Effects of a modular teacher coaching program on child attention problems and disruptive behavior and on teachers’ self-efficacy and stress

Abstract

Symptoms of attention-deficit/hyperactivity and oppositional defiant disorder are associated with a multitude of psychosocial developmental risks, e.g. academic underachievement. Various cognitive behavioral interventions have proven to be effective in reducing problem behavior in school settings. Drawing on this previous work and on our parent-focused preventive and therapeutic programs, we developed the school-based coaching for elementary school teachers of children with attention deficits or disruptive behavior problems (SCEP). Based on functional behavior assessment, SCEP addresses teachers of children with severe externalizing behavior problems in an individualized modular manner. It consists of a one-day training course and fortnightly one-to-one or team-coaching sessions. We analyzed the effects of SCEP in a within-subject control group design (N = 60), with student attention problems and rule-breaking behavior during class as the primary outcome measure. SCEP was found to reduce problem behavior during lessons, with small to medium effect sizes (d = 0.42–0.6). After the intervention, teachers reported changes in their use of praise and felt more confident managing the class (d = 0.58). The results of SCEP are discussed in light of multi-tiered preventive approaches that suggest extensive individualized interventions based on functional behavior analysis for children with severe problem behavior.

Keywords

ADHD externalizing problem behavior primary school school intervention functional behavior assessment

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent, pervasive childhood mental health disorder. Severe symptoms of ADHD impair school performance in approximately 10-12% of the general education population (Fabiano & Pyle, 2018). Associated learning, behavior and comorbid mental health problems affect all aspects of psychosocial development (Fergusson et al., 2013), and students with ADHD receive a high proportion of special education services at school (Loe & Feldman, 2007). Typical school-related problems include off-task behavior, lack of organizational skills, and low academic productivity and achievement (de Ridder et al., 2012; Major et al., 2013). Disruptive or oppositional behavior problems may occur comorbid to ADHD symptoms or independently (Frick & Nigg, 2012). Students with these externalizing disorders have a two- to threefold elevated risk of leaving school early (Erskine et al., 2016).

Multi-tiered systems of support (MTSS) such as Response to Intervention (RTI; Jimmerson et al., 2016) or Positive Behavior Intervention and Support (PBIS; Sugai & Horner, 2009) have gained increasing interest in terms of supporting students with academic or behavioral challenges within the general education setting. MTSS include tiers of intervention of increasing intensity and complexity. At tier 1, support is provided to all students, with a focus on universal prevention. At tier 2, support is provided to students whose behaviors are not successfully responsive to tier 1. At tier 3, students with identified academic, behavioral, or mental health difficulties are provided with individualized interventions.

Following the notion of systematically combining evidence-based interventions with increasing intensity, Fabiano and Pyle (2018) outline a multi-tiered framework for school interventions for students with ADHD, while Waschbusch et al. (2018) carried out a similar rationale for aggression and defiance in youth. On a class-wide universal level (tier 1), classroom management strategies such as using clear commands, modifying teacher attention, and regularly reviewing rules are employed to support all students, including those with ADHD (Fabiano et al., 2018; Pfiffner & DuPaul, 2015). Within this context, the Good Behavior Game (GBG), a group contingency management strategy in which students are rewarded for appropriate on-task behavior, has proven to be effective for team-based classroom behavior management, showing positive effects both on on-task behaviors and on disruptive behavior in classrooms (Leflot et al., 2013; Mitchell et al., 2015). For those students who continue to struggle despite strong universal support, targeted interventions can be provided within tier 2, for instance through individualized token economies or daily behavior report cards. Such report cards constitute one of the most widely studied behavioral interventions. They were found to successfully increase on-task behavior and to decrease disruptive behavior (Volpe & Fabiano, 2013), and have been examined for students with ADHD (Iznardo et al., 2017; Owens et al., 2012; Pyle & Fabiano, 2017) and other behavior problems (Vannest et al., 2010). Organizational skills training is a well-established intervention for students with ADHD in tier 2 (Evans et al., 2018), while for youth with aggressive or defiant behavior problems, social emotional learning components are recommended as student-centered interventions in tier 2 (Lochman & Wells, 2003). Tier 3 interventions are regarded as more specialized and individualized (Sugai & Horner, 2009); they are resource-intensive and target only the more complex cases that have failed to respond to tier 1 and 2 interventions. Generally speaking, tier 3 interventions are a continuation of tier 2 strategies with individualized modifications and with higher intensity. It has been recommended that tier 3 interventions should be based on the assessment of the function of the behavior (Bruni et al., 2017; Pinkelman & Horner, 2017). Thus, situational antecedents that precede the problem behavior are identified, as well as consequences that follow the behavior and which might be responsible for maintaining problem behavior or for sustaining appropriate behavior. This functional model is then used to replace the problem behavior with a functionally equivalent goal behavior (Fabiano, 2016). In a meta-analysis of 82 single case studies of students diagnosed with ADHD, Miller and Lee (2013) reported larger effects for psychosocial interventions that were planned on the basis of individual functional behavior analysis.

Despite extensive empirical evidence on effective interventions for ADHD in school settings (Daley et al., 2014; DuPaul & Stoner, 2014; Evans et al., 2014, 2018; Fabiano et al., 2015; Hodgson et al., 2014), these interventions are rarely used in individualized education plans in general or special education settings (Spiel et al., 2014). Instead, teachers do not feel adequately prepared for dealing with behavioral problems in ADHD students (Moore et al., 2017). Teachers’ feelings of self-efficacy, defined as teachers’ belief in their competencies to implement strategies to affect students’ behavior and learning in an effective manner (Tschannen-Moran & Hoy, 2001), negatively correlate with classroom disturbances and teachers’ emotional exhaustion (Dicke et al., 2014) and positively correlate with students’ learning success (Klassen & Tze, 2014). Job-embedded professional development such as teacher training or coaching might be useful in order to increase teachers’ knowledge, change attitudes, and improve the strategies teachers use in the classroom (Scuitto et al., 2000).

Studies on the effects of teacher training regarding ADHD school interventions are rare, but needed (Fabiano & Pyle, 2018). Latouche and Gascoigne (2017) were able to increase teachers’ ADHD knowledge and feelings of self-efficacy using a brief single-session training workshop. After a seven-week web-based intervention for teachers with ADHD students, participants reported higher knowledge of ADHD and greater perceived control in the classroom and competence in teaching (Barnett et al., 2012). Fabiano and colleagues (2018) found improvements in teachers’ use of behavior management and instructional strategies after a four-session teacher coaching approach in combination with ongoing classroom observations(Fabiano et al., 2018). The results of this randomized controlled trial are in line with previous work examining coaching on either instructional or behavioral management strategies, which used less rigorous designs (Reddy & Dudek, 2014).

The present study is the first pilot study on our modular teacher coaching program SCEP (school-based program for externalizing behavior problems), which we developed on the basis of the above-reported evidence on ADHD school interventions as well as on therapeutic and preventive programs from our group (Doepfner et al., 2004, 2016, 2019; Hanisch et al., 2010; Hautmann et al., 2009; Plueck et al., 2006).

Based on functional behavior analysis, SCEP can be used as an indicated intervention tool (tier 3) in a MTSS. SCEP also comprises tier 1, e.g. classroom management, and tier 2 interventions, e.g. direct behavior ratings. In line with other social learning theory-based school interventions, our program seeks to improve child behavior by changing antecedents and teachers’ responses to child problem and goal behavior (Fabiano et al., 2015). SCEP differs substantially from previous professional development programs, as it comprises twelve manualized intervention modules that can be selected and combined tailored to the individual student’s and teacher’s needs. Our approach is novel for three reasons: (1) to our knowledge it is the first school-based intervention for children with ADHD that systematically uses functional behavior analysis in a school setting to construct an individual intervention plan for one specific target child, (2) this individual intervention plan then comprises a selection of standardized and manualized intervention modules, enabling individualization as well as standardization, and (3) we were especially interested in changes in child behavior during class as a result of SCEP. Thus, in contrast to previous studies, that focused on increases of teachers’ knowledge and changes in teaching strategies, the present study primarily examined decreases in child attention problems and rule-breaking behaviors. Further, we were interested whether SCEP positively affects symptoms of attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD). As teachers feel especially strained by students’ lack of motivation, discipline, or concentration (Moore et al., 2017; Nash et al., 2016), we assumed that decreases in child problem behavior might be accompanied by reduced stress in teachers.

Method

Intervention

Based on the empirical foundation outlined above and evaluated programs for prevention and treatment of ADHD and ODD from our group, we developed the SCEP coaching for elementary school teachers of children with attention and disruptive behavior problems in ordinary school classes. SCEP comprises: (1) a one-day training course for the whole teaching staff regarding classification, causes and general interventions for externalizing problem behavior, and (2) six one-to-one or team-coaching sessions for interested teachers taking place every two weeks. The participating teachers chose one target child with externalizing problem behavior from their class. Examples of specific problem behaviors are “does not start working during self-regulated independent learning”, “is distracted during class”, “shouts out the answer without being called on”, and “picks on other students while changing from one activity to the other”.

The coaching program comprises 12 modules (see Table 1). Modules 1 and 2 are mandatory and were covered in 1-2 coaching sessions. General knowledge on child externalizing problems is applied to the target child by working out a functional behavior analysis and by defining “SMART” (Specific, Measureable, Achievable, Realistic, Timely) goal behaviors to be reached during the coaching. After a classroom observation, the coach and teacher adapt the functional behavior analysis if needed. Then, they cooperatively design an intervention plan by selecting modules from the SCEP manual that are recommended for the specific problem by means of a decision tree. If, for example, during the classroom observation the setting of the classroom did not seem to meet the student’s needs, SCEP module 3 might be selected. If the coach additionally observed hostile interactions between the teacher and the target student, he/she might recommend to next work on a positive student-teacher relationship (SCEP module 5). As the SCEP coaching comprises six sessions, and 1-2 sessions were required for mandatory modules 1 and 2, four further modules could be selected for the remaining four coaching sessions. The 12 SCEP modules are grouped into four general goals: I. change the situation in the classroom, II. change teachers’ response to child behavior, III. increase self-management strategies, and IV. improve cooperation with parents. More specifically, the adaptation of the school and class framework involves, for example, classroom management strategies, feedback given to the child, teacher-child relationship and stress reduction for the teacher. Changing the antecedents and consequences of the problem behavior (II.) comprises defining and communicating rules and systematic reinforcement procedures. In addition, the child’s self-management skills might be supported by modules covering self-monitoring and “if-then plans” (III.). Lastly, one module aims to empower the teacher to communicate effectively with parents (IV. see Table 1 for an overview).

Table 1.

Modules of SCEP coaching.

Basis (mandatory)
Module 1	Acquire knowledge on attention problems and disruptive behavior in school settings
Module 2	Describe and understand problem behavior; define goal behavior
I. Change the situation in the classroom
Module 3	Change the setting in which children work
Module 4	Clarify routines and procedures
Module 5	Build teacher- student relationship
Module 6	Reduce teacher’s stress
II. Change teacher’s response to child behavior
Module 7	How to communicate commands clearly
Module 8	Reward goal behavior
Module 9	Behavior report cards, Token economies
III. Increase self-management strategies
Module 10	If- then plans
Module 11	Self- monitoring
IV. How to cooperate with parents
Module 12	How to talk to parents and how to establish further support

The SCEP manual (Hanisch et al., 2018) provides detailed instructions, material (e.g. worksheets, charts, role-play instructions), and a description of potential difficulties to be aware of for all of the 12 modules. Between sessions, the teacher completes a homework assignment (e.g. documenting the amount of specific praise, or monitoring the new changes in routines). Each coaching session starts with reflection on the homework assignment, then introduces the topic of the respective module, involves active practice (e.g. role-play of a difficult interaction with the parents), and ends with a homework assignment to be carried out during the following two weeks.

Clinically trained child and adolescent psychotherapists conducted the coaching. Supervision was provided by the first author, who is as well trained in clinical child psychology and psychotherapy. All coaches worked as researchers on the development and evaluation of the program.

Study design

SCEP was evaluated using a within-subject control group design (Charness et al., 2012; Hautmann et al., 2008): Every student underwent baseline testing (t0), which was followed by a 12-week waiting period and another assessment prior to the coaching (t1). Subsequently, teachers took part in the coaching (intervention phase, approx. 12 weeks), at the end of which a third assessment occurred (t2). This was followed by a 12-week follow-up phase, which concluded with a final measurement time point (t3). Seasonal effects were controlled by using two time-delayed participant groups (early group (EG) and late group (LG)).

Samples

The size of the overall sample was based on an a priori power calculation by means of G*Power (Faul et al., 2007), which yielded an adequate sample size of 60 target children.

Figure 1 depicts a flow chart representing the recruitment process. A total of 313 schools in the German Federal state of North Rhine-Westphalia (Cologne n = 149, Düsseldorf n = 88, Rhein-Kreis-Neuss n = 76) were sent information about the research project by the respective education authority. Through information events at 18 interested schools, a total of 15 schools were finally recruited to participate in the project. Using block randomization, eight schools were allocated to the early intervention group and seven schools to the late intervention group. Initially, 64 teachers made a commitment to attend coaching sessions, of whom 32 teachers indicated that they would participate in a team together with co-teachers. However, the intended target sample size was not achieved, as both teachers and students failed to participate for several reasons (change of school, repeating school grade, illness). Thus, already recruited schools were used to recruit more participants among teacher colleagues, resulting in the participation of a further 11 teachers (EG 2) and amounting to a total sample of N = 60. Of these, n = 50 teachers participated in six coaching sessions, n = 7 in five sessions, n = 2 in four sessions and n = 1 in two sessions. Table 2 provides a description of the sample. Sociodemographic information of teachers and students was collected. Boys were overrepresented (60%) in the sample. The mean age was 8.15 (SD 1.09) years. Teachers were mainly female (66.7%) and had a mean age of 39.32 (SD 10.65) years. Parents were asked to complete a brief questionnaire regarding the child’s potential diagnoses and treatments. According to parents’ information, 21% (n = 12) of the target children were identified with a mental disorder (ADHD n = 6; oppositional defiant disorder/conduct disorder n = 6). Among these children, nine received any type of therapeutic treatment (psychological psychotherapist, psychiatrist, occupational therapy, public social-psychiatric services) and six took psychotropic medication.

Figure 1.

Flow chart.

Table 2.

Characteristics of the sample.

	n	Min–Max	M (SD)
Schools	15
Class teachers per school		3–7	4.06 (1.22)
Target child
Age in years	60	6.83–10.67	8.15 (1.09)
Class teacher
Age in years	60	27–63	39.32 (10.65)
Professional experience in years	59	0–33	10.16 (7.40)
	N (total)	n	%
Target child
Sex (male)	60	49	81.7
Living with
Mother		24	40.0
Father		1	1.7
Mother & father		31	51.7
Grandparents		1	1.7
No information		3	5
Grade
Grade 1		2	3.3
Grade 2		26	43.3
Grade 3		19	31.7
Grade 4		13	21.7
Class teacher
Sex (male)		3	33.3
Employment
Full-time		40	66.7
Half-day		20	33.3
Further training child problem behavior		35	58.3

Assessments

Child symptoms

Ratings of child symptoms were all carried out by the teachers. Child problem behavior during lessons was assessed using a German version of the Swanson et al. SKAMP Scale (Swanson, 1992), hereinafter referred to as SKAMP-ge (Doepfner et al., 2006). The SKAMP-ge has been well investigated regarding validity, reliability and sensitivity to change (Breuer et al., 2009; Murray et al., 2008). The scale consists of a total of ten items, with six items assessing attention and four items assessing compliance with rules in the classroom (deportment) (Doepfner et al., 2006). For our data, the internal consistencies were α = .85 for the attention problems scale, α = .74 for the deportment scale and α = .85 for the overall scale. The SKAMP-ge functioned as the primary outcome measure.

In addition, we administered the Problem Checklist for Attention Deficit and Hyperactivity Disorder (PCL-ADHD; Doepfner et al., 2008), with analyses conducted using the total score (α = .88). The PCL-ADHD assesses the diagnostic criteria of DSM-IV and ICD-10 for ADHD and is part of the Diagnostic System for Mental Disorders (DISYPS-II; Doepfner et al., 2008; Rodenacker et al., 2017). Furthermore, we applied the Problem Checklist for Oppositional Defiant Disorder (PCL-ODD), which is likewise part of the DISYPS-II (Doepfner et al., 2008; Ise et al., 2014). Due to the age group and preventive nature of the study, only the scale Oppositional-Aggressive Behavior was considered (α = .90).

Ratings of individual problem behavior

In the first coaching session, teachers and coaches identified up to three individually formulated target problems. Before each subsequent coaching session, teachers rated the current intensity of these problem behaviors on a six-point scale, where a 1 represented low intensity and a 6 represented high intensity. As the target problems were not defined until the start of the coaching, there are no reference data from the waiting period. Individual problem behavior was clustered into either ADHD or symptoms of oppositional defiant or conduct disorder.

Teacher behavior

To assess teacher behavior, the Teacher Strategies Questionnaire (Webster-Stratton, 2001) was translated into German by means of a forward-backward translation process. The questionnaire consists of two parts: In Part A, Managing Classroom Behavior measures the teacher’s self-confidence in dealing with problems in the classroom. The internal consistency of this scale in the present study lay at α = .81. In Part B, Specific Teaching Techniques, comprising 38 items, assesses the frequency and perceived usefulness of teachers’ application of diverse educational strategies. Teachers rated the application of these strategies both towards the target child (tc) and the entire class (ec). The individual items are distributed across six subscales. In the present study, only the frequency ratings of the Proactive Strategies (PS) and Inappropriate Strategies (IS) subscales were collected on a 5-point scale (0 = seldom/never to 5 = very often). The internal consistency regarding the whole-class assessment in the present study was α = .67 for PS_ec and α = .68 for IS_ec. Regarding the target child, the Cronbach’s alpha was α = .68 for PS_tc and α = .71 for IS_tc .

Teacher stress and self-efficacy

To assess teacher stress, we used the 14-item Depression subscale of the Depression Anxiety Stress Scales (DASS; Lovibond & Lovibond, 1995). Items are scored from 0 to 3, with higher scores indicating more severe problems. The internal consistency lay at α = .92.

The 10-item Teacher Self-Efficacy Scale (Schwarzer & Schmitz, 1999) aims to assess teachers’ personal convictions regarding their ability to manage professional challenges. In this study, the Cronbach’s alpha for the scale amounted to α = .71.

Statistical analyses

Data were analyzed with multilevel modeling (MLM; Goldstein, 2011; Hox et al., 2010; Raudenbush & Bryk, 2002; Snijders & Bosker, 2012) using IBM SPSS Statistics 24 (IBM, 2016). In contrast to repeated measures ANOVA, MLM can handle missing data by using all available data (Maas & Snijders, 2003). As measurements were nested in persons for the analysis, two levels were considered: level 1 for the repeated measures, level 2 for the persons. Piecewise models were used and the growth rate during the waiting period (pre1 to pre2) and the intervention period (pre2 to post) were assessed separately. The intercept was set as random considering interindividual differences at the beginning of the study, and for reasons of model identification (only two assessments for each time period), the slopes were fixed. We hypothesized that during the waiting period, the growth rate would not significantly differ from zero (“no change” hypothesis). The treatment was considered to be effective when the observed improvement during the intervention period was significantly stronger than the change during the waiting period (i.e. incremental effect). The alpha level was set at α≤ .05. The primary outcome was the SKAMP-ge total score, and secondary analyses were conducted for the SKAMP-ge subscales (attention problems and deportment) and for all other child symptom- and teacher-related measures. Due to the exploratory nature of these secondary analyses, no Bonferroni correction was applied.

The effect sizes of the different phases were calculated with the formula:

d = \frac{slope coefficient b * rating i n t ervall}{s t a n dard deviation of AM (t 0)}

Effect sizes were computed for the waiting period (d_w), intervention phase (d_i), and follow-up phase (d_fu), and the net effect size (d_net = d_w – d_i) was calculated. Effect sizes were classified as “small” (0.20 ≤ d < 0.50), “medium” (0.50 ≤ d < 0.80) or “large” (0.80 ≥ d) (Cohen, 1988). Power calculations indicated that medium effects could be detected with the sample sizes described in Table 3.

Table 3.

Means (standard deviations) of the outcome variables at four time points (Pre1, Pre2, Post, Follow-up); significance test and net effect size of the intervention phase (incremental effect).

				Intervention effect
		n	M (SD)	p	d_net
Primary outcome variable
SKAMP-ge
Total score	Pre1	54	2.02 (0.56)	.021	–0.60
	Pre2	54	1.98 (0.52)
	Post	52	1.54 (0.67)
	Follow-up	36	1.40 (0.62)
Secondary outcome variables
SKAMP-ge
Attention	Pre1	54	2.14 (0.66)	.023	–0.56
	Pre2	54	2.13 (0.62)
	Post	52	1.70 (0.76)
	Follow-up	36	1.55 (0.71)
Deportment	Pre1	54	1.85 (0.65)	.086	-0.42
	Pre2	54	1.75 (0.59)
	Post	52	1.30 (0.69)
	Follow-up	36	1.18 (0.62)
PCL-ADHD
Total Score	Pre1	54	1.87 (0.50)	.245	–0.27
	Pre2	54	1.77 (0.50)
	Post	52	1.48 (0.59)
	Follow-up	36	1.50 (0.52)
PCL-ODD
ODD symptoms	Pre1	54	1.60 (0.71)	.467	–0.13
	Pre2	54	1.47 (0.78)
	Post	52	1.14 (0.70)
	Follow-up	36	1.16 (0.82)
TSQ target child
Total score	Pre1	48	1.63 (0.45)	.396	0.21
	Pre2	52	1.61 (0.47)
	Post	52	1.65 (0.43)
	Follow-up	36	1.54 (0.47)
Proactive strategies	Pre1	48	1.85 (0.58)	.066	0.48
	Pre2	52	1.91 (0.66)
	Post	52	2.28 (0.68)
	Follow-up	36	2.05 (0.74)
Inappropriate strategies	Pre1	48	1.24 (0.53)	.285	–0.25
	Pre2	53	1.18 (0.66)
	Post	52	0.92 (0.50)
	Follow-up	36	0.91 (0.49)
TSQ class
Confidence managing classroom	Pre1	50	3.63 (1.18)	.016	0.58
	Pre2	53	3.65 (1.13)
	Post	53	4.36 (1.04)
	Follow-up	36	4.27 (1.15)
Proactive strategies	Pre1	51	2.08 (0.54)	.057	0.44
	Pre2	52	1.95 (0.55)
	Post	53	2.13 (0.64)
	Follow-up	36	2.01 (0.66)
Inappropriate strategies	Pre1	51	1.32 (0.48)	.301	0.24
	Pre2	53	1.18 (0.66)
	Post	53	0.98 (0.43)
	Follow-up	36	0.98 (0.46)
Total score	Pre1	51	1.75 (0.37)	.037	0.43
	Pre2	52	1.61 (0.47)
	Post	53	1.62 (0.39)
	Follow-up	36	1.56 (0.40)
DASS
Stress scale	Pre1	51	1.11 (0.53)	.556	–0.13
	Pre2	54	0.98 (0.53)
	Post	52	0.79 (0.52)
	Follow-up	36	0.68 (0.48)
TSES
Total score	Pre1	51	1.81 (0.32)	.088	0.40
	Pre2	54	1.83 (0.35)
	Post	53	1.96 (0.31)
	Follow-up	36	1.97 (0.32)

Results

Table 3 summarizes the means (and standard deviations) of the outcome variables at the four assessment points (pre1, pre2 and post, follow-up), together with the p-values for the incremental effect and the net effect sizes.

For the primary outcome variable, a significant medium-sized intervention effect was found (p = .021, d= −0.6). This effect was stable three months after the intervention (β = −0.183235; n.s.) and of small size (−0.22 ≤ d ≤ −0.30). Figure 2 depicts the data for the total SKAMP-ge scale.

Figure 2.

Course of the SKAMP-ge total score.

The secondary analysis of the subscales of the SKAMP-ge revealed significant medium-sized treatment effects for the attention problems scale (p = .023, d = −0.56), but despite a small net effect size (β = −0.128273, d = −0.20), no significant effects were found for the deportment scale.

For the PCL-ADHD, a small, non-significant intervention effect (d = 0.27) was found with respect to problem behavior of the target child.

63.3% of the individually defined problem behaviors related to ADHD symptoms, with 35.8% describing attention problems, 8.3% hyperactivity, and 15.6% impulsivity. 32.1% problem behaviors were oppositional symptoms, and 3.7% were aggressive dissocial behaviors. For the change of the individually formulated target problem during the coaching period, we found effect sizes regarding problem behavior intensity of d = −3.19 for attention problems, hyperactivity and impulsivity (β = −0.379918, p = 0.00) and d = −1.47 for oppositional and aggressive behavior (β = −0.261548, p = 0.00).

For teachers, the experienced self-confidence in classroom management, assessed using the TSQ scale managing classroom behavior, increased significantly in the intervention phase compared to changes in the waiting phase, with a medium effect size (d = 0.58). Furthermore, during the intervention phase, the use of positive educational strategies towards the target child (d = 0.48) and towards the entire class (d = 0.44) slightly increased, with non-significant small and medium effect sizes, respectively. The use of inappropriate strategies (TSQ) already decreased significantly for the entire class during the waiting phase (β = −0.207707; p = .002; d = −0.043). It decreased further during the intervention phase, but did not reach significance when compared to the waiting phase. Nevertheless, the decrease was maintained even three months after the intervention.

For the Depression Anxiety Stress Scales (DASS), no intervention effect was found. However, small but non-significant effects emerged for the waiting period (β = −0.107872, d = −0.20) and the follow-up phase (β = −0.109128, d = −0.21).

Regarding self-efficacy assessed by the TSES, a medium, non-significant effect (d = 0.40) was found for the intervention phase compared to the waiting phase. This effect remained stable over the follow-up phase.

Discussion

With respect to the main goal of this pilot study, SCEP reduced problem behavior during school lessons. For our primary outcome measure (SKAMP-ge), a medium-sized effect emerged. Likewise, we found significant medium-sized effects for attention problems during class. These positive intervention effects are especially noteworthy as ordinary teachers without special education qualification ran the interventions in ordinary school classes.

Effects on rule-breaking behavior were small and non-significant. In line with our findings, small to medium effect sizes have been previously reported for school-based interventions for children with ADHD and aggression (Fabiano & Pyle, 2018; Waschbusch et al., 2018). Previous work suggests that prevention yields larger effects on more severely impaired children (Hautmann et al., 2011). As the above cited meta-analyses were conducted in clinical samples, who are likely more impaired than the children in our sample, we interpret the medium-sized intervention effects in our study as a promising first hint for the effectiveness of SCEP.

With respect to symptoms of ADHD and ODD, we found small, but non-significant symptom changes. Again, this result confirms previous findings of differing effect sizes for various outcome measures (Sonuga-Barke et al., 2013). Our primary outcome measure was the SKAMP-ge scale. As the coach and teacher work on modifying specific school lesson-related problem behaviors during the SCEP intervention, the SKAMP-ge represents a rather proximal measure. Thus, behaviors rated in the SKAMP-ge might have been very close to the child’s most prominent individual problem behavior. This hypothesis is confirmed by the strong reduction of the individual target problems during the intervention. Of course, this latter finding needs to be interpreted with caution, as we do not have reference data on this measure during the waiting period, and teachers rated improvements of individual problem behavior in the presence of the coach.

Although the intervention was successful in decreasing student problem behavior during class, the practical implications of this finding are less clear and need to be further evaluated in upcoming studies. It has been suggested to consider measures of clinical significance or benchmarks driven from well controlled intervention studies rather than p-values or effect sizes (Jacobson & Truax, 1991; McAleavey et al., 2019) to estimate the practical benefit of interventions. Despite the missing control condition and the risk of bias, the bi-weekly rating of individual problem behavior might have added extra practically relevant information suggesting that teachers subjectively perceived a decrease in problem behavior during class. One might argue that this perception could be strongly biased by social desirability or effort justification. On the other hand teachers perception or cognition might from there on be further influenced by the reported improvement of child problem behavior.

There was no significant intervention effect for the deportment scale of the SKAMP-ge, even though this scale likewise measured problem behavior that came very close to the individually formulated problem behavior. For this scale, teachers had already reported a decrease in problem behavior during the waiting period, which therefore reduced the overall net effect size. In contrast to the SKAMP-ge, the other measures assessing DSM diagnostic criteria for ADHD and ODD (PCL-ADHD and PCL-ODD) are more peripheral to the target of our intervention. Presumably, these items may be less sensitive to change, especially as they are not restricted to the behavior during school lessons (Fabiano et al., 2015).

In line with previous work (Fabiano & Pyle, 2018; Waschbusch et al., 2018), we hypothesized that child problem behavior would decrease by training teachers to change antecedents and consequences of child problem behavior following a functional behavior assessment. With respect to interactions with the entire class, teachers reported that they significantly changed their teaching strategies, which is in line with previous professional development training interventions (Fabiano et al., 2018). However, SCEP-coached teachers did not report differentially responding to the target child. Fabiano et al. (2018) found better classroom management strategies measured by classroom observations after a comparable teacher coaching intervention. One might speculate that teachers’ reports on their responses to the target child’s problem behavior at baseline and in the t1 measures may have been affected by a positive response bias, with teachers tending to present themselves in a more positive light (Legato, 2011). However, this potential bias might have been reduced due to the collaborative relationship between coach and teacher during the 12-week coaching period, resulting in more reliable, less socially desirable ratings. Classroom observations might be a more valid measure to further explore this idea in upcoming studies on SCEP.

As teachers’ feelings of self-efficacy have been reported to correlate with students’ learning success (Klassen & Tze, 2014), it is especially noteworthy that after SCEP, teachers felt more confident in managing the classroom, as measured by the TSQ class. However, this was not fully reflected in scores on the TSES scale, which assesses teachers’ personal convictions regarding their ability to manage professional challenges. Here, we found a non-significant, small effect. Again, one might speculate that the TSES may have been too general an instrument to capture the specialized nature of the intervention. The items of the TSQ might have provided a better fit to the targets of the intervention, and might therefore have been more sensitive to change. Another explanation might be that our coaching placed too much emphasis on the persisting problems, thus potentially failing to invite teachers to attend to their competencies and progress. Presumably, tier 1 and 2 strategies may be more likely to enable the experience of mastery. In line with this, less intense teacher professional development programs that focused on the transfer of knowledge on ADHD interventions found positive effects on teachers’ self-efficacy (Barnett et al., 2012; Latouche & Gascoigne, 2017).

We did not detect positive treatment effects on teacher stress. This corresponds to previous data from our group on parenting-related stress among parents of children with externalizing problem behavior (Hanisch et al., 2010; Hanisch et al., 2014), in which we did not find reductions of parents’ stress immediately after a parent intervention. A common feature of the two studies is that the intervention might initially increase the burden on the caregivers due to the time and effort invested in modifying the caregivers’ own behavior. A further explanation for the non-significant net intervention effect might be that the prospect of participating in the teacher coaching already caused some relief during the waiting phase (van de Ven et al., 2017). Another perspective could be that the DASS might not have been a good choice to measure the aspect of teacher wellbeing that we targeted on. Teacher stress might have been measured more validly by a questionnaire constructed for school settings, e.g. the index of teaching stress (Greene et al., 1997), the teacher subjective wellbeing questionnaire (Renshaw et al., 2015) or the German teacher anxiety and stress inventory (Lukesch & Stahl, 2011).

In summary, this first study on SCEP yields promising results in terms of its effectiveness in reducing child problem behavior during class and in changing teachers’ confidence in at least one of our measures. Despite small to medium effect sizes for child ADHD and ODD symptoms, for teachers’ responses to the target child, and for teachers’ stress, these measures did not yield significant intervention effects.

Limitations and future directions

Several limitations have to be considered. First, our sample was highly selective, as we recruited 15 schools out of a total of 313 informed schools. Presumably, schools that were either especially strained by children with externalizing problem behavior or otherwise particularly motivated might have been overrepresented in the present sample, calling the generalizability of our results into question. Moreover, teachers participated voluntarily, which further limits the generalizability of results to teachers who may be more inclined to participate in programs of this kind (Corkum et al., 2015).

Second, the sample size might have been too small to render small- to medium-sized treatment effects significant (Heinrichs et al., 2017). Due to a significant number of dropouts, which presumably have affected our results, a second recruitment phase was necessary, in which we used the already participating schools to recruit more participants among teacher colleagues who were not yet involved. Exchange between the newly recruited teachers and colleagues who had already participated in SCEP might have already catalyzed changes in teachers’ behavior during the waiting period and might have thus decreased the net treatment effects.

A third potential limitation of our study lies in the reduced experimental control: We opted for a within-subject control group design rather than a randomized waitlist control group design because we feared that we would be unable to recruit a sufficient number of waitlist control group schools. Furthermore, our design would have enabled us to compare individual courses during the waiting and the coaching phase from an exploratory perspective if no group effects had been found. We decided on individually combining standardized intervention modules based on functional behavior analysis for each child to increase the practical use of our findings. Thus, the intervention as well as the evaluation procedure result resembles an effectiveness rather than an efficacy trial with low internal validity but high ecological validity (Connor-Smith & Weisz, 2003; McAleavey et al., 2019). On the one hand, this might have increased the measurement error and variance and decreased the likelihood of significant treatment effects. On the other hand, however, it enables the findings to be generalized to real-world school settings more easily (Hoagwood et al., 1995; Nathan et al., 2000; Weiss et al., 1999; Weisz & Jensen, 1999). Secondary analysis should exploratively examine whether individual courses of decreasing problem behavior differ according to the use of specific intervention modules, e.g. decrease in ODD symptoms after practicing specific praise. Due to the restricted sample size of the present study, mediating or moderating effects of treatment composition cannot be analyzed statistically but should be examined in upcoming studies.

Nevertheless, one might argue that this SCEP effectiveness study should have been preceded by a randomized controlled trial with high experimental control to ensure that treatment effects could be detected under highly controlled circumstances (Michelson et al., 2013).

Furthermore, we included a follow-up period of only three months. In future studies, this period needs to be prolonged to ensure sustainability of effects.

Some measures (the deportment scale of the SKAMP-ge, subscales of the TSQ) had internal consistency below .80 which might have affected results on these measures.

In our study, assessment and treatment were provided free of charge to schools and the target child in order to reduce the risk of special selection of students based on monetary factors. For routine practice, the intervention is time-consuming for participating teachers and costly for schools if they have to pay for external personnel to deliver SCEP. Thus, SCEP should address only those children who continue to show behavior problems after evidence-based classroom interventions have been tried out, as suggested within a MSST framework. Schools could then decide which school staff in the respective school, e.g. special education teachers or school psychologists, could be trained to administer SCEP. Due to their qualification and experience with various mental health and learning problems school psychologists seem to especially predisposed. School, teacher and child factors that moderate treatment effects need to be identified in future research to ensure that SCEP is offered to those schools and students that would benefit the most.

Our evaluation of SCEP is exclusively based on teachers’ reports. Cognitive factors like attention or appraisal bias as well as effort justification might have affected measures to a large extent (Hautmann et al., 2013). Nevertheless, these cognitive processes might also be responsible for changes in teachers’ feelings of stress and self-efficacy and might represent the start of an upward spiral of change in teachers’ attitudes and behaviors towards the target child, thus initiating changes in child behavior.

Once the aforementioned factors can be pinpointed and insights are gained from routine use, an implementation strategy can be derived that will allow a widespread application of SCEP within the school setting. Given the multitude of negative effects of externalizing problem behavior, our findings suggest that continued efforts to understand the specific effects of school-based coaching for teachers on reducing this problem behavior are important. Overall, this study demonstrates that externalizing problem behavior can be effectively reduced by teacher-based coaching and that future studies should now examine how to effectively implement these findings in school structures.

To increase treatment effects or target high-risk samples, school-based interventions could be combined with parent training (Hanisch et al., 2010; Hautmann et al., 2009) or individualized child psychotherapy to form a multimodal intervention Doepfner & Hanisch, 2020; Webster-Stratton, 2001).

If SCEP were to be included in a MTSS, a screening for behavior and learning problems could be used to identify students at risk. Next, teachers could be trained in classroom management strategies like the GBG for implementation on tier 1. After consecutive screening, students for tier 2 could be identified, and the use of daily behavior report cards could be taught and implemented. For those students who continue to struggle, teachers might then receive the SCEP coaching. We have outlined a similar approach for German primary schools, which is currently being tested in a pilot study (Hanisch et al., 2019). School psychologists, special education teachers, or clinical child psychologists might serve as trainers or coaches in this professional development program.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the ethics committee of the University Hospital of Cologne. Informed consent was obtained.

Footnotes

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: All authors are authors of the German-language treatment manual that is available in German and receive royalties.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Federal Ministry of Education and Research (BMBF) (Support Code 01JH1204A/B); Prävention und Intervention bei expansivem Problemverhalten in der Schule: Entwicklung und Evaluation eines Lehrercoachings.

Author biographies

Charlotte Hanisch is a Professor at the Faculty of Human Sciences, Department of Special Education, University of Cologne. Also, a clinical child and adolescent psychotherapists with main research interest in multimodal child and adolescent psychotherapy.

Ilka Eichelberger is a Research Associate at the Faculty of Human Sciences, Department of Special Education, University of Cologne. Also, a clinical child and adolescent psychotherapists with main research interest in multimodal child and adolescent psychotherapy. Dr Ilka Eichelberger now works in an outpatient clinic.

Stefanie Richard is a Research Associate at the Faculty of Human Sciences, Department of Special Education, University of Cologne. Also, a clinical child and adolescent psychotherapists with main research interest in multimodal child and adolescent psychotherapy.

Manfred Doepfner is a Professor at the Department for Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy and School for Child and Adolescent Cognitive Behavior Therapy, Medical Faculty of the University of Cologne. One of his research focus lies on the development and evaluation of prevention and intervention programs for youth mental health.

ORCID iD

Charlotte Hanisch

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