....................49
Appendix A: Studies Included in Meta-Analysis..........................................................................73
Appendix B: Coding Guide.......................................................................................................... 77

ix
List of Figures
Figure 1. PRISMA Chart .............................................................................................................. 33
Figure 2. Funnel Plot of Cooperative Learning Effects................................................................ 42

1
Learning Together: A Meta-Analysis of the Effect of Cooperative Learning on
Achievement among Black and Latinx Students
In the United States, there has been a sustained focus on educational reform for several
decades following the publication of the now famous A Nation at Risk report (“A Nation at Risk:
The Imperative for Educational Reform,” 1983). That report fed perceptions that the American
educational system was in decline and led to calls for urgent action to improve the quality of
education and address inequitable student outcomes. A popular resource among educators
seeking to identify the most effective practices is John Hattie’s book Visible Learning (2009)
which summarized the findings of 800 meta-analyses in an attempt to provide educators with an
evidence-based tool to identify which teaching and learning practices have the largest effects on
student learning outcomes. Visible Learning (2009) and its related publications, including the
most recent update called Visible Learning: The Sequel (Hattie, 2023) which includes 2,103
meta-analyses, have become international best-sellers. The general aim of the book is to combine
the findings of meta-analyses on all the possible factors on student achievement to determine
which have the greatest impact on student achievement.
Visible Learning: The Sequel classifies the meta-analyses into domains and sub-domains
More than 300 factors that influence student achievement are divided into ten domains: student,
home, school, teacher, classroom, teacher, curriculum, student learning, teaching strategies,
technology, school and out-of-school strategies. Some of the most impactful influences Hattie
identified in the original Visible Learning (VL1; 2008) included: self-reported grades, Piagetian
programs, providing formative evaluation, micro-teaching, and acceleration. At a time when
educators are called upon to apply evidence-based approaches similar to the medical field, many
practitioners have found a quantitative approach helpful because it allows for sorting the effect

2
sizes and prioritizing those practices with the largest effect sizes and therefore most impactful
(Wrigley, 2018).
Over the last 50 years, a preference for small group, interactive, and cooperative forms of
learning featuring peer-to-peer interaction has emerged, rooted in the belief that students can
learn more by working together than individually or competitively (Jacobs, 2015; Johnson &
Johnson, 2009). David and Roger Johnson (2009) describe a shift towards increased social
interaction which was spurred by social scientists in the late 1960s who stressed the benefits of
socialization and learning. This shift towards socialization and interaction challenged the belief
in rugged individualism which was the dominant learning model of the time. In the individual
learning model students learned in isolation, frequently at their own pace, without interacting
with classmates. A desire to positively influence relationships between students of different
ethnic and racial backgrounds was another reason noted for the increase in interest in applying
cooperative learning in educational settings (Lucker et al., 1976; S. Sharan, 1980; Slavin, 1979,
1985).
It is generally regarded to be a robust and reliable finding that cooperative learning has a
positive effect on student achievement (Hattie, 2023; Johnson & Johnson, 2009) and it is taught
as a high-impact practice in teacher education (Brame & Biel, 2015). The emphasis of
cooperative learning approaches is increasing student-student interaction, in particular, what has
been termed, promotive interaction. In the popular resource, Visible Learning (2023),
cooperative learning is contrasted with individualistic learning and competitive learning
approaches in the influences of cooperative versus individualistic learning and cooperative
versus. competitive learning. The two influences have similar weighted mean effect sizes listed:
cooperative versus competitive learning (.58) and cooperative versus individualistic learning

3
(.62). Visible Learning broadly examined the effectiveness of these influences across many
contexts and learners in the research literature.
Educational policy has increasingly focused on equitable access to education, and
educators are called upon to apply evidence-based interventions and strategies to improve
educational outcomes for all students. Modeled on the field of medicine, evidence-based
education values randomized-control trials, closely matched controls, and meta-analyses (Biesta,
2010; Clearinghouse, 2012; Joyce & Cartwright, 2020; Slavin, 2002). Hattie’s research has been
used to form the basis of policy and improvement recommendations in the United States (e.g.,
Fullan & Others, 2011). Visible Learning does not examine moderators for effects or describe the
populations or settings studied. This leaves a question as to whether the magnitude of the effect
of cooperative learning or any influence on student achievement is generalizable.
Peer-reviewed research has largely been conducted in institutions of higher education
which are connected to the larger structure of settler colonialism (Patel, 2021). Minoritized
populations have been pushed to the margins and denied access to an adequate education in a
myriad of ways (Patel, 2021). Wrigley and McCusker (2019) argue that a truly evidence-based
approach ought not simplify learning interactions or remove them from sociocultural contexts,
which did not investigate the effectiveness of any influences by learner racial identity, assumes
that learners are interchangeable in the educational system. This suggests we ought to re-examine
“what works” for minoritized students.
Visible Learning (2023) explains, “In all comparisons, cooperative methods beat
competitive and beat individualistic learning—pointing again to the power of peers in the
learning equation: cooperative versus. individualistic learning (d = 0.55), cooperative versus
competitive learning (d = 0.53), and competitive versus individualistic learning (d = 0.24)” (p.

4
385). The purpose of the present study is to examine the effectiveness of cooperative learning
among diversely composed student groups by analyzing selected studies from the Visible
Learning synthesis included in the two influences: cooperative versus individualistic learning
and cooperative versus competitive learning. Studies examining the effectiveness of this
instructional approach on achievement that include at least 40% of participants identified as
African American, Black, Hispanic, or Latino/a/x were included.
Review of the Prior Literature
Defining the Constructs
Cooperative learning has become one of the most popular and effective instructional
strategies in the United States (Slavin et al., 2008, 2009). With the popularity and variety of
different cooperative learning approaches, it becomes vital to define cooperative learning
precisely because, as Johnson and Johnson (1999) put it, “There is nothing magical about
working in a group. … To use cooperative learning effectively, one must know what is and is not
a cooperative group” (p. 68).
Defining Cooperative Learning
Cooperative learning is a structured and systematic instructional methodology in which
students assist one another in small groups to achieve a common goal or complete a task, and it
is distinct from the relatively less structured strategies considered collaborative learning
(Johnson & Johnson, 2013). In this approach, groups are typically heterogeneous, consisting of
students with varying abilities and backgrounds, and each member is responsible for contributing
to the group's success (Johnson & Johnson, 2013). The aim of the activities is to promote active
engagement and participation among students while fostering a sense of shared responsibility
and community within the classroom (Slavin, 1995). The instructor must take a more structured

5
approach to achieve this aim. Common procedures include communicating a common goal to
group members, offering a reward for achieving the group’s goal, assigning complementary and
interrelated roles, holding group members accountable for their individual learning, and
providing feedback about how groups can function more effectively (Springer et al., 1999).
Several cooperative learning strategies have been defined in the empirical research and
are represented in Hattie’s synthesis (2023). Below I will describe several of the most popular
approaches to structuring cooperative learning including learning together, teams–games–
tournament, jigsaw, constructive controversy, and group investigation.
Learning Together. This has been one of the more popular approaches which is wellrepresented in the research and noted as an adaptive conceptual framework in which teachers can
be trained (Johnson et al., 2000; Van Ryzin et al., 2020). It requires five essential elements for
effectiveness identified by Johnson and Johnson (1999, 2002, 2009): positive interdependence,
individual accountability, promotive interaction, appropriate use of social skills, and group
processing.
● Positive interdependence is the perception that the group members are linked in a
way so that each member cannot be successful without their fellow group members
also succeeding. Learning tasks might be assigned that require group members to
work in coordination to achieve a common goal, such as solving a common problem,
creating a group presentation, or writing a paper with a shared grade.
● Individual accountability exists when the performance of each individual group
member is assessed, and the results are shared with the individual and group. The
assessment of individual learning ensures that each member of the group is learning
and contributing. Ways to structure individual accountability include giving

6
individual assessments, selecting one student’s work product at random to represent
the group, or even having individual students to share with another student what they
have learned. When students work cooperatively, they promote the success of other
students.
● Promotive interaction is necessary to trigger certain cognitive activities (1999). Some
examples of promotive behaviors include explaining how to solve problems, social
modeling, sharing knowledge with classmates, and encouraging and praising the
efforts of other students.
● Development of social skills is another necessary and deliberate component for
successful cooperative learning. Johnson and Johnson caution that asking socially
unskilled learners to cooperate as a group will not necessarily result in effective
cooperation, therefore purposeful incorporation of leadership, trust-building, conflict
management, and other social skills is needed.
● Group processing occurs when students collectively discuss how well they are
functioning as a team and performing to achieve the shared goals. While cooperative
learning without group processing is more effective than individualistic learning,
cooperative learning with structured cooperative group discussion has been
demonstrated to be most effective (Yager et al., 1985).
Teams–Games–Tournament. The teams–games–tournament method of cooperative
learning was developed by DeVries and Edwards (DeVries, 1976) as a strategy to introduce
games into the classroom. Students are assigned by stratified sampling based on academic ability
and background demographic factors such as race and sex so that each group approximates the
classroom as a whole. Teams sit together physically in class and regularly participate in team

7
meetings and peer tutoring. Students participate in regular games designed to assess knowledge
of relevant concepts and skills. Team grades are assigned based on the sum of individual
teammate performance in tournaments. Tournament sessions are structured around games where
students at comparable ability levels compete to earn individual points, which are then summed
to determine the team score. Because this method involves competition between groups, this
method has been described as “intergroup competition” to distinguish it from the methods which
do not feature competition between groups (Johnson et al., 1981).
Jigsaw Method. The Jigsaw method of cooperative learning was developed by Elliot
Aronson in the 1970s as a method for racial integration to student self-esteem and interpersonal
interactions (Aronson & Bridgeman, 1979) but has also been widely recognized as an academic
intervention. Nalls and Wickerd (2022) note that “the majority of recent studies in the literature
on the jigsaw method emphasize its academic benefits and effect on increasing collaboration
among students, but not its effects on increasing inclusion, empathy, and social acceptance” (p.
6). The method requires first purposely dividing the students into five to six-person teams diverse
in gender, ethnicity, race, and ability, dividing the lesson or academic task into segments, and
assigning each student to a segment. Next, students who are assigned the same segment across
groups form temporary expert preparation groups where the students have time to cooperate to
learn their assigned segment before the students rejoin their original group to present their
segment to their group and support group mates’ learning by answering questions as the group’s
“expert” on that segment.
Constructive Controversy. Constructive Controversy, also sometimes called Academic
Controversy, focuses on creating intellectual conflict to encourage deeper engagement in the
content and sustain attention over time (Johnson, Johnson, & Smith, 2000). When students’

8
ideas, conclusions, or opinions are incompatible with those of another student, and the students
attempt to reach an agreement, then constructive controversy exists. Students’ interactions
include cooperation and conflict, allowing students to engage in deliberate discourse and creative
problem-solving. As an instructional approach, students may be assigned to small groups and
assigned positions on an issue. To encourage engaging meaningfully with the opposing position,
students adopt and attempt to present strong cases for the opposing positions after developing
and presenting arguments for their assigned positions.
Group Investigation. Group Investigation, developed by Sharan and Sharan (1990),
involves students working in small groups to conduct an in-depth investigation of a topic or
problem. In this method, each group plans their research and divides the work among the group
members. The students within the group then collaboratively research, analyze, and synthesize
information on their assigned subtopic.
While there is considerable variation in cooperative learning methods, “What
characterizes these methods is that students spend much of their class time working in small,
heterogeneous learning groups, in which they are expected to help one another learn” (Slavin,
1983, p. 431). This variability speaks to the popularity of the general strategy but also creates
potential flexibility for applying cooperative learning strategies. For the purpose of this
synthesis, cooperative learning is defined more broadly as a structured and systematic
instructional methodology in which students work together in small groups to achieve a common
goal or complete a task. In order to include the studies from Visible Learning (2023), this broader
definition is applied, however the use or presence of many of the essential elements as defined
by Johnson and Johnson (1999) were examined as moderators.

9
Johnson and Johnson (1999) emphasized the importance of accurately distinguishing
cooperative and non-cooperative groups. They identified five essential characteristics that are
also defined as part of their cooperative learning approach called learning together. If some or all
these elements are absent, there may be a group that is not a cooperative learning group. A
pseudo learning group occurs when students are assigned to work together but there is no
interdependence between learners. For example, if students are assessed and rewarded
individually, they may seek information or resources from one another but have little motivation
to teach what they know to other group members.
For the present study, any studies including strategies described as cooperative and
included within either influence, cooperative versus competitive or cooperative versus
individualistic learning, were included. As described in greater detail in the methodology,
whether the strategy applied in the study included four of the five essential features identified by
Johnson and Johnson (1999) were coded for consideration as moderators.
Theoretical Foundations for Cooperative Learning
Cooperative learning has broad theoretical roots in many psychological perspectives,
including cognitive–developmental, social cognitive, behavioral learning, and social
interdependence (Johnson & Johnson, 2015). Johnson and Johnson (2015) concluded that among
these theoretical perspectives, social interdependence theory was most frequently cited in the
existing literature on the topic which suggested it was most capable of guiding research and
practice on cooperative learning. Allport’s (1954) contact theory is also relevant for
understanding the role of positive peer relationships between cooperative learning and student
achievement among diverse and multi-ethnic classrooms. This synthesis will review the
following relevant theoretical frameworks: cognitive–developmental; social cognitive;

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behavioral learning; social interdependence; and interactive, constructive, active, and passive
(ICAP) framework.
Cognitive–Developmental Theories
The cognitive–developmental perspective has its roots in the work of Jean Piaget, who
believed children construct knowledge through their experiences of the world. This principle
about how knowledge is created is at the core of constructivism, which Schunk (2020) cautions
is strictly an epistemology rather than a theory. Constructivists view knowledge as subjective;
based on an individual’s beliefs, cognitions, and experiences. Learning occurs as a result of the
reciprocal interaction of persons and their environments (Cobb & Bowers, 1999). Therefore, a
central assumption of the constructivist approach is that learning is optimized when structured in
ways that allow learners opportunities to actively engage in the content by interacting with
materials (e.g., experiential learning) and other learners.
Piaget as a cognitive constructivist believed learning occurs through a process of
(dis)equilibrium, a conflict between existing schemas and the environment, which prompts
adaptation through accommodation and assimilation processes (Schunk, 2020). He claimed
social arbitrary knowledge, such as language, values, rules, morality, and symbol systems, could
only be learned through interactions with others (Piaget, 1926, as cited in Slavin, 1987). In
cooperative learning settings when peers interact, they are more likely to be exposed to different
perspectives and alternative explanations which may create disequilibrium and lead to new
learning opportunities.
While Piaget primarily explained learning through the interaction of the individual with
their environment, Vygotsky proposed that knowledge is constructed through social interactions,
and was considered a social constructivist (Schunk, 2020). In the sociocultural perspective,

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learning is facilitated by social interaction with a teacher, another learner, or a peer and mediated
by cultural tools such as language, which facilitate learning. A more knowledgeable person, a
peer or teacher, provides guidance and scaffolding to support the learner in reaching a greater
potential level of learning. Within a cooperative group activity, students would provide one
another with support and guidance, which would bring new learning within a reachable zone of
proximal development. With students scaffolding the learning of other students compared to only
the teacher supporting individual students, students can master more content and reach greater
levels of achievement. In heterogeneous groups, students are also exposed to the differing
approaches, viewpoints, and experiences of their classmates which further expands their learning
and provides students with opportunities to increase their sense of relatedness and liking for
those different than themselves.
Cognitive and motivational theories provide useful frameworks for understanding why
active and interactive strategies may be impactful. However, the cognitive engagement
framework interactive, constructive, active, and passive (ICAP) provides a concise framework to
highlight the mechanism at work during cooperative learning interactions. ICAP stands for the
four cognitive engagement modes: interactive, constructive, active, and passive (Chi & Wylie,
2014). Each mode is defined by the behaviors that teachers or others could observe. The passive
mode is characterized by listening to instructions, reading a text, or observing a video without
doing anything else. The active mode might be characterized by students repeating or taking
verbatim notes during a lecture, underlining or highlighting text, or manipulating the video
playback by pausing, rewinding, and so on. With constructive engagement students are
generating information: asking questions and drawing concept maps, self-explaining after
reading, and synthesizing information across different texts, explaining concepts in the video,

12
and contrasting with prior knowledge. The interactive mode of engagement consists of
dialoguing: defending and arguing in dyads or small groups, answering comprehension questions
with a partner, and debating with a peer about a concept in the video.
The ICAP framework posits different levels of learning hierarchically with each mode
with the greatest level achieved in interactive modes of engagement, followed by constructive
which is the next greatest, followed by active, and the least effective is passive. Each mode of
engagement is associated with different knowledge-change processes that promote successively
deeper levels of understanding. In the ICAP hierarchy, interactive engagement requires that each
person in the group contributes constructively to the interaction. This means that a physical or
surface-level interaction that involved sharing solutions, would be a passive interaction and not a
constructive interaction. Interactive dialogues, such as elaborating on or explaining a solution,
may require several exchanges of ideas and knowledge between learners that result in new ideas.
Other examples provided include defending and arguing a position, criticism, and justification of
answers, asking and answering one another’s questions, explaining to one another, and
elaborating on one another’s answers. Chi and Wylie distinguish constructive interactions as
being marked by discourse and dialogue between learners. The researchers clarify that dialogue
is not necessarily interactive if it is not shared and there is no frequent turn-taking. An example
of a dialogue that is not interactive is one where one person dominates the conversation, and the
other partner simply agrees.
Social Interdependence Theory
In the context of cooperative learning methods, the social interdependence theory is
helpful for understanding how to structure activities to promote the types of cooperative behavior
that cognitive learning theories suggest would support achievement within a group. Johnson and

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Johnson (Johnson, 2003; Johnson & Johnson, 2005, 2015) explain the theory of social
interdependence is an extension of the work of Koffka (1935) by Lewin (1936), who proposed
that groups were dynamic wholes and the interdependence among their members could vary,
explaining variability in the level of cooperation observed between members.
Attempting to understand how cooperation could be promoted in groups, Deutsch (1949)
highlighted positive interdependence as the key ingredient for encouraging collaborative
behavior. In social interdependence theory, how goals are structured determines how group
members interact (Johnson, 2003). Deutsch defined a cooperative situation as one where the
individuals have promotively interdependent goals, meaning an individual cannot achieve their
goal without the other individuals achieving their respective goals. He suggests the cohesiveness
of the group increases as a function of the strength of the goals perceived to be promotively
interdependent and the degree of perceived positive interdependence. Deutsch described two
types of interdependence: positive and negative. With positive interdependence individual group
members perceive that they can only achieve their objective if their group mates also attain
theirs. In the case of negative interdependence, individuals perceive they can only achieve their
objective if their group mates fail to attain theirs. Conceptually this is a competitive goal
structure. Johnson (2003) describes “the basic premise of social interdependence [as] the ways in
which participants’ goals are structured determine how they interact, and the interaction pattern
determines the outcomes of the situation” (p. 936).
Therefore, promoting increased learning and student achievement hinges on successfully
creating positive interdependence to motivate learners to engage in promotive interaction to
facilitate each other’s learning. If a group activity creates goals for learners that promote
interdependence between group members, there would be an increase in cooperative behaviors

14
between group members, such as providing mutual assistance and sharing information and
resources. Conceptually, with students serving as teachers for other students to support the
learning of other students, this is similar to decreasing the class size or increasing the ratio of
teachers to students.
The Role of Race and Ethnicity in Cooperative Learning
Critical race theory (CRT) provides a framework for understanding the unique challenges
faced by minoritized students in the United States and the potential benefits of cooperative
learning in promoting more inclusive educational environments and fostering their sense of
belonging. CRT emphasizes the structural and institutional nature of racism, highlighting how
policies, practices, and curricula can contribute to unequal educational experiences and outcomes
for minoritized students (Ladson-Billings, 1998; Ladson-Billings & Tate, 2016; Yosso, 2005).
Cooperative learning promotes collaboration, equal status and participation, and shared
responsibilities which meet the criteria for beneficial intergroup contact (Allport, 1954) and
foster a supportive and inclusive environment to validate the experiences and identities of
minoritized students (Freire, 1970; Johnson & Johnson, 1989). By providing opportunities for
students to share their perspectives, experiences, and cultural assets, cooperative learning can
enhance the learning process and challenge stereotypes and biases (Gay, 2018; Ladson‐Billings,
1995; Ladson-Billings, 1998). As a teaching strategy consistent with culturally responsive
teaching (Gay, 2002) and culturally relevant pedagogy (Ladson‐Billings, 1995), cooperative
learning has the potential to increase minoritized students’ sense of belonging (Gowing, 2019).
Increased sense of belonging has been demonstrated to be linked to a host of positive outcomes,
including student achievement (Goodenow & Grady, 1993).

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Boykin and Allen (2000) suggested that children of African descent are socialized
through culturally structured events to develop themes of communalism. Boykin (1997)
describes the concept of communalism:
Communalism denotes awareness of the fundamental interdependence of people. One’s
orientation is social rather than being directed toward objects. There is overriding
importance attached to social bonds and social relationships. One acts in accordance with
the notion that duty to one’s social group is more important than individual rights and
privileges. Hence, one’s identity is tied to group membership rather than individual status
and possessions. Sharing is promoted because it affirms the importance of social
interconnectedness. Self-centeredness and individual greed are frowned upon. (p. 411)
A similar theme exists in Latinx culture, called familisimo, and is described as the strong
identification and attachment of persons with their nuclear and extended families and involves
the elevation of the needs of the family (both nuclear and extended) over the needs of the
individual (Smith-Morris et al., 2013). Kagan and Knight (1981) found that Mexican American
children engaged in more prosocial behavior than Anglo-American children. Boykin and Allen
(2004) further proposed that including such cultural themes in academic settings allows Black
children to leverage their existing competencies to develop new ones, leading to improved
academic performance.
Intergroup Contact Theory
In addition to positively influencing achievement, cooperative learning is theorized to
benefit social relationships, particularly among students of different racial/ethnic backgrounds
(S. Sharan, 1980; Slavin, 1985, 1995; Van Ryzin et al., 2020). Intergroup contact theory suggests
that contact between people of diverse backgrounds is more likely to reduce prejudice and

16
improve interpersonal relationships if it meets certain criteria including equal status between
group members, common goals, positive contact norms, intergroup cooperation, support of
authorities, and personal accountability (Allport, 1954; Brown & Hewstone, 2005; Paolini et al.,
2010). When these conditions are met, then social contact is more likely to lead to integration
and positive outcomes (Molina & Wittig, 2006; Pettigrew & Tropp, 2006, 2013). Paolini et al.
(2010) found that when there is negative contact between group members, the salience of group
membership (i.e., race) increases and prejudice can increase. They warn that in unsupervised
conditions when the contact valence (i.e., whether contact is positive or negative) cannot be
adequately influenced then negative contact and high salience of group membership is more
likely to occur.
It has been noted that the method of cooperative learning, particularly as defined by
Johnson and Johnson (1999) has much overlap with contact theory applied to educational
settings (Slavin, 1985). According to Van Ryzin et al. (2020), the necessary conditions include:
individuals being brought together as equals, with differences in social status being explicitly
minimized; pairs or groups of individuals must be given a common goal to direct their
interactions and being incentivized to work together to achieve their goal; the contact must
involve an extended amount of face-to-face interaction time, preferably including mutual
disclosure to assist in discovering areas of commonality; and those in positions of authority (i.e.,
teachers) must explicitly encourage and support positive, cooperative interactions and discourage
prejudice or bias. Consistent with predictions from contact theory, cooperative learning has been
shown to increase peer relatedness, perceptions of academic support, and engagement in learning
with greater increases for students of color (Van Ryzin et al., 2020). In the absence of the
outlined conditions, intergroup contact, such as with competition, will increase, rather than

17
reduce, intergroup tensions (Cohen & Lotan, 1997; Paolini et al., 2010). Therefore, with diverse
classrooms, it may be essential to appropriately structure cooperative learning to avoid
disadvantaging students of color by creating a negative or stressful classroom environment.
Together the social cognitive, ICAP, interdependence, and contact theories provide an
understanding of how cooperative learning may influence learning and motivation to increase
student achievement. Social interdependence theory explains that when there is positive
interdependence between students in a group such that they perceive that they can only attain
their goal if their groupmates also attain theirs, it leads to cooperative behavior between group
members, such as peer tutoring. Social cognitive theory predicts that student motivation and
learning is aided by having competent peer models. ICAP framework provides the cognitive
explanation for understanding how elaboration, dialogue, perspective-taking, and other
interactive behaviors lead to greater cognitive engagement and, therefore, increased learning.
Lastly, contact theory helps to explain why minoritized students in diverse classrooms might
especially benefit from the implementation of cooperative learning instructional strategies.
Review of Empirical Literature on Cooperative Learning
A large body of research has examined the effect of cooperative learning on achievement
and several syntheses, including meta-analyses, have been conducted on the cooperative learning
instructional approach. In these syntheses, cooperative learning has largely been regarded as an
effective instructional strategy. In 2009, Johnson and Johnson described “the widespread and
increasing use of cooperative learning [as] one of the great success stories of social and
educational” (p. 365), which has been validated and refined through a large body of studies.
Coleman (1959) advocated for replacing interpersonal competition in classrooms with
between-group competition accompanied by within-in group cooperation and for incorporating

18
educational content into games that would make learning like a sport. He drew on Deutch’s idea
that the individual will take actions to support and facilitate the action of others if they perceive
it will aid them in their own goal attainment. Other research had also demonstrated that with
cooperation, communication, friendliness, and feelings of group cohesion (Grossack, 1954).
Johnson and Ellison (1973, as cited in Johnson & Johnson, 1974) found that after Grade 6
learners provided the opportunity to experience a cooperatively structured classroom, all of them
reported a preference for the cooperative classroom structure.
While some early studies examining how structuring goals for learners in groups
influenced achievement produced contradictory findings, the earliest meta-analysis of
cooperative learning resolved these (Hall, 1988; Johnson et al., 1981). For example, Hayes
(1976) found that individualistic and cooperative goal structures were equally effective, Michaels
(1977) had concluded that interpersonal competition is most effective for increasing the
academic achievement of students, and Shlomo Sharan (1980) had concluded that cooperation
leads to higher levels of achievement and greater social integration among students of different
racial groups. The results from Johnson et al. (1981) and Hall (1988) both supported the general
conclusion that cooperative learning was associated with higher achievement than both
individualistic and competitive learning conditions.
Earlier studies on cooperative learning manipulated reward structures to demonstrate
interdependence and cooperation between learners. For example, Hamblin et al (1973) divided
fifth grade students into four groups where each group experienced four reward conditions in a
randomly assigned order. The four conditions varied the proportion of group-based reward.
Students exhibited proportionally higher levels of time on task and peer tutoring in relation to the
proportion of group-based reward.

19
Other studies demonstrated that students who participated in cooperatively structured
group activities displayed more cooperative behaviors. Ryan and Wheeler (1977) randomly
assigned fifth and sixth grade students from a middle-class suburban school to cooperative or
competitive conditions. Students assigned to the cooperative condition worked on cooperative
inquiry tasks in groups during one lesson a day for 18 days, whereas students in the competitive
group worked on lessons alone. Following the 18 days, all students were randomly assigned to
groups of six to play a simulation game where students were free to adopt any strategy. The
students who had previously participated in the cooperative learning groups exhibited a
significantly greater number of cooperative behaviors during the simulation game compared to
their fellow students who had been previously assigned to the competitive condition.
Cooperative behaviors included providing assistance to others, proposing and participating in
group strategies. This pattern was replicated in Hertz-Lazarowitz et al. (1980) study of Israeli
elementary school students; students’ cooperative behaviors transferred to interactions with peers
who were not members of their learning groups and to situations which were not structured by
the teacher. In Nichols (1996) students randomly assigned to cooperative learning high school
geometry classrooms using STAD demonstrated greater achievement on the IOWA Basic Skills
assessment and students reported greater use of deeper cognitive strategies, increased selfefficacy, and increased valuing of the subject. A second later post-test after all classrooms had
resumed to traditional individualistic instruction suggesting that the changes did not endure long
after the cooperative learning strategy was removed.
The meta-analyses included in Hattie’s synthesis (2023) span elementary, secondary, and
post-secondary settings. Each one has found support that cooperative learning strategies are
effective at promoting learning and are more effective than comparisons (Hall, 1988; Hilk, 2013;

20
Johnson et al., 1981; Johnson et al., 2000; Qin et al., 1995; Springer et al., 1999). Therefore,
these meta-analyses have also focused on explaining heterogeneity in the research literature.
Characteristics Contributing to Variation in Intervention Effects
Potential sources of variability in the effectiveness of cooperative learning have been
examined in the meta-analyses and include learner age, domain of study, decade, and the
proportion of Black and Latinx learners. In this section, I identify some of the factors which may
explain heterogeneity. These factors may have to do with the characteristics of the sample (i.e.,
the proportion of Black and Latinx students), the setting, the way cooperative learning was
implemented, or the comparison group.
Characteristics of the Sample
Age of Learner. The differences in cognitive–developmental readiness of students at
various ages and grade levels may influence the effectiveness of cooperative learning methods
on achievement. Studies of the effectiveness of cooperative learning in secondary and postsecondary or adult populations have generally had higher mean effect sizes (Johnson, et al.,
1981; Qin et al., 1995). Teaching social skills is a recommended part of the cooperative learning
approach (Goodwin, 1999). The effectiveness of cooperative learning in increasing achievement
and improving social skills among elementary-age students has also been demonstrated (Bruce,
2009; Slavin, 1980, 2015), however, because younger students are less likely to have mastered
the linguistic or social skills needed for successful cooperative learning, they may not derive as
much benefit relative to their older peers.
Racial Composition. Two meta-analyses included in the cooperative learning influence
show greater effectiveness of cooperative learning approaches with Black or Latinx students
compared to White students or in majority Black or Latinx populations (Hall, 1988; Springer et

21
al., 1999). Hall (1988) examined whether the effect of cooperative learning differed between
White and minority students in three studies and found a greater effect size of cooperative
learning on academic achievement for minority students. Springer et al. (1999) found larger
positive effects for cooperative and collaborative learning on student achievement when groups
were composed predominantly or exclusively of Black and/or Latinx students. This study will
examine the proportion of Black, the proportion of Latinx, and the combined proportion of
Latinx and Black learners as moderators.
Schooling in the United States has largely been Eurocentric with the aim to deculturalize
and assimilate students from other ethnic groups (Spring, 2016). Despite the Brown v. Board of
Education ruling in 1954, it was not until after the United States Supreme Court decided in 1967
in Green v. County Board of New Kent County that school districts must adopt effective plans to
integrate that the degree of school segregation began to decline substantially (Reardon & Owens,
2014). Students experience a sense of belonging when they feel supported by and connected to
their peers (Gowing, 2019; Nasir et al., 2011). Students from racialized groups have historically
experienced lower levels of belonging than their White, non-racialized peers (Gray et al., 2018).
Promoting cooperative learning relationships, consistent with the theme of communalism, may
contribute to an increased sense of belonging, which in turn has been demonstrated to be related
to positive academic outcomes and an increased sense of intrinsic motivation (Wentzel et al.,
2016).
Characteristics of the Setting
Decade. Qin et al. (1995) conducted a meta-analysis examining the effectiveness of
cooperative learning strategies compared to competitive learning strategies on problem-solving.
They found differences by decade with negative effects in the 1950-60s, the highest effect size in

22
the 70s (1.03) then declining to mid-size effect (.52) in the 80s and later. Qin et al (1995) do not
explain the difference and caution that the finding may be suspect given the small number of
studies before the 60s. Year or decade of publication will not be included as a moderator in the
present review because there is no rationale in the prior literature to expect differences over time.
Subject Domain. Qin et al. (1995) found larger effects for non-linguistic problems than
linguistic problems. Springer et al. (1999) focus their meta-analysis on the effects of cooperative
learning methods on achievement, persistence, and attitudes at the postsecondary level in science
technology engineering, and mathematics (STEM) fields. They found robust positive effects of
both cooperative and collaborative learning methods on achievement, attitudes, and persistence
across STEM disciplines. Stiff and Harvey (1988) noted that Black students entered school with
similar ability in mathematics as their White peers and therefore attributed the lower
achievement of Black students in mathematics to instruction which is inconsistent with the
cultural norms and preferred learning style of Black students. They describe Black students as
field-dependent learners who value benefiting the group and, therefore, have the cultural norm
that cooperative behavior is acceptable and desirable. If students of color have field-dependent
and cooperative learning styles, cooperative learning methods as a culturally consistent approach
would match their preference and be expected to support a greater sense of belonging.

23
Comparison Condition. The studies examining the effectiveness of cooperative learning
instructional strategies have contrasted these strategies with a variety of “traditional approaches”
including what have been described as individualistic and competitive learning strategies.
Competitive learning strategies are marked by negative interdependence between learners,
whereas individualistic learning exists when there is no interdependence between learners
(Deutsch, 1949; Johnson, 2003). To the extent that creating negative interdependence is
predicted to negatively impact peer relationships and elicit conditions of contact, which contact
theory predicts would increase racial tension, it is expected to be less effective than
individualistic approaches, which do not influence interdependence between learners. For diverse
classrooms, in particular (i.e., not all students of color), this difference may be even larger.
Roseth et al.’s (2008) meta-analyses examined the effect of cooperative learning on both
social relationships and achievement. They drew three major conclusions: cooperation promoted
higher levels of achievement than competitive or individualistic learning, cooperation promoted
more positive social relationships than competitive or individualistic learning, and cooperative
learning was associated with a positive relationship between social relationships and
achievement. Roseth et al. (2008) suggest that the more successful students are at building
positive peer relationships, the more likely students will be to achieve, and that the use of
cooperative learning strategies may increase achievement by supporting the development of
positive peer relationships Increased positive peer relationships in a school setting has been
theorized to increase students’ sense of belonging, particularly among students of color (Van
Ryzin et al., 2020). Similar to the Roseth et al. (2008) meta-analysis except among college
students, Hilk (2013) compared the effects of cooperative with competitive, and individualistic
learning strategies on achievement and social relationships. She found positive effects for

24
cooperative learning on both student achievement and peer relationships but did not examine
whether a positive relationship between cooperative learning and peer relationships, predicts
student achievement as Roseth et al. (2008) did in their study.
Characteristics of the Cooperative Learning Method
Type of Cooperative Learning Intervention. Johnson et al. (2000) conducted a metaanalysis comparing the effectiveness of various methods of cooperative learning for supporting
student achievement limited only to efficacy studies. Efficacy studies are those from real-world
settings in contrast to short-term laboratory. Two independent variables were coded in the metaanalysis: the method of cooperative learning and the classification of the method on a continuum
of direct to conceptual. Direct cooperative learning methods were those with well-defined
procedures that teachers were expected to follow exactly. By comparison, more conceptual
methods were those which gave teachers conceptual frameworks to adapt and incorporate as
appropriate. The classification of the method on the continuum from direct to conceptual was
determined by scoring each method on five criteria which were then summed for a total score
used to rank the methods. By comparing the relative effect sizes of each method on student
achievement, Johnson et al. (2000) rank the effectiveness of the method of cooperative learning
relative to competitive methods or individualistic methods. Every method of cooperative learning
methods was effective in promoting achievement compared to competitive and individualistic
methods. They found that the method with the largest effect sizes was Johnson and Johnson’s
Learning Together which promoted higher achievement than competitive (.85) or individualistic
efforts (1.04). Academic Controversy, where students are assigned opposing positions to argue
with a partner, produced the next highest effect sizes compared to competitive (.67) or
individualistic (.91) methods. Examining the rank of each method by the strength of the

25
correlation between the total score on the direct-conceptual continuum and the effect size
revealed a significant correlation such that more conceptual cooperative learning methods were
associated with relatively larger effect sizes.
Intergroup Competition. One of the early controversies among early studies related
specifically to whether intergroup competition was a necessary component of effective
cooperative learning. Johnson et al. (1981) suggested the pattern of results in their meta-analysis
indicated that cooperative learning without intergroup competition was more effective than
cooperative learning featuring intergroup competition. Johnson et al. (1984) explained that
intergroup competition lessened the effectiveness of cooperative learning because it creates a
focus on individual performance wherein higher-performing students would see lowerperforming students as lower status and be less likely to interact with them. Slavin (1983),
however, believed intergroup competition increased the effectiveness of cooperative learning.
Johnson et al. (1981) and Hall (1988) came to opposite conclusions regarding whether
intergroup competition enhances the effectiveness of cooperative learning. This variability may
be expected if intergroup competition is related to the essential elements for effective
cooperative learning, but is not one of the essential elements. Methods that use intergroup
competition, such as teams–games–tournaments, have group members matched at similar ability
levels compete on behalf of their teams. This creates some reward interdependence that would
encourage promotive peer interaction such as peer tutoring. Further, the structure where each
team member is assessed (i.e., competes) also promotes individual accountability which Johnson
and Johnson (1999) identify as an essential feature needed to make cooperative learning “work.”
Methods that include competition between groups may not foster increased sense of
belonging to the classroom or school community to the same degree as cooperative learning

26
methods which emphasize cooperation as a classroom culture. In cooperative groups where there
is competition between groups, social relationships may only improve with the group mates. If
this is the case, then among samples with representative proportions of Black and Latinx
students, the types of cooperative learning methods which include intergroup competition may
be less effective.
Group Incentives and Task Specialization. Slavin (1983) conducted a synthesis
examining two main components of cooperative learning strategies: a cooperative incentive
structure and a cooperative task structure. In cooperative task structures, two learners are
instructed to work together on a common goal. With cooperative incentive structures, the
learners are interdependent on each other for a reward or incentive which all group members
share, only if the group is successful. This has also been described as incentive interdependence.
Slavin classified task structures into two categories: task specialization methods, where each
group member is responsible for a specialized task (e.g., jigsaw) also described as role
interdependence, and group study methods, where learners study together but do not have
separate tasks for each learner. He classified incentive structures into three types: no group
reward, a group reward based on a single group deliverable (e.g., worksheet or report), or a
group reward based on an average of individual student performance (e.g., the average of
individual quiz grades). Slavin found that depending on which task structure was used, the
incentive structure became important. With group study cooperative learning methods, the
majority of the studies with group rewards for individual learning showed positive effects (89%),
but none of those with either a reward for a group product or an individual reward had a positive
effect on student achievement. When the cooperative learning approach used task specialization,
83% of the studies with group reward for a group product and the single group reward for

27
individual learning had positive effects on student achievement, whereas only one study with
individual reward had a positive effect. Therefore, among the cooperative learning methods that
do not use task specialization to create interdependence between learners, the importance of the
cooperative incentive structure (i.e., group reward) is greater.
This pattern of findings is consistent with how Johnson and Johnson (1999) define a
cooperative effort. A group that has no role interdependence and no group incentive is not a
cooperative group would be a pseudo group. In groups where there is a reward for a group
product there might conceivably be an absence of interdependence between learners. For
example, the most knowledgeable and competent group member may undertake the work and
submit the group assignment. In this case, this more competent student does not learn more than
if they were working independently. Because the more competent learners have no incentive to
assist less competent students and those less competent students have no incentive to also work
hard to learn, they might be expected to learn less in such groups.
As discussed by Slavin (1980), task and reward structures in cooperative learning may
differ. For example, the jigsaw strategy features task specialization where each student’s
contribution is needed in order to succeed as a group. Other strategies may include group work
where students work together to complete a task, or group study where students work together to
master the material. Group study, without another strategy to create interdependence among
group members, may not promote equal status, which contact theory and the Learning Together
method of cooperative learning emphasize is important, particularly for improving peer
relationships. With respect to reward structures, individual reward structures exist when each
group member earns a reward or grade for their respective performance in contrast with a group
reward structure where each group member earns the same reward dependent on the performance

28
of the group as a whole (i.e., reward interdependence). Group incentives promote
interdependence and may therefore be expected to produce greater gains in achievement relative
to other cooperative methods featuring individual learning structures in addition to
individualistic and competitive reward structures. Role interdependence, however, may not
encourage as much interaction between students. Therefore, methods that rely on task
specialization or resource sharing to create interdependence between learners may be less
effective at improving peer relationships. Because the improvement of peer relationships is
important to the classroom and school climate, the strategy for creating interdependence between
learners may be important for Latinx and Black learners.
Length of Cooperative Activity. The total length of the cooperative learning activity
may also play a role in the size of the effect. Very short activities, where a group is formed for
the class period are expected to be less effective because students would not have as much
opportunity to interact and also to improve peer relationships. Consistent with this expectation,
Qin et al. (1995) found that the briefest studies of one day or less had the smallest mean effect
(.41) while studies between two and nine days had the highest (.91). Studies 10 to 29 days and 30
or more days had similar mean effect sizes (.58 and .57, respectively) which were higher on
average than studies of one day or less, but lower than studies where the activity lasted between
two and nine days.
Elaborative or Promotive Interaction. As predicted by the ICAP framework, deeper
learning is expected with increasing levels of cognitive engagement. Interaction is associated
with higher levels of engagement, but only interaction between peers involving dialogue meets
this criterion. For instance, if group members divide tasks or share resources but do not engage in
much dialogue through the activity, the ICAP framework would predict lower effectiveness

29
relative to an activity that elicits rich explanatory dialogue between group members. Whether
the activity met the ICAP criteria for interactive (i.e., involving dialogue) were coded for each
study.
There have been many meta-analyses comparing cooperative and individualistic and
cooperative and competitive learning, which have all found that cooperative learning is more
strongly associated with increased student achievement. However, most of the studies do not
apply modern meta-analytic techniques, such as hierarchical linear modeling. Hattie’s Visible
Learning synthesis (2023), which combines these meta-analyses, did not consider the diversity of
the analytic samples. The present meta-analysis will apply modern meta-analytic approaches to
examine the effectiveness of cooperative learning relative to individualistic and competitive
learning approaches among any studies with diverse samples which include Black and Latinx
students.
The Present Synthesis
Cooperative learning approaches, as an active learning strategy, have gained widespread
popularity for their potential to improve achievement, attitudes about learning, and peer
relationships. The current synthesis offers an opportunity to examine the level of support in the
cited research base on the effectiveness of cooperative learning among representative samples of
students which includes reflecting at least 40% of Black and Latinx students. This would build
upon Hattie’s syntheses by confirming or disconfirming the effectiveness of cooperative learning
methods for groups of students that are representative of the diversity of United States
classrooms. There are six proposed research questions:
1. What is the estimated average effect size of the relationship between cooperative
learning and achievement among diverse samples of students? How does this

30
compare to that observed in prior syntheses? Hypothesis: Estimated average effect
sizes are predicted to be larger for this synthesis of diverse study samples.
2. Does the estimated average effect differ depending on the proportion of Black,
Latinx, or combined proportion of Black and Latinx learners? Hypothesis: With
increasing proportions of Black and Latinx learners, the effectiveness of cooperative
learning is expected to increase.
3. Does the effectiveness of cooperative learning vary with the grade span of the
learners or the domain of study? Hypothesis A: Elementary-aged learners will
experience smaller gains from cooperative learning interventions compared to
secondary and college-aged learners. Hypothesis B: Learners studying STEM
subjects will experience larger gains from cooperative learning contexts compared to
other subjects.
4. Does the effectiveness of cooperative learning methods differ based on whether it is
compared to individualistic or competitive learning strategies? Hypothesis: Average
effect sizes for cooperative learning are predicted to be larger when compared to
competitive learning than when compared to individualistic learning.
5. Are differences in the effectiveness of cooperative learning methods explained by the
characteristics of the cooperative learning strategy, such as the use of role
specialization, group incentives, individual accountability, and the generation of
promotive interaction between learners? Hypothesis: Studies which include the
essential characteristics of cooperative learning are expected to have larger average
effects on average.

31
6. Are differences in the effectiveness of cooperative learning explained by the study
methods, such as use of random assignment, matching or equating techniques, or the
level of assignment. Hypothesis: Studies applying techniques to produce equivalence
between groups, such as random assignment or equating techniques, will have smaller
effects on average than those that do not create equivalence between comparison
groups.
Methods
The basis for the current synthesis is in Hattie’s Visible Learning (2023) and all of the
included studies were obtained from the meta-analyses included in that online resource. This
section will describe the methodology of this study, which is part of a collective project where
doctoral student researchers are screening, coding, and analyzing various influences.
Literature Search
The reports for this meta-analysis were retrieved from The Visible Learning MetaX
website for the identified related influences, cooperative learning versus individualistic learning
and cooperative learning versus competitive learning. The meta-analyses and reports for these
two influences were located through the university library resources and databases such as
ProQuest, ERIC, PsycINFO, Google Scholar, and electronic document delivery.
For the influence of cooperative versus individualistic learning, Visible Learning listed
five meta-analyses. The earliest listed was Johnson and Johnson (1987) which was located and
determined to be an instructional book that did not provide references to individual studies or
effect sizes. It was therefore excluded from this analysis. One of the five meta-analyses (Roseth,
et al., 2006) was a conference presentation that was not available, however, the authors published
a subsequent meta-analysis the following year (Roseth, et al, 2008) which is assumed to be the

32
same and used as such. The remaining four meta-analyses were located and included in the
search process.
Figure 1, the PRISMA chart, depicts the screening phases and describes the number of
records identified, included, and excluded, and the reasons for exclusions (Page et al., 2021). All
five of the previously mentioned meta-analyses for the influence cooperative versus competitive
learning were also included among the total eight meta-analyses from cooperative versus
individualistic learning. These included Johnson and Johnson (1987) which was an instructional
book and therefore eliminated, the published Roseth et al. (2008) meta-analysis which replaced
Roseth et al. (2006), and three others (Hall, 1988; Johnson et al, 2000; Hilk, 2013). There were
three which were included in the cooperative versus competitive learning influence, but not
cooperative versus individualistic learning: Johnson et al. (1981), an early meta-analysis that also
included studies with cooperative, competitive, and individualistic goal structures; Qin et al.,
(1995) which examined cooperative versus competitive efforts; and Johnson et al. (1983) which
was eliminated because it examined the effect of interdependence on interpersonal attraction.

33
Figure 1
PRISMA Chart
Therefore, in total, across the two influences, six unique meta-analyses were included
(Hall, 1988; Hilk, 2013; Johnson et al, 1981; Johnson et al., 2000; Qin et al., 1995; Roseth et al.,
Meta-analyses identified from
Hattie’s Visible Learning metaanalysis reports (n =13)
Records removed before screening:
Duplicate meta-analyses (n =5)
Record ineligible (n =2)
Meta-analyses retained (n =6)
Records screened
(n =711)
Records excluded
Duplicate records (n =150)
Reports sought for retrieval
(n =561)
Reports not retrieved
Record unavailable (n =56)
Reports assessed for sample
eligibility
(n =505)
Reports excluded:
Not in the USA (n = 75)
No race/ethnicity data (n = 343)
Less than 40% Black or Latinx
(n = 56)
Studies included in review
(n =13)
Reports of included studies
(n =13)
Identification of studies via Hattie’s Visible Learning Meta-Analyses
Identification Screening Included
Reports excluded:
No comparison group (n =6)
No achievement measure (n =8)
Insufficient information (n =5)
Reports assessed for
methodological eligibility
(n =32)

34
2008;). From within these six meta-analyses, a total of 711 reports were identified to screen for
the inclusion criteria which are described in the next section and entered into a spreadsheet.
Among the 711 reports, 150 (21%) were duplicates and therefore removed leaving 561 unique
reports to be retrieved. The researcher attempted to locate each study through publicly available
online databases (e.g., ProQuest, ERIC, PsycINFO, Google Scholar) and university library
resources including document delivery and interlibrary loan. Of the 561 listed reports, 56 (9.9%)
could not be located through any of the previously mentioned alternatives. The 505 reports
which were retrieved reports were screened for inclusion criteria. Characteristics related to the
initial screening were recorded in the spreadsheet such as whether the report was available, the
source it was retrieved from, whether the research was conducted in the United States, whether it
described the racial/ethnic composition of the sample, and the proportions of Asian, Black,
Latinx, White, and other ethnic groups included in the sample.
Inclusion Criteria
To be included in the meta-analysis, a study needed to be retrievable and available
through library resources, document delivery, or interlibrary loan, and meet the following
criteria: (a) conducted in the United States, (b) must be reported in English, (c) include a sample
that was at least 40% Black or Latinx, (d) use a two-group comparison design to assess the
effect of cooperative learning on achievement compared to either individualistic or competitive
learning, (e) achievement must be measured at the student level, and (f) reports must provide
enough information to retrieve or calculate an effect size. The studies were screened in two
stages: initially for being conducted in the United States and providing an ethnicity distribution
of the sample indicating at least 40% Black or Latinx participants. Thirty-two studies (6.3%)
from among the 505 which were initially retrieved and screened met inclusion Criteria A, B, and

35
C. Nearly 15% of the reports featured research not conducted in the United States. Shlomo
Sharan’s (1980) paper, for example, was among the screened reports which were excluded
because the research was conducted in Israel. Reports were searched for information about the
racial identity of the study sample including for words such as race, ethnicity, Black, Hispanic,
Latin, Spanish, and so forth, and about two-thirds (67.8%) of the reports were determined not to
include a description of the racial/ethnic composition of the sample. Ultimately, 32 of the
remaining 88 (36.4%) included a sample of participants at least 40% of whom were identified as
Black or Latinx.
In the second stage of screening, studies were screened again during coding and six
studies (18.8%) lacked a comparison group, eight (25%) did not have an achievement measure at
the student level, and five (15.6%) did not provide enough information to compute effect sizes.
There were 13 studies (40.6%) that met the final screening criteria and were included in the
meta-analysis. A list of the included studies and their characteristics can be found in Appendix
A.
Data Extraction
A detailed coding guide was developed through iterative coding training and practice
sessions of the dissertation research group members led by the co-chairs. A copy of the final
version of the coding guide is included in Appendix B. The specific coding guide and coding
template for this dissertation were developed by expanding the common guide to include codes
that are uniquely relevant to this synthesis of cooperative learning.
Codes that are common to the broader project and to numerous influences of learning
were developed by the project co-chairs. Over the summer term of 2024, twelve weekly coding
practice and training sessions provided calibration opportunities for this author and other

36
dissertation students participating in a workgroup. Each week, the project co-chairs assigned
doctoral students in the research group between one and three reports of increasing difficulty to
read and code using the coding guide and the common coding template. Prior to students
beginning to code studies independently, doctoral students participated in an assessment to
ensure an understanding of the coding process and definitions. After demonstrating competence
by exceeding the required 80% agreement with the experienced primary coder, data extraction
commenced.
Research Study Characteristics
Broad descriptive information about the studies in our dataset was coded, including the
author, the year of publication or production, the type of report, the data source, the outcome
domain, and whether the dataset overlaps (i.e., is shared with) any other report in the synthesis.
The type of report was later recoded to group the studies into published and unpublished for
moderator analyses examining potential publication bias. The subject domain of the outcome
measures was also coded per the coding guide in Appendix B, however prior to analysis,
domains were recoded to allow for moderator analysis of STEM domains compared to other
domains. The characteristics of the achievement outcome measures were coded, including the
outcome types, such as national or state standardized tests, GPA, or letter grades.
The included intervention studies were also coded for research design and quality
characteristics for use in moderator analysis. The codes include random assignment procedures
(yes/ no/ unclear), matching or equating techniques (yes/no/unclear), and the level of assignment
(student/teacher/classroom/school/other). For studies with multiple samples or multiple
outcomes, all effects were coded separately.

37
Sample and Setting Characteristics
Characteristics related to the sample and the participants were coded, such as
racial/ethnic composition of the sample, grade level of the students, the number of participants in
the intervention and control conditions, the proportion of learners by race/ethnicity, the
proportion of female students, the proportion of socio-economically disadvantaged students, and
proportion of limited English proficiency (i.e., emergent bilingual learners) for possible inclusion
in moderator analyses. The proportion of Black and Latinx learners are used in moderator
analyses.
Characteristics of the settings were coded as part of a common coding guide including the
state in which the research was conducted, the region of the country, and the school level of the
setting (i.e., preschool, elementary, middle, high school, and so on). Studies were coded for the
setting for inclusion in moderator analyses.
To determine the effect of a condition, it must, of course, be compared with some
alternative, and this is why studies were screened to include only those reporting effects in twogroup comparison designs. The effectiveness of cooperative learning has most frequently been
contrasted with individualistic and competitive learning styles. Therefore, studies were coded to
indicate which type of comparison group control condition was used for inclusion in moderator
analysis. If a control group was described as traditional or normal, it was assumed to be
individualistic learning.
Cooperative Learning Intervention
Influence or intervention characteristics were coded including the name of influence as it
is defined in each report and how the intervention was manipulated by the researcher.
Characteristics related to the way in which cooperative learning is operationalized in each study

38
were coded, including the duration of the intervention, group reward (yes/no/unclear), role
specialization (yes/no/unclear), individual accountability (yes/no/unclear), promotive interaction
(yes/no/unclear), social skills support (yes/no/unclear) and whether there was any competition
between groups (yes/no/unclear). Role interdependence existed if there was task specialization.
For example, Holliday (1995) included task specialization which involved the teacher passing
out expert cards to each member of the group that identified the topics each student was required
to learn and teach to the other members of the group. Individual accountability was coded as yes
if a group member’s performance was visible to other members of the group, such as individual
scores being averaged for a group score. In another illustration of individual accountability,
students were instructed to discuss the problems within the group in order to reach a group
consensus regarding the solutions, and once a consensus had been reached, each student then
individually justified in writing the selections of the group. If the study described students
interacting in promotive ways, such as offering explanations or assistance to other students (e.g.,
peer tutoring), it was considered to have promotive interaction.
Interrater Agreement
After all the data were extracted for the studies meeting the inclusion criteria by the
author, they were checked for accuracy by an experienced graduate student collaborator to
ensure the reliability of the coding and data extraction. The interrater agreement rate was
calculated as the total number of data points that agreed divided by the total number of data
points extracted. There was a high rate of agreement between researcher and validator with an
agreement rate of 98.89%.

39
Computing Effect Sizes
The reports included in this synthesis are experimental or quasi-experimental with all
studies assigning students or classrooms to cooperative learning or a comparison condition and
comparing measures of achievement between groups. Therefore, I computed Cohen’s d which is
the standardized mean difference between the cooperative learning and control groups using the
Campbell Institute’s online Effect Size Calculator (Wilson, n.d.). Effect sizes were computed
directly from the means, standard deviations, and sample sizes for the intervention and control
groups whenever possible. However, if effect sizes could not be calculated this way, they were
computed from F-ratios, t-statistics, or chi-square statistics. For any studies that include more
than one cooperative learning condition compared to a single control condition, the effect sizes
for each intervention condition were calculated separately. Prior to analysis, all d were converted
to Hedges’ g (1980) as a correction for any potential small sample bias (Borenstein et al., 2009).
Data Analysis Strategy
The extracted intervention data were meta-analyzed using the metafor and clubSandwich
R packages (Pustejovsky, 2019; Viechtbauer, 2010). Random-effects modeling were applied
throughout the analyses. A multi-level modeling approach is adopted in conjunction with robust
variance estimators to account for the dependency between multiple effect size estimates within
studies and guard against potential model misspecification, (RVE; Pustejovsky & Tipton, 2020).
Publication bias was examined through funnel plot asymmetry by conducting an Egger’s
regression test (Egger et al., 1997) and examining whether publication status was a moderator in
meta-regression models.
The pooled effect sizes for the effect of cooperative learning on achievement were
estimated by fitting a random-effects model to estimate t. The heterogeneity among effect sizes

40
were assessed and indicated by Q, τ2
, and I2
statistics. I report 95% confidence intervals (CI) for
the weighted average effect (Borenstein et al., 2009). To further explain heterogeneity in the
effect size estimates, mixed-effects meta-regression models were used. The effect of moderators
are examined in separate models. Due to the small sample of total effects, covariates to control
for indicators of methodological quality are not investigated.
The moderators examined include comparison group type (individualistic or
competitive), grade range, domain, the proportion of the sample that was Black, the proportion of
the sample that was Latinx, and characteristics of the cooperative learning implementation such
as the use of intergroup competition, group rewards, task specialization, promotive interaction,
individual accountability, and support for social skills.
Numerous meta-analyses have compared cooperative and individualistic learning, and
cooperative and competitive learning and have consistently found that cooperative learning is
more strongly associated with increased student achievement. Many of the studies are, however,
older and do not apply modern meta-analytic techniques. Further, these meta-analyses did not
consider the diversity of their analytic samples. The present meta-analysis will apply modern
meta-analytic approaches to examine the effectiveness of cooperative learning relative to
individualistic and competitive learning approaches among any studies with diverse samples
which include Black and Latinx students.
Results
In total, 13 studies and reports were included in the final sample, which met the inclusion
criteria (nine published and four unpublished). From these studies, there were 30 samples and 37
effect sizes. The studies were published between 1979 and 2009.

41
Assessing for Outliers
The dataset was examined for extreme outliers which are defined as those effects which
were more than three standard deviations greater or smaller than the overall estimated effect size.
One potential outlier was identified (Bonaparte, 1990) with larger positive effects than the
distribution of other studies. All effects were retained for meta-analyzing.
Overall Effects for Cooperative Learning
As expected, the pooled average effect for the difference between cooperative learning
and control conditions was positive and statistically significant for achievement (g = 1.29, p <
.001, see Table 1). With 95% confidence, the average effect lies between 0.7075 and 1.8673.
This is a larger effect than is typical for educational assessments as well as those listed for
cooperative learning which ranged from .24 when compared to competitive learning and .55
compared to individualistic learning in Visible Learning (Hattie, 2023). The variability among
effect sizes that can be attributed to between-study differences is estimated to be 98.22%. That is
a large amount of variance which warrants further investigation in moderator analyses.
Table 1
Overall Average Effect of Cooperative Learning Interventions on Achievement
Outcome k NS NES g 95% CI τ
2
I
2 Q
Achievement 13 28 37 1.287*** 0.70/1.87 1.699 98.22 524.77****
Note. k = number of studies. NS = number of samples. NES = number of effects. g = Hedges’ g
(average pooled effect). CI = confidence interval (low estimate / high estimate). *** p < 0.001
**** p < 0.0001.

42
Publication Bias
Publication bias occurs when studies with statistically significant results are more likely
to be published (Sutton, 2009). A funnel plot was used to examine for publication bias (see
Figure 2). Funnel plots graph the effect sizes on the x-axis in relationship to their respective
standard errors on the vertical axis. Asymmetric patterns are suggestive of publication bias. An
Egger’s regression test (Egger et al., 1997) was also used to test for publication bias. Visual
inspection of Figure 2 suggests asymmetry as the outcomes for g (k = 37) are not spread evenly
along the funnel shape. Several effects fall toward the lower right suggesting that larger effects
may be less precise. The results from the modified Egger’s regression model provide evidence of
funnel plot asymmetry (b = 7.38, SE = .94, t(35) = 7.86, p < .001).
Figure 2
Funnel Plot of Cooperative Learning Effects

43
To further evaluate for the presence of publication bias in the included studies and their
related effects, effects were coded as unpublished (0) or published (1) based on the publication
status. The dummy variable for publication status (unpublished vs. published) was then used in a
meta-regression to test whether effect sizes were different based on publication type. The results
are presented in Table 2 with study methods moderators. The effects did differ by publication
status with the average effect being greater for unpublished studies (g = 2.250) than published
studies (g = 0.614; b = 1.64, p < .05). This significant difference is in the opposite direction than
expected and should therefore be interpreted with caution; it may have been influenced by the
decision not to exclude any studies as outliers. Due to the presence of asymmetry and larger
effect sizes among unpublished studies, caution is advised in interpreting the results.
Moderator Analyses
I examined moderators of the effects between cooperative learning and achievement (see
Tables 2–14). Limited variability and missing information prevented a reliable analysis with
sufficient statistical power for some moderators. Moderator analyses involving fewer than three
studies in a subgroup are not reported.
Study Methods Moderators
The first set of moderators examined were characteristics of the study methods and
included: whether random assignment was used; whether matching or equating was used; and
level of assignment (see Table 2). Twelve of 13 studies provided information about assignment
to control and experimental conditions and were included in the moderator analysis for random
assignment. Studies in which students were not assigned to cooperative learning or comparison
conditions randomly (g = 0.43) did not yield a significantly weaker average positive effect on
achievement compared to studies where students were assigned randomly (g = 1.56; b = 1.14, p

44
= .07). Only studies with random assignment yielded a significant positive average effect on
achievement. Twelve of 13 studies provided information about whether matching or equating
procedures were used and included for moderator analysis. Studies in which no matching or
equating was used (g = 0.26) yielded a significantly weaker average effect on achievement
compared to studies where matching procedures were utilized (g = 2.25; b = 1.99, p < .001).
Only studies using matching procedures yielded significant positive effects. Twelve of the
studies were included in the moderator analysis of the level of assignment. One study was
excluded because it described a change in the course format over time. The remaining studies
assigned students to cooperative learning or comparison conditions or classrooms or teachers to
cooperative learning or comparison conditions. Studies describing the assignment of participants
to cooperative or comparison conditions at the teacher or classroom level were combined for
analysis. There was no difference in average effect for studies in which participants were
assigned to conditions at the teacher/class level (g = 1.68) compared to those in which individual
students were assigned to conditions (g = 0.75; b = –.93, p = .07).

45
Table 2
Results of Moderator Analyses of Study Methods
Moderator k NS NES b (SE) g 95% CI
Publication status
Unpublished 4 12 12 – 2.250*** 1.47/3.03
Published 9 16 25 –1.64 (.059)* 0.614 –0.01/1.24
Random assignment
No RA 5 5 8 0.43 0.42/1.27
RA 7 19 21 1.14 (.514) 1.56** 0.69/2.43
Matching techniques
Not used 8 13 18 - 0.26 0.02/0.54
Used 4 14 18 1.99 (.420)*** 2.25*** 1.38/3.11
Level of assignment
Teacher/classroom 8 17 22 - 1.68** 0.73/2.62
Student 4 10 14 0.926 (.490) 0.75** 0.29/1.21
Note. k = number of studies. NS = number of samples. NES = number of effects. b =
unstandardized regression slope coefficient (moderator effect). SE = standard error. g = Hedges’
g (average pooled effect). CI = confidence interval (low estimate / high estimate). **p < .01.***p
< .001
Sample and Setting Moderators
Moderators related to the sample and setting are summarized next (see Table 3). The first
two moderators relate to the racial composition of the sample: the proportion of the sample that
was Black and the proportion of the sample that was Latinx. The effects included samples
ranging from 2.8% to 100% Black participants with an average of 69.69%. The average effect
size between cooperative learning and achievement when the sample is 0% Black is negative and
not significant at 0.085 (p = .616). For each unit increase in the proportion of Black participants,
the effect increased about .01 (b = .013009, p =.006). The effects were from samples ranging

46
from 0% to 78% Latinx participants with an average of 11.96%. The average effect size between
cooperative learning and achievement when the sample had no Latinx participants was 0.65
which is positive (p = .002). The percentage increase in Latinx participants did not significantly
change the average effect size (b = .01, p = .063). The combined proportion of Black and Latinx
participants was also considered and ranged from 37.5–100% and averaged 81.64% among the
available 29 effect sizes without missing data. The model estimated a negative non-significant
average effect size for effects with samples comprised of no Black or Latinx participants (g = –
0.45, p = .35) and non-significant change for each unit increase in the proportion of Black and
Latinx participants (b = .01, p = .054).
School level moderated the magnitude of the relationship between cooperative learning
and achievement such that the effect sizes among samples of elementary students were larger (g
= 2.20) than those among samples of secondary level students (g = 0.70; b = –1.50, p = .023) as
well as those among college students (g = 0.49; b = 1.71, p = .023). When comparing secondary
and college, effect sizes among college samples were not significantly smaller (g = 0.49; b =
¬.209, p = .561).
A moderator analysis contrasting STEM and non-STEM disciplines was conducted to
determine whether there were larger effect sizes on average in STEM disciplines, as
hypothesized. This hypothesis was confirmed. While in non-STEM disciplines, the average
effect size was positive (g = 0.824), the average effect size in STEM disciplines was significantly
larger (g = 2.720; b = 1.90, p = .009).
The comparison group was the next moderator examined to determine whether the
average effect size of cooperative learning differed when it was compared with individualistic or
competitive control groups. The average effects did differ as predicted; cooperative learning had

47
a larger average effect on achievement when compared to a competitive learning control group
(g = 2.334) rather than an individualistic comparison group (g = 0.508; b = 1.83, p =.004)
Table 3
Results of Moderator Analyses for Sample and Setting Characteristics
Moderator k NS NES b (SE) g 95% CI
Ethnicity
% Black sample 13 20 29 0.01 (0.006)**
% Latinx sample 13 20 29 0.01 (0.005)
% Black and latinx 13 20 29 0.01 (0.005)
School level
Elementary 13 28 28 2.20** 0.94/3.46
Secondary 4 4 10 1.50 (0.605)* 0.70** 0.25/1.15
College 5 6 9 1.71 (0.640)* 0.49 0.25/1.23
Subject domain
Non-STEM 6 12 14 0.824** 0.31/1.35
STEM 3 10 10 1.90 (0.648)** 2.720** 1.34/4.09
Comparison group 11 16 20 0.508*** 0.25/1.12
Individual 4 12 17 1.83 (0.564)** 2.334** 0.76/3.55
Competitive 11 16 20 0.508*** 0.25/1.12
Note. k = number of studies. NS = number of samples. NES = number of effects. b =
unstandardized regression slope coefficient (moderator effect). SE = standard error. g = Hedges’
g (average pooled effect). CI = confidence interval (low estimate / high estimate). STEM =
science technology engineering mathematics *p < .05 **p <. 01 *** p < .001.

48
Characteristics of Cooperative Learning Moderators
Characteristics of the cooperative learning strategy were examined as moderators next
(see Table 4). These included whether there was competition between the cooperative groups,
called inter-group competition, as well as whether essential characteristics of the cooperative
learning strategy as defined by Johnson and Johnson (1999).
The average effect of cooperative learning on achievement when there was no intergroup
competition was positive (g = 0.696, p = .006). However, the average effect when there was
intergroup competition was significantly greater (g = 1.99; b = 1.291, p = .01). The duration of
the intervention was examined next and found not to have varied to the extent expected. One
study did not specify the duration of the intervention and was excluded from the moderator
analysis. Four of the remaining 12 studies (25%) used cooperative learning interventions of one
day or less in duration and were coded as the reference group, while the other eight (75%) were
ten or more days. The effects from longer interventions (g = 0.34) were not significantly larger
than those from shorter interventions of one day or less (g = 0.79; b = –0.48, p = .09).

49
Table 4
Results of Moderator Analyses for Cooperative Learning Characteristics
Moderator k NS NES b (SE) g 95% CI
Intergroup competition
No competition 7 10 12 0.696** 0.25/1.09
Competition 6 15 17 1.29 (0.46)* 1.99*** 1.14/2.89
Duration
One day or less 4 10 14 0.79*** 0.33/1.25
Ten or more days 8 10 15 –0.48 (0.44) 0.34 –0.11/0.72
Group incentive
No group reward 4 7 11 1.01** 0.47/1.56
Group reward 9 21 26 0.38 (0.44) 1.39** 0.60/2.18
Role interdependence 11 16 20 0.508*** 0.25/1.12
No role dependence 8 22 28 1.51*** 1.16/2.22
Role dependence 5 6 9 1.02 (0.43)* 0.49 0.18/0.80
Indiv. accountablity
No indiv.
accountability
2 5 9 1.12*** 0.76/2.70
Indiv. accountability 11 23 28 0.21 (0.42) 1.33*** 0.00/1.00
Promotive interaction
No promotive inter. 8 14 18 0.67*** 0.33/0.78
Promotive inter. 5 14 19 1.25 (0.55)** 1.92** 1.00/3.05
Social skills support
No support 8 21 28 1.56*** 0.81/2.32
Social skills support 5 7 9 –1.07 (0.40)* 0.49* 0.07/0.91
Note. K = number of studies. NS = number of samples. NES = number of effects. b =
unstandardized regression slope coefficient (moderator effect). SE = standard error. g = Hedges’
g (average pooled effect). CI = confidence interval (low estimate / high estimate). Indiv. =
individual. Inter.= interaction. *p < .05 **p <. 01 *** p < .001.
The essential characteristics of cooperative learning were examined next, beginning first
with the use of group rewards. Nine of the total thirteen studies (69.2%) described whether there

50
was a group reward used to create interdependence between members. Among these studies,
there was a positive estimated average effect that was numerically greater (g = 1.39), but not
significantly greater than those that did not (g = 1.01; b = 0.38, p = .40). The use of role
interdependence varied with five of 13 studies (38.5%) including it as a feature in the
implementation of cooperative learning. Those studies without role interdependence had a
positive estimated average effect (g = 1.51), which was significantly greater than those that
included role interdependence (g = 0.49; b = –1.02, p = .045). Among the thirteen included
studies, the majority (84.6%) specifically described an implementation of cooperative learning
which featured individual accountability for group members. On average the effect size between
cooperative learning and achievement was positive and significant among studies with individual
accountability (g = 1.33) and without individual accountability between group members (g =
1.12; b = 0.21, p = .63). Another characteristic of cooperative learning examined as a moderator
was promotive interaction, which five of 13 studies mentioned in describing their cooperative
learning manipulation. As predicted, the effects from those studies were larger on average (g =
1.92) than those that did not indicate any promotive interaction (g = 0.67; b = 1.25, p = .03).
Lastly, five of the included studies (38.5%) described social skills support or instruction as part
of the described implementation of cooperative learning. The effects from those studies which
did not mention supporting social skills had significantly higher effect sizes on average (g =
1.56) than those that did mention social skills support (g = 0.49; b = –1.07, p < .001), but both
were significantly positive.
Discussion
This meta-analysis represents a research synthesis of the effect of cooperative learning on
student achievement among diverse samples of students consisting of at least 40% Black and

51
Latinx students. Using the evidence provided in the 13 experimental and quasi-experimental
studies, consistent with my hypotheses, I found statistically significant positive effect sizes for
cooperative learning among representatively diverse student samples.
The Effect of Cooperative Learning on Achievement
The average effect size (g = 1.29) was larger than those reported in Visible Learning
(2023). Among typical effect sizes for educational interventions, this is a very large magnitude
difference compared to the control conditions. For example, Hattie (2023) has identified .40 as
the hinge point for worthwhile educational interventions. The average effect size in this synthesis
is also larger than the effect sizes reported by Hattie (2023) for cooperative learning which
ranged from .24 when compared to competitive learning and .55 when compared to
individualistic learning in Visible Learning. The samples included in this synthesis were diverse;
on average about 82% of the samples consisted of Black or Latinx students. Among these
samples with higher proportions of students of color, the average effect among cooperative
learning interventions was more than twice the magnitude. This is further suggested by the
finding that greater proportions of Black students were associated with larger effect sizes for
cooperative learning methods. These findings are consistent with theory and prior research
predicting larger gains or greater benefits of cooperative learning for minoritized students than
competitive learning conditions (Coleman, 1998; Hurley et al., 2009). Other studies have also
demonstrated that Black and Latinx students gain more from the implementation of cooperative
learning than white students (Lucker et al., 1976; Slavin & Oickle, 1981). Jagers (1992) found
that African American students favored interdependent learning conditions compared to
independent learning conditions which they suggest demonstrates that Latinx and Black students
experience cooperative learning contexts to be more culturally relevant. They argue the larger

52
benefits may be expected because students can apply their existing competencies toward learning
new skills.
Setting and Sample Moderators
This synthesis revealed larger effect sizes among elementary students (g = 2.20) than
those among samples of secondary level students (g = 0.70) and college students (g = 0.49),
while secondary and college did not differ significantly. This was the reverse of what was
expected based on the findings from Qin, Johnson, and Johnson (1995) which found smaller
effect sizes on average for elementary students compared to secondary or adult students. Qin et
al. speculated that younger learners might not have comparable linguistic skills to older learners.
This synthesis does not find support for this explanation. One possibility suggested by Webb and
Farivar (1994) who included cooperative skill-building activities for middle school students in
their implementation of cooperative learning to teach communication and helping skills, is that
older students may lack the necessary skills to work cooperatively because they have had more
years in schools where they have been forbidden from working with classmates. Another reason
for why there might be greater effectiveness among elementary school students is that the
traditional elementary classroom is self-contained. A self-contained classroom is one in which
the same group of students are instructed for multiple subjects by one educator throughout all or
most of the day. This allows for students participating in cooperative learning to spend a greater
amount of time with members of their group where they may behave in prosocial ways towards
their group, improving social relationships among students.
Partly due to the active participatory nature of cooperative learning methods, cooperative
learning strategies have been popular in science, technology, engineering, and mathematics
disciplines (Springer et al., 1999). Qin et al. (1995) reported larger effect sizes for non-linguistic

53
domains which is consistent with the finding in this synthesis that cooperative learning
interventions in STEM disciplines had larger effect sizes on average than did those in non-STEM
disciplines. These disciplines may provide more opportunities for real-world problems which
provide another avenue for learning to be culturally relevant because students are more likely to
explore problems that are personally meaningful and relevant, including social justice issues that
are important in their community.
How effective an intervention appears to be is influenced by the comparison or reference
group. If a new pharmaceutical treatment is compared with a pure placebo, there would be a
greater difference than if the new treatment was compared to the leading effective treatment. In
the context of this cooperative learning synthesis, the most common comparison group was
individualistic learning. With individualistic reward structures the success of another student has
no effect on the success of another student and with competitive reward structures (i.e.,
interpersonal competition) the success of one student diminishes the likelihood of success for
others. Motivational research has demonstrated that competition can diminish intrinsic
motivation (Deci & Ryan, 1985). In this meta-analysis among diverse student groups,
cooperative learning had a larger average effect on achievement when compared to a competitive
learning control group (g = 2.334) rather than individualistic comparison group (g = 0.508).
There is evidence of cultural differences; for example Hurley et al. (2009) found that while
European white participants performed best as a group under interpersonal reward condition,
African American students performed better as a group under communal or intergroup
competition learning conditions than interpersonal competition.

54
Cooperative Learning Moderators
Slavin and Johnson and Johnson were influential proponents for cooperative learning, but
they disagreed about whether cooperative learning instructional strategies should feature
competition between group members (e.g., Johnson et al., 1980, Slavin, 1990; Slavin, 1999).
Among the included studies featuring diverse groups of learners, the studies featuring
competition between groups had larger effects on average (g = 1.29) than did those which did not
have competition between groups (g = 0.70), though both types of interventions were positive
and featured large average effects exceeding the .40 recommended threshold for effective
educational interventions. Coleman (1959) compared cooperative learning strategies to team
sports. If competition is used as part of the cooperative learning strategy it appears to impart
additional benefits to achievement. Using educational games that reward the success of the team
are intended to encourage group members to value the group’s success (Slavin, 1996).
Cooperative learning strategies which created interdependence through the reward
structure as well as created through other means were both effective at increasing achievement
compared to individualistic or competitive learning strategies. The ability to detect a difference
may be reduced due to the small number of studies included in this synthesis. However, the
significantly positive average effect for cooperative strategies which did not describe methods of
creating interdependence through reward structures speaks to the potential of promoting
interdependence through other means such as simply communicating the expectation or
providing the opportunity for students to work cooperatively.
Cooperative learning approaches that did not feature role dependence were associated
with larger effect sizes. These might be more effective at promoting achievement because they
do not ask group members to develop expertise in one area or with one sub-task. The included

55
studies required individual measures of achievement, therefore strategies that promote
specialization within a group activity may promote cooperation, however they may not promote
equivalent learning on more comprehensive measures of student performance.
Individual accountability was a feature of the vast majority of studies. In this synthesis
only two studies did not describe how they created individual accountability. The effects from
those studies without individual accountability had positive effects which were not smaller than
those from the other 11 studies which did mention individual accountability. While these studies
did not mention it, they might have still included strategies that created individual accountability
such that all learners participated in the learning task, thus increasing their learning.
Cooperative learning approaches that featured promotive interaction described strategies
to increase the amount of elaboration and explanation provided to one another by students.
Consistent with cognitive learning theories, these strategies appear to promote greater learning
and achievement. The studies that attended to this aspect of the cooperative learning experience
produced effects that were more than twice as large as those that did not, attesting to the power
of this component to achieve maximum benefits from the cooperative learning approach. The
interaction and opportunity for sharing one’s perspective would also signal the value and worth
of the experiences and knowledge of students from minoritized backgrounds and contribute to
increasing critical consciousness (Freire, 1973).
While it was expected that explicit attention to social skills would be a beneficial feature
of the cooperative learning approach, the data from this synthesis did not support that conclusion.
Instead, the studies that mentioned social skills, while positive and large enough to suggest
effectiveness (g = 0.49) had smaller effect sizes on average than when there was not a focus on
social skills (g = 1.56). The majority of studies did not make mention of how the strategy

56
promoted positive social behavior and it is a limitation that many descriptions of cooperative
learning approaches were not sufficiently detailed and while not mentioned, they may have
nonetheless included strategies to support positive social behavior and the development of social
skills, as recommended by Johnson and Johnson (1999).
Practice and Policy Implications
Based on the results of this research synthesis, I suggest some guidelines for
educators and education policymakers.
1. Educators of all types and age groups can use cooperative learning as an instructional
strategy to improve learning outcomes for their students. There is robust evidence for
the benefits of cooperative learning for achievement among students of a variety of
ages and cultures. STEM domains may be particularly well-suited for cooperative
learning instructional strategies.
2. Creating positive interdependence through group reward structures was an effective
essential strategy for increasing the effectiveness of cooperative learning. Ensuring
the presence of promotive and elaborative dialogue between students and individual
student accountability to group mates would be of additional benefit.
3. For diverse groups of students with high proportions of minoritized students,
intergroup competition may provide an effective strategy for promoting cooperative
behavior within a group.
4. For diverse groups of students with high proportions of Black and Latinx students,
cooperative learning is consistent with culturally responsive and relevant education
and supports dramatic increases in achievement.

57
Limitations and Recommendations Future Research
This synthesis only includes studies that were reported by Hattie in the selected
influences on cooperative learning (i.e., cooperative versus individualistic learning and
cooperative versus competitive learning). This facilitates comparison to prior reviews, which did
not restrict the sample but limited the number of reports and observations overall, thereby
reducing statistical power to explore possible moderators. There are also not many recent studies.
Most studies were conducted prior to 2000. This may be due to the perception that cooperative
learning has been sufficiently studied and its effectiveness has been broadly demonstrated. The
research question further reduces the included studies to only those that include information
about the racial composition, indicating a sufficiently diverse sample, which additionally reduced
statistical power. I would encourage researchers to be diligent about collecting and reporting
sample demographics and school and classroom setting characteristics. These details are critical
for understanding the degree to which instructional strategies generalize to various learners and
learning contexts. A further limitation is that the method of cooperative learning or the specific
strategies employed may be missing in many studies, which may make moderation analyses
difficult to conduct or interpret.
Finally for the moderator analyses, as with all research syntheses, it is important to note
that synthesis-generated evidence should not be misinterpreted as supporting conclusions about
causality. These findings should be taken to provide directions for future research on cooperative
learning. The findings provide considerations for appropriate uses of large archives of syntheses
of educational interventions.

58
Conclusions
In conclusion, this meta-analysis has provided compelling evidence regarding the
significant positive effect of cooperative learning on student achievement, particularly among
racially diverse samples featuring substantial percentages of Black and Latinx students. The
findings indicate that cooperative learning interventions yielded notably larger effect sizes
compared to individualistic and competitive learning conditions, with average effect sizes
surpassing the threshold for meaningful educational interventions. There were greater effects
with increasing proportions of Black students. Cooperative learning demonstrated greater
efficacy among elementary students compared to secondary and college students, challenging
prior assumptions about age-related differences in the effectiveness of such interventions.
Furthermore, strategies incorporating intergroup competition and promoting elaborative dialogue
among students were associated with enhanced achievement outcomes. While this synthesis
offers valuable insights into the potential of cooperative learning as a culturally responsive and
relevant instructional strategy, it also underscores the need for future research to address
methodological limitations and explore additional moderators to further elucidate the
mechanisms driving its effectiveness across diverse educational contexts. Ultimately, these
findings have significant implications for educators and policymakers seeking evidence-based
strategies to improve learning outcomes for diverse student populations, particularly in STEM
disciplines where cooperative learning approaches have shown unique promise.

59
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References marked with an asterisk indicate studies included in the meta-analysis.

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Appendix A: Studies Included in Meta-Analysis
First author Year CL
type
GL Pub. RA Match. LOA %
B
%
L
CG IGC Dom. GR TS IA SS PI d
Allen 1984 STAD HS P Y Y C 70 0 I Y Social Y Y Y N N 0.6965
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 2.4637a
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 7.1474b
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 1.7281c
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 3.4888d
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 3.5466e
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 4.0458f
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 1.5065g
Bonaparte 1990 STAD ES U Y Y C X X C Y Math Y Y Y N Y 4.4603h
Ellison 1994 GS HE P Y N S 100 0 C Y Read. Y N Y N N 0.8046
Ellison 1994 GS HE P Y N S 100 0 I N Read. Y N Y N N 0.05
Frierson 1987 Other HE P N N C 93.9 0 I N Other N Y Y Y Y 1.3892
Garibaldi 1979 Other HS P X Y S 100 0 I N Read. N N N N N 1.5202
Garibaldi 1979 Other HS P X Y S 100 0 I N Other N N N N N 0.5862
Garibaldi 1979 Other HS P X Y S 100 0 C N Read. N N N N N 2.6729

74
First author Year CL
type
GL Pub. RA Match. LOA %
B
%
L
CG IGC Dom. GR TS IA SS PI d
Garibaldi 1979 Other HS P X Y S 100 0 C N Other N N N N N 0.1944
Garibaldi 1979 Other HS P X Y S 100 0 I Y Read. N N N N N 1.1362
Garibaldi 1979 Other HS P X Y S 100 0 I Y Other N N N N N 1.5321
Garibaldi 1979 Other HS P X Y S 100 0 C Y Read. N N N N N 2.2235
Garibaldi 1979 Other HS P X Y S 100 0 C Y Other N N N N N 1.2293
George 1994 Other HE P N N C 72.1 0 C X Other Y Y Y N N 0.504
George 1994 Other HE P N N C 72.1 0 C X Other Y Y Y N N 0.6772
George 1994 Other HE P N N C 72.1 0 C X Other Y Y Y N N 0.4149
George 1994 Other HE P N N C 72.1 0 C X Other Y Y Y N N 0.0159
Holliday 1995 Jigsaw HS U N N C 46.2 0 X Y Social Y Y Y Y Y 0.3796i
Holliday 1995 Jigsaw HS U N N C 46.2 0 X Y Social Y Y Y Y Y 0.2735j
Johnson 1989 Other HE P Y Y S 100 0 I N Other Y N Y N N 1.1086
Nowak 1996 LT ES U N X T 35 2.5 I N Math Y Y Y Y N 0.8059
Prezsler 2009 Other HE P N N O 2.8 49.9 I N Sci N N N N N 0.2994
Samaha 2000 Other MS P Y N O 10 X I N Other Y N Y N N 0.0271k
Samaha 2000 Other MS P Y N O 10 X I N Other Y N Y N N 0.2162l
Samaha 2000 Other MS P Y N O 10 X I N Other Y N Y N N 0.3793m

75
First author Year CL
type
GL Pub. RA Match. LOA %
B
%
L
CG IGC Dom. GR TS IA SS PI d
Scott 1984 STAD ES U N N C 23.7 10.1 I Y Read N N Y N N 0.0753
Stevens 1991 GS ES P Y N C 71.2 10.1 I X Read Y N Y Y Y 0.2095
Stevens 1991 GS ES P Y N C 71.2 10.1 I X Read Y N Y Y Y 0.8467
Stevens 1991 GS ES P Y N C 71.2 10.1 I X Read Y N Y Y Y 0.0092
Stevens 1991 GS ES P Y N C 71.2 10.1 I X Read Y N Y Y Y 0.1944
Note. CL = cooperative learning; STAD = Students Teams Assessments Divisions; GS = group study; LT = learning together; GL =
grade level; ES = elementary school; MS = middle school; HS = high school; HE = higher education; Pub. = publication type; P =
published; U = unpublished; RA = random assignment used; match = matching methods used; Y = yes; N = no; X = unclear or
missing; LOA = level of assignment; C = classroom, S = student; T = teacher; O = other; %B = percentage Black; %L = percentage
of Latinx students; CG = comparison group; I = individualistic; C = competitive; IGC = intergroup competition; Dom = outcome
domain, Social = social science; Read = reading or English language arts; Sci = science; GR = group rewards; TS = task
specialization; IA = individual accountability; SS = support for social skills; PI = promotive interaction.
a
school A masters level students; b
school A non-masters level students; c
school B masters level students; d
school B non-masters level
students; e
school C masters level students; f
school C non-masters level students; g
school D masters level students; h
school D non-

76
masters level students; i
students not eligible for free or reduced lunch; j
eligible for free or reduced lunch; kmixed gender groups;
l
female only groups; mmale only.

77
Appendix B: Coding Guide
Code name Code description Code options
Coder information
C-1 Date coded [text entry]
C-2 Coder [text entry]
Meta-analysis characteristics
M-1 Meta-analysis’ first author’s last name [text entry]
M-2 Meta-analysis Google Drive link [text entry]
Report characteristics
R-2 Article Google Drive link [text entry]
R-3 First author’s last name [text entry]
R-4 Year [text entry]
R-5 Title [text entry]
R-6 APA reference [text entry]
R-7 Publication type 1. Journal article
2. Book or book chapter
3. Dissertation
4. Master’s thesis
5. Policy report
6. Government report
7. Conference paper
8. Other
–99. Can’t tell
R-8 Data sources 1. Independent study
2. Regional/national data set
3. Other
–99. Can’t tell
R-9 Dataset name [text entry];
–99 Missing/can’t tell/not applicable
R-10 Data collection year indicated 0. No
1. Yes
R-11 Year(s) data collected [text entry];
–99 Missing/can’t tell/not applicable
R-12 On what page(s) did you find the data
source?
[text entry]
R-13 Overlapping datasets [text entry];
–99 No
Setting characteristics
S-1 Study number 0. Single study
1. Study 1
2. Study 2
3. Study 3
etc.

78
Code name Code description Code options
S-2 Location [text entry];
–99 Missing/can’t tell/not applicable
S-3 Region 1. Northeast
2. South
3. Midwest
4. West
5. National
–99. Can’t tell
S-4 On what page(s) did you find the
location?
[text entry];
–99 Missing/can’t tell/not applicable
S-5 School level 1. Preschool
2. Elementary school: K–5
3. Middle school: 6–8
4. High school: 9–12
5. Undergraduate
6. Graduate school
7. Other (specify)
–99. Can’t tell
S-6 Other school level (specify) [text entry];
–99 Missing/can’t tell/not applicable
Participant and sample characteristics
P-1 Sample 0. Overall sample
1. Subgroup
P-2 Subgroup specification [text entry];
–99 Missing/can’t tell/not applicable
P-3 Subgroup overlap 0. No
1. Yes
–99. N/A
P-4 Subgroup overlap explanation [text entry];
–99 Missing/can’t tell/not applicable
P-5 Sample size (at start) [text entry];
–99 Missing/can’t tell/not applicable
P-6 On what page(s) did you find the
sample size?
[text entry];
–99 Missing/can’t tell/not applicable
P-7 Sample characteristics 1. Sample at start
2. Analysis sample
3. Both, but they are the same
4. Both, and they are not the same
5. Neither
–99 Missing/can’t tell/not applicable
P-8 Sample characteristics specification [text entry];
–99 Missing/can’t tell/not applicable
P-9 %White [text entry];
–99 Missing/can’t tell/not applicable

79
Code name Code description Code options
P-10 %Black [text entry];
–99 Missing/can’t tell/not applicable
P-11 %Hispanic [text entry];
–99 Missing/can’t tell/not applicable
P-12 %Asian or Pacific Islander [text entry];
–99 Missing/can’t tell/not applicable
P-13 %Native American or American
Indian
[text entry];
–99 Missing/can’t tell/not applicable
P-14 %Other [text entry];
–99 Missing/can’t tell/not applicable
P-15 On what page(s) did you find the
racial/ethnic distribution?
[text entry];
–99 Missing/can’t tell/not applicable
P-16 Grade level -1. Preschool 8. Grade 8
0. Kindergarten 9. Grade 9
1. Grade 1 10. Grade 10
2. Grade 2 11. Grade 11
3. Grade 3 12. Grade 12
4. Grade 4 13. Undergraduate
5. Grade 5 14. Graduate
6. Grade 6 15. Other (specify)
7. Grade 7 –99. Can’t tell
P-17 Grade level (if other) [text entry];
–99 Missing/can’t tell/not applicable
P-18 On what page(s) did you find the
grade level?
[text entry];
–99 Missing/can’t tell/not applicable
P-19 % Female [text entry];
–99 Missing/can’t tell/not applicable
P-20 On what page(s) did you find the %
female statistic?
[text entry];
–99 Missing/can’t tell/not applicable
P-21 % Low income / economically
disadvantaged
[text entry];
–99 Missing/can’t tell/not applicable
P-22 On what page(s) did you find the %
low income statistic?
[text entry];
–99 Missing/can’t tell/not applicable
P-23 % Special education [text entry];
–99 Missing/can’t tell/not applicable
P-24 On what page(s) did you find the %
Special education statistic?
[text entry];
–99 Missing/can’t tell/not applicable
P-25 % English learners [text entry];
–99 Missing/can’t tell/not applicable
P-26 On what page(s) did you find the %
English learner statistic?
[text entry];
–99 Missing/can’t tell/not applicable
Predictor/influence
I-1 Report’s name for influence [text entry]

80
Code name Code description Code options
I-2 Influence definition [text entry];
–99 Missing/can’t tell/not applicable
I-3 On what page(s) did you find the
influence definition?
[text entry];
–99 Missing/can’t tell/not applicable
I-4 How is the influence measured? [text entry];
–99 Missing/can’t tell/not applicable
I-5 On what page(s) did you find the
description of how the influence
was measured?
[text entry];
–99 Missing/can’t tell/not applicable
I-6 Reliability 0. No
1. Yes
–99. Unsure, not applicable
I-7 Alpha coefficient (reliability) [text entry];
–99 Missing/can’t tell/not applicable
I-8 Alpha coefficient from what source? 1. Data from this coded study
2. Data from the study for which the survey
was derived
–99. Unsure, not applicable
I-9 On what page did you find the alpha
coefficient?
[text entry];
–99 Missing/can’t tell/not applicable
I-10 How was the influence manipulated
by the researcher?
[text entry];
–99 Missing/can’t tell/not applicable
I-11 On what page(s) did you find the
description of how the researcher
manipulated the influence?
[text entry];
–99 Missing/can’t tell/not applicable
I-12 What type of formative assessment
intervention was used?
1. Professional development
2. Curriculum-embedded assessment
3. Technology-mediated formative
assessment
4. Other
–99. Unsure
I-13 Other type used? [text entry]; –99 not applicable
I-14 What was the duration of the
intervention in years?
[text entry]; –99 not applicable
I-15 Who was the primary feedback
source?
1. Instructor
2. Peers
3. Self
4. Computer-generated
5. Other
Cooperative learning
CL-1 Comparison 1. Individualistic
2. Competitive
CL-2 Cooperative learning method [Text]
-99. Unsure, N/A

81
Code name Code description Code options
CL-3 Intergroup competition 0. No
1. Yes
-99. Unsure, N/A
CL-4 Duration of cooperative
learning
1 day or less
2. 2 to 9 days
3. 10 or more days
-99. Unsure, N/A
CL-5 Focus on social skills 0. No
1. Yes
-99. Unsure, N/A
CL-6 Group Reward 0. No
1. Yes
-99. Unsure, N/A
CL-7 Role interdependence 0. No
1. Yes
-99. Unsure, N/A
CL-8 Individual Accountability 0. No
1. Yes
-99. Unsure, N/A
CL-9 Promotive Dialogue 0. No
1. Yes
-99. Unsure, N/A
Outcome measures
O-1 Outcome type 1. Standardized test (e.g., NAEP, state
standardized assessment, WoodcockJohnson test)
2. Grades (e.g., course, GPA)
3. Knowledge diagnostic test developed by
the researcher/instructor
4. Local assessment (e.g., local school
district)
5. Other achievement
O-2 Outcome name [text entry];
–99 missing/can’t tell/not applicable
O-3 Outcome description [text entry];
–99 missing/can’t tell/not applicable
O-4 On what page(s) did you find the
description of the outcome?
[text entry];
–99 missing/can’t tell/not applicable
O-5 Domain of outcome 1. Mathematics
2. English language arts
3. Science
4. Social science
5. General academics
6. Other (specify)

82
Code name Code description Code options
O-6 Domain of outcome (specified) [text entry];
–99 missing/can’t tell/not applicable
O-7 What is the unit of analysis? 1. Student
2. Teacher
3. Classroom
4. School
5. Other (specify)
–99. Unsure/not applicable
O-8 Other unit of analysis [text entry];
–99 missing/can’t tell/not applicable
O-9 Timing of influence & outcome
measure collection
1. Simultaneously
2. Longitudinally
–99. Unsure
O-10 Specify timing [text entry];
–99 missing/can’t tell/not applicable
O-11 On what page(s) did you find the
timing of data collection described?
[text entry];
–99 missing/can’t tell/not applicable
Research design and effect sizes
E-1 Sample size (for relationship/effect) [text entry];
–99 missing/can’t tell/not applicable
E-2 On what page(s) did you find the
sample size?
[text entry];
–99 missing/can’t tell/not applicable
E-3 Direction of relationship between
influence and outcome
0. Null/no relationship
1. Positive
2. Negative
3. Mixed
–99. Unclear
E-4 Evidence of direction 1. Sign of correlation coefficient
2. Comparing means or rate of success
3. Indication in text
–99. Can’t tell/unclear
E-5 On what page(s) did you find the
direction of the relationship?
[text entry];
–99 missing/can’t tell/not applicable
E-6 In what table did you find the
direction of the relationship?
[text entry];
–99 missing/can’t tell/not applicable
E-7 Type of research design 1. Descriptive study
2. Correlational study
3. One-group/single-group preexperimental design
4. Quasi-experiment
5. Rct/true experiment (2+ groups)
–99. Can’t tell

83
Code name Code description Code options
E-8 Is there a treatment group and a
control group? 0. No
1. Yes
–99. Unclear
E-9 Is there random assignment to
treatment and control groups? 0. No
1. Yes
–99. N/a
E-10 On what page did the researchers
specify random assignment?
[text entry];
–99 Missing/can’t tell/not applicable
E-11 Level of assignment 1. Student
2. Teacher
3. Classroom
4. School
5. Other (specify)
–99. Unsure/not applicable
E-12 Other level of assignment [text entry];
–99 missing/can’t tell/not applicable
E-13 Is there matching of treatment units to
comparison units? 0. No
1. Yes
–99. N/a
E-14 Matching characteristics [text entry];
–99 missing/can’t tell/not applicable
E-15 On what page(s) did the researchers
indicate matching and matching
characteristics?
[text entry];
–99 missing/can’t tell/not applicable
E-16 Did the researchers report priorinfluence or pre-test statistics?
0. No
1. Yes
–99. Can’t tell
E-17 On what page(s) did the researchers
report pre-test statistics?
[text entry];
–99 missing/can’t tell/not applicable
E-18 In what table did the researchers
report pre-test statistics?
[text entry];
–99 missing/can’t tell/not applicable
E-19 Regression
0. No
1. Yes
–99. Can’t tell
E-20 On what page did the researchers
specify using regression?
[text entry];
–99 missing/can’t tell/not applicable
E-21 Multi-level/hierarchical modeling
0. No
1. Yes
–99. Can’t tell

84
Code name Code description Code options
E-22 On what page did the researchers
specify multi-level modeling?
[text entry];
–99 missing/can’t tell/not applicable
Experimental studies
EE-1 What is Nₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-2 On what page did you find Nₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-3 In what table did you find Nₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-4 What is Nc? [text entry];
–99 Missing/can’t tell/not applicable
EE-5 On what page did you find Nc? [text entry];
–99 Missing/can’t tell/not applicable
EE-6 In what table did you find Nc? [text entry];
–99 Missing/can’t tell/not applicable
EE-7 What is Mₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-8 On what page did you find Mₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-9 In what table did you find Mₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-10 What is SDₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-11 On what page did you find SDₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-12 In what table did you find SDₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-13 What is Mc? [text entry];
–99 Missing/can’t tell/not applicable
EE-14 On what page did you find Mc? [text entry];
–99 Missing/can’t tell/not applicable
EE-15 In what table did you find Mc? [text entry];
–99 Missing/can’t tell/not applicable
EE-16 What is SDc? [text entry];
–99 Missing/can’t tell/not applicable
EE-17 On what page did you find SDc? [text entry];
–99 Missing/can’t tell/not applicable
EE-18 In what table did you find SDc? [text entry];
–99 Missing/can’t tell/not applicable
EE-19 What is the effect size (d)? [text entry];
–99 Missing/can’t tell/not applicable
EE-20 What is the variance (v)? [text entry];
–99 Missing/can’t tell/not applicable

85
Code name Code description Code options
EE-21 Screenshot of effect size calculation [Image, –99 not applicable]
EE-22 What is SEₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-23 On what page did you find SEₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-24 In what table did you find SEₜ? [text entry];
–99 Missing/can’t tell/not applicable
EE-25 What is SEc? [text entry];
–99 Missing/can’t tell/not applicable
EE-26 On what page did you find SEc? [text entry];
–99 Missing/can’t tell/not applicable
EE-27 In what table did you find SEc? [text entry];
–99 Missing/can’t tell/not applicable
EE-28 What is the effect size (d)? [text entry];
–99 Missing/can’t tell/not applicable
EE-29 What is the variance (v)? [text entry];
–99 Missing/can’t tell/not applicable
EE-30 Screenshot of effect size calculation [Image];
–99 not applicable
EE-31 What is the t-statistic? [text entry];
–99 Missing/can’t tell/not applicable
EE-32 On what page did you find the tstatistic?
[text entry];
–99 Missing/can’t tell/not applicable
EE-33 In what table did you find the tstatistic?
[text entry];
–99 Missing/can’t tell/not applicable
EE-34 What is the effect size (d)? [text entry];
–99 Missing/can’t tell/not applicable
EE-35 What is variance (v)? [text entry];
–99 Missing/can’t tell/not applicable
EE-36 Screenshot of effect size calculation [Image];
–99 not applicable
EE-37 What is the p-value of the t-test? [text entry];
–99 Missing/can’t tell/not applicable
EE-38 On what page did you find the pvalue?
[text entry];
–99 Missing/can’t tell/not applicable
EE-39 In what table did you find the p-value? [text entry];
–99 Missing/can’t tell/not applicable
EE-40 What is the effect size (d)? [text entry];
–99 Missing/can’t tell/not applicable
EE-41 What is variance (v)? [text entry];
–99 Missing/can’t tell/not applicable
EE-42 Screenshot of effect size calculation [Image];
–99 not applicable

86
Code name Code description Code options
EE-43 How many groups are compared in the
F-test?
[text entry];
–99 Missing/can’t tell/not applicable
EE-44 What is the F-statistic of the F-test? [text entry];
–99 Missing/can’t tell/not applicable
EE-45 On what page did you find the Fstatistic?
[text entry];
–99 Missing/can’t tell/not applicable
EE-46 In what table did you find the Fstatistic?
[text entry];
–99 Missing/can’t tell/not applicable
EE-47 What is the effect size (d)? [text entry];
–99 Missing/can’t tell/not applicable
EE-48 What is variance (v)? [text entry];
–99 Missing/can’t tell/not applicable
EE-49 Screenshot of effect size calculation [Image];
–99 not applicable
EE-50 Frequency of yes/favorable outcome
for treatment group
[text entry];
–99 Missing/can’t tell/not applicable
EE-51 Frequency of no/unfavorable outcome
for treatment group
[text entry];
–99 missing/can’t tell/not applicable
EE-52 Frequency of yes/favorable outcome
for control group
[text entry];
–99 missing/can’t tell/not applicable
EE-53 Frequency of no/unfavorable outcome
for control group
[text entry];
–99 missing/can’t tell/not applicable
EE-54 On what page did you find the
contingency table/data for the
contingency table?
[text entry];
–99 missing/can’t tell/not applicable
EE-55 In what table did you find the
contingency table/data for the
contingency table?
[text entry];
–99 missing/can’t tell/not applicable
EE-56 What is the effect size (d)? [text entry];
–99 missing/can’t tell/not applicable
EE-57 What is the variance (v)? [text entry];
–99 missing/can’t tell/not applicable
EE-58 Screenshot of effect size calculation [image];
–99 not applicable
EE-59 d-index calculated?
0. No
1. Yes
–99. not applicable
EE-60 Effect size from original meta-analysis[text entry];
–99 missing/can’t tell/not applicable
EE-61 On what page did you find the effect
size from the original metaanalysis?
[text entry];
–99 missing/can’t tell/not applicable

87
Code name Code description Code options
EE-62 In what table did you find the effect
size from the original metaanalysis?
[text entry];
–99 missing/can’t tell/not applicable

Abstract (if available)

Abstract This meta-analysis applies interdependence theory from the social science literature to understand the impact of cooperative learning on students’ achievement among racially diverse study samples. This study aimed to determine if the effectiveness of cooperative learning differed based on sample composition. Also, this study sought to determine which cooperative learning characteristics were most predictive of student achievement. I applied multi-level mixed effects modeling to meta-analyze secondary data from studies within the meta-analyses included in John Hattie’s Visible Learning (2023) with samples of at least 40% Black or Latinx students. Findings from this study indicate that cooperative learning has a significant positive average effect for diverse groups of learners and there were stronger effects for samples with larger proportions of Black students. There were larger effects in science, technology, engineering, and math domains and among elementary school-aged learners. Creating positive interdependence through group reward structures was an effective essential strategy for increasing the effectiveness of cooperative learning. Using group rewards to create positive interdependence, encouraging promotive interaction between students, and ensuring individual student accountability to groupmates increased the effectiveness of cooperative learning. This study supports cooperative learning as a culturally relevant and responsive educational approach and affirms the effectiveness of cooperative learning strategies for diverse student populations such as those served in many public schools in the United States.

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Asset Metadata

Creator Aharonian, Ani A. (author)

Core Title Learning together: a meta-analysis of the effect of cooperative learning on achievement among Black and Latinx students

School Rossier School of Education

Degree Doctor of Education

Degree Program Educational Leadership

Degree Conferral Date 2024-08

Publication Date 07/01/2024

Defense Date 04/23/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag cooperative learning,meta-analysis,racially diverse,Visible Learning

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kho, Adam (committee chair), Patall, Erika (committee chair), Hirabayashi, Kimberly (committee member)

Creator Email aaaharon@usc.edu,aaharonian@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113997AJB

Unique identifier UC113997AJB

Identifier etd-AharonianA-13167.pdf (filename)

Legacy Identifier etd-AharonianA-13167

Document Type Dissertation

Format theses (aat)

Rights Aharonian, Ani A.

Internet Media Type application/pdf

Type texts

Source 20240701-usctheses-batch-1176 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu