Effects of e Coaching on the Occurrence,Equity,and Variety of Behavior Specific Praise During Mursion ™ Simulations

Abstract

Engaging in deliberate practice opportunities during special education teacher preparation may increase the implementation and sustainability of evidence-based practices with fidelity in K-12 classrooms. We used a concurrent multiple baseline design across participants to evaluate the effects of providing immediate feedback through eCoaching with online Bug-In-Ear (BIE) technology on special education teacher candidates’ acquisition and sustained use of behavior specific praise (BSP) during Mursion™ classroom simulations. We also measured equitable praise rates and used the Behavior Specific Praise Observational Tool (BSP-OT) to evaluate teacher candidates’ praise variety. Results showed a functional relation between the intervention and teacher candidates’ use of BSP. Maintenance, equity, and praise variety data indicated promising social validity findings.

Keywords

autism spectrum disorder Mursion™online BIE teacher preparation

Behavior specific praise (BSP) is a developing evidence-based practice ([EBP]; Zoder-Martell, Floress, Bernas, Dufrene, & Foulks, 2019) that includes explicit acknowledgment and precise reinforcement of effort-controlled behavior (e.g., “Nice job staying on task and contributing to the class discussion!”) rather than ability (“Wow, you are smart!”; Royer, Lane, Dunlap, & Ennis, 2019). When teachers use BSP, desired behaviors are reinforced and students with challenging behavior are indirectly reminded of classroom and schoolwide expectations (Royer et al., 2019). BSP has been linked to improved student engagement (Ennis, Royer, Lane, & Dunlap, 2020a). Moreover, BSP is supported by the Council for Exceptional Children’s (CEC) Social/Emotional/Behavioral High Leverage Practices (HLP 7, 8) for promoting a positive and engaging learning environment (McLeskey et al., 2017). By contrast, general praise ([GP]; e.g., “Good job!”) is less effective as it does not clearly communicate why the praise is being given (Ennis, Royer, Lane, & Dunlap, 2020b). Nonetheless, GP is observed more frequently than BSP in K-12 classrooms (Markelz, Riden, Floress, Heath, & Pavelka, 2022).

Considering the empirical evidence supporting the use of BSP, it is essential for special educators to develop proficiency in delivering BSP while enrolled in educator preparation programs (EPP). In addition to preparing future educators to implement BSP, there are other important aspects of BSP that must be considered and empirically evaluated (Markelz et al., 2022). First, students who routinely do not meet specified behavioral expectations will likely benefit from receiving increased rates of BSP (Royer et al., 2019). Second, increasing the originality of praise statements (i.e., praise variety) may strengthen the effects of the verbal reinforcement (Markelz et al., 2022).

Given the known benefits, BSP instruction should be included in EPP coursework and clinical experiences, though the adoption and sustained use of such practices requires more than simply mastering knowledge through coursework (Scheeler, 2008). Specifically, teacher educators must provide teacher candidates (i.e., individuals enrolled in an EPP for the purpose of seeking PK-12 licensure; Council for the Accreditation of Educator Preparation [CAEP], 2022; National Council for Accreditation of Teacher Education [NCATE], 2008) with deliberate and authentic practice opportunities and feedback to promote the acquisition and implementation of EBPs with fidelity (Dawson & Lignugaris/Kraft, 2017; Horn & Rock, 2022; Peterson-Ahmad, Pemberton, & Hovey, 2018). Deliberate practice opportunities are those in which individuals “exert effort to improve their performance…[and] repeatedly perform the same or similar tasks” (Ericsson, Krampe, & Tesch-Romer, 1993, p. 367).

Online Simulated Learning

Immersive virtual reality (VR) is an innovative way for teacher candidates to engage in deliberate practice opportunities in a controlled environment prior to real world application (Billingsley, Smith, Smith, & Meritt, 2019). Immersive VR has been demonstrated to (a) improve instructional practice and increase emotional awareness in teachers and teacher candidates, and (b) provide teachers and teacher candidates an opportunity to be immersed in a simulated environment that closely resembles a K-12 classroom as they practice applying acquired skills “without risk or failure with actual students” (Billingsley et al., 2019, p. 84).

One pioneering example of immersive VR used successfully by teacher educators to teach EBPs to teacher candidates, is Mursion™, formerly referred to as TeachLivE™. Mursion™ is a mixed-reality environment comprised of human-controlled avatars and computerized components (Dieker, Rodriguez, Lignugaris/Kraft, Hynes, & Hughe, 2014; Hartle & Kaczorowski, 2019). One of the critical features of Mursion™ is that teachers and teacher candidates have opportunities to practice EBPs in a safe environment, which has the potential to improve their effectiveness and efficacy as teachers in the classroom (Dieker et al., 2014; Landon-Hays, Peterson-Ahmad, & Frazier, 2020). Simulations are not designed to replace on-site clinical experiences; rather, Mursion™ simulations are designed to provide teacher candidates with repeated opportunities to practice and refine instructional and behavioral practices with avatars in a “safe space” prior to working with students in a real-world classroom (Cohen, Wong, Krishnamachari, & Berlin, 2020; Vince-Garland, Vasquez, & Pearl, 2012). Teacher candidates have reported Mursion™ practice opportunities as being beneficial in advancing instructional and behavioral practices (Hudson, Voytecki, Owens, & Zhan, 2019; Landon-Hays et al., 2020).

An emphasis on providing coaching and feedback to strengthen classroom simulation experiences is prevalent in the Mursion™ literature (e.g., Cohen et al., 2020; Dawson & Lignugaris/Kraft, 2017; Elford, Carter, Aronin, & Crow, 2013; Peterson-Ahmad et al., 2018; Vince-Garland et al., 2012; Vince-Garland, Holden, & Garland, 2016). For example, Vince-Garland et al. (2012, 2016) measured the effects of individualized clinical coaching (ICC) during Mursion™ on the implementation fidelity of an EBP, system of least prompts. ICC entailed verbally reviewing operational definitions, modeling (Vince-Garland et al., 2012), and prompting online verbal discussions after completing a written feedback form (Vince-Garland et al., 2012; 2016). A functional relation was demonstrated in both studies, and participants continued using the target EBP once the intervention was removed.

Dawson and Lignuaris/Kraft (2017) extended the Mursion™ literature as they examined the effects of Mursion™ with delayed feedback on the acquisition and generalization of specific praise, praise around, and error correction. The researchers delivered verbal and written feedback on target practices at the conclusion of each simulation. Generalization data revealed that participants generalized the target practices to the classroom to a certain extent but using specific praise (also referred to as BSP) posed challenges for several participants (Dawson & Lignuaris/Kraft, 2017).

Although the findings of the aforementioned studies support embedding Mursion™ in special education EPPs to promote the acquisition of EBPs by teacher candidates (Dawson & Lignuaris/Kraft, 2017) and novice teachers (Vince-Garland et al., 2012, 2016), the timing of feedback delivery needs further investigation. The preference for and the effectiveness of feedback delivery (i.e., immediate vs. delayed) has been a controversial topic in the field of education with some researchers supporting the use of delayed feedback (e.g., Corral, Carpenter, & Clingan-Siverly, 2021) and others proposing the delivery of immediate feedback (e.g., Fu & Li, 2020; Scheeler & Lee, 2002) to promote learning. Because the previous studies on the acquisition of EBPs during Mursion^TM simulations have examined the effects of delayed feedback, it is critical that additional studies are conducted to also examine the effects of immediate feedback on the acquisition, generalization, and maintenance of EBPs by teacher candidates.

eCoaching During Simulation

eCoaching is one approach used to deliver immediate feedback in the classroom, and it is also applicable in Mursion^TM classroom simulations. The eCoaching process consists of teacher candidates wearing a Bluetooth earpiece (i.e., bug-in-ear [BIE] technology) to facilitate two-way communication with university faculty without requiring their physical presence. Immediate feedback is delivered in “real-time” to an individual while they are actively engaged in the task(s) being targeted during the intervention; therefore, the use of immediate feedback delivery during teacher training affords teacher candidates an opportunity to improve targeted behavior continuously (Randolph, Duffy, Brady, Wilson, & Scheeler, 2020; Scheeler & Lee, 2002).

The results of several empirical research studies on the use of eCoaching during special education teacher preparation demonstrate an increase in practical application of target instructional and behavioral practices with high fidelity (Nagro et al., 2022; Rock et al., 2009, 2012, 2014; Scheeler, McKinnon, & Stout, 2012; Sinclair, Gesel, LeJeune, & Lemons, 2020). Further, providing immediate feedback through eCoaching has been shown empirically to increase practical application and sustained use of behavioral practices, including BSP (e.g., Horn et al., 2022; Randolph, Chubb, Hott, & Cruz-Torres, 2021) and opportunities to respond ([OTR]; Randolph et al., 2020). Thus, it is plausible to posit the delivery of immediate feedback through eCoaching with BIE to teacher candidates actively engaged in Mursion™ simulations could optimize learning outcomes (Cohen et al., 2020).

Several researchers (Elford et al., 2013; Cohen et al., 2020) have investigated the effects of eCoaching with BIE on teacher candidates’ performance during Mursion^TM simulations. Elford et al. (2013) measured the effects of immediate feedback delivered through eCoaching with BIE on increasing teacher candidates’ delivery of positive feedback to student avatars. The eCoach provided immediate feedback to each of the four teacher candidates during two randomly assigned Mursion^TM simulations. Results showed that teacher candidates’ delivery of positive feedback to student avatars increased from 20% to 30% across all teacher candidates after the intervention was introduced (Elford et al., 2013). Further, two teacher candidates gave positive feedback consistently (100%) after eCoaching with BIE was removed.

In a larger-scale experimental investigation, Cohen and colleagues (2020) compared the effects of three conditions, within the context of Mursion™ simulations, on the development of redirecting off-task student behavior. In the first condition (i.e., “Coaching Only),” teacher candidates received delayed feedback at the conclusion of the Mursion™ simulation on their performance and ability to redirect student behavior. In the second condition (i.e., “BIE coaching”), eCoaching with BIE was used to provide teacher candidates with immediate feedback in response to the first three off-task behaviors displayed by the student avatars. In the third condition (i.e., “Self-Reflection”), the researchers provided teacher candidates the opportunity to “pause,” reflect, and repeat the simulation (Cohen et al., 2020). That is, teacher candidates were given a 5-min self-reflection period between simulations where they were prompted to use three questions to guide their reflections. Subsequently, they had an opportunity to repeat the Mursion™ simulation. Results showed that teacher candidates who received one of the coaching conditions (i.e., coaching only or BIE coaching) engaged in higher performance rates of the target practice (i.e., redirecting off-task behavior), compared to teacher candidates who received only the self-reflection condition (Cohen et al., 2020).

An unexpected finding of the study conducted by Cohen et al. (2020) was that the effect size for teacher candidates who received eCoaching with BIE was not larger than the effect size for teachers who received delayed coaching at the conclusion of the simulation. Cohen and colleagues concluded that providing more coaching within the context of simulation does not result in larger improvements in teachers’ implementation of target practices. Notably, however, immediate feedback provided through eCoaching with BIE was limited to teacher candidates receiving verbal prompts during the first three of the six occurrences of the target behavior (Cohen et al., 2020). A question that remains unanswered in this study is whether the delivery of intermittent feedback impacted the effects of the BIE coaching conditions.

As special educator shortages continue to impact public schools nationwide, it is imperative that EPPs prepare future special education teachers to implement EBPs, such as BSP, when they enter the K-12 classroom. Thus, research on the acquisition and sustained use of BSP in special education EPPs, including measures for equity and praise variety, is warranted (Ennis et al., 2020b). Mursion™ affords deliberate practice opportunities, and a need remains for experimentally evaluating the effects of specific coaching procedures, including providing immediate feedback through eCoaching with BIE technology, during Mursion™ simulations. Therefore, the purpose of this study was threefold. First, we experimentally evaluated the effects of providing immediate feedback by means of eCoaching via online BIE within the context of Mursion^TM (i.e., mixed-reality classroom) on increasing teacher candidates’ use of BSP. Second, we evaluated praise variety across teacher candidates to examine the novelty of their BSP statements. Third, we assessed the equitable delivery of BSP within the context of Mursion^TM, when teacher candidates received immediate feedback via eCoaching with online BIE. Our research questions were:

1. Is there a functional relation between immediate feedback delivered via eCoaching with online BIE during a mixed reality classroom simulation and an increased use of BSP in master’s students enrolled in a special education program?

2. How does immediate feedback, delivered via eCoaching with online BIE during a mixed reality classroom simulation, impact master’s students’ praise variety?

3. How does immediate feedback, delivered via eCoaching with online BIE during a mixed reality classroom simulation, impact master’s students’ use of equitable BSP?

4. Does immediate feedback delivered via eCoaching with online BIE produce maintenance of BSP use in master’s students enrolled in a special education program?

Method

Participants and Setting

After obtaining approval from the university Institutional Review Board (IRB), students enrolled in an online, graduate-level, advanced behavior management practicum course with a 45-hour early field experience requirement (hereinafter referred to as “behavior course”), were invited to participate in this study. Four graduate students volunteered to participate, all of whom expressed interest in receiving application-based training to improve their implementation fidelity of behavior-related practices. Khayla, Tina, Linsley, and Kim attended a special education master’s degree program at an accredited urban university located in the southeastern region of the United States. The university served approximately 24,000 college students who were 46% White, 29% Black or African American, 8% Hispanic, 6% Multi-Ethnic, 5% Asian, 3% International, and 3% Unknown or Other.

All participants were female special education teacher candidates enrolled in a degree-seeking program while simultaneously working towards full licensure in K-12 special education. All participants reported that they completed one behavior management practicum course prior to the one they were enrolled in at the time of the study. Khayla and Tina shared they were “somewhat familiar” with eCoaching and BIE, meaning they read or heard about BIE but did not have first-hand experience. Linsley and Kim reported they were “unfamiliar” with eCoaching procedures and BIE technology. All four participants indicated they were “somewhat familiar” with Mursion™, meaning they read or heard about classroom simulations.

The university had a licensing agreement with Mursion™, which enabled mixed-reality classroom simulations to be offered to students. Due to the COVID-19 global pandemic, the campus-based Mursion™ Lab was closed; thus, participants engaged in scheduled Mursion™ simulations remotely via Zoom. Each participant logged into Zoom from their home-based computer for all sessions. Similarly, the eCoach accessed each scheduled classroom simulation from her home-based computer. The geographic location of participants ranged from the same city as the university to the opposite end of the state.

Materials, K-12 Student Avatars, and eCoach

Internet access was required for all Mursion™ simulations. The simulation specialist provided the eCoach (i.e., person delivering the immediate feedback via BIE) and each participant with private access to a secure cloud-based meeting where they were able to live-stream and record the classroom simulation. The eCoach was also the facilitator and assumed responsibility for recording and uploading each session’s recording to a private cloud-based account that was accessible only to the first author and independent observers. The eCoach, Ophelia, was a former special education teacher and full-time doctoral student in a special education program at the time of the study. She served as a graduate teaching assistant for the advanced behavior course in which participants were enrolled. Thus, Ophelia was well versed in BSP, including providing varied and equitable praise rates. Additionally, Ophelia had experience as a data recorder and/or eCoach in two earlier research investigations.

Each Mursion™ session included three middle-school student avatars in a general education classroom. Nate, a student avatar with a diagnosis of autism spectrum disorder (ASD), displayed off-task behaviors (e.g., speaking out of turn, making comments that were not relevant to the topic). Jasmine and Dev, neither of whom had a disability, were student avatars who displayed high levels of engagement and on-task behaviors during classroom simulations.

Participants used computers with a built-in webcam to engage in the sessions, while the eCoach used a 32 GB seventh Generation iPad™ to view sessions and provide immediate feedback during the intervention condition. The eCoach and each participant wore a Bluetooth Skullcandy Ink’d Wireless™ headset. Two-way communication was transmitted through Bluetooth headsets (i.e., BIE devices) as the eCoach was in a different geographic location than participants during sessions. During the intervention condition only, the eCoach had a printed list of varied BSP statements to inform her immediate feedback.

Experimental Design

A concurrent multiple-baseline research design (Ledford & Gast, 2018), replicated across four participants, was used to evaluate the effects of eCoaching with online BIE on increasing the delivery of BSP. The conditions of the design included baseline, intervention, fading, and maintenance. The criteria for entering the intervention phase consisted of at least five sessions and stable data, or a trend in the opposite direction predicted by the intervention (Horner et al., 2005; Zimmerman et al., 2018). We used visual analysis within and across conditions to evaluate behavior change and determine whether a functional relation existed between the intervention and the target behavior (Ledford & Gast, 2018). We used the six visual analysis elements (i.e., level, trend, variability, overlap, consistency, and immediacy) to interpret the graphed data, (Ledford & Gast, 2018). We calculated effect size using Tau-U and reported the effects of the intervention as a small (<.20), moderate (.20–.60), large (.60–.80), or very large effect (>.80; Vannest & Ninci, 2015). To assess participants’ responses to social validity surveys, we analyzed data numerically and identified themes related to the study goals, procedures, and outcomes.

Dependent Variables and Measures

Our dependent variable was BSP. As part of the course curriculum, all participants had access to an online module that included information about BSP. We were interested in learning if skill demonstration carryover would be observed in Mursion™ simulations. BSP was defined as a positive verbal praise statement provided to a student avatar by a participant that included a description of the behavior being reinforced and was emitted within 3s of its occurrence. We used several recording methods to collect data on three dimensions of BSP. First, frequency was used to record the number of BSP statements participants were giving to student avatars. These data were converted to percentage of BSP and rate of BSP per minute. Second, we used the Behavior-Specific Praise—Observation Tool (BSP-OT; Markelz et al., 2022) to analyze BSP variety. We used rate per minute to record BSP variety. Varied praise was calculated by dividing the number of different descriptive words or adjectives (e.g., good, excellent) used by the total number of BSP statements given (Markelz et al., 2022). To meet criteria and ensure accurate representation of praise variety, only sessions where the rate per minute exceeded 1.0 were analyzed. Third, we examined the equity of BSP by comparing the rate in which each participant directed BSP statements to individual student avatars or provided group praise (i.e., two or more students being praised simultaneously). Specifically, we were interested in determining if increased rates of BSP were directed toward Nate, compared to Dev and Jasmine. We used rate per minute to report equitable BSP given to an individual or group of student avatars.

Procedures

All Mursion™ sessions were recorded, thus, the researchers were able to see and hear each mixed-reality classroom simulation while simultaneously listening to immediate feedback given to participants by the eCoach throughout the intervention condition. That is, all audio (i.e., the Mursion™ simulation and immediate feedback delivered via BIE) was transmitted wirelessly through BIE devices, enabling the researchers to hear all aspects. Mursion™ sessions were live streamed via Zoom and each participant engaged in simulations remotely. To control for observer carryover effects, participants only had access to the Mursion™ sessions in which they were actively participating, and they were unable to join or view other participants’ sessions. Sessions were scheduled weekly by the first author, and when participants logged into Mursion™, they were automatically placed in a virtual waiting room.

First, the eCoach, who also acted as the facilitator, logged into the Mursion™ session and the simulation specialist (i.e., trained Mursion™ actor who was responsible for controlling student avatars in real time) granted her access to the virtual classroom. The simulation specialist conducted a routine technology check to ensure there were no issues with avatar voices, body movements, and glitching. Next, the simulation specialist granted the scheduled participant access to the virtual classroom where the eCoach read aloud general information about Mursion™, including student avatar capabilities (e.g., “Students can respond verbally and raise their hands.“) and limitations (e.g., “Students cannot give choral responses.“). Each Mursion™ session lasted 8 min, beginning when the eCoach said, “Begin simulation” and ending when she said, “End simulation.” The simulation topic remained the same across all conditions. The eCoach recorded each session and uploaded the recording to a secure cloud-based platform that was only accessible to the research team for coding purposes. Data collection took place 3–4 days a week, as determined by Mursion™ simulation availability.

Baseline

During baseline, participants engaged in a mixed-reality Mursion™ classroom simulation that entailed working with a small group of middle-school student avatars in an inclusion classroom. We designed the simulation to allow participants to lead small group lessons in which student avatars shared their ideas for designing a television commercial about their school and its available resources. Participants were familiar with the general topic of the simulation, but they were not given additional directions and did not receive immediate feedback via BIE during the baseline condition. Participants wore BIE earpieces to become familiar with the technology, though they were powered “off” and the eCoach did not provide any feedback.

Intervention

Each intervention session followed the general login procedures described above. Prior to each session, participants turned the BIE device to the “on” position and conducted an auto check to ensure that the two-way communication with the eCoach was working. Feedback delivery from the eCoach immediately followed (i.e., within 3 s) the target behavior being reinforced (i.e., BSP) and was instructive, specific, and positive (Scheeler, Ruhl, & McAfee, 2004, Scheeler, Morano, & Lee, 2018). The eCoach modeled varied praise statements (e.g., “Excellent job providing specific praise to Nate!” or “Yes! That was specific!“) as she delivered immediate, specific, and positive feedback to participants. The eCoach also gave instructive feedback (e.g., “Be specific” or “Say, ‘I like how you are raising your hand, Nate!’“) when participants missed an opportunity to praise a student avatar or when praise delivery was general (e.g., “Good job”) rather than specific. Participants continued in the intervention condition until they reached the mastery intervention criterion (i.e., 90% BSP for three consecutive sessions) and then entered the fading condition.

Fading

We used the fading procedures described by Scheeler et al. (2018) and Horn et al. (2022) to fade the intervention over three consecutive sessions. Fading consisted of gradually removing the BIE device that facilitated the two-way communication between the teacher candidate and eCoach. First, the participant wore the BIE device without receiving immediate feedback from the eCoach. Second, the participant placed the BIE device in plain sight, next to the computer, while engaging in the Mursion™ simulation. Finally, the BIE device remained in the storage case and was not placed in proximity.

Maintenance

We conducted maintenance sessions to measure each participant’s ability to deliver BSP to student avatars in a simulated classroom without receiving immediate feedback from the eCoach via BIE. We conducted one maintenance session approximately 10 days postintervention for each participant. Only one follow-up session was conducted for each participant due to high demands for the Mursion™ lab, which resulted, in part, from the ongoing global COVID-19 pandemic that adversely impacted in person clinical experiences across the United States.

Interobserver Agreement

The eCoach served as the primary data recorder. Another special education doctoral student served as the reliability observer throughout the study. Both the primary recorder and the reliability observer were certified special education teachers with a master’s degree in special education and their research interests included teacher preparation and improving instructional practices for students with ASD. The first author trained the primary recorder and the reliability observer prior to data collection. The interobserver agreement (IOA) training criterion was 100% across mixed-reality practice sessions. We calculated point-by-point agreement by dividing the total number of agreements by the total number of agreements plus disagreements and multiplied the quotient by 100 (Ledford & Gast, 2018). We scored an agreement if the primary recorder and the reliability observer recorded the same instance of the behavior as occurring or not occurring. The reliability observer scored 71% of all sessions selected randomly across participants and conditions. Table 1 shows the mean and range of IOA data. The mean agreement across participants was 99% (range, 93.33%–100%).

Table 1.

Mean and Range for Interobserver Agreement Data.

Participant	Baseline		Intervention		Fading		Maintenance
Participant	M (%)	Range (%)	M (%)	Range (%)	M (%)	Range (%)	M (%)	Range (%)
Khayla	100	–	100	–	100	–	100	–
Tina	100	–	98.3	93.3 – 100	100	–	100	–
Linsley	100	–	98.1	94.4 – 100	94.1	–	100	–
Kim	100	–	97	94.1 – 100	100	–	100	–

Note. M = mean.

Treatment Integrity

A trained doctoral student assessed treatment integrity (TI) across 20% of intervention sessions. We modified a checklist developed by Scheeler and colleagues (2018) to align it with study participants and procedures. The reliability observer assessed TI of the eCoach on the following behaviors: (a) eCoach and teacher candidate logged in to simulated Mursion™ classroom via Zoom with video on, (b) the eCoach and teacher candidate connected to the audio headset to enable two-way communication, (c) the eCoach provided immediate feedback to teacher candidate (corrective and/or verbal praise) within 3 s of the target behavior, and (d) the eCoach provided verbal and immediate feedback to participants. TI was 100% throughout the intervention condition.

Social Validity

At the conclusion of the study, we administered a social validity questionnaire (adapted from Scheeler et al., 2018) to the participants to assess the acceptability and feasibility of receiving immediate feedback via eCoaching with online BIE during a mixed-reality classroom simulation. The electronic questionnaire consisted of 12 open and close-ended questions and is available upon request from the corresponding author.

Results

Behavior Specific Praise Used in a Simulated Classroom

Figure 1 shows the percentage of BSP given by participants to student avatars during each simulated Mursion™ session across the conditions of the study. Figure 1 also depicts the BSP rate per minute and variety across each of the conditions (i.e., baseline, intervention, fading, and maintenance) of the study. Baseline data indicated that the four participants did not provide BSP consistently (i.e., range, from 0% to 50%) or frequently (i.e., range, from 0 to .4 per min) before the intervention was introduced. Intervention data revealed an increase in the use and rate of BSP by all four participants after the intervention (i.e., immediate feedback delivered via eCoaching with online BIE) was introduced. Fading and maintenance data show that all four participants continued to use BSP consistently and frequently during each session after the intervention was faded and removed.

Figure 1.

Behavior specific praise given by teacher candidates during classroom simulations.

The first tier in Figure 1 displays Khayla’s performance data. Baseline data revealed that Khayla’s mean level of performance using BSP was 18% and showed a gradual descending trend prior to intervention. As soon as the intervention was introduced, Khayla’s mean performance level using BSP immediately increased to 91% and remained stable throughout the intervention. Khayla’s BSP rate per minute during baseline was low (M = .2) but increased during the intervention (M = 1.8). Data showed stable performance and no overlap between baseline and intervention or fading conditions; therefore, a very large effect size of 1.00 ${C I}_{90 %}$ [.45, 1.00] (p = .009) was demonstrated. She continued to give high levels (M = 88%) of BSP s when the intervention was faded, and follow-up data revealed additional growth (M = 94%). Kayla gave student avatars BSP at a mean rate per minute of 1.7 when the intervention was faded, and that rate increased (M = 1.9) during the maintenance condition.

The second tier in Figure 1 shows Tina’s performance data. Baseline data revealed a variable level of performance with a mean level of performance of 21%. The last three baseline data points indicated a steep descending trend to 0 levels prior to the intervention. Upon introduction of the intervention, Tina’s performance level using BSP increased to 89% and remained stable throughout the intervention. Tina’s BSP rate per minute during baseline was low (M <.1) and increased to 1.8 during the intervention condition. Data showed stable performance during the intervention and fading conditions, no overlap with baseline, and Tina’s Tau-U was 1.00 ${C I}_{90 %}$ [.52, 1.00] (p = .0006), suggesting a very large effect. Tina continued to engage in high levels (M = 95%) of using BSP when the intervention was faded, and maintenance data indicated that Tina’s percentage of using BSP was 85%. Tina continued giving student avatars high rates of BSP as the intervention was faded (M = 2.1) and removed (M = 1.4).

The third tier in Figure 1 depicts Linsley’s performance data. Baseline data revealed moderate variability during the first five sessions, with a mean performance level of 12%. During the last four baseline sessions, Linsley’s performance maintained near 0 levels. Intervention data showed that Linsley’s performance level using BSP increased to 98% immediately after the intervention was introduced and remained stable throughout the intervention. Linsley’s BSP rate per minute during baseline was low (M < .1) and increased to 2.6 during the intervention condition. Data showed a stable level of performance during the intervention and fading conditions, and no overlap between these conditions and baseline; thus, demonstrating a very large effect size of 1.00 ${C I}_{90 %}$ [.45, 1.00] (p = .003). She continued to engage in a high percentage of BSP when the intervention was faded (M = 92%). Linsley’s percentage of using BSP decreased to 79% during the maintenance condition. BSP was observed at a rate of 1.7 during fading, and 1.4 during the maintenance condition.

The fourth tier in Figure 1 depicts Kim’s performance data. Baseline data revealed 0 level performance using BSP during the first six baseline sessions, with an increase to a mean performance level of 6% during the last four sessions. Data showed a descending trend to 0 levels prior to intervention. Intervention data showed that Kim’s performance level using BSP increased to 98% immediately after the intervention was introduced and remained stable throughout the intervention. Kim’s BSP rate per minute during baseline was low (M < .1) and increased to 2.6 during the intervention condition. Data showed a stable level of performance during the intervention and fading conditions, with no overlap between these conditions and baseline. Kim’s Tau-U was 1.00 ${C I}_{90 %}$ [.45, 1.00] (p = .003), showing a very large effect. She continued to engage in a high percentage of BSP during fading (M = 93%). Kim’s level of performance using BSP was 89% during the maintenance condition. Kim continued using high rates of BSP during fading (M = 2.7) and maintenance (M = 2.0).

Behavior Specific Praise Variety

The bar graphs in Figure 1 show the percentage of praise variety across participants. The low rates of BSP observed during the baseline condition (i.e., rate per minute did not exceed 1.0) limited our ability to calculate praise variety during this condition. Therefore, we reported the percentage of praise variety for intervention, fading, and maintenance conditions only. Intervention data revealed that as the percentage and rate of BSP increased when the intervention was implemented, the percentage of praise variety increased too. Khayla’s mean praise variety was 36% during the intervention condition, 29% during the fading condition, and 25% during the maintenance condition. Tina’s mean praise variety was 37% during the intervention condition, 27% during the fading condition, and 37% during the maintenance condition. Linsley’s mean praise variety was 28% during the intervention condition, 36% during the fading condition, and 45% during the maintenance condition. Finally, Kim’s mean praise variety was 22% during the intervention condition, 24% during the fading, and 31% during the maintenance condition.

Equitable Praise

Table 2 shows BSP delivered to (a) each student avatar individually, (b) a group of student avatars including Nate, and (c) a group of student avatars that did not include Nate. During the baseline condition, the mean rate participants directed BSP to Nate individually was .5, and low rates of BSP were also directed to Dev and Jasmine (Dev: M = .2; Jasmine: M = .1). Participants were not observed giving BSP to groups of student avatars during baseline.

Table 2.

Rate of Behavior Specific Praise Given to Each Student.

Average BSP Per Student	Khayla				Tina				Linsley				Kim
Average BSP Per Student	Baseline	Intervention	Fading	Maintenance	Baseline	Intervention	Fading	Maintenance	Baseline	Intervention	Fading	Maintenance	Baseline	Intervention	Fading	Maintenance
Nate	1.4	6.2	6.3	5	.43	6.1	7.3	6	0	6	3.3	2	.22	6.2	5.7	7
Dev	.2	3	4	2	.14	2.7	3	1	.3	5.8	3	1	0	5.6	5.7	5
Jasmine	0	2.6	2	3	0	2.6	3	0	.2	4.2	2.7	0	0	5.8	5.7	7
Group with nate	0	1.0	1.0	5.0	0	2.4	2.7	3	0	2.8	4	8.0	0	2.2	2.7	1.0
Group without nate	0	1.4	.33	0	0	.8	.67	1	0	1.4	1	0	0	1.6	1.7	0

During the intervention condition, participants directed BSP to Nate individually at a rate of 6.1, compared to a mean rate of 4.3 for Dev and a mean rate of 3.8 for Jasmine. During the intervention condition, the mean BSP rate for a group of student avatars that included Nate was 2.1 and 1.3 for a group of student avatars that did not include Nate.

During the fading condition, the mean rate participants gave BSP to Nate individually was 5.7, compared to a mean rate of 3.9 for Dev, and 3.4 for Jasmine. As the intervention was faded, BSP was delivered to a group of student avatars that included Nate at a mean rate of 2.6, compared to .9 for BSP delivered to a group of student avatars that did not include Nate.

During the maintenance condition, Nate received individual BSP at a mean rate of 5.0, whereas Dev received BSP at a mean rate of 2.3, and the mean rate for Jasmine was 2.5. BSP directed toward a group of student avatars that included Nate was observed at a mean rate of 4.3, compared to .3 when Nate was not included.

Social Validity

All four participants reported that they “liked” receiving immediate feedback via eCoaching with online BIE during a mixed-reality classroom simulation. Participants reported that the intervention helped them understand how to use BSP and increased the frequency with which they delivered praise to students. Specifically, one participant noted, “The impact was substantial because I needed to improve giving specific praises and from the feedback via eCoaching, I was able to correct myself and use BSP correctly.” Another participant echoed a similar response, “I feel that eCoaching in a mixed-reality classroom is very effective professional development. I personally was able to put to practice what I learned from the textbook and research. I would say research to practice through eCoaching is a successful tool to learn new skills.” Participants noted how realistic the Mursion™ classroom was and reported their acquired skills could easily be generalized and applied when working with actual students, “I found that I enjoyed presenting more opportunities for the students to respond just so I could give them more praise. This makes me think of my teaching style once I go back to teaching. I think this experience will make me redesign my teaching to be more interactive and will help me build a more student driven agenda to accomplish the curriculum.”

Discussion

The purpose of this study was to extend the existing Mursion™ and eCoaching literature by experimentally evaluating the effects of immediate feedback delivered via eCoaching with online BIE on the use, equity, and variety of BSP by special education teacher candidates during a classroom simulation. Baseline data indicated that the four participants did not provide BSP consistently (i.e., range, from 0% to 50%) or frequently (i.e., range, from 0 to .4 per minute) before the eCoaching intervention. This lack of carryover from the BSP module not only confirms foundational research on the lack of knowledge transfer and the research to practice gap (e.g., Fixsen, Naoom, Blase, Friedman, & Wallace, 2005; Joyce & Showers, 2002; Scheeler et al., 2004) but lends further support for this study.

A functional relation was demonstrated between the intervention and the target behavior when participants received immediate feedback (i.e., feedback given within 3s of the target behavior) via eCoaching with online BIE while teaching in Mursion™. Furthermore, high percentages of BSP statements were sustained over time when eCoaching was removed. During the intervention condition, teacher candidates were observed differentiating BSP rates, directing more individual BSP statements to Nate, the student avatar with ASD, as well as small groups that included Nate. Equitable delivery of BSP continued, and even improved, as the intervention was faded and removed altogether. Given the low rates of BSP during the baseline condition, we were unable to calculate praise variety. Nonetheless, all four teacher candidates used variety while giving BSP when receiving immediate feedback via eCoaching with online BIE and sustained or increased praise variety when the intervention was faded and removed.

Impact of eCoaching in Mursion™ Simulations

Our results are consistent with previous studies reporting the benefits of providing teacher candidates with multiple opportunities to practice implementing newly acquired skills in a simulated environment (i.e., Mursion™; Dawson & Lignugaris/Kraft, 2017; Elford et al., 2013; Fraser et al., 2020; Vince-Garland et al., 2012; Peterson-Ahmad et al., 2018). Also similar to previous findings (e.g., Horn, Gable, Bobzien, Tonelson, & Rock, 2020; 2022; Korner & Brown, 1952; Rock, 2019), participants indicated it took between three and four sessions to feel comfortable receiving immediate feedback by means of eCoaching with BIE. Our research makes several unique contributions to the literature that warrant further discussion. The first contribution is the experimental evaluation of the effects of eCoaching through online BIE during a Mursion™ classroom simulation to improve a target practice (i.e., BSP), followed by fading BIE. Providing participants with immediate individualized feedback consistently (rather than intermittently) until they reached acquisition, before removing BIE led to sustainability of the newly acquired practice. That is, implementing the systematic fading procedure used by Scheeler et al. (2018) and Horn et al. (2022) to remove the BIE device allowed us to confirm the impact of the eCoaching intervention accurately. Documenting participants’ performance during the fading condition showed longer term effects of the intervention once immediate feedback delivered through eCoaching with online BIE was removed, and, thus, validated the efficacy of providing immediate feedback via eCoaching with online BIE during Mursion™ simulations. Further, participants noted how authentic the Mursion™ simulations were and highlighted how they felt their newly acquired skills could easily be transferred to in-person learning, which aligns with previous Mursion™ research (e.g., Elford et al., 2013).

Praise Variety

The second contribution is the improved and sustained high rate of praise variety. To our knowledge, this study was the first to utilize the BSP-OT (Markelz et al., 2022) to evaluate praise variety. All four teacher candidates varied their BSP statements during the intervention condition, and three of the four participants increased the percentage of BSP variety over time. Interestingly, Linsley used the lowest overall percentage of BSP across conditions yet had the highest percentage of praise variety. By contrast, Khayla used the highest overall percentage of BSP but used less variety while delivering BSP. One possible explanation for these results could be related to the individual participants focus during the simulated classroom activity. For example, Linsley may have focused her attention on the occurrence of high magnitude, low frequency behaviors, resulting in fewer, but more individualized and variable forms of BSP. On the other hand, Khayla may have attempted to acknowledge behaviors equally, regardless of magnitude. As such, although she used BSP, there would likely be greater repetition of BSP statements.

Equitable Rates of Behavior-Specific Praise

The third contribution is the examination of equitable rates of BSP in a simulated classroom that included students with and without disabilities. Data showed that equitable praise rates continued, or even improved, after participants received immediate feedback by means of eCoaching with BIE during a Mursion™ simulation. All four participants directed higher rates of BSP to Nate, compared to Dev and Jasmine during the intervention condition, and high rates of group directed BSP were observed when the group included Nate. Notably, these high rates were sustained as the intervention was faded and removed. One potential explanation of this finding is that participants learned to provide differentiated levels of BSP to match individual student needs and promote engagement, although this aspect was not directly targeted in our intervention. Specifically, students with ASD need various levels of support to meet their academic, social, and behavioral needs (Wolfe, McCammon, & Check, 2022) and, consequently, participants in this study may have provided higher rates of BSP to facilitate Nate’s engagement during the simulation.

Limitations

The findings of this study should be interpreted within the context of some limitations. First, because of school closures resulting from the COVID-19 pandemic, we were unable to collect generalization data to measure the transfer of BSP to the classroom. Therefore, it is unknown whether receiving immediate feedback through eCoaching with BIE while using BSP during Mursion™ contributed to the implementation of this practice with fidelity in a K-12 classroom. Second, we conducted only one maintenance session because of limited availability for simulations and the end of the semester; thus, longer-term effects of the intervention are unknown. Third, we did not measure praise variety during the baseline condition because the rate of BSP given was low. In other words, when using the BSP-OT to calculate praise variety, the percentage would be 100% if only one BSP statement was given. Thus, we found the BSP-OT tool to be applicable only when higher rates of BSP were observed. Fourth, Tau-U indicated very large effect sizes for all participants in this study which may have biased the interpretation of the results. Therefore, readers should be cautious not to rely only on effect sizes when interpreting the results, but also use visual analysis which allows for a more conservative interpretation of results (Fingerhut, Xu, & Moeyaert, 2021; Kazdin, 2021).

Implications for Practice

Based on our findings and those of previous researchers (Dawson & Lignugaris/Kraft, 2017; Elford et al., 2013), we recommend embedding mixed-reality classroom simulations in teacher preparation programs as a form of practice-based learning that can be enhanced by adding eCoaching with online BIE. However, it is important to note that participation in classroom simulations should not replace early field experiences with actual students in classroom settings but be used to prepare teacher candidates prior to entering the K-12 classroom. When applicable, we recommend teacher candidates engage in simulated practice opportunities that include eCoaching with BIE. However, we recognize that many EPPs do not have access or the financial means to purchase a Mursion™ licensing agreement.

eCoaching with BIE is a cost-effective way to provide practice-based learning opportunities whereby teacher candidates can receive immediate feedback while implementing newly acquired skills (Horn & Rock, 2022), sans the simulated environment (e.g., role playing, practicum experiences). In short, it is essential to consider and adequately address the digital divide in special education EPPs (Horn & Rock, 2022) and we recommend considering accessibility to required technology and geographic location (e.g., urban, suburban, rural) when determining what is feasible for each individual program (i.e., Mursion™ with eCoaching vs. eCoaching only). Finally, given the diverse needs of students who receive special education services, teacher educators should address equity in BSP delivery (i.e., differentiating rates of BSP based on student needs) to adequately prepare future educators to meet the needs of students with and without disabilities. Finally, we recommend teacher educators incorporate praise variety in BSP instruction, as doing so may enhance the impact and delivery of praise statements.

Suggestions for Future Research

Based on the extant literature, coupled with findings from our study, we offer several implications for research. First, future researchers should include generalization measures to determine if skills, such as BSP, acquired during classroom simulations are transferred and applied in the classroom context with novel students and across various activities. Second, we recommend researchers include additional maintenance data to assess long-term effects of participating in Mursion™ simulations. Third, we suggest future researchers experimentally evaluate the effects of this intervention when applying other instructional and behavioral EBPs and high-leverage practices. Fourth, we recommend researchers consider evaluating structured approaches to fading, including scaffolding the in-ear feedback. Finally, we believe more comparison studies are needed to identify the unique benefits and drawbacks of eCoaching with online BIE coaching compared to other coaching methods (e.g., elbow coaching) during mixed-reality Mursion™ simulations.

Conclusion

Our findings demonstrate the efficacy of providing immediate feedback via eCoaching with online BIE to special education teacher candidates as they acquire a new behavioral practice and implement it with fidelity. We observed immediate behavior change across participants during Mursion™ simulations as a result of the eCoaching intervention. Teacher candidates continued to give high rates of BSP after the intervention was discontinued. Unique to this investigation, immediate feedback transmitted via eCoaching with online BIE was consistent, rather than intermittent, throughout the intervention condition. Additionally, the eCoach provided feedback and verbal reinforcement reliably (i.e., no missed opportunities), as confirmed by TI data. Our results contribute to an emerging literature base that confirms providing immediate feedback during mixed-reality classroom simulations optimizes teacher candidates’ learning outcomes efficiently (Elford et al., 2013). Moreover, teacher candidates varied BSP statements during and after the intervention condition, and equitable BSP rates were observed as well. Our research supports the continued use of online BIE during Mursion™ simulations to facilitate practical application and sustained use of instructional and behavioral practices.

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

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Annemarie L. Horn

Marcia L. Rock

Author Biographies

Annemarie L. Horn, PhD is an Assistant Professor of Special Education and K-12 Special Education Adapted Curriculum Program Coordinator at Old Dominion University. Dr. Horn’s research centers on integrating technology enabled coaching and practice-based learning in special education teacher preparation as well as professional learning and development for in-service teachers and paraeducators.

Marcia L. Rock, PhD is a Professor in the Department of Specialized Education Services, Graduate Studies Director, and Ph.D. Program Director, at the University of North Carolina Greensboro. Dr. Rock’s research interests include pre- and in-service teacher development, technology enabled coaching, workforce systems thinking and change, research practice partnerships, inclusive and special education, behavioral health, and social, emotional learning.

Laura Chezan, PhD is an Associate Professor of Special Education in the Department of Communication Disorders and Special Education at Old Dominion University. Her research interests focus on quality of life, social-communication interventions for individuals with autism, and positive behavior support.

Jonna Bobzien, PhD is an Associate Professor of Special Education and the Department Chair of Communication Disorders and Special Education at Old Dominion University in Norfolk, VA. Dr. Bobzien’s expertise centers on the training and implementation of evidenced-based practices with students diagnosed with multiple and/or severe disabilities.

Olga Karadimou, MSEd is a PhD candidate in the special education program at Old Dominion University. Ms. Karadimou's areas of research include preservice teacher preparation, professional development, eCoaching with Bug-in-Ear technology, and increasing practical application of evidence-based practices in school settings, especially in rural areas.

Aeshah Alturki, MA is a doctoral student in the special education Ph.D. program at Old Dominion University. Ms. Alturki’s research interests include evidence-based practices for young children with autism spectrum disorder, parent-implemented interventions, and early childhood special education.

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