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Individual, generalized, and moderated effects of the good behavior game on at-risk primary school students: A multilevel multiple baseline study using behavioral progress monitoring

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Abstract

The current study examined the impact of the Good Behavior Game (GBG) on the academic engagement (AE) and disruptive behavior (DB) of at-risk students' in a German inclusive primary school sample using behavioral progress monitoring. A multiple baseline design across participants was employed to evaluate the effects of the GBG on 35 primary school students in seven classrooms from grade 1 to 3 (M age = 8.01 years, SD age = 0.81 years). The implementation of the GBG was randomly staggered by 2 weeks across classrooms. Teacher-completed Direct Behavior Rating (DBR) was applied to measure AE and DB. We used piecewise regression and a multilevel extension to estimate the individual case-specific treatment effects as well as the generalized effects across cases. Piecewise regressions for each case showed significant immediate treatment effects for the majority of participants (82.86%) for one or both outcome measures. The multilevel approach revealed that the GBG improved at-risk students' classroom behaviors generally with a significant immediate treatment effect across cases (for AE, B = 0.74, p < 0.001; for DB, B =-1.29, p < 0.001). The moderation between intervention effectiveness and teacher ratings of students' risks for externalizing psychosocial problems was significant for DB (B =-0.07, p = 0.047) but not for AE. Findings are consistent with previous studies indicating that the GBG is an appropriate classroom-based intervention for at-risk students and expand the literature regarding differential effects for affected students. In addition, the study supports Frontiers in Education 01 frontiersin.org Leidig et al. 10.3389/feduc.2022.917138 the relevance of behavioral progress monitoring and data-based decision-making in inclusive schools in order to evaluate the effectiveness of the GBG and, if necessary, to modify the intervention for individual students or the whole group.
feduc-07-917138 July 25, 2022 Time: 15:44 # 1
TYPE Original Research
PUBLISHED 29 July 2022
DOI 10.3389/feduc.2022.917138
OPEN ACCESS
EDITED BY
Sarah Powell,
University of Texas at Austin,
United States
REVIEWED BY
Stefan Blumenthal,
University of Rostock, Germany
Jana Jungjohann,
University of Regensburg, Germany
*CORRESPONDENCE
Tatjana Leidig
tleidig@uni-koeln.de
SPECIALTY SECTION
This article was submitted to
Educational Psychology,
a section of the journal
Frontiers in Education
RECEIVED 10 April 2022
ACCEPTED 28 June 2022
PUBLISHED 29 July 2022
CITATION
Leidig T, Casale G, Wilbert J,
Hennemann T, Volpe RJ, Briesch A and
Grosche M (2022) Individual,
generalized, and moderated effects
of the good behavior game on at-risk
primary school students: A multilevel
multiple baseline study using
behavioral progress monitoring.
Front. Educ. 7:917138.
doi: 10.3389/feduc.2022.917138
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Hennemann, Volpe, Briesch and
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reproduction is permitted which does
not comply with these terms.
Individual, generalized, and
moderated effects of the good
behavior game on at-risk
primary school students: A
multilevel multiple baseline
study using behavioral progress
monitoring
Tatjana Leidig 1*, Gino Casale 2, Jürgen Wilbert 3,
Thomas Hennemann 1, Robert J. Volpe 4,
Amy Briesch 4and Michael Grosche 2
1Department of Special Education and Rehabilitation, Faculty of Human Sciences, University
of Cologne, Cologne, Germany, 2Institute of Educational Research, University of Wuppertal,
Wuppertal, Germany, 3Institute of Inclusive Education, Faculty of Human Sciences, University of
Potsdam, Potsdam, Germany, 4Department of Applied Psychology, Northeastern University, Boston,
MA, United States
The current study examined the impact of the Good Behavior Game (GBG)
on the academic engagement (AE) and disruptive behavior (DB) of at-risk
students’ in a German inclusive primary school sample using behavioral
progress monitoring. A multiple baseline design across participants was
employed to evaluate the effects of the GBG on 35 primary school students
in seven classrooms from grade 1 to 3 (Mage = 8.01 years, SDage = 0.81 years).
The implementation of the GBG was randomly staggered by 2 weeks across
classrooms. Teacher-completed Direct Behavior Rating (DBR) was applied
to measure AE and DB. We used piecewise regression and a multilevel
extension to estimate the individual case-specific treatment effects as well
as the generalized effects across cases. Piecewise regressions for each
case showed significant immediate treatment effects for the majority of
participants (82.86%) for one or both outcome measures. The multilevel
approach revealed that the GBG improved at-risk students’ classroom
behaviors generally with a significant immediate treatment effect across
cases (for AE, B= 0.74, p<0.001; for DB, B= –1.29, p<0.001).
The moderation between intervention effectiveness and teacher ratings
of students’ risks for externalizing psychosocial problems was significant
for DB (B= –0.07, p= 0.047) but not for AE. Findings are consistent
with previous studies indicating that the GBG is an appropriate classroom-
based intervention for at-risk students and expand the literature regarding
differential effects for affected students. In addition, the study supports
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the relevance of behavioral progress monitoring and data-based decision-
making in inclusive schools in order to evaluate the effectiveness of the GBG
and, if necessary, to modify the intervention for individual students or the
whole group.
KEYWORDS
classroom behavior, good behavior game, multilevel analysis, piecewise regression,
single case design
Introduction
The national prevalence rates of mental health problems
in Germany indicate that approximately 17 to 20% of
schoolchildren demonstrate psychosocial problems (i.e.,
externalizing problems such as aggressive behavior or
hyperactivity, and internalizing problems such as depressiveness
or anxiety) with various degrees of severity (Barkmann and
Schulte-Markwort,2012;Klipker et al.,2018). These problems
can negatively affect classroom behaviors and the social and
academic performance of individual students (Kauffman and
Landrum,2012) and their peers (Barth et al.,2004). As the
majority of students with behavioral problems in Germany are
educated in inclusive schools without special education services
(Volpe et al.,2018), teachers need strategies to successfully
deal with their behavior problems, promote social-emotional
development, and continuously evaluate the effectiveness of the
implemented strategies.
Research reviews have demonstrated that group
contingencies such as the Good Behavior Game (GBG;
Barrish et al.,1969) are effective interventions for managing
externalizing behavior problems in general education
classrooms (Maggin et al.,2017;Fabiano and Pyle,2019).
The GBG is an easy-to-use interdependent group contingency
intervention with extensive empirical support that utilizes
students’ mutual dependence to reduce problem behaviors and
to improve prosocial and academic behaviors (Flower et al.,
2014). The main features of this game are easily comprehensible
(Flower et al.,2014): (1) selecting goals and rules, (2) recording
rule violations, (3) explaining the rules of the game and
determining the rewards, (4) dividing the students in two or
more teams to play against each other, and (5) playing the
GBG for a specified amount of time. In the classic variant,
the team receives a mark (“foul”) when a team member
breaks a rule. At the end of the game, the team with the
fewest fouls wins. In multi-tiered-systems of support (MTSS;
Batsche,2014), the GBG is typically integrated in regular
classroom instruction in tier 1 as a part of a proactive classroom
management approach (Simonsen and Myers,2015). When
the GBG is played in conjunction with behavioral progress
monitoring, this combination provides an appropriate way to
facilitate data-based decision-making in supporting students
at risks for emotional and behavioral disorders (EBD) within
a MTSS: If an individual student does not respond to the
GBG, this information can be used to decide whether further
interventions should be added in tier 2 (Donaldson et al.,
2017). In Germany, evidence-based practice within MTSS is
still in its infancy (e.g., Voß et al.,2016;Hanisch et al.,2019);
this also applies to globally known interventions such as the
GBG and data-based decision-making based on behavioral
progress monitoring. Given the potential benefits of the GBG
for students with or at risk for EBD, additional information is
needed on its impact and suitability in a German population.
Evidence base of the good behavior
game
For group design studies in general, the GBG meta-
analysis by Flower et al. (2014)indicated moderate effects
(Cohen’s d= 0.50) on problem behaviors (e.g., aggression,
off-task behavior, and talking out), whereas the meta-analysis
of randomized-controlled trials of the GBG by Smith et al.
(2021)resulted in only small but significant effect sizes for
conduct problems (i.e., aggression or oppositional behavior)
(Hedges’ g= 0.10, p= 0.026) and moderate but not significant
effect sizes for inattention (i.e., concentration problems and
off-task behavior) (Hedges’ g= 0.49, p= 0.123). For conduct
problems, the comparatively smaller effects are likely due
to the rigorous standards of randomized-controlled trials,
implying, for instance, less biased estimates of study effects; for
inattention, this may reflect the significant heterogeneity of the
findings and the overall small number of included studies (Smith
et al.,2021). Meta-analyses of single-case research of the GBG
(Bowman-Perrott et al.,2016;Flower et al.,2014) and class-
wide interventions for supporting student behavior (Chaffee
et al.,2017) found a significant and immediate treatment effect
for reducing challenging behaviors (e.g., disruptive behavior,
aggression, off-task behavior, talking out, and out-of-seat)
(Flower et al.,2014:β=0.2038, p<0.01) and medium to
high effects across both general and special education settings
(Bowman-Perrott et al.,2016: TauU = 0.82; Chaffee et al.,
2017: TauU = 1.00) with larger effects on disruptive and
off-task behavior (e.g., out-of-seat, talking out, interrupting,
pushing and fighting) (TauU = 0.81) than on-task behavior (e.g.,
working quietly, following teacher’s instructions, and getting
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materials without talking) (TauU = 0.59), and higher effects
for students with or at risk for EBD (TauU = 0.98) than
for students without any difficulties (TauU = 0.76) (Bowman-
Perrott et al.,2016). Although the meta-analyses revealed the
effectiveness of the GBG across settings, it must be noted that
the majority of research examined the impact of the GBG in
general education settings with typically developing students
(Moore et al.,2022). Even though the studies included often lack
concrete information on the implementation of mainstreaming
or inclusive education in the sample, against the background of
the development of the school systems since the 1990s, it can be
assumed that many studies of the last 30 years were conducted
in mainstreaming or inclusive settings.
When interpreting the meta-analytic findings, some
methodological aspects have to be considered. In some studies
using a group design, students were nested within classrooms.
This approach is helpful to infer the effectiveness for a
population of students based on inferential statistics. However,
it does not automatically allow to distinguish which students
the GBG was effective for and which students did not benefit at
all (or even increased their problem behavior). In fact, in group
research designs, the behavior of individual students differing
from the mean of all students is considered a measurement
error, and the groups effect size does not tell us anything about
case-specific treatment effects (Lobo et al.,2017). In principle,
this also applies to subgroup analyses, even if they examine the
effects more specifically. Furthermore, as Smith et al. (2021)
noted, the available group design studies and single case studies
measure similar but finally different outcomes: While the
results of group design studies allow conclusions regarding
general, cross-situational changes in student behavior (trait), no
conclusions can be drawn about the effects of GBG on targeted
behavior in the classroom situations in which GBG is played
(state). The reverse is equally true. Against this background, the
comparability of results of group studies and single-case studies
is constrained. In addition, regarding the meta-analyses of
single-case research, it is important to consider that most of the
included studies investigated the impact of the GBG on a group
or a class, not on individual students (Donaldson et al.,2017).
Evidence base for effects on at-risk students in
single-case research using progress monitoring
There is limited information about the impact of the GBG
on students with or at risk for EBD in single-case research
using progress monitoring. As Bowman-Perrott et al. (2016)
critically noted, most studies included in their meta-analysis
focused on typically developing students with a normal range
of disruptive behaviors. From a methodological perspective, the
information provided by single-case research is limited due to
the composition and nature of the samples and the methods of
data analysis used. With only a few exceptions, the vast majority
of single-case studies investigated a class or a group as a single-
case and analyzed data at the classroom level (Donaldson et al.,
2017). This approach is helpful to test the positive impact of
the GBG on a group as a whole, but it does not allow analysis
at the level of individual students (Donaldson et al.,2017;
Foley et al.,2019). Furthermore, the sample sizes are relatively
small (i.e., three to 12 cases), and the studies varied widely
with regard to sample characteristics (e.g., setting, classroom
size, class composition). As a result, neither the findings of
individual studies nor the aforementioned meta-analyses can
simply be generalized to specific populations of students, e.g.,
at-risk students (Bowman-Perrott et al.,2016;Donaldson et al.,
2017).
In only three of the four studies reporting individual student
data included in the meta-analysis by Bowman-Perrott et al.
(2016), the target students were explicitly identified as the most
challenging students in their class (i.e., students displaying more
disruptive behaviors than peers) (Medland and Stachnik,1972;
Tanol et al.,2010;Hunt,2012). We found six further studies
investigating the impact of the GBG on students with or at risk
for EBD at the level of individual students (Donaldson et al.,
2017;Groves and Austin,2017;Pennington and McComas,
2017;Wiskow et al.,2018;Foley et al.,2019;Moore et al.,
2022). In seven of the aformentioned nine studies, all of the
target students showed improvements, although to differing
degrees. However, Donaldson et al. (2017)and Hunt (2012)
found individual non-responders. Furthermore, Donaldson
et al. (2017)and Moore et al. (2022)reported decreasing
positive effects of the GBG over time for some children
who frequently exhibited disruptive behavior. As Donaldson
et al. (2017)concluded, only teachers who play the GBG with
progress monitoring at the level of individual students can avoid
unnecessary implementation of individualized interventions,
and identify students who need additional support beyond the
class-wide intervention.
In the few studies that have examined the effects of the
GBG at the level of individual students, one can analyze the
differential impact on individual students, but one cannot
conclude whether the GBG is effective for a specific group
of students, e.g., with or at risk for EBD. No study at the
individual level or at the group level used an inferential
statistic approach to examine whether the effects are statistically
significant, which is important to generalize the results (Shadish
et al.,2013). Even in single-case research, researchers are
not only interested in specific individual treatment effects
to support evidence-based decisions, but also whether the
effects can be generalized to other cases. To our knowledge,
no study evaluating the efficacy of the GBG has used
inferential statistics to generalize the average treatment effect
across cases within the same study. Accordingly, neither
the results of studies at classroom level nor those at the
level of individual students are representative for students
with or at risk for EBD, so that the evidence for this
group remains comparatively weak (Bowman-Perrott et al.,
2016;Donaldson et al.,2017). In addition to case-specific
inferential statistics, multilevel models enable investigating the
overall effectiveness across participants considering the average
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treatment effect, variations across cases and possible influential
factors (Moeyart et al.,2014).
Psychosocial problems as moderator for the
effects of the good behavior game
Additional research on the students’ individual
characteristics is needed to assess the possible influential
factors (Bowman-Perrott et al.,2016;Maggin et al.,2017).
The results of group design studies suggest that students’
psychosocial problems (i.e., externalizing problems such as
aggressive behavior or hyperactivity, and internalizing problems
such as depressiveness or anxiety) are associated with the
effectiveness of the GBG: They indicate differential effects of the
GBG on externalizing and internalizing problems depending
on the students’ individual risk levels and types (e.g., van Lier
et al.,2005;Kellam et al.,2008;Spilt et al.,2013) with partly
contradictory results for students with or at risk for severe
externalizing behavior problems (e.g., aggressive, violent, and
criminal behavior) and for students with combinations of risks
(e.g., combination of social and behavior risks). Analyzing
single-case research, Bowman-Perrott et al. (2016)identified
the EBD risk status as a potential moderator for the effect
on externalizing classroom behaviors (i.e., larger effects for
students with EBD or at risk for EBD), however without further
investigation of the risk level and subtype of behavior problems.
Therefore, the role of students’ psychosocial problems as a
potential moderator requires further investigation.
The current study
In sum, the existing research adds different pieces to the
puzzle whether GBG is an evidence-based intervention, partly
also for students with or at risk for EBD (Bowman-Perrott
et al.,2016). There is some support for the effectiveness of the
GBG either for individual students or for groups of students,
e.g., with or at risk for EBD (Joslyn et al.,2019;Smith et al.,
2021). However, our study is the first study about the GBG
that uses a large sample of individual at-risk students nested in
classrooms and tested the hypothesis of individual, general, and
differential effectiveness within the same study using inferential
statistics on individual and group level at the same time.
Typically, the number of cases in single-case research varies
between 1 and 13 (Shadish and Sullivan,2011). Our large
sample of 35 students and our methodological approach give
us the opportunity to examine case-specific treatment effects as
well as effects across cases under stable conditions. Combining
group statistics and single-case statistics makes all Council of
Exceptional Children’s standards for classifying evidence-based
special education interventions (Cook et al.,2015) applicable for
our study (see Supplementary Material).
Therefore, the current study is designed to extend previous
research by investigating the impact of the GBG on at-risk
students’ classroom behaviors in inclusive settings in Germany
using behavioral progress monitoring and analyzing the data
using regression analyses to estimate case-specific treatment
effects as well as effects across students. In particular, we
investigate the interaction between the impact of the GBG and
students’ psychosocial problems due to the potential influence
of students’ individual risks. We hypothesized moderate to large
effects on at-risk students’ academic engagement and disruptive
behavior for the majority of students and across all cases with an
immediate treatment effect, and only a small additional slope
effect. Furthermore, we anticipate that students’ psychosocial
problems moderate the intervention effects. We hypothesize
that the impact of the GBG would interact with the magnitude of
students’ behavioral problems. Specifically, we assume that the
higher students’ externalizing problems are, the more effective
the GBG will be.
Method
Participants and setting
In Germany, inclusion in schools is primarily understood
as learning together of students with and without special
educational needs in general school settings (KMK,2011).
This study was conducted in an inclusive primary school in a
midsize town in western Germany (North Rhine-Westphalia)
with 12 first-through-fourth-grade classrooms. Due to the legal
requirements of North Rhine-Westphalia, support in the areas
of learning, speech and behavior can be provided in all grades
regardless of the formally identified special educational needs,
so that there is no administrative ascriptive diagnosis for some
of the students with special educational needs. Furthermore,
as a rule, no formal diagnosis of special educational needs
in learning, behavior, and speech is provided for first and
second graders. In the inclusive primary school in our study,
approximately 50 children were with or at risk for learning
disabilities and/or EBD. In line with official school records,
approximately 70% of all students had a migration background.
After the introduction of the project during the teachers’
conference, seven general education teachers decided to
participate in the study with their classes. Each of these
classroom teachers nominated five students based on their
professional experience and judgment as the most challenging
students in their classroom (n= 35; five first graders,
15 second graders and 15 third graders; five girls and 30
boys). Based on teacher ratings on the Strengths and Difficulties
Questionnaire (SDQ; Goodman,1997), 22 (63%) of the
nominated students showed externalizing risks, whereas only
two (6%) demonstrated internalizing risks, two students (6%)
showed risks in both areas, and nine students (26%) exposed
no psychosocial risks. Further demographic information and the
SDQ data are summarized in Table 1.
After consultation with the teachers to identify the
classroom situation believed to be the most problematic
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TABLE 1 Student demographic information and data of the behavioral screening instrument (SDQ).
SDQ
Classroom (Grade) Student Gender Age Migration background Externalizing scoreaInternalizing scoreb
A (1st) S1 Boy 6 0 9+1
S2 Boy 6 0 11++ 0
S3 Girl 7 0 6 0
S4 Boy 7 1 5 3
S5 Boy 6 1 7+1
B (2nd) S6 Boy 7 1 8+1
S7 Boy 8 1 4 3
S8 Boy 7 1 13+++ 3
S9 Boy 7 1 15+++ 6+
S10 Boy 7 0 18+++ 4
C (2nd) S11 Girl 7 1 10++ 3
S12 Boy 7 0 13+++ 1
S13 Boy 9 0 13+++ 3
S14 Boy 7 1 10++ 4
S15 Boy 8 1 8+1
D (2nd) S16 Boy 7 1 4 0
S17 Boy 7 0 6 3
S18 Boy 7 0 9+4
S19 Boy 7 1 4 7+
S20 Girl 7 1 6 10+++
E (3rd) S21 Boy 9 1 3 1
S22 Boy 8 1 6 0
S23 Boy 9 0 8+5
S24 Boy 9 0 6 1
S25 Boy 8 0 12+++ 3
F (3rd) S26 Girl 9 0 13+++ 2
S27 Boy 8 1 11++ 2
S28 Girl 8 1 7+3
S29 Boy 8 1 10++ 1
S30 Boy 8 0 10++ 9++
G (3rd) S31 Boy 8 0 6 2
S32 Boy 8 1 9+0
S33 Boy 8 0 9+3
S34 Boy 9 1 11++ 1
S35 Boy 8 0 10++ 0
For the migration background, 0 = no and 1 = yes. For SDQ externalizing and internalizing scores, += slightly raised, ++ = high and +++ = very high.
aCategorization by Goodman et al. (2010): 7-9 = slightly raised, 10-11 = high, 12-20 = very high. bCategorization by Goodman et al. (2010): 6-7 = slightly raised, 8-9 = high, 10-
20 = very high.
concerning externalizing classroom behaviors, we defined the
target instructional period for playing the GBG for each
classroom (i.e., individual work in math for classroom B, D,
and G, and individual work in German language for classrooms
A, C, E, and F).
Measures
Direct behavior rating
Direct Behavior Rating (DBR; Christ et al.,2009) was
used to measure academic engagement (AE) and disruptive
behavior (DB). DBR has already been adapted, evaluated, and
implemented for German classrooms using the operational
definitions of AE and DB provided by Chafouleas (2011)
(Casale et al.,2015,2017). AE included students’ active or
passive participation in ongoing academic activities such as
engaging appropriately in classroom activities, concentrated
working, completing tasks on time and raising their hands.
DB was defined as behaviors disrupting others or affecting
students’ own or other students’ learning, such as speaking
without permission, leaving one’s seat, noisemaking, having
undesirable private discussions and fooling around. For
both dependent variables, we used a single-item scale
(SIS) with these broadly defined items representing a
common behavior class (Christ et al.,2009). Previous
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research focusing on the German DBR scales supports
their generalizability and dependability across different
raters (i.e., general classroom teachers and special education
teachers), items, and occasions (Casale et al.,2015,2017).
These studies showed that the DBR-SIS provides dependable
scores (8 > 0.70) for an individual student’s behavior after
four measurement occasions. Furthermore, measurement
invariance testing across high-frequency occasions with short
intervals showed the sensitivity of DBR (Gebhardt et al.,
2019). In addition, previous research from the United States
supports the reliability, validity and sensitivity of DBR to
changes to the SIS regarding the targeted behaviors (e.g.,
Briesch et al.,2010;Chafouleas et al.,2012). The teachers
observed the behavior of the five nominated students in the
target classroom situation during baseline and intervention.
Immediately after each observation, the teachers rated both
AE and DB for each of the five students on a paper-pencil
questionnaire using a 6-point Likert-type scale (i.e., 0 = never,
5=always).
Strengths and difficulties questionnaire for
teachers
Participants’ internalizing and externalizing problems were
assessed with the German version of the worldwide used
behavioral screening questionnaire SDQ (Goodman,1997)
for teachers. It consists of 25 items equally divided across
five subscales (hyperactivity, conduct problems, emotional
problems, peer problems, and prosocial behavior). Besides the
5-factor model, a 3-factor model containing the factors of
externalizing problems, internalizing problems, and prosocial
behavior is used to interpret results (Goodman et al.,2010).
Evaluations of the German version indicate an acceptable
fit both of the 5-factor model (Bettge et al.,2002) and the
3-factor model (DeVries et al.,2017), and a good internal
consistency for the teacher version of the SDQ (Cronbachs
αbetween 0.77 and 0.86; Saile,2007). After each item was
scored by teachers on a 3-point Likert-type scale (0 = not
true,1=somewhat true,3=certainly true), we calculated
the internalizing and externalizing subscales by summing the
conduct and hyperactivity scales for the externalizing subscale
and summing the emotional and peer problems scales for the
internalizing subscale (Goodman et al.,2010).
Design and procedures
A multiple baseline across-classroom design with a 2-week
staggered randomized-phase start was used. Each classroom was
randomly assigned to a single a priori designated intervention
start point (Kratochwill and Levin,2010). After the baseline
phase, which was previously determined to be at least 8 but
not more than 38 days, the intervention phase (70 to 100 days)
started with a 2-week interval between the groups depending on
the start point. Due to holidays and cancelled lessons because
of school events, full-day conferences, part-time employment of
teachers, and in-service training, the number of days with the
opportunity to play the GBG fluctuated between 61 to 87 days.
The GBG was played de facto 3 to 5 days a week. Phase lengths
for each classroom are displayed in Table 2.
Teacher training and mentoring
To support an effective implementation, we developed a
program with the components shown in Figure 1 according to
recommendations from the literature (Hagermoser Sanetti et al.,
2014;Poduska and Kurki,2014) and considering the specific
school setting. In addition, the teachers received training on
the use of DBR based on Chafouleas (2011), consisting of the
theoretical background, the practical use of DBR, and feedback
by a research assistant.
In total, the training before the implementation of the
GBG included 14 hours for classroom management and GBG
(i.e., 10 hours in-service training and 4 hours self-study)
and 5 hours for DBR (in-service training). Self-study was
implemented as an e-learning course with text, audio, video, and
TABLE 2 Design data, treatment integrity, and treatment usability for each classroom.
Treatment Usability (M)
Classroom A B Treatment IntensityaTreatment IntegritybAcceptability Understanding Collaboration Feasibility
A 8 100 97.70% 92.86% 5.29 4.75 5.33 5.17
B 8 100 79.76% 100% 5.43 5.25 6.00 5.17
C 18 90 87.01% 100% 4.29 4.75 4.67 4.67
D 38 70 81.97% 89.29% 4.86 4.75 5.33 5.00
E 18 90 70.89% 100% 4.71 5.25 5.00 4.67
F 28 80 83.10% 88.10% 4.71 4.50 5.67 4.67
G 28 80 64.79% 88.57% 5.00 5.00 5.00 4.83
A = baseline phase length in days; B = intervention phase length in days; M= mean values. For treatment usability, higher scores are indicative of more extreme responding in the direction
of the scale assessed.
aTreatment intensity as number of days the GBG was played based on the adjusted opportunities to play, expressed as percentage. bTreatment integrity rated by observer.
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FIGURE 1
Components of teacher training and mentoring.
interactive elements. The in-service training featured input with
video examples, discussion, and small group practice including
feedback from both trainers and peers. In addition, the teachers
received a detailed printed intervention manual with all steps
and materials. Immediately before the start of the intervention
phase, details regarding the introduction phase of the GBG
and specific individual questions were clarified in a two-hour
implementation planning meeting. During the intervention
phase, we provided six brief appointments (15 to 20 minutes)
for performance feedback spread over the entire intervention
period following work shadowing, in each case combined with
talks about pending issues and difficulties, and one appointment
(1 hour) for a detailed consultation after ten weeks based on
the results of the behavioral progress monitoring. Motivational
interviewing/trouble shooting was offered two times on site by
the trainers (1 hour) and additionally by email.
Baseline
Teachers completed the paper-pencil-based DBR-SIS for
each student each day at the end of the target instructional
period. The assessment units were 10 minutes long.
Intervention
The classic version of the GBG was played (Flower et al.,
2014) with dividing the students in two or more teams to
play against each other, marking “fouls for rule-breaking, and
rewarding the team with the fewest fouls as winner of the game.
The teachers divided their class into five to six teams using the
existing assigned group seating arrangements, with five to six
students on each team. The teams were maintained throughout
the intervention phase. Contrary to previous practices reported
by Donaldson et al. (2017), no student was placed on his or
her own team because of behavior, e.g. disruptive behavior. If
problems arose on a team, the teacher spoke with the team to
find common solutions. After the teams had given themselves
names (e.g., “Lions” and “The cool kids”), each team received a
sign with the team name, which was affixed to the team table.
A poster at the front of the classroom listed three to four rules
that had been previously jointly compiled (e.g., “I am quiet” “I
work intently” “I sit in my seat”). Moreover, a timer was placed
near the poster to be clearly visible to all teams, and the names of
teams were written on the board to mark the fouls. Respecting
the specific classroom situation and the relationship with the
students, the goals, rules, rule violations, and rewards slightly
deviated across classrooms (e.g., rewarding with goodies such as
chocolate or gummy bears, small prizes such as stickers or pens,
or activities such as group games or extra reading time). After
10 minutes playing in the defined period, the teachers counted
the fouls, named the winning team and delivered the reward to
the winning team.
Treatment integrity
At six measurement points, trained observers collected
treatment integrity data on the accuracy with which the
teacher implemented the GBG using a seven-item treatment
integrity checklist adapted from the Treatment Integrity
Planning Protocol (TIPP; Hagermoser Sanetti and Kratochwill,
2009) with dichotomous ratings (agreement disagreement).
In addition, the teachers completed an analogous treatment
integrity checklist with additional options for comments (e.g.,
reasons for not playing the game or information on problems)
and noted the number of fouls overall and per team on each
day. The total adherence across all classrooms throughout the
six measurement points was 94.12% (range = 88.1% to 100%).
For these days, the interobserver agreement (observer teacher)
was 100%. Teachers’ ratings over the whole treatment period
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showed that the overall treatment integrity measured by daily
self-assessment was 96.15% (range = 85.5% to 100%).
Treatment usability
Treatment usability from the teachers’ points of view was
assessed with the German version of the Usage Rating Profile
(URP; Briesch et al.,2017) consisting of 20 items loading on four
factors. The teachers indicated the extent to which they agreed
with each of the items using a 6-point Likert scale (i.e., 1 = strong
disagreement,6=strong agreement). The results of the URP
revealed agreement regarding the usage of the GBG intervention
across all teachers for each factor: M= 4.9 (SD = 0.38) for
acceptability, M= 4.9 (SD = 0.28) for understanding, M= 5.2
(SD = 0.45) for home-school collaboration, and M= 4.88
(SD = 0.23) for feasibility. Treatment integrity and usability are
shown in Table 2.
Data analysis
To examine the impact of the GBG on AE and DB on
each single case and across cases, descriptive statistics and
inferential statistics were used to analyze the data. Therefore,
after calculating phase means and effect sizes for each single
case, we conducted regression analyses for each single case
and across cases to replicate the results as well as to obtain
statistical significance and overall quantification (Manolov and
Moeyaert,2017). The multilevel approach to estimate the overall
effects was also used to analyze the interaction between students’
psychosocial problems and the intervention effects. First, we
reported descriptive statistics and calculated the non-rescaled
non-overlap of all pairs (NAP; see Alresheed et al.,2013)
with medium effects indicated by values of 66% to 92%, and
strong effects indicated by values of 93% to 100%. Subsequently,
we analyzed the data using a piecewise regression approach
(Huitema and McKean,2000). This procedure enables the
control of developmental trends in the data (trend effects)
and the differentiation between continuous (slope effect) and
immediate (level effects) intervention effects. We conducted
piecewise regressions for each single case and a multilevel
extension (see Van den Noortgate and Onghena,2003;Moeyart
et al.,2014) for all cases with measurements at level 1 nested
in subjects at level 2. The multilevel analyses were set up as
a random intercept and random slope model with all three
parameters (trend, slope, and level effects) as fixed and random
factors. To test the significance of the random effects, we applied
a likelihood ratio test for each random slope factor comparing
the full model against a model without the target factor.
To analyze the moderating effects of students’ internalizing
and externalizing problems on the intervention effects, we
inserted cross-level interactions into the model. All analyses
were conducted using R (R Core Team,2018) and the scan
package (Wilbert and Lüke,2018).
Results
Descriptive statistics
Table 3 summarizes descriptive statistics, including
the NAP as a common effect size for both AE and DB.
The non-rescaled NAP indicated a medium or strong
effect for both dependent variables for all participants,
varying between 77.8% and 99.8% for AE and between
78.3% and 100% for DB. In detail, there were strong
effects for 12 (34.29%) and medium effects for 23
(65.71%) participants regarding AE, and strong effects
for 18 (51.54%) and medium effects for 17 (48.46%)
participants regarding DB.
Inferential statistics
We analyzed the data case by case, conducting piecewise
regression analyses to calculate the impact of the GBG on
AE and DB for each case. Subsequently, we used a multilevel
extension to calculate the impact of the GBG for all cases with
measurements at level 1 nested in subjects at level 2.
Piecewise regression for each single case
Fourteen cases showed both significant level increases in
AE and decreases in DB (p<0.05). Three participants (S2,
S3, and S20) showed significant level increases only for AE
(p<0.05), whereas 12 participants demonstrated significant
level decreases only for DB (p<0.05). The slope effect was
significant for increases in AE as well as decreases in DB for three
participants (p<0.05 for S10, S26, and S33). Four participants
(S19, S21, S29, and S35) demonstrated neither a significant level
nor a significant slope effect for one of the dependent variables
(p>0.05). The results for each case are shown in Table 4.
Multilevel analyses
First, we conducted multilevel analyses of all single cases.
Overall, for AE and DB, we found significant level effects.
On average, AE increased by 0.74 points (p<0.001) on a
6-point Likert-type scale, and DB decreased by 1.29 points
(p<0.001). Furthermore, the significant slope effect for DB
indicated a decrease of 0.01 points (p= 0.002) per measurement
occasion. The similar increasing slope effect for AE failed to
reach statistical significance (p= 0.055). Second, we calculated
the random effects regarding the variability between cases.
We found significant level effects for both of the dependent
variables. For AE, the estimated standard deviation for the level
effect was SD = 0.65 (p<0.001), whereas for DB, it was SD = 0.81
(p<0.001). The estimated standard deviation for the slope effect
was SD = 0.00 (p= 1.000) for AE and SD = 0.15 (p= 0.034) for
DB. The results for both fixed and random effects are shown in
Table 5.
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TABLE 3 Descriptive statistics for the 35 single-cases for both dependent variables.
Case nAnBAcademic Engagement Disruptive Behavior
misAmisBMA(SD)MB(SD) NAP (%) misAmisBMA(SD)MB(SD) NAP (%)
S1 8 100 1 24 2.0 (1.2) 3.5 (1.1) 88.6 1 24 1.1 (0.4) 0.2 (0.4) 93.9
S2 8 100 1 17 2.1 (1.2) 3.6 (1.1) 86.9 1 18 1.1 (0.7) 0.4 (0.8) 83.8
S3 8 100 1 20 3.6 (0.8) 4.1 (0.8) 77.8 1 20 1.1 (0.7) 0.2 (0.6) 90.2
S4 8 100 1 20 3.4 (0.5) 4.7 (0.7) 95.9 1 21 2.3 (1.1) 0.1 (0.3) 99.4
S5 8 100 1 19 3.1 (1.1) 4.3 (0.8) 87.8 1 20 1.1 (0.9) 0.1 (0.3) 88.7
S6 8 100 1 36 1.9 (0.9) 3.5 (0.8) 94.2 1 38 2.1 (1.1) 0.0 (0.2) 99.8
S7 8 100 2 41 2.3 (1.2) 2.5 (1.2) 79.6 2 41 1.8 (0.8) 0.2 (0.4) 98.6
S8 8 100 1 38 1.9 (1.3) 3.2 (0.9) 88.9 1 38 2.1 (1.1) 0.0 (0.1) 99.9
S9 8 100 1 34 1.4 (0.8) 3.6 (0.8) 97.8 1 35 3.7 (0.5) 0.0 (0.3) 100.0
S10 8 100 2 41 2.2 (1.5) 3.3 (0.9) 88.5 2 42 1.0 (0.6) 0.0 (0.1) 96.1
S11 18 90 5 33 1.9 (0.3) 2.6 (0.6) 90.0 5 34 2.9 (0.9) 0.2 (0.7) 98.7
S12 18 90 5 43 3.8 (0.6) 4.4 (0.7) 89.5 5 43 2.7 (0.9) 0.2 (0.5) 99.3
S13 18 90 7 37 2.4 (0.5) 2.7 (0.5) 88.3 7 37 1.8 (0.6) 0.2 (0.5) 98.7
S14 18 90 5 31 2.4 (0.7) 3.0 (0.5) 88.1 5 32 1.8 (0.7) 0.1 (0.3) 99.4
S15 18 90 5 34 2.9 (0.3) 3.7 (0.5) 93.7 5 35 1.8 (0.7) 0.1 (0.4) 98.9
S16 38 70 11 23 3.7 (0.5) 4.1 (0.6) 84.5 11 23 1.5 (0.8) 0.8 (0.7) 86.9
S17 38 70 11 20 3.7 (0.7) 4.3 (0.8) 86.2 11 22 1.0 (0.7) 0.4 (0.5) 87.0
S18 38 70 13 28 4.0 (0.5) 4.7 (0.5) 91.1 13 29 0.7 (0.6) 0.1 (0.4) 90.6
S19 38 70 13 26 3.1 (0.8) 4.1 (0.8) 91.7 13 27 0.6 (0.8) 0.3 (0.5) 85.3
S20 38 70 11 20 3.5 (0.7) 4.7 (0.6) 94.2 11 22 0.1 (0.3) 0.0 (0.0) 78.3
S21 18 90 7 38 3.7 (0.6) 3.5 (0.7) 79.9 7 40 0.9 (0.7) 0.7 (0.8) 85.4
S22 18 90 4 36 3.6 (0.7) 4.4 (0.6) 88.9 5 38 1.4 (0.9) 0.6 (0.6) 90.3
S23 18 90 4 37 3.3 (0.6) 3.7 (0.9) 85.0 4 38 1.3 (0.6) 0.5 (0.6) 90.5
S24 18 90 4 43 4.0 (0.8) 4.4 (0.6) 84.9 4 45 1.3 (0.6) 0.6 (0.6) 90.7
S25 18 90 3 55 3.3 (0.8) 4.2 (0.6) 93.6 3 57 2.0 (0.7) 0.7 (0.6) 97.3
S26 28 80 12 29 3.3 (0.8) 3.3 (0.6) 82.2 12 29 2.0 (1.1) 1.7 (0.8) 84.5
S27 28 80 12 26 3.5 (0.6) 3.4 (0.7) 79.3 12 26 2.1 (1.0) 1.8 (0.9) 83.6
S28 28 80 13 29 2.1 (0.5) 2.7 (0.8) 90.4 13 29 3.1 (0.7) 2.0 (0.9) 94.0
S29 28 80 14 32 2.4 (0.6) 3.0 (0.9) 91.2 14 32 2.8 (0.8) 1.6 (0.9) 94.8
S30 28 80 16 30 3.2 (0.6) 3.9 (0.7) 93.6 16 30 2.1 (0.9) 0.6 (0.7) 96.7
S31 28 80 12 36 2.7 (1.3) 4.8 (0.6) 97.7 12 36 1.2 (1.4) 0.1 (0.4) 92.4
S32 28 80 12 36 3.2 (1.2) 4.9 (0.3) 97.4 12 36 1.1 (0.7) 0.0 (0.2) 96.5
S33 28 80 12 35 1.9 (1.1) 4.9 (0.4) 99.8 12 36 1.7 (1.2) 0.1 (0.3) 96.2
S34 28 80 12 36 4.2 (0.6) 4.9 (0.4) 93.8 12 36 0.6 (0.6) 0.0 (0.2) 92.5
S35 28 80 12 36 4.1 (0.8) 4.8 (0.6) 93.1 12 36 0.4 (0.6) 0.1 (0.5) 88.6
n= number of data points, which was the same for all of the students within the same group in each classroom; mis = missing data points; M= mean, SD = standard deviation; NAP = non-
rescaled non-overlap of all pairs. Missing data points resulted from the school and teacher factors (e.g., public and movable holidays, cancelled lessons, part-time employment, and illness)
as well as student illness, incomplete DBR, etc.
Interaction with students’ psychosocial
problems
We inserted cross-level interactions between level effects
and the different SDQ subscales into the model. For AE, we did
not find significant interactions with the level of the intervention
phase for either the SDQ total score (B= 0.03, p= 0.292) or one
of the subscales (Bint = 0.03, p= 0.505; Bext = 0.03, p= 0.423).
The analysis for DB showed a significant interaction for the
externalizing subscale (B=0.07, p= 0.047) and no significant
interactions for the other scales (Btotal =0.05, p= 0.076;
Bint =0.02, p= 0.927). Table 6 contains the results for both
dependent variables.
Discussion
The purpose of the current study was to evaluate the
impact of the GBG on at-risk students’ AE and DB in
an inclusive primary school in Germany using behavioral
progress monitoring. Extending previous studies, we used
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TABLE 4 Piecewise regression analyses for the 35 single cases for
both dependent variables.
Case Academic Engagement Disruptive Behavior
Trend Level Slope Trend Level Slope
S1 0.22 2.12* 0.22 0.08 1.31* 0.08
S2 0.23 1.85* 0.24 0.08 0.71 0.09
S3 0.08 1.26* 0.07 0.16 0.65 0.16
S4 0.09 1.58* 0.09 0.12 2.68* 0.12
S5 0.03 0.82 0.03 0.3* 0.06 0.3*
S6 0.23 0.72 0.23 0.23* 1.34* 0.23*
S7 0.15 0.14 0.16 0.06 1.5* 0.06
S8 0.06 0.84 0.07 0.13* 1.64* 0.13*
S9 0.16 2.4* 0.17 0.09* 3.33* 0.09*
S10 0.36* 1.93* 0.36* 0.11* 1.33* 0.11*
S11 0.03 1.16* 0.04 0.07 2.3* 0.07
S12 0.06 0.59 0.04 0.05 2.08* 0.05
S13 0 0.36 0 0.04 1.14* 0.04
S14 0.02 0.62* 0.02 0.07* 2.06* 0.07*
S15 0.03 0.34 0.02 0.02 1.45* 0.02
S16 0.01 0.56* 0.01 0.02 1.21* 0.01
S17 0.01 0.54 0.02 0 0.73* 0.01
S18 0 0.53* 0 0.01 0.55* 0.01
S19 0.03* 0.18 0.02 0.02 0.04 0.02
S20 0.01 0.74* 0.01 0 0.16 0
S21 0.05 0.06 0.05 0.05 1.03 0.04
S22 0.06 1.28* 0.05 0.11* 1.94* 0.11*
S23 0 0.11 0.01 0.02 0.83* 0.02
S24 0.05 0.77* 0.05 0.04 1.17* 0.03
S25 0.01 0.62 0.01 0.03 0.99* 0.04
S26 0.05* 0.39 0.05* 0.07* 1.4* 0.06*
S27 0.01 0.15 0.01 0.06* 0.36 0.06*
S28 0.01 0.51 0.01 0 1.21* 0
S29 0.02 0.08 0.04 0.01 0.27 0.01
S30 0.03 0.52 0.04 0.02 1.44* 0.02
S31 0.01 1.88* 0.01 0.05* 1.77* 0.05*
S32 0.01 1.7* 0.01 0.01 1.02* 0.01
S33 0.04* 3.51* 0.04* 0.06* 2.38* 0.06*
S34 0.01 0.7* 0.02 0 0.48* 0
S35 0 0.55 0 0.03 0.1 0.02
* = significant (p<0.05).
piecewise regression for each of the 35 single cases and
a multilevel extension to examine both level and slope
effects. Furthermore, we examined the interaction with
students’ psychosocial problems as potential influencing factors
moderating the effectiveness.
Main findings
The individual-level data analyses revealed that the majority
of at-risk students benefited from the GBG. Whereas the non-
rescaled NAP indicated a medium or strong effect for both
dependent variables for all participants, the inferential statistics
did not reveal statistically significant improvements for all cases.
Piecewise regressions for each single case enabled us to identify
significant immediate treatment effects for 14 participants for
both outcomes (40.0%), for three participants for AE only
(8.57%), and for 12 participants for DB only (34.29%). However,
six students (17.14%) showed no significant level effects for AE
or DB. The results at the individual level support and extend
the limited prior research, indicating that the classroom-based
GBG intervention is effective for improving at-risk students’
classroom behaviors, but there are students who do not respond
to the intervention (Hunt,2012;Donaldson et al.,2017;Moore
et al.,2022). Consistent with previous research reporting class-
wide data (Flower et al.,2014;Bowman-Perrott et al.,2016),
the multilevel approach revealed that the GBG improved AE
and reduced DB for students with challenging behavior with
a significant immediate treatment effect across cases. In line
with the meta-analysis by Bowman-Perrott et al. (2016), the
GBG was more effective in reducing DB than increasing AE.
Similar to Flower et al. (2014), we found statistically significant
slightly decreasing DB throughout the intervention phase. In
contrast to the single case studies by Donaldson et al. (2017)
and Moore et al. (2022), in which some participants with or at
risk for EBD showed slightly increasing trends over the course
of the intervention, this slope effect indicates a continuous and
decreasing change in DB.
The findings from the present study extend prior meta-
analysis results (Bowman-Perrott et al.,2016) by investigating
students’ psychosocial risks as potential moderators for the
impact of the intervention. We found significant moderating
effects for students’ externalizing problems on the intervention
effect for DB, meaning that the higher students’ externalizing
problems were, the more effective the GBG was. These findings
correspond with the results of reviews on the subject of
interventions for aggressive behavior (Waschbusch et al.,2019)
as well as longitudinal studies evidencing the GBG as an effective
TABLE 5 Fixed and random effects of the multilevel
piecewise-regression models for academic engagement and
disruptive behavior.
Fixed effects Random effects
Parameter B SE t p Estimated SD L p
Academic Engagement
Intercept 3.02 0.14 21.83 <0.001** 0.74 109.59 <0.001**
Trend 0.00 0.00 0.92 0.358 0.01 1.94 0.746
Level Phase B 0.74 0.13 5.86 <0.001** 0.65 65.71 <0.001**
Slope Phase B 0.01 0.00 1.92 0.055 0.00 0.02 1.000
Disruptive Behavior
Intercept 1.50 0.13 11.63 <0.001** 0.71 210.92 <0.001**
Trend 0.01 0.00 3.19 0.001** 0.02 12.90 0.012**
Level Phase B 1.29 0.15 8.77 <0.001** 0.81 138.84 <0.001**
Slope Phase B 0.01 0.00 3.18 0.002** 0.15 10.43 0.034*
* = significant (p<0.05); ** = significant (p<0.001).
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TABLE 6 Interaction of psychosocial problems with the impact of the good behavior game.
Scale B SE df t p B SE df t p
Academic Engagement Disruptive Behavior
SDQ total
Intercept 2.94 0.12 2376 24.25 <0.001** 1.61 0.12 2349 12.24 <0.001**
Trend 0.00 0.00 2376 3.72 <0.001** 0.00 0.00 2349 0.30 0.766
Level Phase B 0.67 0.17 2376 5.30 <0.001** 1.20 0.13 2349 9.33 <0.001**
SDQ total 0.06 0.03 33 2.22 0.034 0.04 0.03 33 1.40 0.172
Level Phase B*SDQ total 0.03 0.03 2376 1.06 0.292 0.05 0.03 2349 1.77 0.076
SDQ int
Intercept 2.94 0.13 2376 22.99 <0.001** 1.61 0.14 2349 11.76 <0.001**
Trend 0.00 0.00 2376 3.70 <0.001** 0.00 0.00 2349 0.34 0.738
Level Phase B 0.67 0.13 2376 5.32 <0.001** 1.20 0.05 2349 9.15 <0.001**
SDQ int 0.05 0.05 33 0.94 0.356 0.01 0.05 33 0.09 0.927
Level Phase B*SDQ int 0.03 0.05 2376 0.67 0.505 0.02 0.05 2349 0.39 0.697
SDQ ext
Intercept 2.94 0.12 2376 23.88 <0.001** 1.61 0.13 2349 12.60 <0.001**
Trend 0.00 0.00 2376 3.70 <0.001** 0.00 0.00 2349 0.30 0.766
Level Phase B 0.67 0.13 2376 5.37 <0.001** 1.20 0.13 2349 9.41 <0.001**
SDQ ext 0.07 0.04 33 –1.98 0.056 0.07 0.04 33 1.93 0.062
Level Phase B*SDQ ext 0.03 0.03 2376 0.80 0.423 0.07 0.04 2349 1.99 0.047*
SDQ total = SDQ total score; SDQ int = SDQ subscale internalizing problems; SDQ ext = SDQ subscale externalizing problems; * = significant (p<0.05); ** = significant (p<0.001).
intervention to reduce aggressive behavior for children with
high risks in general (van Lier et al.,2005) and in boys with
persistent high risks (Kellam et al.,2008). However, even though
the majority of students with externalizing risks benefited
from the intervention, three of the non-responders showed
high risks assessed by their teachers. This finding leads to
the assumption that for children with high externalizing risks,
further individual factors moderate the effectiveness of group
contingencies (Maggin et al.,2017). No effects were found for
internalizing risks. Considering that the majority of the students
nominated by teachers showed no internalizing problems,
we cannot deduce effects for students with internalizing
risks from our study.
Overall, our findings indicated that the impact of the
classroom-based GBG program varied as a function of
individual children. For both outcomes, and in particular for AE,
there was large variability between individuals. There are several
explanations for this finding. Although no clear pattern emerges
in our data, it is possible that aspects of treatment integrity and
usability may have affected the outcomes, particularly among
the non-responders (Moore et al.,2022). From a methodological
point of view, for some students in our sample, the high
AE values at baseline led to minimal room for improvement,
suggesting a possible ceiling effect (Ho and Yu,2015). Likewise,
there might have been floor effects for some of the students with
low DB at baseline. Furthermore, it is important to consider the
situation in which the GBG was played. It is possible that the
target situation was not the most difficult part of the lesson for
all of the nominated students; thus, their AE and, in part, their
DB might not have been as problematic as usual. In addition,
struggling with learning strategies, as a common problem of
students with challenging behavior (Kauffman and Landrum,
2012), could affect the effectiveness of the GBG regarding AE.
The implementation of additional components such as self-
monitoring strategies (Bruhn et al.,2015) could be necessary to
increase AE for non-responding students (Smith et al.,2021).
Interestingly, we could not find any interaction of
externalizing problems and intervention effectiveness for
AE. In addition to the aforementioned possible ceiling
effects, the similarity of the measured constructs must be
considered. Although hyperactivity and conduct problems
can negatively impact school functioning and academic
performance (Mundy et al.,2017), the externalizing subscales
of the SDQ as a standardized measure for assessing child
mental health problems are more closely linked to disruptive
classroom behaviors than to academic engagement as a typical
school-related construct. Further associated factors, such
as psychological and cognitive dimensions of engagement,
including having sense of belonging or motivational
beliefs (Wang and Eccles,2013) or the level of academic
enabling skills (Fabiano and Pyle,2019), could moderate the
intervention effects on AE.
Limitations
The findings from this study should be interpreted by
considering several potential limitations. First, not all of
the nominated students in our sample had high ratings in
the externalizing and/or internalizing subscales of the SDQ.
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Therefore, it can be assumed that not all of the nominated
students were at risk for EBD. Although research has indicated
that teachers are competent in identifying students in their
classrooms with problem behaviors (Lane and Menzies,2005),
we believe that in addition to teacher nomination, future
research should use other methods to identify students with
challenging behavior (i.e., systematic behavioral assessment
in the baseline).
Second, teachers both delivered the intervention and rated
students’ performance. The ‘double burden’ of teaching and
rating as well as the teachers’ acceptance of the intervention
could affect their ratings. However, despite limited associations
between behavioral change and acceptability, research has
demonstrated the sensitivity of the DBR-SIS completed by
implementing teachers (Chafouleas et al.,2012;Smith et al.,
2018). Furthermore, we were unable to conduct systematic
direct observations by trained observers or video recordings. As
such, we decided to use the DBR-SIS as an efficient tool with
acceptable reliability, validity and sensitivity within our aims.
Third, our dependent variables were broad categories
combining different behaviors. These target behaviors enabled
us to compare our results, particularly with the findings
of the existing meta-analysis. On the other hand, students’
individual changes in specific behaviors could not be tested.
Furthermore, we only investigated one potential moderator
for the impact of the intervention. However, the differentiated
analysis of these risks for two key aspects of classroom
behaviors extends previous research independently of the need
for further investigation.
Implications for research and practice
Analyzing the responses to the GBG using behavioral
progress monitoring helps to identify students who need
additional support on tier 2 in a MTSS and to recognize
at an early stage if positive effects are decreasing over time
for individual students (Donaldson et al.,2017;Moore et al.,
2022). The selected combination of data analysis methods
enables precise alignment with our aims and considers the
characteristics of the data (Manolov and Moeyaert,2017):
Due to our large sample and the analytical method chosen,
we were able to investigate the impact of the GBG on
individual at-risk student classroom behaviors as well as its
effectiveness across cases. We therefore believe that our study
is a methodologically sound study about the GBG that could
be used to substantiate the evidence classification of the GBG.
Considering the fact that the single-case studies of the GBG
work with small samples and use methods for data analysis that
do not address the problem of autocorrelation, misestimates
of effectiveness due to the methods chosen are plausible
(Shadish et al.,2014), and the number of non-responders tends
to be underestimated. The number of non-responders indicates
that we should be very careful with the transfer of results
of studies investigating a class or a group as a single-case
to the behavioral development of individual students with
challenging behavior. Therefore, further single-case research
with larger-than-usual samples and meta-analytical approaches
are necessary to extend the findings regarding the impact of the
GBG on at-risk students’ classroom behaviors as well as further
potential moderators. As shown in our study, externalizing
risks seem to moderate the impact of the GBG on reducing
disruptive behavior. Thus, our findings imply that in future
intervention studies, the effects should be controlled for possible
influences of externalizing risks. Furthermore, in addition to
potential individual factors, functional characteristics (Maggin
et al.,2017), environmental moderators, such as the classroom
level of aggression (Waschbusch et al.,2019), peer factors, and
school climate (Farrell et al.,2013), should be examined.
Overall, the results of our study suggest that the GBG
facilitates at-risk students’ behavioral development in
inclusive settings in Germany. In particular, in our sample,
students who were assessed by teachers as exhibiting high
externalizing behavior problems can benefit. These results
should encourage teachers to implement this classroom-based
intervention and to monitor its effect using behavioral
progress monitoring. Our findings also suggest that a
combination of the behavioristic method of the GBG with
cognitive methods such as self-monitoring (Bruhn et al.,
2015) could be necessary to enable the effects regarding
AE. In our study, treatment integrity was high, and
teachers assessed the intervention as suitable for their
daily work. However, whether the intervention can be
sustainably implemented depends on several factors. Despite
the simple rules of the game, coaching throughout the
implementation and positive impacts, teachers do not see
sufficient possibilities to integrate the GBG naturally in
their daily work (Coombes et al.,2016). Furthermore, the
teachers in our study likewise reported that they did not
find the time to play the GBG due to school events or
learning projects. In addition, some found it difficult to
consistently implement behavioral progress monitoring. To
increase sustainability, the maintenance of both the GBG
and progress monitoring should be carefully planned and
monitored. If this is successful, the GBG, in conjunction
with behavioral progress monitoring, is an appropriate
classroom-based intervention to improve at-risk students’
classroom behaviors and to adjust students supports data-based
at an early stage.
Data availability statement
The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.
Frontiers in Education 12 frontiersin.org
feduc-07-917138 July 25, 2022 Time: 15:44 # 13
Leidig et al. 10.3389/feduc.2022.917138
Ethics statement
Ethical review and approval were not required in accordance
with the local legislation and institutional requirements.
Following the school law and the requirements of the
ministry of education of the federal state North Rhine-
Westphalia (Schulgesetz für das Land Nordrhein-Westfalen),
school administrators decided in co-ordination with their
teachers about participation in this scientific study. Written
informed consent from the participants’ legal guardian/next of
kin was not required to participate in this study in accordance
with the national legislation and the institutional requirements.
Verbal informed consent to participate in this study was
provided by the participants’ legal guardian/next of kin.
Author contributions
MG, GC, RV, JW, and TL: methodology. JW: software. JW
and TL: formal analysis. GC and TL: investigation. TH, GC, and
TL: resources. TL, GC, and JW: data curation. TL: writing
original draft and visualization. TH and RV: supervision. GC
and TL: project administration. All authors: conceptualization
and writing review and editing.
Funding
This study was supported by the Marbach Residency
Program (Jacobs Foundation). We acknowledge support for the
Article Processing Charge from the DFG (German Research
Foundation, 491454339).
Conflict of interest
The authors declare that the research was conducted
in the absence of any commercial or financial relationships
that could be construed as a potential conflict of
interest.
Publisher’s note
All claims expressed in this article are solely those
of the authors and do not necessarily represent those
of their affiliated organizations, or those of the publisher,
the editors and the reviewers. Any product that may be
evaluated in this article, or claim that may be made by
its manufacturer, is not guaranteed or endorsed by the
publisher.
Supplementary material
The Supplementary Material for this article can be
found online at: https://www.frontiersin.org/articles/10.3389/
feduc.2022.917138/full#supplementary-material
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