Effects of Online Tiered Training to Increase Teachers’ Use of Positive Behavior Supports

Abstract

To develop and support teachers’ skills in classroom management, resource-efficient teacher training approaches are needed. This study evaluated the effects of an online tiered training intervention on teachers’ use of class-wide positive behavior supports, either behavior-specific praise (BSP) or opportunities to respond (OTRs), using a randomized controlled trial conducted in an online graduate course in classroom management. Fifty-nine participants (52 teachers, 6 paraprofessionals, and 1 after-school instructor) completed the study. We randomly assigned 29 teachers to receive tiered training for BSP and 30 teachers to receive tiered training for OTRs. We found tiered training increased participants’ use of the target practice, BSP or OTR, with medium to large effect sizes, and most teachers required support beyond universal training. Teachers rated the training they received as highly acceptable, feasible, and useful.

Keywords

graduate teacher education tiered support classroom management self-monitoring self-management peer coaching

Effective classroom management plays a key role in promoting positive student outcomes (Oliver et al., 2011). Positive, consistent, responsive classroom management is linked to increased learning and positive outcomes for students (Sutherland et al., 2018; Trussell, 2008; Wang et al., 1994). Students in poorly managed classrooms experience less time academically engaged, have worse academic outcomes (Gage et al., 2018; Reinke et al., 2008), and are at risk for adverse long-term behavioral outcomes, including the development of emotional and behavioral disorders (Greer-Chase et al., 2002). Teachers are also negatively impacted by student challenging behavior. Challenging behavior predicts teacher burnout (Aloe et al., 2014; Reinke et al., 2013), attrition (Tsouloupas et al., 2010), and lower teaching self-efficacy (Reinke et al., 2013).

Teachers receive limited training on classroom management in their teacher preparation programs (Cooper et al., 2018; McGuire et al., 2024) and report not having the skills they need to effectively manage student behavior (Bowsher et al., 2018; Darling-Hammond et al., 2009). Once in the classroom, teachers generally get additional training through professional development (PD) opportunities provided by their school or district, or via graduate education (Webber et al., 2023); although, access to and quality of these in-service PD opportunities varies. Graduate programs provide one vehicle for providing teachers with foundational training in managing student behavior.

Online Graduate Teacher Education

Graduate education is increasingly taking place online. Among students enrolled in graduate courses in the United States, 56% were enrolled in at least one distance education course, and 40% were enrolled in exclusive distance education courses in Fall 2021 (National Center for Education Statistics, 2023). Graduate programs in education follow a similar trend—in 2012, 15% of programs were online, compared to 40% in 2021 (Capranos et al., 2023; Wiley Education Services, 2019). Online courses may employ synchronous, asynchronous, or combined approaches. During online synchronous instruction, the instructor and students meet live, often using videoconferencing software. In asynchronous instruction, students complete learning activities prepared by the instructor on their own time, without “real time” interaction with the teacher. Synchronous learning activities produce better student outcomes relative to asynchronous learning (Moallem, 2015; Stover & Miura, 2015), but asynchronous courses increase flexibility and accessibility and are preferred by teachers in graduate programs (Smith, 2013).

Instructional modalities for asynchronous instruction typically include recorded lectures, discussion boards, and other application activities. While these activities may help teachers gain knowledge of best practices, the PD literature suggests teachers need ongoing support to apply new techniques and improve student outcomes (Yoon et al., 2007). Teachers might require differentiated instruction to acquire new skills. Teachers vary in their response to specific training modalities and need different types and intensities of training to increase behavior support use (e.g., LaBrot et al., 2020; Myers et al., 2011; Sanetti & Collier-Meek, 2015). Coaching with performance feedback has been shown to be highly effective in improving teacher implementation of behavior support practices (Stormont et al., 2015; Wilkinson et al., 2020). However, coaching is highly resource-intensive and may be difficult for course instructors to implement in large classes. Training approaches that incorporate differentiation and balance teacher needs and resource constraints are needed.

Tiered Training Frameworks

One training approach that aims to use resources efficiently and individualize teacher support is tiered training (Bloomfield et al., 2024). Tiered training frameworks mimic multi-tiered systems of support for students and are resource-efficient because resource-intensive training procedures (e.g., coaching) are only provided to teachers who need it. Typically, at Tier 1, all teachers receive training using evidence-based practices, such as explicit instruction and practice with feedback in a large-group format (Simonsen et al., 2014). Coaches monitor teacher implementation, and teachers who need additional support receive Tier 2. Tier 2 includes ongoing performance feedback, which can be delivered via self-management to keep coaching resources low (Freeman et al., 2018; Simonsen et al., 2014) or by a supervisor (e.g., Owens et al., 2020). Teachers who do not respond to Tier 2 receive Tier 3. Tier 3 is intensive and individualized training, including frequent performance feedback from a coach (e.g., Ennis et al., 2020; Freeman et al., 2018).

Evaluations of tiered training frameworks support that teachers might need a continuum of training intensity to use behavior support practices. These studies also point to areas in which additional research is needed to further develop and evaluate this approach. First, existing studies have applied tiered training for educators to in-service PD (Bloomfield et al., 2024) and in-person pre-service teacher training (LaBrot et al., 2022). There are positive effects of tiered training on teacher behavior in these contexts. Similarly, there is a need for resource-efficient training in online graduate teacher education. Therefore, it would be helpful to evaluate this approach for use in online graduate courses. Second, most existing studies have used supervisory coaching at Tier 3, which is resource intensive (e.g., Myers et al., 2011; Owens et al., 2020). An adaptation to enhance the feasibility and sustainability of Tier 3 may be to use peer coaching instead. During peer coaching, teachers are partnered together and trained to observe and provide feedback on target practices. Previous research has demonstrated peer coaching increases use of positive behavior supports (e.g., Golden et al., 2021; Torelli, Noel, Morris, & Hendrickson, 2024) and is effective when implemented online (Torelli, Noel, Gross, & Morris, 2024). Third, in most of the existing studies of tiered training, researchers used single-case designs to evaluate individual tiers of the framework (e.g., Freeman et al., 2018; Grasley-Boy et al., 2023; LaBrot et al., 2020). These designs have primarily informed the effects of Tier 2 without providing experimental data on the effects of Tiers 1, 3, or the intact tiered training framework as a packaged intervention. Group designs could provide a holistic appraisal and set expectations about teacher responses to tiered training.

Study Purpose

The purpose of this study was to develop and evaluate an online tiered training model intended to increase teachers’ use of positive behavior supports for use in graduate teacher education. We investigated the effects of the intact tiered training framework on teachers’ use of class-wide positive behavior supports through behavior-specific praise (BSP) and opportunities to respond (OTRs). We selected BSP and OTRs because of the strong research evidence for both practices to improve student outcomes (e.g., Common et al., 2020; Royer et al., 2019). BSP and OTRs are low-effort instructional practices teachers can implement across a variety of instructional contexts and are easily adapted to meet the individual needs of students. We also sought to further improve the feasibility of tiered training by providing peer coaching at Tier 3, rather than supervisory coaching (Thompson et al., 2012). We addressed the research questions:

What is the effect of online tiered training on teachers’ use of BSP or OTRs?

What proportion of teachers respond to Tier 1 or require Tier 2 or Tier 3 to increase their use of BSP or OTRs?

To what extent do teachers report online tiered training to be acceptable, feasible, and useful?

Method

Participants and Setting

After obtaining Institutional Review Board approval, we recruited participants from an online graduate education course in classroom management at a public university in the Southern United States across two semesters. All students enrolled in the course were eligible to participate in the study (N = 95). The course objectives related to understanding the relations between child behavior and their environment; implementing and monitoring positive behavior supports; and engaging in meaningful PD related to students’ social, emotional, and behavioral outcomes. The fourth author, who was not associated with the graduate course, consented participants. Study participation did not influence course assignments or grades, and participants were not compensated for their participation. All students were enrolled in master’s degree programs in education and worked full-time in schools, either as teachers or paraprofessionals in the Southern United States.

We enrolled a total of 62 participants (65% of registered students), in two cohorts, one from fall (n = 30) and spring (n = 32) academic semesters. Fifty-nine participants completed the study. Two participants took medical leave of absences from their education programs during the study, and one participant dropped the associated course prior to study data collection. Fifty-two participants were teachers, six participants were paraprofessionals, and one participant was an after-school instructor. Thirty-one participants were fully certified, 26 participants held temporary teaching licenses, and 2 participants were not certified. Participants had 0 to 28 years of teaching experience (M = 3.6, SD = 5.9) and worked in P–12 classrooms in general education or special education settings. Table 1 summarizes participant characteristics by group.

Table 1.

Participant Demographics.

Characteristic	BSP group	OTR group	Total (%)
Participants	n = 29	n = 30	N = 59
Role
Teacher	23	29	52 (88.1)
Paraprofessional	5	1	6 (10.2)
After-school instructor	1	0	1 (1.7)
Grade Level
Elementary school	18	17	35 (59.3)
High school	6	8	14 (23.7)
Middle school	4	4	8 (13.6)
Pre-kindergarten	0	1	1 (1.7)
Setting
Special education	20	16	36 (61.0)
General education	9	14	23 (39.0)
Highest Degree
Bachelor’s	17	18	35
Master’s	7	8	15
Average years’ experience	4.1 (SD = 7.4)	3.1 (SD = 4.2)	3.6 (SD = 5.9)

Note. Some percentages do not add to 100 due to missing data. BSP = behavior-specific praise; OTR = opportunities to respond.

Study activities were completed online and teachers implemented positive behavior supports in person in their P-12 classrooms. Participants completed training activities asynchronously and implemented target practices during regular instruction. They submitted 10-minute video recordings of the target teacher-directed instructional activity each week, which the research team coded and analyzed.

Materials

We used the course learning management system (LMS; Blackboard) to house all modules and quizzes. Modules consisted of recorded lectures and slides developed and recorded by the first author. Students watched modules using Perusall software, which allowed them to create time-stamped annotations, comments, and questions for the instructor and other students. Course instructors provided written feedback on quizzes directly in the LMS. Participants uploaded and stored videos, self-monitoring or self-management data, and peer coaching forms to a secure cloud storage system (Microsoft OneDrive). Each participant had their own folder, to which only the participant, research team, and peer coach, when applicable, had access. We provided participants with templates in Microsoft Excel for entering and analyzing self-monitoring or self-management data. At Tier 3, participants used a peer coaching form in Microsoft Word. We distributed questionnaires online using Qualtrics. We used the Countee app (Countee program; Hernandez Eslava, 2020) to collect data on BSP and OTRs. All materials, including modules, are available by contacting the first author.

Study Design

We used a randomized controlled trial design to evaluate the effects of tiered training on teachers’ use of BSP or OTRs. Each semester, we randomly assigned participants to receive tiered training for BSP or OTRs following baseline data collection. Thus, all participants received intervention but for only one target practice. Each group (i.e., BSP or OTR) served as a counterfactual for the other group. Thus, the BSP groups’ change in OTRs served as a comparison for the change in OTRs for the OTR group and vice versa. After the conclusion of the study, participants received training in the other practice.

Dependent Measures

Structured Direct Observation of Target Practices

We collected data on teacher behaviors by coding weekly participant-submitted 10-minute videos of one consistent teacher-directed instructional activity with timed-event recording using Countee software. When videos were longer than 10 minutes, we coded the first 10 minutes of the video. We coded a BSP when the teacher provided a verbal acknowledgment of a specific behavior that clearly indicated the student(s) being praised (Royer et al., 2019). Examples included, “Fantastic job staying focused on your work, Ellen” and, “Thank you for moving silently to the next station, group 2.” We excluded behavior narration (e.g., “Ken is showing me he is ready”) and sarcastic or punitive comments such as “Allen did the right thing by putting his book away; the rest of you are not following directions.”

We coded an OTR when the teacher provided an instructional statement requiring a visible academic response (MacSuga-Gage & Simonsen, 2015). We coded two types of OTRs: unison and individual. We coded a unison OTR when the teacher provided an OTR to the entire class or the entire group of students receiving instruction. Examples of unison OTRs included choral, gestural, and written responses. We coded an individual OTR when the teacher provided an OTR, to which only one student responded at a time. Examples of individual OTRs were questions in which students raised their hands to answer, and the teacher called on one student at a time. We added unison and individual OTRs in a session for the OTR-dependent variable, which was used to assess teacher progress in all analyses.

For each target practice, we aggregated (i.e., averaged) session data across each participant’s first three sessions to achieve a baseline score. Similarly, we aggregated each participant’s final three sessions to achieve a posttest score for each target practice (tiered training condition). Averaging scores across sessions were used to improve the temporal stability of the measure to be more representative of the teacher’s use of the target practice during the targeted teacher-directed instructional activity (Yoder et al., 2018). We aggregated sessions by calculating an average frequency for each practice across the three sessions. For most participants, the final three sessions for the posttest dependent variable were sessions 12–14 (n = 47), but the total number of sessions varied slightly by the participant due to differing teaching schedules (M = 13.5, range 9–14).

Observer Training and Interobserver Agreement (IOA) on Target Practices

The first author trained observers on data collection procedures by first reviewing the coding manual and data collection software procedures. Then, observers practiced coding with sample videos and live feedback and independently coded training videos until reaching at least 90% agreement with a master code on three different videos across all dependent variables. We monitored the reliability of direct observation data on at least 25% of randomly selected sessions across participants and conditions. We assessed IOA using the Countee app website. We used a frequency within interval agreement method for IOA. Each session was divided into 5-second intervals. We divided the smaller frequency of a behavior by the larger frequency within each interval, then averaged agreement across intervals and multiplied by 100%. Mean agreement across cohorts, participants, conditions, and behaviors was 98.3% (range, 95.8%–100%). Mean agreement was 99.0% (range, 91.4%–100%), 96.6% (range, 86.7%–100%), and 96.4% (range, 86.4%–100%) for BSP, unison OTRs, and individual OTRs, respectively.

Social Validity

We assessed teachers’ perceptions of tiered training through items adapted from the Intervention Rating Profile-15 (IRP-15; Martens et al., 1985) as used by Simonsen et al. (2020). The IRP-15 measures teacher acceptability of school-based interventions and has a single factor structure of “general acceptability” with the internal consistency of Cronbach’s alpha (α) = .98 (Martens et al., 1985). The questionnaire had 14 items participants rated on a 6-point Likert-type scale (Strongly disagree = 1 to strongly agree = 6). We distributed the questionnaires after participants submitted their final session video. Fifty-two of 59 participants completed the questionnaire. The current sample’s internal consistency was adequate (α = .95).

Study Procedures

Participants completed study activities over 14 weeks. Baseline data were collected over the first 3 weeks, and the intervention was implemented during the remaining 11 weeks. Throughout the study phases, we collected data via participant-submitted videos of teacher-directed instruction during one target instructional activity. Each video was 10 minutes long and was coded as one session. Prior to the first video submission, course instructors (first and second authors) met with participants via Zoom to describe the study and course expectations. In this meeting, participants selected a time of day during which they would record their session videos. Participants selected a time of day when they were providing teacher-directed instruction and had difficulty managing student behavior. Instructors told participants to keep the instruction type and content area consistent across sessions. Across training conditions, instructors provided emailed feedback once every three weeks. The lead course instructor (first author) had a Ph.D. in special education, was a BCBA-D, and was a former public school teacher. The second-course instructor was an advanced doctoral student with a master’s degree in special education and a former public school teacher.

Participants did receive grades for completing study activities that were also part of regular course activities. Specifically, participants received grades for (a) submitting videos each week, (b) completing self-monitoring, self-management, or peer coaching each week, (c) accuracy of responding on the module quiz, and (d) meeting the target practice use criteria in at least two sessions by the end of the semester. Participants were not graded on the accuracy of their self-monitoring, self-management, or peer coaching data.

Baseline

We collected baseline data for three sessions during the first 3 weeks of the semester. Participants did not receive training on the target practice during or prior to baseline. During baseline, participants completed course activities (e.g., modules, quizzes) on classroom management principles unrelated to the target practices (i.e., establishing and teaching classroom expectations, developing routines). The fourth author randomized participants to a target practice group following baseline. Twenty-nine participants were randomized to the BSP group, and 30 participants were randomized to the OTR group. An independent samples t-test revealed the groups did not differ on teachers’ years of experience and two chi-square tests revealed the groups did not differ on teachers’ grade or education level.

Tier 1: Online Module + Self-Monitoring

Tier 1 was introduced in week 4 and continued for 3 weeks before re-assigning tiers. Tier 1 involved participants completing online modules on both the target practice and self-monitoring and then video recording 10-minute sessions during their target, teacher-directed instructional time using the target practice and submitting self-monitoring forms. The target practice and self-monitoring modules included a short lecture on the practice (M = 23 minutes) and a quiz. The lecture defined the practice, described how to use it, provided examples and non-examples, described recommended frequencies for using the target practice, and provided prompts for planning to incorporate the practice into instruction. The lecture described tools for self-monitoring (e.g., tallies on a sheet of paper, golf counter, moving paper clips between pockets) and asked participants to select one for their use. The quiz assessed participants’ understanding of the practice and how to use it and required them to share their plan for incorporating the practice into instruction. Course instructors provided written feedback on quiz responses in the LMS within 1 week, prior to participants recording their next session.

For self-monitoring, we gave participants operational definitions of the target practice and told them to select a recording method from the provided examples. We instructed them to self-monitor twice weekly for 10 minutes—once during their video session and once during another time that was not recorded. We provided participants with an Excel template where they entered their self-monitoring data, which was saved in their cloud folder. We asked participants to enter data at least weekly. Participants completed a minimum of 3 weeks at Tier 1 before the research team evaluated their progress and re-assigned tiers. We provided emailed performance feedback on their use of the target practice once every 3 weeks (i.e., at least once per tier). We also emailed participants when data were missing. Emailed feedback included BSP related to use of the target practice and individualized suggestions for how to further increase or maintain use of the target practice.

Tier Re-Assignment

After participants submitted videos in weeks 6 and 9, the research team evaluated participant progress and determined whether to keep the participant in the same tier, increase their tier, or begin to fade support (week 9 only). For this process, we first evaluated whether the participant met or exceeded the criterion for their practice in the most recent session, at a minimum. Participants who met the criterion remained in their current tier, except Tier 2 participants in week 9, for whom we began fading support. If the participant had not yet met the criterion but showed a change in level from baseline and an increasing trend approaching the criterion, they remained in their current tier. All other participants moved to a more intensive tier. For BSP, the criterion was five BSP in 10 minutes (O’Handley et al., 2018). For OTRs, the criterion was 30 OTRs in 10 minutes (MacSuga-Gage & Simonsen, 2015). The first and second authors made tier assignments. We established reliability on tier assignments by agreeing on five consecutive tier determinations after independently analyzing participant data. After establishing reliability, we collected IOA on tier determinations for 25% of randomly selected participants at each tier assignment point. IOA on tier determinations was 100%.

Tier 2: Self-Management

If participants did not respond to Tier 1, they received Tier 2 beginning in week seven or 10, which replaced self-monitoring with self-management. During self-management, participants (a) set a goal frequency for the target practice for each self-monitoring session, (b) continued self-monitoring use of the target practice for two 10-minute sessions per week, (c) used self-prompts to remind them to self-monitor and meet their goal, (d) graphed their data at least weekly, (e) evaluated their own progress by reviewing graphed data, and (f) provided self-reinforcement for meeting their goal (Simonsen et al., 2014). Examples of self-prompts teachers used included writing their goal on the board in the classroom, putting a sticky note with their goal on their computer, and using a calendar reminder to remind them of their goal. Examples of self-reinforcement teachers used if they met their goal for a certain number of days (self-determined) included buying themselves a treat (e.g., coffee, food item, and shoes) and engaging in a preferred activity (e.g., taking a walk, taking a nap, and shopping). We gave participants a new Excel template to enter their self-monitoring data, where they entered their goals for each self-monitoring day and graphed their data to visually show their progress.

At the start of Tier 2, participants completed an asynchronous online module on self-management. The module reviewed self-monitoring and described each part of a self-management intervention, its benefits, and steps for use. It included prompts for planning self-management intervention and reviewed target practice criteria to inform goal setting. It also asked participants to reflect on their self-monitoring procedures and to modify them if needed. Participants completed a quiz on self-management in which they shared each their self-management plan, and the instructor provided written feedback. We continued to review participants’ self-management data weekly, provide email feedback once every three weeks, and email participants when data were missing.

Tier 3: Peer Coaching

Participants who did not respond after 3 weeks of Tier 2 support continued self-management and received Tier 3 support for 5 weeks. Tier 3 support was peer coaching with another person enrolled in the classroom management course and assigned the same target practice (the partner was not necessarily in the research study). During peer coaching, participants watched their partner’s video each week and completed a structured form, which they submitted to their partner’s cloud storage folder within 24 hours of a video submission. On the form, participants tallied the frequency of the target practice, gave two to four examples of when the teacher used the practice, described two to four opportunities when the teacher could have used the practice, and marked whether the teacher met their goal (adapted from Golden et al., 2021). Participants reviewed the feedback they received within 24 hours of submission. They initialed, dated, and moved feedback to a “reviewed” folder to show they reviewed it.

At the start of Tier 3, participants met with a course instructor in a small group (one to three students) to review how to use the target practice and to discuss their progress, goal, self-management procedures, challenges, potential solutions, and peer coaching procedures. Meetings lasted 15–30 minutes, depending on the number of participants. Participants also completed an asynchronous, online module on peer coaching. The module provided an overview of the benefits of peer coaching, described implementation steps, and described how to use the peer coaching form. Participants completed a quiz on peer coaching, which assessed their understanding of the procedures. They received written feedback on their responses. We continued to reviewparticipants’ self-management data weekly, provided emailed performance feedback once every 33 weeks, and emailed participants when peer coaching forms were not submitted or reviewed on time.

Fading

For participants who were assigned to Tier 2 in week six, during the week nine tier re-assignment, we began to fade Tier 2 support for participants who met the target practice criterion during the two most recent sessions. Faded Tier 2 more closely resembled Tier 1 self-monitoring practices. During fading, participants continued to self-monitor and graph their data, but participants were no longer required to use self-prompts and self-reinforcement. We continued to review participants’ self-management data weekly, provided emailed performance feedback once in this time, and emailed participants when data were missing.

Treatment Integrity

We assessed treatment integrity of tiered training by component using permanent product data and data from the LMS. We measured the integrity of the following components: modules, self-monitoring, self-management, and peer coaching. We evaluated the treatment integrity of the modules by assessing if participants completed modules on the target practice, self-monitoring, and, when applicable, self-management and peer coaching using data from the course LMS.

We assessed treatment integrity of self-monitoring and self-management on a weekly basis. To do this, we reviewed each participant’s spreadsheet and calculated the sum of the number of days participants submitted self-monitoring data out of two opportunities. We then calculated the total number of days participants provided data across all weeks, divided this sum by the total opportunities for self-monitoring data, and multiplied this quotient by 100% to calculate the percentage of self-monitoring opportunities completed on time. We assessed the treatment integrity of peer coaching using permanent product data from peer coaching forms. We summed the number of peer coaching forms the participants reviewed, divided this sum by the number of peer coaching forms they received, and multiplied by 100% to calculate the percentage of peer coaching forms reviewed. Across components and tiers, average treatment integrity was 82.9% (range, 66.7%–100%). Average Tier 1 treatment integrity (modules, self-monitoring data) was 94.5%, average Tier 2 integrity (module, self-management data) was 75.0% (n = 31), and average Tier 3 integrity (module, peer coaching forms) was 73.4% (n = 15).

Data Analysis

To test whether tiered training influenced change in participant’s use of OTRs, participants’ baseline use of OTRs and condition (coded as 0 = counterfactual group and 1 = tiered training OTR group) was regressed on posttest OTR use. Posttest OTR use was calculated using an average frequency from the final three intervention sessions. For participants without any missing observations, this was sessions 12–14 (n = 47). For participants missing one observation, this was sessions 11–13 (n = 9). The three remaining participants were missing between 3–5 observations; their posttest OTR was calculated using their final three sessions. Teacher education, years of teaching experience, and grade level were included as covariates given prior work, suggesting teachers’ use of evidence-based classroom management approaches may vary along these dimensions (Domitrovich et al., 2008; Hollo & Hirn, 2015; Reddy et al., 2013). A similar model was used to test whether tiered training influenced change in participant’s use of BSP. Participants’ baseline use of BSP and condition (coded as 0 = counterfactual group and 1 = tiered training BSP group) was regressed on posttest BSP use. We calculated posttest BSP using the same procedures as posttest OTR use. Teacher education, years of teaching experience, and grade level were again included as covariates. Effects are interpreted as the extent to which tiered training predicted change in participant’s use of OTRs or BSP. We conducted analyses using Mplus version 8.9 (Muthen & Muthen, 2018).

Missing data patterns revealed four teachers were missing data on years of experience, nine teachers were missing data on education level, and one teacher was missing data on grade. We used the full information maximum likelihood estimator to account for these missing data. This estimator retains the statistical power of the full analytic sample while minimizing bias in parameter estimates when data cannot be presumed to be missing completely at random (Enders, 2001).

Results

Effects of Online Tiered Training on Teachers’ Use of BSP and OTRs

Outcomes related to teachers’ use of BSP and OTRs are in Table 2. After controlling for pretest scores, we found tiered training significantly influenced teachers’ use of OTRs compared to the counterfactual group (B = 10.79, p = .003; d = .56). Similarly, we found tiered training significantly influenced teachers’ use of BSP when compared to the counterfactual group after controlling for pretest scores (B = 6.34, p = <.001; d = 2.18). These results suggest that tiered training increased the use of positive behavior supports.

Table 2

Pretest Scores and Treatment Condition as Predictors of Posttest Outcomes.

Treatment Conditions	B	S.E.	p-value
BSP Group
Baseline BSP	6.34	0.78	0.02
Years’ Experience	−0.01	0.08	0.88
Highest Degree	−0.36	1.05	0.73
Grade Level	−0.72	0.87	0.41
Group	6.34	0.78	0.00
OTR Group
Baseline OTR	0.81	0.12	0.00
Years’ Experience	−0.64	0.40	0.11
Highest Degree	3.07	5.25	0.56
Grade Level	5.29	3.80	0.16
Group	10.79	3.61	0.00

Note. For BSP, group was coded as 0 = Counterfactual and 1 = BSP. For OTR, group was coded as 0 = Counterfactual and 1 = OTR. BSP = behavior-specific praise; OTR = opportunities to respond.

Tiers of Support

The second goal was to examine the level of training teachers needed to increase BSP or OTRs. Across BSP and OTR groups, 22% of teachers responded to Tier 1 alone (n = 13). Fifty-three percent of teachers received Tier 2 (n = 31) and 25% received Tier 3 (n = 15). Within the BSP group, 28% responded to Tier 1 alone (n = 8). Forty-eight percent of teachers received Tier 2 (n = 14) and 24% received Tier 3 (n = 7). Within the OTR group, 17% of teachers responded to Tier 1 alone (n = 5). Fifty-seven percent of teachers received Tier 2 (n = 17) and 27% of teachers received Tier 3 (n = 8). Among teachers who received Tier 2, 38 teachers began Tier 2 in week 7 and seven began in week 10. While we observed increases in the target practice for each group, suggesting tiered training was effective, we also observed variability in the use of target practices, especially for OTRs. (Table 3 shows descriptive statistics.)

Table 3.

Observed Frequencies of Target Behaviors.

Target practice	Baseline			Tiered training
Target practice	M	SD	Range	M	SD	Range
Behavior-specific praise group
BSP frequency	0.8	1.2	0–4.7	7.1	4.3	0–18.7
OTR/Counterfactual frequency	18.3	12.1	0–53	17.9	13.6	0–66.3
Opportunities to respond group
BSP/Counterfactual frequency	1.5	2.1	0–9.3	1.1	1.3	0–5.0
OTR frequency	23.7	18.8	0.7–82.0	34.3	22.3	13.3–127.7

Note. Data represent average target practice frequencies during participants’ initial three sessions (Baseline) and final three sessions (tiered training). BSP = behavior-specific praise; OTR = opportunities to respond.

Figure 1 shows line graphs of teachers’ use of the target practice by session and the highest tier in which they participated. The top tier of the figure shows BSP data. Teachers who received Tier 1 only showed an immediate, large increase in BSP with tiered training that maintained throughout intervention. The Tier 2 group shows an immediate but smaller increase in BSP with Tier 1 (first three sessions of tiered training) and an increasing trend after Tier 2 was introduced (session 7 or 10). The Tier 3 group showed little change in use of BSP until Tier 3 was introduced (session 10) and an increasing trend with Tier 3. The bottom figure shows OTR data. Teachers who received Tier 1 alone showed only a small increase in OTRs with tiered training, but the level was consistent with the OTR criterion (30 per session), suggesting these teachers may have already been using sufficient frequencies of OTRs. The Tier 2 group did not show a change in level of OTRs until Tier 2 was introduced, and the Tier 3 group did not show a change in level until Tier 3 was introduced.

Figure 1.

Mean Frequency of Practice Use By Session and Highest Training Tier Received

Social Validity of Tiered Training

Socially validity questionnaire ratings showed high acceptability with a total mean score of 75.0. By target practice group, the overall mean scores were 74.1 and 76.0 for BSP and OTRs, respectively. By highest tier received, the mean overall scores were 75.8, 76.7, and 70.0 for Tiers 1, 2, and 3, respectively. Mean scores across items ranged from 4.8 to 5.5 on a 6-point scale and indicated participants found tiered training to be useful, feasible, and acceptable. For example, participants rated the training they received as highly effective (M = 5.5), appropriate for a variety of teachers (M = 5.5), and beneficial for increasing their use of specific classroom management skills (M = 5.5). The lowest mean rating was the extent to which training was consistent with other trainings participants had in the school setting (M = 4.8). This may be due to the use of ongoing supports (i.e., self-monitoring, self-management, peer coaching) which are less commonly used during in-service trainings.

Discussion

We conducted a randomized controlled trial to evaluate the effects of online tiered training on teachers’ use of BSP and OTRs in an online graduate classroom management course. Results suggested tiered training increased teachers’ use of target practice (BSP or OTRs) with medium to large effect sizes favoring the treatment groups. Most teachers required support beyond Tier 1 (online module + self-monitoring) to increase use of the target practice. Seventy-eight percent of teachers required Tier 2 or Tier 3 training. Further, teachers rated tiered training as highly acceptable, feasible, and useful. Results provide preliminary support for the use of tiered training in an online graduate course to increase teachers’ use of BSP and OTRs. We discuss three key implications of the results below.

To our knowledge, this is the first study to experimentally evaluate the effects of an intact tiered training framework. We found positive effects on teachers’ use of BSP and OTRs. These findings replicate and extend previous research showing functional relations between Tier 2 and teachers’ use of target practices. In addition, much of the prior research on tiered training targeting positive behavior supports has focused on increasing teachers’ use of BSP (e.g., Gage et al., 2017; Simonsen et al., 2014; Thompson et al., 2012), with fewer studies targeting other practices (see Ennis et al., 2020; LaBrot et al., 2020 for exceptions). The results of this study replicate the positive effects of tiered training on BSP and provide promising initial support for tiered training on OTRs for teachers in graduate programs. Interestingly, we observed a larger effect size for BSP relative to OTRs. Teachers may be more easily able to increase BSP compared to OTRs when provided with tiered training on one target practice. We also observed large variability in OTRs across groups and phases. We attempted to control for instructional context by having participants focus on one teacher-directed instructional context, but participants were heterogenous—varying in grade level taught, content area, and educational setting. For example, there were special education teachers teaching reading with activities such as letter-sound fluency, and there were high school teachers teaching using discussion-based activities. This resulted in a high degree of variability between teachers.

Our results extend previous research on tiered training showing positive effects when implemented online and using peer coaching at Tier 3. Course instructors used an asynchronous approach, giving participants flexibility on when to complete modules, record session videos, upload data, and complete peer coaching each week. Treatment fidelity was moderate to high, and teachers reported training to be acceptable, suggesting this approach was accessible to most teachers. The structure of training also allowed flexibility for course instructors to review and provide feedback on participant submissions. Another benefit of the asynchronous online model was that it allowed us to implement tiered training in a rural region where instructors would not have been able to travel to all participants’ schools due to time and distance. Further, we limited supervisory coaching resources by providing participants with performance feedback only once every three weeks across tiers and by using peer coaching at Tier 3. Our results suggest these adaptations may be viable strategies to enhance the sustainability of tiered training.

Research suggests teachers vary in the amount and type of support they need to increase their use of positive behavior supports (e.g., LaBrot et al., 2020; Sanetti & Collier-Meek, 2015). In the present study, we found most teachers (78%) required Tier 2 or Tier 3 training. These results extend previous research on tiered training to provide initial data on the proportion of teachers who may respond to Tier 1 among teachers enrolled in graduate programs. The data suggest asynchronous, online modules and self-monitoring may be insufficient to increase many teachers’ use of BSP and OTRs in online graduate courses. While most teachers received Tier 2 or Tier 3, medium to large effect sizes showing increased use of target practices suggest teachers responded to Tier 2 or 3 training. These results align with previous research showing most teachers responded to Tier 2 training and only a few needed Tier 3 training (e.g., Ennis et al., 2020; Freeman et al., 2018; LaBrot et al., 2020). Although most teachers needed support beyond Tier 1, about one-quarter of teachers increased their use of the target practice without Tier 2 or 3 (22%). This suggests resources can be saved by using tiered training, as some teachers will not require targeted or intensive support to improve their practice.

Future Directions

Results of this study point to several avenues for future research. First, this study did not attempt to isolate the effects of individual tiers on teacher behavior. Future research should evaluate the effects of each tier on teacher behavior in the context of a research design that also allows for evaluating the effects of the model. These studies will inform which tiers may need to be further adapted to improve their effectiveness. Second, based on the low number of responders to Tier 1, future research should develop and evaluate adaptations to Tier 1 to increase its effectiveness. It is possible the online delivery of Tier 1 impacted its effectiveness. Finally, future research should seek to identify factors that predict response to training, including for whom and under what conditions (e.g., target practices selection, training modality, grade level) different intensities and types of training may be effective. Increased understanding of treatment moderators may help instructors optimize resources in implementing tiered training by preemptively assigning teachers to tiers or training approaches (e.g., Artman-Meeker et al., 2022). Researchers may consider using sequential multiple assignment randomized trials (SMARTs) for these research questions (Nahum-Shani & Almirall, 2019).

Limitations

This study had six primary limitations. First, we relied on participants to submit video recordings for session data. Teacher behavior in these videos might have differed from their behavior at other times. We attempted to control for this procedurally by giving teachers instructions on the context in which to record sessions (i.e., teacher-directed instruction of the same type of content each week). This also helped ensure our response criterion for OTRs (30 per 10 minute) was appropriate for the instructional context. While similar differences in teacher behavior might be observed with an in-person observer, and recorded data may not be representative of teacher behavior at other times in the school day, the data showed an increase in the target practice during the target instructional time for most teachers by the end of the study. Second, the study included a moderate number of teachers but a small number of teachers per group and per tier. Given this small sample interpreting between-group differences should be done with caution due to the lack of statistical power. Future work should use samples large enough to detect group differences across groups and tiers and in diverse settings to advance our understanding of the effects of intact-tiered training on teacher practice. Third, observers were not naive to the treatment group, introducing a possible risk of bias. To mitigate this limitation, we collected IOA on at least 25% of sessions across participants and conditions and maintained high levels of IOA. Fourth, the training activities participants completed in this study were embedded in an online graduate course. The contingencies and requirements in place in a course are different than for in-service PD. Thus, results may have limited generalizability to in-service PD. Fifth, as this study was embedded in a graduate course, participants received grades related to study activities (i.e., completing self-monitoring, self-management, or peer coaching, using the target practice by the end of the semester), which may have incentivized them to complete training activities and use the target practice. It is possible grades function similarly to evaluative observations by school staff in how they affect teacher behavior, and it is also possible these two incentive systems function differently. Consequently, results may not generalize to contexts with different incentive systems. Finally, we were unable to collect follow-up or generalization data due to the constraints of the semester calendar. As a result, we do not know the extent to which teacher behavior maintained when tiered training was removed or the extent to which it generalized to other relevant contexts. Future research should seek to measure generalization and maintenance of teacher behavior.

Conclusion

Online tiered training showed initial support for increasing teachers’ use of class-wide behavioral supports among teachers enrolled in online graduate education programs. These findings replicate and extend previous research showing the positive effects of individual training tiers on teachers’ use of positive behavior supports in in-service and in-person pre-service settings. Future research is needed to optimize individual tiers in the model, understand who responds to different types and intensities of training, and test this model using larger samples.

Footnotes

Acknowledgements

We thank Kaitlyn Morris, Evyn Hendrickson, Amy Pence, and Sydney Hawthorne for their assistance with data collection.

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

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by grant P20GM103436-22 (KY INBRE) from the National Institute of General Medical Sciences, National Institutes of Health and by the Center for Innovative Teaching and Learning, Western Kentucky University.

ORCID iD

Jessica N. Torelli

Author Biographies

Jessica N. Torelli is an assistant professor of special education at the University of Georgia. Her research focuses on applying behavior support practices in classroom settings to promote students’ social, emotional, behavioral, and academic outcomes.

Anna H. Miller is a doctoral candidate in special education at Vanderbilt University. Her research focuses on math intervention for students with persistent difficulties in mathematics.

Kristen L. Granger is an assistant professor of special education at Vanderbilt University. She studies dynamic systems within early childhood and elementary school classrooms that influence the social, behavioral, and academic development of students with and at risk for high incidence disabilities.

Christina R. Noel is an associate professor of special education at Western Kentucky University. Her research focuses on behavioral interventions for students with autism spectrum disorder and positive behavior supports.

Thomas J. Gross is an associate professor at Appalachian State University. He conducts applied research in child, family, and learning support systems, including multitiered systems of support.

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