Behavior Management Training for Newly Graduated Teachers: A Randomized-Controlled Trial

Abstract

Students who engage in challenging behavior should receive preventive and intervening supports and services in general education settings based on their individual needs. These supports are necessary for students to be successful in school, yet preservice teachers receive limited education and training in both classroom and behavior management. As such, the purposes of this study were to identify the effects of an online behavior management training on newly graduated elementary education teachers immediately after completing their teacher preparation programs and to explore their perceptions regarding the training. A randomized-controlled trial was used, with 39 participants in the intervention group and 44 participants in the waitlist-control group. Results indicated participants in the intervention group showed a statistically significant increase in both knowledge and self-efficacy compared with participants in the waitlist-control group, and insignificant gains in their ability to analyze the use of behavior management strategies. Participants reported feeling the training was highly effective and were eager to implement the strategies with their future students.

Keywords

behavior management preservice teachers online training teacher preparation

Many students can engage in challenging behavior, with some engaging in more frequent rates, higher intensity, and/or more significant forms of challenging behavior. Those students who engage in challenging behavior can have short- and long-term consequences such as less instructional time (Sutherland et al., 2008), higher rates of referrals to intervention and/or special education services (Crane et al., 2013; DiPrete & Jenninges, 2012), and a higher likelihood of failing school (Noltemeyer et al., 2015) than their peers who do not engage in frequent challenging behavior. The impact of challenging behavior is not only felt by students but also teachers. Harris and colleagues (2019) interviewed teachers about their perceptions on teacher attrition, and teachers informed them that student behavior was one of the reasons teachers leave the profession. Djonko-Moore (2016) reported similar findings using 2007–2009 survey data from the National Center for Education Statistics. They reported that “students’ behavior significantly predicted a 193.1% increase in the odds of teachers moving schools” (p. 1079).

Preservice Teacher Training

To better support students who engage in challenging behavior, teachers would benefit from additional training in behavior management. Researchers have found that training in behavior management increases knowledge in behavior management strategies and self-efficacy in implementing behavior management strategies (Bosch & Ellis, 2021; Tsouloupas et al., 2014). As teachers are learning to support their students, it is helpful for them to use both prevention and response strategies (Dunlap et al., 2010) as well as function-based interventions (Dunlap & Fox, 2011) that best meet the needs of each student. To support a student who engages in challenging behaviors, a teacher can first collect behavior data such as antecedent-behavior-consequence (ABC) data. Then, the teacher can analyze the data to identify a hypothesized function (i.e., gaining access to an object, activity, or person; escaping or avoiding an activity or person; and sensory stimulation) of the behavior. Finally, the teacher can implement a behavior change plan that includes both antecedent and response strategies as well as function-based interventions (Cooper et al., 2020; O’Neill et al., 2014; Umbreit et al., 2007). Such practices align with applied behavior analysis (ABA) principles, which are evidence-based and support the behavioral development of students who engage in challenging behavior (Cooper et al., 2020).

However, preservice teachers receive limited preparation in classroom or behavior management. For example, State and colleagues (2011) examined the course syllabi from 41 randomly selected colleges and universities and found that elementary general education programs were not preparing preservice teachers to educate students with social-emotional and behavioral delays. Specifically, they found that four programs did not provide any instruction related to behavior management, 17 programs provided some class time to intervention discussion, and eight programs discussed classroom management but not challenging behavior.

Similarly, Freeman and colleagues (2014) examined legislative and state policies related to classroom management for elementary general education majors. They found that 45 states required instruction that focused on classroom management, although only 28 states required that instruction focus on research-based practices. Preservice teachers were more likely to receive instruction about actively engaging students, with 31 states requiring this type of instruction. In contrast, only three states required instruction related to responding to appropriate behavior and four states required instruction related to responding to inappropriate behavior. Such findings align with what other researchers have reported regarding limited training in both classroom and behavior management (McGuire et al., 2023).

Christofferson and Sullivan (2015) found that stand-alone courses related to classroom management were most beneficial in increasing preservice teachers’ sense of preparation and ability to implement classroom management strategies. Begeny and Martens (2006) reported similar findings, also reporting that preservice teachers had limited coursework dedicated to behavioral concepts, strategies, programs, and assessment. Such findings indicate that although some teachers may be engaging in classroom management coursework, fewer teachers have opportunities to engage in behavior management coursework. Putman (2012) also examined stand-alone classroom management courses by comparing stand-alone classroom management courses with the implementation of in vivo experiences and classroom management courses. He found that when preservice elementary education teachers engaged in classroom management courses and in vivo experiences, their sense of efficacy increased significantly more than preservice teachers who engaged in coursework alone.

Online Training for Preservice Teachers

Although in vivo courses can support preservice teachers’ skill development, the use of online training opportunities for teachers can also help them gain new skills (e.g., Billingsley & Scheuermann, 2014). Powell and Bodur (2019) define online trainings, or “online teacher professional development” as “courses, workshops, or learning modules that are delivered in an online format for teacher [professional development]” (p. 21). They further explain that online trainings can have several goals, purposes, or formats. Such formats can include synchronous or asynchronous courses, videos, websites, interactive blogs, podcasts, or even social media (Powell & Bodur, 2019). Because of the variety of goals, purposes, and formats available for online trainings, the needs of the participants should be considered when developing a specific training. Then, the goals, purposes, and format can be tailored for those participants.

When teachers participate in online training opportunities, 83.3% have reported them to be moderately to extremely beneficial and 74.3% of teachers have felt the content presented in online trainings is moderately to largely applicable to their teaching (Parsons et al., 2019). When looking specifically at classroom and behavior management strategies, researchers have worked to identify ways in which an online training can increase teachers’ strategy use. Marquez and colleagues (2015) reported the effects of a classroom management training implemented with elementary education teachers in three separate studies. The training was developed using three online video modules, an interactive planning tool, and downloadable summaries of the strategies participants learned in the modules. Findings indicated the online training led to increased knowledge and self-efficacy for participants who completed the training compared with those in a waitlist-control group.

As discussed by Powell and Bodur (2019), online trainings can extend beyond synchronous or asynchronous courses, including videos, blogs, and podcasts. For example, Hirsch et al. (2015) reported on the use of video acquisition podcasts compared with traditional lectures to teach preservice general education teachers about the FBA process. Results indicated participants in the video acquisition podcast group showed significantly higher gains in knowledge and application of FBA principles following the intervention when compared with participants in the traditional lecture.

Online trainings, in addition to in vivo trainings, can be effective when tailored toward specific participants, including preservice teachers. However, preservice teachers still have limited knowledge and experiences related to behavior management. In a recent review of literature, no studies could be found that indicated preservice elementary education teachers were receiving in vivo or online training related to behavior management strategies (McGuire et al., 2023). Therefore, there is a clear need for the development of a behavior management training to support the teachers who work with students who engage in challenging behavior in general education settings. Providing behavior management training through an online experience can allow preservice teachers opportunities to engage in content in synchronous or asynchronous formats; view videos, blogs, or podcasts; or interact with material in more effective and efficient ways than may be possible through in vivo formats (Powell & Bodur, 2019).

Adult Learning Theory

Embedding adult learning theory principles can offer opportunities for participants such as preservice teachers to engage in content that is tailored to their needs. According to adult learning theory, adults are able to engage in self-paced learning opportunities that employ goal-specific tasks and activities as long as they have prior knowledge and experiences related to the content being presented (Merriam, 2001). In addition, Dunst (2015) reported learning opportunities adults engage in should include the following key features: (a) explanation of key concepts, (b) authentic opportunities to use new skills, (c) opportunities to reflect, (d) feedback, (e) follow-up, and (f) opportunities for engagement. As such, training opportunities preservice teachers engage in should incorporate characteristics of adult learning theory to ensure they are able to actively engage and learn new content in a way that is meaningful for them.

In addition to adult learning theory, implementing behavior skills training (BST) can allow instructors to utilize specific teaching techniques with learners such as instruction, modeling, rehearsal, and feedback (Miltenberger, 2012). This means the instructor first provides instruction on a given topic, then models its use, provides opportunities for practice, and finally implements corrective feedback or specific praise. The use of BST with teachers can increase their ability to implement specific academic and/or behavioral strategies. For example, Sarokoff and Sturmey (2013) found that three teachers who participated in a BST package about discrete trial training increased their correct teaching responses from 43%, 49%, and 43% to 97%, 98%, and 99%, respectively, following the training. The use of BST, coupled with adult learning theory principles, may have similar results with preservice and/or novice teachers who engage in a training about behavior management.

Purpose of the Study

To ensure students are being supported effectively, strategies based on ABA principles can be taught to preservice teachers prior to beginning their work in the classroom. However, preservice teachers have little opportunity to engage in coursework related to classroom management and even fewer opportunities to engage in coursework or additional trainings about behavior management. As such, the purposes of this training were to identify the effects of an online behavior management training on teachers and to explore their perceptions regarding the training. Two research questions guided the study:

Research Question 1: Do newly graduated elementary education teachers who complete an online behavior management training show greater (a) understanding of, (b) ability to analyze the use of, and (c) self-efficacy in applying behavior management strategies compared with participants in a waitlist-control group?

Research Question 2: How do newly graduated elementary general education teachers perceive the acceptability, feasibility, and effectiveness of the training?

Method

Our research team consisted of a doctoral student and two faculty members. The first author and primary investigator had taken courses, conducted prior quantitative studies, and was mentored by the second and third authors. The first two authors have prior experience working with new teachers and students with challenging behavior, and are board-certified behavior analysts (BCBAs). The third author is an educational statistics expert.

Participants

This study focused on elementary education teachers who recently graduated from teacher preparation programs in the U.S. Midwest. Recently graduated teachers were selected because they had already completed their degree and student teaching experiences but not yet begun teaching. Therefore, they would have completed all previous undergraduate coursework and their student teaching experience, which would allow them to build upon their current knowledge base. In addition, participants could also extend what they already knew in preparation for their first year of teaching. Participants from the Midwest were chosen for inclusion because of similarities in licensing processes. For example, some states outside the Midwest require additional education to obtain a teaching license. Inclusion criteria were (a) recently graduated (i.e., Fall 2020 or Spring 2021) from a public or private university in the Midwest, (b) student-taught in an elementary general education classroom (i.e., kindergarten through sixth grade), and (c) completed a traditional certification program. Participants were excluded if they completed an alternative certification program to control for variations in how teachers are prepared in such programs.

A total of 83 participants were included in the study: 39 participants in the intervention group and 44 in the waitlist-control group. Most participants were women (n = 81) and White (n = 70). Sixty-seven participants graduated in Spring of 2021 and 14 participants graduated in Fall of 2020. One participant graduated in 2016 but chose to delay her student teaching experience to Spring of 2021. Another participant confirmed she graduated in Fall of 2020 or Spring of 2021 but did not indicate in which term she graduated (see Table 1 for participant information).

Table 1.

Demographic Information for Newly Graduated Teacher Participants

Demographic Variables	Intervention (n = 39)		Control (n = 44)		Total (N = 83)
Demographic Variables	n	%	n	%	n	%
Gender
Male	0	0	2	4.55	2	2.41
Female	39	100	42	95.5	81	97.6
Age, M (SD)	22.7 (3.3)		22.8 (3.4)		22.8 (3.3)
Race
Asian	2	5.1	2	4.5	4	4.8
Black or African American	1	2.6	0	0	1	1.2
White	33	84.6	37	84.1	70	84.3
Two or more races	1	2.6	4	9.1	5	6.0
Other	1	2.6	0	0	1	1.2
Ethnicity
Hispanic/Latino	2	5.1	5	11.4	7	8.4
State of college attendance
Illinois	12	30.8	10	22.7	22	26.5
Indiana	7	17.9	11	25	18	21.7
Iowa	2	5.1	1	2.3	3	3.6
Kansas	2	5.1	3	6.8	5	6.0
Michigan	0	0	3	6.8	3	3.6
Minnesota	3	7.7	5	11.4	8	9.6
Missouri	1	2.6	5	11.4	6	7.2
Nebraska	1	2.6	1	2.3	2	2.4
Ohio	1	2.6	2	4.5	3	3.6
South Dakota	3	7.7	0	0	3	3.6
Wisconsin	5	12.8	3	6.8	8	9.6
Date of graduation
Fall 2020	8	20.5	6	13.6	14	16.9
Spring 2021	30	76.9	37	84.1	67	80.7
Other	1	2.6	1	2.3	2	2.4
Grade levels certified to teach
PreK–3rd	2	5.1	1	2.3	3	3.6
PreK–5th	4	10.2	1	2.3	5	6.0
PreK–6th	1	2.6	0	0	1	1.2
PreK–8th	1	2.6	0	0	1	1.2
K–6th	14	35.9	21	47.7	35	42.2
K–8th	5	12.8	4	9.1	9	10.8
K–9th	1	2.6	1	2.3	2	2.4
1st–6th	10	25.6	12	27.3	22	26.5
1st–8th	1	2.6	3	6.8	4	4.8
1st–9th	0	0	1	2.3	1	1.2

Note. PreK = prekindergarten, K = kindergarten.

Measures

Demographic Questionnaire

Participants completed a demographic questionnaire via a Google form. Information collected from participants included age, gender, ethnicity, college or university attended, grade level where student teaching was completed, and information about previous training related to behavior management. There was a combination of open-ended and multiple-choice questions on the demographic questionnaire.

Pre- and Post-Measures

Three scales were used in the study: (a) knowledge, (b) application, and (c) self-efficacy in applying behavior management strategies (the scales are available from the first author upon request). These scales were administered prior to and after the intervention group completed the training. The first scale focused on knowledge of behavior management strategies and was adapted from the Function-Based Support Knowledge Test (Renshaw et al., 2008) and the Parenting Knowledge Assessment (Lee, Hacker, et al., 2022). Renshaw et al. (2008) did not report the psychometric properties for the Function-Based Support Knowledge Test. Lee, Hacker, et al. (2022) reported a Cronbach’s alpha from a previous unpublished study to be .73. The current scale included 25 multiple-choice questions about behavior management strategies. To adapt the knowledge scale, the first author identified items from both measures that aligned with the training and then changed incidental information (e.g., setting), so it more closely related to the included participants. The scale considered multiple aspects of knowledge (e.g., data collection, data analysis, knowledge strategies), meaning it did not measure a single latent construct. Therefore, Cronbach’s alpha was not calculated because the unidimensional factor assumption was violated.

The second scale focused on the participants’ ability to analyze the use of behavior management strategies (this was referred to as “application” on the scale). Because of the ongoing COVID-19 pandemic, direct teacher-to-student contact was limited, and therefore vignettes were used in the second scale for application purposes. The application scale was adapted from the Identifying Behavioral Interventions based on a functional behavioral assessment measure (Borgmeier & Loman, 2012) that included two vignettes each on pre- and post-measure. Borgmeier and Loman (2012) did not report psychometric properties for their scale. The vignettes provided information about two students, antecedents to their behaviors, consequences for their behaviors, and hypothesized functions of the behaviors. Then, participants were asked to provide responses regarding replacement behaviors, antecedent strategies, reinforcers, and consequences. Each vignette had five multiple-choice questions, with three of the questions requiring one answer and two requiring two answers, for a total of 14 possible points on the scale. Minimal adaptations were made to this scale (e.g., setting). For the same reason, Cronbach’s alpha was not calculated on the knowledge scale (i.e., it tested a variety of constructs, and therefore the unidimensional factor assumption was violated), it was not calculated on the application scale.

The third scale was developed by the first and second authors and focused on participants’ self-efficacy in applying behavior management strategies. The scale was identified as a confidence scale to participants as this is a more colloquial term often referring to a general belief about one’s own abilities. The first two authors developed the scale by identifying the overarching themes of the online training and creating questions that aligned with the themes. The scale had 16 Likert-type questions with a scale of 1 (strongly disagree) to 5 (strongly agree). Questions focused on participants’ self-efficacy in using strategies taught in the behavior management training. Sample items included, “When considering behavior management strategies for students who engage in challenging behavior, I am knowledgeable in collecting ABC data,” and “I feel confident in my ability to teach a replacement behavior based on the function of a student’s behavior.” The purpose of this scale was to measure participants’ self-efficacy in applying strategies upon entering their first year of teaching instead of their ability to use the strategies upon entering the classroom. The Cronbach’s alpha on the premeasure and postmeasure were .91 and .95, respectively.

Social Validity Questionnaire

Following the implementation of the intervention, participants completed a social validity questionnaire created by the first and second authors that included three sections: (a) acceptability, (b) feasibility, and (c) effectiveness. There were 19 questions, with each section having five or six Likert-type questions that ranged from 1 (strongly disagree) to 5 (strongly agree) and one open-ended question asking whether the participant would like to share anything else about the training related to the given topic (i.e., acceptability, feasibility, or effectiveness). The first two authors created the measure by identifying items that aligned with each of the three themes (i.e., acceptability, feasibility, and effectiveness) and the online training. The Cronbach’s alpha for the social validity questionnaire was .92.

Intervention: Behavior Management Training

The online training included modules developed by the first and second authors. The modules were based on the Challenging Behavior Online Modules (Lee, Hacker, et al., 2022; Lee, Meadan, & Xia, 2022; Terol et al., 2023), but adapted to fit the current audience (i.e., elementary general education teachers) and age group (i.e., elementary-age students). Because the participants had previously completed student teaching experiences, they had prior knowledge working with students in elementary school, in alignment with adult learning theory (Merriam, 2001). The training was composed of four video modules that consisted of Google slide decks, narrations, video examples, vignettes, and real-world scenarios that helped participants engage in the content in meaningful ways. Dunst (2015) reported that when adults engage in learning opportunities, they benefit from authentic opportunities to use their skills and opportunities for engagement. The inclusion of narration, video examples, vignettes, and real-world scenarios allowed participants the opportunity to use the skills they were learning by using life-like scenarios and to be more engaged in the content. Narration of the presentations was audio recorded and uploaded to Edpuzzle, an online tool that allows for integration of questions in videos. The use of Edpuzzle allowed content to be explicitly taught, modeled for participants, and then opportunities for practice, as outlined in BST (Miltenberger, 2012). Videos were then integrated into Canvas. A Manual of Terms was also created for participants to use as a supplemental resource that provided definitions of terms used throughout the modules. Copies of the slides and Manual of Terms were available on Canvas.

The modules were based on ABA principles. Module 1 covered (a) definitions of behavior, (b) functions of behavior, and (c) data collection. Module 2 covered (a) antecedent-based strategies, including providing choices, Premack Principle, priming, and schedules and routines, and (b) consequence-based strategies, including behavior-specific praise and token economy. Module 3 covered (a) replacement behaviors, (b) reinforcement, and (c) punishment. Finally, Module 4 reviewed concepts taught in the first three modules (see Supplemental Figure 1 for outline of modules). Throughout each module, participants were provided with scenarios to explain each concept and strategy and were asked questions to test their understanding of concepts through the Edpuzzle feature. After each question there was an explanation of the correct answer. Participants were provided an opportunity to ask questions at the end of each module through the Edpuzzle feature and the first author provided responses, allowing opportunities for feedback and follow-up (Dunst, 2015; Miltenberger, 2012). Each module was approximately 24 min long (M = 24 min, 15 s; range = 22 min, 18 s–28 min, 14 s).

To ensure participants completed each module, Canvas was set up so participants could not advance to the next module without completing the previous one. In addition, Edpuzzle does not allow videos to be marked as “complete” without the video being watched to the end. Each video was set so participants could not skip sections or speed up the video. The combination of the settings of Edpuzzle, questions embedded into the videos, and settings of Canvas ensured participants completed the video modules in their entirety.

Pilot

To ensure content validity of all measures, four BCBAs were asked, via email, to review the measures for accuracy, content, and coverage of material. Feedback from the BCBAs was integrated into the measures and changes were made accordingly. Similarly, a BCBA who was not part of the research team reviewed the training material and provided feedback to members of the research team. Changes were integrated into the modules prior to them being piloted. The measures and training were then piloted with two elementary education majors who did not meet all inclusion criteria (e.g., were not graduating in Fall 2020 or Spring 2021). Pilot participants provided feedback about the format of the training, materials presented, pacing, measures, and information that helped the researchers adjust the training prior to implementing it with study participants. For example, pilot participants provided information regarding errors in questions on the measures so they could be corrected.

Procedures

After receiving institutional review board (IRB) approval, recruitment began in April 2021. A priori G*Power analysis (Faul et al., 2009) indicated that, to achieve 80% power, a sample size of 78 participants was needed with a medium effect size and the nominal alpha of .05. To account for possible attrition a goal of 100 participants was set. Recruitment continued until 132 participants were recruited. Recruitment flyers were initially shared with public and private colleges and universities throughout the Midwest, and then in Facebook groups for teachers and on the first author’s Facebook page. A Google form was used to screen potential participants, with a link included on the recruitment flyer. If they met inclusion criteria, a consent form and demographic questionnaire were sent to the participants (n = 117) via a Google form. Participants had to provide consent prior to advancing to the demographic questionnaire. If participants chose not to provide consent, they were thanked for their time and exited the Google form. Ninety-seven participants provided consent to participate and completed the demographic questionnaire. Information from the demographic questionnaire was reviewed by the first author and compared with the screening form to ensure information aligned and they met inclusion criteria.

A research assistant naïve to study conditions randomly assigned participants into either the intervention or waitlist-control group using R version 3.5.3. To do this, the research assistant was given a list of participant identification numbers, entered the numbers into R, and used a code to generate a list of numbers within a range. Two lists were generated, one list was assigned to Group 1 and the other assigned to Group 2. The research assistant then sent the list to the first author who assigned all participants from Group 1 into the intervention group and all participants from Group 2 into the waitlist-control group. Once participants completed the consent form and demographic questionnaire, they were enrolled into one of two Canvas (a learning management system) courses. Participants assigned to the intervention group were enrolled in Canvas course A (n = 48) and participants assigned to the waitlist-control group were enrolled in Canvas course B (n = 49). Study procedures began in the middle of May 2021 to ensure participants had completed their undergraduate requirements. Participants in both groups were sent an introductory email with information about the online training, a timeline for completing study procedures, and how to enroll in Canvas. To begin, all participants completed the premeasure via Canvas, which included three scales: knowledge assessment, application assessment, and self-efficacy assessment.

Following completion of the premeasure, participants assigned to the intervention group began the online training and had 10 days to complete four modules. Following completion of the modules, all participants completed the postmeasure and participants in the intervention group completed the social validity questionnaire. Then, participants in the waitlist-control group gained access to the training. Waitlist-control group participants also had 10 days to complete the four modules before completing the social validity questionnaire. At the end of the study, 39 participants in the intervention group and 44 in the waitlist-control group remained. All participants who withdrew from the study did so after completing the premeasure (see Table 2 for attrition information). To thank participants for completing the online training and all measures, they each received an egift card.

Table 2.

Attrition Information.

Group	Completed screener	Met inclusion criteria	Provided consent	Randomized	Completed premeasure	Completed postmeasure
Intervention	132	117	97	48	43	39
Waitlist-control	132	117	97	49	45	44

Data Analysis

Independent-samples t tests were conducted to analyze similarities and differences between the intervention and waitlist-control groups. The t tests were run on each subscale and on the following demographic variables: gender, race/ethnicity, and state of college attendance. To analyze the intervention data, multivariate analysis of variance (MANOVA) was used. Each participant’s scores were averaged per subscale (i.e., knowledge scale, application scale, and self-efficacy scale). A 2 (Group A vs. Group B; a between-subject factor) × 2 (Time 1 vs. Time 2; a within-subject factor) MANOVA was run to indicate whether there was a statistically significant main effect of the training on the outcome variables (i.e., knowledge, application, and self-efficacy) or an interaction effect. Wilk’s Λ from the MANOVA analysis indicated whether at least one outcome showed a significant mean difference. To identify which of the outcome variables was significant (i.e., if there was a significant improvement in knowledge, application, or self-efficacy), separate analysis of variance (ANOVA) analyses were conducted for each outcome variable. In addition, all MANOVA and ANOVA analyses were run using a confounding variable of previous training, which indicated whether participants completed a previous training such as an undergraduate course on classroom management or a professional development activity during student teaching (N = 35 in total, n =16 in the intervention group, n = 19 in the waitlist-control group). Participants who had completed a previous training were coded as 1, and those who had not participated in a previous training were coded as 0. Previous training was added as a potential confounding variable because whether or not participants received previous training could impact the effectiveness of the current training.

The data from the social validity questionnaire were analyzed using descriptive statistics to indicate overall satisfaction with the training itself. The questionnaire had three sections: (a) acceptability, (b) feasibility, and (c) effectiveness. Means and standard deviations were calculated on each section to identify the overall satisfaction of the training and open-ended responses were reviewed to identify similarities and differences.

Results

The research questions focused on newly graduated teachers’ knowledge, application, and self-efficacy in using behavior management strategies after participating in an online behavior management training compared with participants in a waitlist-control group. Independent-samples t tests were run on demographic data and the subscales to identify similarities and differences between the intervention and waitlist-control groups. There were no significant differences between the groups in terms of gender, t(81) = 1.346, p = .18, Cohen’s d = 0.154; race/ethnicity, t(81) = –0.608, p = .55, d = 0.87; or the state of college attendance, t(81) = –1.011, p = .32, d = 2.968. There were also no significant differences between groups on the knowledge premeasure, t(81) = –1.918, p = .06, d = 2.54; application premeasure, t(81) = 0.439, p = .66, d = 2.75; or self-efficacy pre-measure, t(81) = –0.597, p = .55, d = 0.58. Nonsignificant t test results suggested the intervention and waitlist-control groups were similar in terms of demographic variables and pre-measures.

Results from MANOVA indicated the main effect of group, Λ = .786, F(3, 79) = 7.092, p < .001; main effect of intervention (i.e., Time 1 vs. Time 2), Λ = .346, F(3, 79) = 49.074, p < .001; and an interaction effect, Λ = .446, F(3, 79) = 32.305, p < .001, were all significant. Because the MANOVA analyses suggested that significant group differences existed regarding at least one of the three outcome variables (i.e., knowledge, application, and self-efficacy), ANOVA analyses were conducted to investigate the main and interaction effects for each of the three outcome variables.

Knowledge

The ANOVA analysis indicated there was an significant main effect of group on the knowledge scale, F(1, 81) = 4.268, p = .042, η² = .05; a significant difference between Time 1 and Time 2, F(1, 81) = 51.938, p < .001, η² = .50; and a significant difference on the interaction effect, F(1, 81) = 42.330, p < .001, η² = .35. The interaction effect suggested that the effectiveness of the training was different between the intervention and control group. The interaction plot (Figure 1) indicated that participants in the intervention group showed a larger increase in knowledge of behavior management strategies compared with the waitlist-control group. There also was a large effect size, further indicating the training was effective for increasing knowledge for participants.

Figure 1.

Interaction effect of Knowledge Scale.

Application

Results from the ANOVA analysis indicated an insignificant main effect of group on the application scale, F(1, 81) = 2.036, p = .157, η² = .03; significant main effect of Time 1 and Time 2, F(1, 81) = 34.780, p < .001, η² = .28; and insignificant interaction effect, F(1, 81) = 3.889, p = .052, η² = .05. The insignificant interaction effect indicated the effectiveness of the training on application skills (i.e., analyzing the application of behavior management skills) appeared to be the same for the two groups. However, the moderate effect size of the interaction indicated that the insignificant results may be due to the limited sample size. Figure 2 further shows that the training appeared to result in larger changes in application scores for the intervention group than for the waitlist-control group.

Figure 2.

Interaction effect of Application Scale.

Self-Efficacy

The ANOVA analysis indicated there was a statistically significant main effect for group on the self-efficacy scale, F(1, 81) = 19.176, p < .001, η² = .19; a significant difference between Time 1 and Time 2, F(1, 81) = 105.656, p < .001, η² = .60; and a significant interaction effect F(1, 81) = 66.522, p < .001, η² = .44. The large effect size, coupled with significant main and interaction effects, indicate the intervention was successful in improving the participants’ self-efficacy in implementing behavior management strategies. Figure 3 indicates the intervention group demonstrated a large increase in self-efficacy in using behavior management strategies compared with participants in the waitlist-control group following the behavior management training.

Figure 3.

Interaction effect of Confidence Scale.

Social Validity Questionnaire

Following completion of the training, participants from both intervention and waitlist-control groups completed a social validity questionnaire that inquired about the acceptability, feasibility, and effectiveness of the training (see Table 3 for summary of findings). Participants provided responses to each item on a scale of 1 to 5 (1 = strongly disagree, 5 = strongly agree). In regard to the acceptability of the intervention, participants in the intervention group provided a mean score of 4.5 (SD = 0.61) and participants in the waitlist-control group provided a mean score of 4.4 (SD = 0.71). Participants shared positive feedback in the open-ended section regarding the acceptability of the training. One participant in the intervention group stated, “I liked how the videos had examples and little question check ins! I learned a lot and I am eager to be with students and implement it!” (ST22P2).

Table 3.

Results From Social Validity Questionnaire.

Social Validity Variables	Interventionn = 38	Controln = 37	TotalN = 75
Acceptability	4.5 (SD = 0.61)	4.4 (SD = 0.71)	4.4 (SD = 0.67)
Feasibility	4.6 (SD = 0.56)	4.6 (SD = 0.52)	4.6 (SD = 0.54)
Effectiveness	4.3 (SD = 0.61)	4.3 (SD = 0.67)	4.3 (SD = 0.64)

Note. Scale = 1 (strongly disagree) to 5 (strongly agree).

Most comments about the acceptability of the training were positive, but there were a few comments that indicated participants were not fully satisfied with the training. For example, one participant in the control group stated,

The only thing that I still do not feel confident was covered was dealing with kids who are still not receptive to any of these methods. I know there will always be exceptions so with the student I have in mind it’s probably more of a social worker or special ed teacher who is needed than my classroom strategies, although they will help a lot. (ST60P2)

Feasibility was assessed next, including questions about navigating the training and Canvas. Participants from both groups reported mean scores of 4.6 (SD = 0.56 and 0.52, respectively). Participants felt the materials and Canvas were easily accessible. One participant in the control group shared, “This course was so clearly and strongly organized . . . and I appreciated that organization because it aided in the flow and comprehension of the content” (ST32P2). There were a few technical glitches participants reported, including one participant who stated she was unable to clearly view some parts of videos and another who stated she had to rewatch the videos a couple times before Canvas would allow her to proceed to the next video. Such technical issues were rarely reported and participants indicated they did not impact their overall ability to engage in the content.

The final section of the social validity questionnaire was about the effectiveness of the training. Questions in this section asked participants if they gained new knowledge and if they felt they could effectively use the strategies with future students. Participants from both groups provided mean scores of 4.3 (SD = 0.61 and 0.67, respectively). Comments about the effectiveness of the training were positive, with a participant in the intervention group stating, “Thank you! I learned a lot and this was a very well developed training! Excited to have new tools for my behavior management toolbox!” (ST49P2). Most comments were positive, but one participant in the intervention group was concerned about using the strategies in her future classroom, stating,

While this training was effective, I feel that implementing them in real life will be a bit more challenging, but I do feel more knowledgeable about ABC data, prevention strategies, token economy, etc. I will definitely be more prepared in my first year as a teacher having taken this training. (ST114P2)

Participants also made recommendations for future trainings. Some recommendations included changes to the videos themselves, including the addition of captions, adding more questions to the modules, having more modules that are shorter in length, and opportunities to ask questions throughout the modules instead of at the end. Two participants also recommended providing more opportunities for practicing specific skills such as writing target behaviors and replacement behaviors. Participants in the waitlist-control group also requested an opportunity to be tested following the completion of the modules instead of only before.

Discussion

The purposes of this study were to (a) test the efficacy of an online training for newly graduated elementary education teachers in the areas of knowledge, ability to analyze, and self-efficacy in applying behavior management strategies compared with participants in a waitlist-control group, and (b) explore the perceptions of newly graduated elementary education teachers regarding the social validity of the online behavior management training. Results indicated the training was effective in increasing participants’ knowledge and self-efficacy in applying behavior management strategies, but their ability to analyze the use of such strategies on the application measure did not increase in a statistically significant manner, even after controlling for previous training (e.g., coursework) participants may have engaged in. Social validity data indicated participants found the training to be highly acceptable, feasible, and effective, and participants from both the intervention group and waitlist-control group were eager in implementing the behavior management strategies in their future classrooms.

Two key findings from the study indicated that (a) a short training can be effective in improving knowledge and self-efficacy for newly graduated teachers, and (b) additional changes may be needed to aid in the applicability of the content. The behavior management training provided short, self-paced modules for newly graduated teachers that resulted in increased knowledge and self-efficacy for participants in the intervention group. It is not surprising that such a training increases participants’ knowledge as this aligns with findings from other studies (Lee, Hacker, et al., 2022; Lee, Meadan, & Xia, 2022; Renshaw et al., 2008). When participants engage in a focused intervention, it is likely their knowledge of the content will increase. In a study conducted by Lee, Hacker, and colleagues, caregivers were provided with online modules related to behavior management strategies they could use with their children. Results indicated a statistically significant increase in knowledge for the intervention group compared with the control group. Similarly, Renshaw and colleagues (2008) used a knowledge assessment to test teachers’ knowledge of behavior management skills following an intervention in a multiple-baseline design study. Participants showed large improvements in their scores following the intervention. This is also expected given the training was developed with adult learning theory and BST in mind. The training specifically integrated components that allowed for interaction between the instructor and participants, including the use of explicit instruction, modeling of new concepts, integration of questions, and immediate feedback (Dunst, 2015; Miltenberger, 2012).

Participants in the current study showed an increase in their self-efficacy in applying behavior management strategies. When looking at prior research, it has been reported that teachers feel more self-efficacious when educating students with prosocial behaviors and report lower levels of self-efficacy when teaching students who engage in challenging behavior (Zee et al., 2016). As teachers begin receiving behavior management training, their levels of self-efficacy increase, such that those who receive limited training report low levels of self-efficacy and those who receive more training report high levels of self-efficacy (Putman, 2012; Tsouloupas et al., 2014). However, it should be noted that the participants in the current study were not interacting with students and therefore may have had a false sense of self-efficacy following the training. In the current study, the implementation of immediate performance feedback (Dunst, 2015; Miltenberger, 2012) may have aided in the increases in self-efficacy because all feedback was provided via behavior-specific praise or corrective feedback. However, participants did not have an opportunity to directly implement the strategies with students and therefore did not have the ability to know whether they could effectively implement the strategies independently.

The current training focused on behavior management strategies based on applied behavior analysis. Previous research has shown that teacher preparation programs are underpreparing preservice teachers to support students who engage in challenging behavior (Freeman et al., 2014; State et al., 2011). Approximately 42% of the participants engaged in a previous training experience about classroom management. However, when previous training was included as a covariate in the analysis, results still concluded the intervention was effective in improving participants’ knowledge and self-efficacy in using behavior management strategies. This finding indicates previous training, which included coursework related to classroom management strategies, is insufficient in preparing preservice teachers to support individual students who engage in challenging behavior. Such a finding adds to the body of literature indicating preservice teachers need to learn strategies to support entire classrooms as well as individual students who engage in challenging behavior.

Participants showed insignificant increases, but moderate effects from the online training in their ability to analyze the use of strategies. The current study did not include an application component that allowed participants to implement strategies they were learning with students in real time. Modules used application-based scenarios, allowing participants to respond to questions as though they were working with a student, but this did not fulfill the needs of an in vivo experience. Prior research has indicated stand-alone courses are most beneficial in teaching management strategies (Christofferson & Sullivan, 2015). Putman (2012) also reported the benefits of preservice teachers engaging in practicum experiences alongside stand-alone courses. Adult learning theory and BST also explain that although real-world examples are important, actually applying skills is more beneficial for adults who are learning new skills (Dunst, 2015; Merriam, 2001; Miltenberger, 2012). Future research may consider using programs that incorporate in vivo experiences when students are not available (e.g., TeachLive; Dieker et al., 2015). For example, TeachLive uses a simulated classroom with avatars for students, which would allow preservice or inservice teachers to practice using behavior management strategies in a simulated environment.

As we consider the findings from our study, we must do so with the understanding that our participants engaged in practicum and student teaching experiences that were different than preservice teachers had prior to the COVID-19 pandemic. It is possible participants who completed a more traditional teacher preparation program prior to the pandemic may have different experiences, making it difficult to generalize our results to a general population of newly graduated teachers. There was a call for policymakers and teacher educators to strengthen teacher preparation programs in the wake of the COVID-19 pandemic, specifically related to the use of evidence-based practices such as those used in this behavior management training (Darling-Hammond & Hyler, 2020). Just as preservice teachers’ preparation shifted prior to and during the pandemic, so have the needs of the students they educate (Bera et al., 2022). Providing preservice and newly graduated teachers with evidence-based behavior management strategies will allow them to meet the needs of their students in a post-COVID world.

On the social validity questionnaire, participants reported feeling their knowledge of behavior management strategies increased and that they were more confident in using such strategies. However, a few participants shared comments regarding future concerns applying behavior management strategies with students who engage in challenging behavior. This aligns with the results of the ANOVA analyses, and specifically the insignificant findings from the application scale. It further highlights the importance of using both didactic and in vivo experiences in a training to allow teachers to learn and apply behavior management strategies.

Limitations

Study results should be viewed with some limitations. First, the study was conducted during the COVID-19 pandemic, which interfered with participants’ teacher preparation experiences. Findings may not be able to be generalized to newly graduated teachers who have completed more traditional teacher preparation, including in-person coursework and student teaching. Next, when examining the interaction plot of the application scale (see Figure 2), there was an increase for the waitlist-control group that was larger than would be expected despite not receiving intervention. This may mean the increase seen in the intervention group did not result solely from the training, or there was maturation for the waitlist-control group. Regardless, this may bring into question the results of the application scale, and therefore they should be viewed as a limitation. Another limitation relates to the insignificant application finding. As mentioned, there were insignificant increases in application for participants in the intervention group. This is likely because participants were applying their skills using videos and vignettes instead of in a real-world context, which is considered best practice. Another limitation relates to the attrition rate, particularly for the intervention group. The attrition rate was 14% (19% for intervention, 10% for control). It is unclear why participants did not complete the training as the research team provided several reminders throughout the study to complete training-related activities. Finally, data were not collected for either group after the study concluded to assess for maintenance of skills over time. Given the current educational climate during the COVID-19 pandemic, the skills they learned may be more necessary than during a typical school year. Yet, without additional data collection, it is difficult to know whether participants were able to maintain their newly acquired skills. Future research should consider maintenance of behavior management strategies across time.

Implications

The current study has implications for practice, research, and policy. First, administrators and school district personnel may consider the use of a behavior management training when they are planning beginning of the year activities. Research indicates preservice teachers have little preparation in the area of behavior management (Begeny & Martens, 2006; McGuire et al., 2023), and if administrators and school leaders intervene early, they may be able to help alleviate some stress new teachers feel related to challenging behavior students engage in. Similarly, teacher preparation programs may consider adding seminars that focus on similar topics covered in the behavior management training from this study. Seminars take less time than traditional courses, but also may help to cover the content addressed in this training. This is not enough to provide preservice teachers the education they need to support students with challenging behavior, but it is a good place to start.

The current study was one of the first to focus on training preservice teachers on behavior management strategies, and specifically ABA strategies, to support students who engage in challenging behavior. It is documented in research that preservice teachers have received few opportunities to learn behavior management strategies through coursework (Begeny & Martens, 2006; Freeman et al., 2014). Future research can focus on expanding similar trainings that include in vivo experiences. Such experiences would allow participants to target a student in their classroom so they would be able to practice implementing strategies in real time. Future research can also focus on implementing behavior management training for teachers in their first 3 years of teaching (i.e., the induction phase; Bartell, 2005). In this case, teachers will have experience working with students and understand the importance of implementing such strategies differently than newly graduated teachers.

Conclusion

Preservice elementary education teachers are underprepared to support students who engage in challenging behavior. Teacher preparation programs provide them with opportunities to engage in classroom management coursework. However, coursework related to behavior management strategies are not typically provided. Findings from this study indicated that offering training by using online modules can provide preservice teachers instruction related to behavior management on a schedule that is more efficient for them while also allowing them to gain the knowledge and self-efficacy they need to support students who engage in challenging behavior.

Supplemental Material

sj-docx-1-bhd-10.1177_01987429231179890 – Supplemental material for Behavior Management Training for Newly Graduated Teachers: A Randomized-Controlled Trial

Supplemental material, sj-docx-1-bhd-10.1177_01987429231179890 for Behavior Management Training for Newly Graduated Teachers: A Randomized-Controlled Trial by Stacy N. McGuire, Hedda Meadan and Yan Xia in Behavioral Disorders

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This study was supported in part by funding from the Office of Special Education Programs, U.S. Department of Education Project STePS (H325D189923). The views or opinions presented in this manuscript are solely those of the authors and do not necessarily represent those of the funding agency.

ORCID iDs

Stacy N. McGuire

Hedda Meadan

Supplemental Material

Supplemental material for this article is available at

References

Bartell

C. A

. (2005). Cultivating high-quality teaching through induction and mentoring. Corwin Press.

Begeny

J. C.

Martens

B. K

. (2006). Assessing pre-service teachers’ training in empirically validated behavioral instruction practices. School Psychology Quarterly, 21(3), 262–285.

Bera

Souchon

Ladsous

Colin

Lopez-Castroman

. (2022). Emotional and behavioral impact of the COVID-19 epidemic in adolescents. Current Psychiatry Reports, 24, 37–46. https://doi.org/10.1007/s11920-022-01313-8

Billingsley

G. M.

Scheuermann

B. K

. (2014). Using virtual technology to enhance field experiences for pre-service special education teachers. Teacher Education and Special Education, 37(3), 255–272. https://10/1177/0888406414530413

Borgmeier

Loman

S. L

. (2012). Identifying behavioral interventions based on a functional behavioral assessment. Portland State University.

Bosch

Ellis

. (2021). Using avatars to address teacher self-efficacy. Journal of Global Education and Research, 5(1), 15–35. https://doi.org/10.5038/2577-509X.5.1.1069

Christofferson

Sullivan

A. L

. (2015). Preservice teachers’ classroom management training: A survey of self-reported training experiences, content coverage, and preparedness. Psychology in the Schools, 52(3), 248–264. https://doi.org/10.1002/pits

Cooper

J. O.

Heron

T. E.

Heward

W. L

. (2020). Applied behavior analysis (3rd ed.). Pearson.

Crane

Winsler

Sands

. (2013). Patterns of referral recommendations for ethnically diverse children in subsidized child care. Exceptional Children, 80(1), 45–64.

10.

Darling-Hammond

Hyler

M. E

. (2020). Preparing educators for the time of COVID. . .and beyond. European Journal of Teacher Education, 43(4), 457–465. https://doi.org/10.1080/02619768.2020.1816961

11.

Dieker

L. A.

Hynes

M. C.

Hughes

C. E.

Hardin

Becht

. (2015). TLE TeachLivETM: Using technology to provide quality professional development in rural schools. Rural Special Education Quarterly, 34(3), 11–16. https://doi.org/10.1177/875687051503400303

12.

DiPrete

T. A.

Jenninges

J. L

. (2012). Social and behavioral skills and the gender gap in early educational achievement. Social Science Research, 41, 1–15. https://doi.org/10.1016/j.ssresearch.2011.09.001

13.

Djonko-Moore

C. M

. (2016). An exploration of teacher attrition and mobility in high poverty racially segregated schools. Race Ethnicity and Education, 19(5), 1063–1087. https://doi.org/10.1080/13613324.2015.1013458

14.

Dunlap

Fox

. (2011). Function-based interventions for children with challenging behavior. Journal of Early Intervention, 33(4), 333–343. https://doi.org/10.1177/1053815111429971

15.

Dunlap

Iovannone

Kincaid

Wilson

Christainsen

Strain

English

. (2010). Prevent-teach-reinforce: The school-based model of individualized positive behavior support. Paul H. Brookes.

16.

Dunst

C. J

. (2015). Improving the design and implementation of in-service professional development in early childhood intervention. Infants & Young Children, 28(3), 210–219. https://doi.org/10.1097/IYC.0000000000000042

17.

Faul

Erdfelder

Buchner

Lang

A. G

. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160.

18.

Freeman

Simonsen

Briere

MacSuga-Gage

A. S

. (2014). Pre-service teacher training in classroom management: A review of state accreditation policy and teacher preparation programs. Teacher Education and Special Education, 37(2), 106–120. https://doi.org.10.1177/0888406413507002

19.

Harris

S. P.

Davis

R. S.

Christensen

S. S.

Hanks

Bowles

. (2019). Teacher attrition: Differences in stakeholder perceptions of teacher work conditions. Educational Sciences, 9(4), 300. https://doi.org/10.3390/educsci9040300

20.

Hirsch

S. E.

Kennedy

M. J.

Haines

S. J.

Thomas

C. N.

Alves

K. D

. (2015). Improving preservice teachers’ knowledge and application of functional behavioral assessments using multimedia. Behavioral Disorders, 41(1), 38–50. https://doi.org/10.17988/0198-7429-41.1.38

21.

Lee

J. D.

Hacker

Meadan

Sleiman

. (2022). Challenging behaviors online modules for parents of young children with disabilities: A pilot feasibility study. Education & Treatment of Children, 45, 341–355. https://doi.org/10.1007/s43494-021-00067-x

22.

Lee

J. D.

Meadan

Xia

. (2022). Impact of challenging behavior online modules on Korean parents of children with developmental disabilities: A randomized controlled trial. Journal of Positive Behavior Interventions, 24(3), 222–235. https://doi.org/10.1177/10983007211071119

23.

Marquez

Vincent

Marquez

Pennefather

Smolkowski

Sprague

. (2015). Opportunities and challenges in training elementary school teachers in classroom management: Initial result from Classroom Management in Action, an online professional development program. Journal of Technology and Teacher Education, 24(1), 87–109.

24.

McGuire

S. N.

Meadan

Folkerts

. (2023). Classroom and behavior management training needs and perceptions: A systematic review of the literature. Child and Youth Care Forum. http://doi.org/10.1007/s10566-023-09750-z

25.

Merriam

S. B

. (2001). Andragogy and self-directed learning: Pillars of adult learning theory. New Directions for Adult and Continuing Education, 89, 3–14. https://10.1002/ace.3

26.

Miltenberger

R. G

. (2012). Behavior modification: Principles and procedures (5th ed.). Cengage Learning.

27.

Noltemeyer

A. L.

Ward

R. M.

McLoughlin

. (2015). Relationship between school suspension and student outcomes: A meta-analysis. School Psychology Review, 44(2), 224–240. https://doi.org/10.17105/spr-14-0008-1

28.

O’Neill

R. E.

Albin

R. W.

Storey

Horner

R. H.

Sprague

J. R

. (2014). Functional assessment and program development for problem behavior: A practical handbook (3rd ed.). Cengage Learning.

29.

Parsons

S. A.

Hutchison

A. C.

Hall

L. A.

Parsons

A. W.

Ives

S. T.

Leggett

A. B

. (2019). U.S. teachers’ perceptions of online professional development. Teaching and Teacher Education, 82, 33–42. https://doi.org/10.1016/j.tate.2019.03.006

30.

Powell

C. G.

Bodur

. (2019). Teachers’ perceptions of an online professional development experience: Implications for a design and implementation framework. Teaching and Teacher Education, 77, 19–30. https://doi.org/10.1016/j.tate.2018.09.004

31.

Putman

S. M

. (2012). Examining variations in programmatic delivery on teaching candidates’ sense of efficacy. Research in the Schools, 19(2), 45–61.

32.

Renshaw

T. L.

Christensen

Marchant

Anderson

. (2008). Training elementary school general educators to implement function-based support. Education & Treatment of Children, 31(4), 495–521. https://doi.org/10.1353/etc.0.0037

33.

Sarokoff

R. A.

Sturmey

. (2013). The effects of behavior skills training on staff implementation of discrete-trial teaching. Journal of Applied Behavior Analysis, 37(4), 535–538. https://doi.org/10.1901/jaba.2004.37-535

34.

State

T. M.

Kern

Starosta

K. M.

Mukherjee

A. D

. (2011). Elementary pre-service teacher preparation in the area of social, emotional, and behavioral problems. School Mental Health, 3, 13–23. https://doi.org/10.1007/s12310.010-9044-3

35.

Sutherland

K. S.

Lewis-Palmer

Stichter

Morgan

P. L

. (2008). Examining the influence of teacher behavior and classroom context on the behavioral and academic outcomes of students with emotional and behavioral disorders. The Journal of Special Education, 41, 223–233. https://doi.org/10.1177/0022466907310372

36.

Terol

A. K.

Lee

J. D.

Martin

M. R.

Meadan

. (2023). Online training for parents of children with developmental disabilities in a low resourced community: A pilot feasibility study. Journal of Policy and Practice in Intellectual Disabilities, 20, 7–17. https://doi.org/10.1111/jppi.12417

37.

Tsouloupas

C. N.

Carson

R. L.

Matthews

R. A

. (2014). Personal and school cultural factors associated with the perceptions of teachers’ efficacy in handling student misbehavior. Psychology in the Schools, 51(2), 164–180. https://doi.org/10.1002/pits.21739

38.

Umbreit

Ferro

Liaupsin

C. J.

Lane

K. L

. (2007). Functional behavioral assessment and function-based intervention: An effective, practical approach. Pearson.

39.

Zee

de Jong

P. F.

Koomen

H. M. Y

. (2016). Teachers’ self-efficacy in relation to individual students with a variety of social–emotional behaviors: A multilevel investigation. Journal of Educational Psychology, 108(7), 1013–1027. https://doi.org/10.1037/edu0000106

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