The Effects of Varied and Non-Varied Praise on Student On-Task Behaviors

Abstract

Research has demonstrated that behavior specificity is a salient characteristic of teacher praise efficacy. Praise variety may also be an important characteristic to reinforce desired student behavior based on research about the quality of reinforcers. In this study, we used an alternating treatments design to examine the effects of varied and non-varied behavior-specific praise (BSP) on two first-grade students’ on-task behaviors in U.S. general education classrooms. Visual and statistical analyses suggest both varied and non-varied BSP increased on-task behavior, with varied BSP resulting in marginally higher levels of on-task behavior. There was no functional relationship between varied and non-varied BSP conditions. Findings from this study contribute to teacher praise literature as the first to empirically investigate the effects of praise variety on student behavior. We discuss the implications of this preliminary research and encourage future inquiry into additional characteristics of praise.

Keywords

behavior-specific praise on-task behavior praise variety teacher training

Teacher praise has been examined for decades by researchers as a form of positive reinforcement to improve student behaviors when attention maintains behavior (Becker et al., 1967; Brophy, 1981; Markelz, Taylor, et al., 2019; Sutherland et al., 2000; White, 1975). It is often stated that teacher praise is a low-intensity, effective, and efficient proactive classroom management strategy for academic and social behaviors (Ennis et al., 2018; Lane et al., 2015). Although there is debate about whether sufficient high-quality research exists to categorically claim teacher praise as an “evidence-based strategy” (e.g., Moore et al., 2019; Royer et al., 2019), researchers have demonstrated the effectiveness of teacher praise in a variety of settings with a variety of populations (Ennis et al., 2020; Moore et al., 2019).

There is consensus among scholars that behavior-specific praise (BSP) may be a more salient reinforcer than general praise (GP) due to the explicit linkage between teacher approval and a specific student behavior (Alberto et al., 2022; Gage & MacSuga-Gage, 2017). An example of GP is “Nice job!” An example of BSP is “I like the way you put your things away then immediately started working on your project, well done!” When students receive feedback about their behavior delivered as a specific positive affirmation, the student’s behavior is rewarded with attention, the student is told which specific behavior resulted in that attention, and their classmates have been reminded of classroom expectations. Specific feedback is a potentially more effective reinforcer than nonspecific feedback because the recipient is oriented to the behavior that elicited reinforcement and is thus able to replicate that behavior in the future (Cooper et al., 2020). Accordingly, BSP is recommended as a Tier 1 (classroom level), Tier 2 (small group level), or Tier 3 (individual student level) intervention to promote desirable behaviors (Floress et al., 2020).

Praise Variety

The past empirical literature has focused on the efficacy of BSP versus GP (Markelz, Taylor, et al., 2019). Yet, scholars have suggested another characteristic of praise may affect efficacy such as praise variety (Floress & Beschta, 2018; Hager, 2012; Markelz et al., 2020). Praise variety is a topographical characteristic grounded in research related to the quality of reinforcer (Markelz et al., 2020). The stronger or more effective a reinforcer is, the greater likelihood of meaningful behavior change. Floress and Beschta (2018) suggested that teachers who praise in various ways (e.g., physical gestures, verbal praise, and written notes) may become a discriminative stimulus for the availability of praise. As students learn which teachers deliver more praise (i.e., provide more reinforcement), they are likely to perform behaviors that in the past have led to teacher praise (Greer, 2002). However, stimulus habituation is a decrease in response to a stimulus after repeated delivery (Thompson & Spencer, 1966). For example, at a children’s birthday party, the first balloon that pops likely startles everyone, but over time, as more balloons pop, the sound becomes less startling because party-goers habituate to the sound. Similar to a balloon popping, a student who first hears a praise statement might take notice more readily, as the statement is novel to the environment. However, teacher praise, like any other stimulus (e.g., balloon popping), may become irrelevant if students habituate to hearing praise.

Shriver and Allen (2008) contend that individuals learn to attend to relevant stimuli and ignore stimuli that have little meaning or importance. Student habituation to praise may diminish the reinforcer strength and the praise efficacy (Markelz et al., 2020). Given the potential for praise variety to impact the efficacy of this widely purported classroom management strategy, an examination of praise variety literature is warranted.

Praise Variety Literature Review

Researchers have suggested that sincere praise will more likely capture students’ attention (Bayat, 2011; Brophy, 1981; Henderlong & Lepper, 2002; McKay, 1992). However, sincerity is a subjective and difficult to quantify term that has been neglected in the literature. Markelz, Riden, Floress, et al. (2022) suggest that praise variety may increase perceived sincerity as the praise statement novelty decreases the likelihood of habituation. Rather than habituating to hearing “Good job,” a student may believe a teacher’s praise statement is more genuine with statements like, “Thank you for putting your supplies away immediately after I asked the class.”

Few studies have empirically examined praise variety. Floress and Beschta (2018) measured “diverse praise” in 28 kindergarten through fifth-grade classrooms and defined it as “the use of verbal statements or gestures of approval that are delivered in a variety of distinguishable ways in response to desired student behavior” (p. 1191). The authors measured the rate of teacher praise delivery method toward various student behaviors. Verbatim GP data were coded into eight categories: (a) work/job, (b) adjective, (c) effort, (d) compliance/appreciation, (e) gesture, (f) tangible, (g) physical, and (h) miscellaneous. Verbatim BSP data were coded into categories based on student behavior variety (e.g., sitting, working, hand-raising, following directions). For example, if the teacher said, “Nice job sitting in your seat” and “Good job sitting down when I asked,” within the same observation, one diverse BSP category was counted because both BSP statements targeted “sitting down.” On average, teachers used 3.7 total diverse praise categories per observation (M = 18.2 min per observation) and more general diverse praise categories (2.2) compared with specific diverse praise categories (1.5; Floress & Beschta, 2018).

In a study examining a student-teacher’s use of effective teaching strategies in a moderate to severe self contained elementary classroom, Hager (2012) measured praise variety by quantifying the adjective used in the praise statement. For example, if the student-teacher said, “Good job working quietly” and “Nice work working quietly,” praise variety was counted twice (i.e., once for “good” and once for “nice”). During baseline, the teacher delivered an average of 7.6 varied praise statements. Following a video self-monitoring intervention, the teacher averaged 14.8 varied statements.

Markelz and colleagues (2020) developed and assessed the reliability of an observation tool to measure praise specificity, contingency, and variety. Similar to Hager (2012) who identified the statement adjective as the key component to varied praise, Markelz et al. explained BSP variety as “the teacher praises a student (or group of students) for a specific behavior using a variety of descriptive language (e.g., ‘Sam, you did a super job raising your hand.’ At the next opportunity, the teacher praises Sam for raising his hand ‘Sam, wonderful job raising your hand’).” Unlike Floress and Beschta (2018) who included student behavior as a required component of diverse praise, if a teacher said, “Great job raising your hand” and “Great job sitting in your seat” those statements would be counted as non-varied since “great job” was used in both statements.

Markelz et al. (2020) determined that statement adjective was appropriate to measure praise variety since GP does not identify a specific behavior. Furthermore, teachers often need to single out a specific student behavior for frequent reinforcement. For example, a student’s behavior intervention plan (BIP) may target sharing with classmates. The teacher would want to identify instances of the student sharing with classmates and deliver a dense schedule of descriptive praise (“I love how you are sharing right now,” “You are doing an excellent job sharing with your friends,” “Awesome job, I love seeing you share with your classmates”).

Using the Behavior-Specific Praise Observation Tool (BSP-OT), Markelz, Riden, Floress, and colleagues (2022) measured natural rates of teacher praise specificity, contingency, and variety. To calculate praise variety the authors (a) counted different adjectives used over a 15-min observation session and then (b) divided the number of different types of praise statements by total statements and multiplied by 100. For example, if a teacher delivered 10 BSP statements and used “great,” “wonderful,” “good,” and “love,” then the teacher used four varied praise statements (4/10 × 100 = 40% praise variety).

Results from Markelz, Riden, Floress, et al. (2022) suggested that special education teachers (n = 12) delivered 44.9% BSP praise variety and 48.6% GP praise variety per 15-min observation. General education teachers (n = 13) delivered 44.5% BSP variety and 51.5% GP variety with no statistical differences between special education and general education teachers (BSP variety [U = 76.0, p = .934]; GP variety [U = 69.0, p = .643]). Preservice special education teachers (n = 17) delivered 51.5% BSP variety and 57.7% GP variety and were excluded from statistical difference calculations due to limited observations. Across teacher groups, low natural rates of BSP and GP were observed per observation session (BSP M = 1.59; GP M = 7.2), which corresponded with prior research measuring natural rates of praise delivery (Floress et al., 2018; Jenkins et al., 2015).

Purpose of the Study

Given the identification of praise variety as a potentially salient characteristic of praise efficacy due to potential stimulus habituation, this study empirically examined the effects of varied and non-varied specific praise on student on-task behavior. Previous research has observed teacher’s natural rates of praise variety (e.g., Markelz, Riden, Floress, et al., 2022); however, no study has empirically examined whether praise variety affects student behavior. The following study adds to the literature on teacher praise by empirically manipulating praise variety to expand researchers’ understanding of praise efficacy. In other words, does varied praise serve as a more salient reinforcer than non-varied praise on student on-task behaviors? The following research questions guided our analysis:

Research Questions

Research Question 1: Is there a functional relation between varied BSP and increased student on-task behavior?

Research Question 2: Is there a functional relation between non-varied BSP and increased student on-task behavior?

Research Question 3: Is there a difference in effectiveness between varied BSP and non-varied BSP on student on-task behavior?

Method

Participant and Setting Information

The study took place in a public K–Grade 5 Title 1 elementary school in the United States with 490 students. Approximately 40% of the student population identified as a racial or ethnic minority. Prior to study initiation, institutional review board approval was obtained from university and school district review boards. A recruitment email was sent to three different principals within the school district to forward to their general and special education teachers. The email asked teacher volunteers to participate in a study that could help with classroom management practices. The inclusion criterion included no planned extended absences. Teacher participants were offered a US$400 incentive to participate. Two teachers from the same public elementary school volunteered to participate in the study. Each teacher selected an instructional period for observation to occur and identified a target student from their classroom, who they believed demonstrated frequent off-task behaviors, to complete a teacher–student dyad. Voluntary consents from teachers and students’ parents were obtained. All names have been changed to protect participant identity.

Two certified general education teachers and one of their students in an inclusive classroom participated in the study. Both participating teachers were White females with bachelor’s degree in elementary education. Dyad A consisted of a teacher and the target student in a classroom with 20 students total. Ms. Alisha was 41 years of age and had a total of 8 years of teaching experience, two of those years in her current position. Alli was a 6-year-old Black girl without a disability. Ms. Alisha reported that Alli frequently demonstrated off-task behaviors such as climbing on furniture, repeatedly seeking teacher attention, and not completing assigned activities. Alli was on a behavior intervention plan for 3 weeks consisting of a check-in/checkout (Klingbeil et al., 2019) when the study started. Ms. Alisha completed a Functional Analysis Screening Tool (FAST; Iwata et al., 2013), which indicated Alli’s off-task behavior was socially maintained (attention/preferred item). Dyad A observations occurred daily during the beginning of reading/writing class where students sat at their desks, clustered in small groups of 4 to 5 students, and participated in teacher-led activities, such as filling in blanks to complete story sentences, spelling practice, and sight word practice.

Dyad B consisted of a teacher and the target student in a classroom with 22 students total. Ms. Brenda was 21 years of age and had 4 years of overall experience and 2 years in her current position. Brice was a 7-year-old Black boy who was medically diagnosed with attention-deficit-hyperactive disorder (unmedicated) and was not receiving special education services. Ms. Brenda identified Brice as a good candidate for the intervention due to excessive off-task behaviors, such as not completing assignments and aggression toward other students. Brice was also receiving a behavior intervention plan for 4 weeks with check-in/checkout. Ms. Brenda completed a FAST, which indicated Brice’s off-task behaviors were equally social and automatically maintained (attention/preferred item and sensory stimulation). Dyad B observations occurred daily during reading instruction where students sat at their individual desks (Brice’s desk was isolated in the front of the classroom) and participated in teacher-led activities, such as choral reading, or sat on the carpet in front of a smartboard and participated in the video-led reading activities.

Study Design

An alternating treatments design (Cooper et al., 2020) was used to assess if a functional relation between varied/non-varied praise and student on-task behaviors exists. This design was used to measure whether rapid changes in praise variety would affect student on-task behaviors. The present study aligns with the What Works Clearinghouse version 4.1 single-case design review standards (What Works Clearinghouse, 2020): (a) the independent variable was systematically manipulated; (b) outcome variables were measured systematically over time by more than one assessor, and interobserver agreement (IOA) was collected for at least 20% of all sessions across all phases; (c) the study included at least three attempts to demonstrate an intervention effect at three different points in time; (d) each phase had a minimum of five data points, and (e) no more than two consecutive treatments were scheduled.

Dependent Variable

The primary dependent variable was student on-task behavior. Student behaviors were measured as a percentage using whole interval recording (Ledford & Gast, 2018) with 20-s intervals. Whole interval recording was selected because it underestimates the duration of behavior (Ledford & Gast, 2018). On-task behavior was defined as participating in a lesson or activity or being focused and attending to a speaker or activity. Examples of student on-task behavior included looking at the teacher when they were talking, working on iPad activity (e.g., phonics practice), and raising one’s hand to participate in classroom discussion. Students were also considered on-task if they were performing typical classroom activities (e.g., taking scrap paper to the trash can or handing out worksheets). Examples of off-task behavior included walking around the classroom without permission, talking with other students during instruction, and head down on desk and not completing assignments.

Secondary dependent variables were teacher praise characteristics delivered toward the target student at any time during observation sessions (i.e., specific/general praise, contingent/non-contingent, and varied/non-varied praise). Definitions of praise characteristics were used from the BSP-OT (Markelz et al., 2020). Specific praise was defined as a positive statement delivered by the teacher toward the target student that identified a behavior and incorporated an expression of approval (e.g., great job raising your hand, Sarah). General praise was defined as a positive statement delivered by the teacher toward the target student that was unspecific to behavior (e.g., “Good work, Sarah”).

Specific and general praise can classify as contingent or noncontingent. Contingency is the relationship between two events, one being a consequence of temporal proximity to the other event (Markelz et al., 2020). Contingent-specific praise was defined as a positive statement provided by the teacher, when a desired behavior occurred (contingent), to inform the target student, specifically, what they did well (e.g., a teacher tells the class to quiet down; Sarah gets quiet, teacher responds, “Great job getting quiet, Sarah”). Contingent general praise was a positive statement provided by the teacher, when a desired behavior occurred to inform the student generally that they did well (e.g., Teacher tells the class to sit down; Sarah sits, teacher responds, “Good job, Sarah”).

Specific and general praise can also classify as varied or non-varied. Praise variety is about the quality of praise being different or diverse (Markelz et al., 2020). Varied specific praise was defined as the teacher praising the student for a specific behavior using a variety of descriptive language (e.g., “Sarah, you did a super job raising your hand.” At the next opportunity, the teacher praises Sarah for raising her hand, “Sarah, wonderful job raising your hand”). Varied general praise was when the teacher praises a student with a variety of descriptive language without specifying a behavior (e.g., “Great work, Sarah!” “Nice job, Sarah”).

Independent Variables

Given the study’s purpose to examine differences between varied and non-varied praise on student on-task behaviors, teacher participants needed to deliver consistent specific and contingent characteristics of praise, prior to independently manipulating varied praise. We trained teachers to ensure high levels of specific and contingent praise were being delivered (i.e., a minimum of 10 statements toward the target student) before entering the treatment phases. Once participants entered the treatment phase, Treatment 1 (T1) was a high rate of varied praise. Treatment 2 (T2) was a high rate of non-varied praise.

Data Collection

Dependent and independent variables were measured with an adapted BSP-OT. Previous research suggests the BSP-OT is a reliable tool to measure teacher praise characteristics with an interclass correlation of .80 and a κ score of .91 (Markelz et al., 2020). The adapted BSP-OT added a student on-task row to simultaneously collect teacher praise characteristic data (independent variable) as well as student on-task behavior data (dependent variable). The adapted BSP-OT is available in the online supplemental materials. The first author trained the primary data collector (a graduate student). Following the 30-min training, the primary data collector watched a 15-min prerecorded video of a teacher/student dyad in an authentic classroom and used the adapted BSP-OT to data collect teacher praise characteristics and student on-task behavior. The primary data collector’s data was assessed for reliability against an accurate data sheet using kappa. A priori κ criterion of .80 was set. The primary data collector scored kappa reliability of .95.

Procedures

Baseline

Observation sessions were set to 15-min in length due to previous praise research on acceptable dependability and generalizability levels of teacher praise (Floress et al., 2021; Gage et al., 2014). A minimum of five baseline observations were conducted, but the research team also analyzed data with a stability envelope criterion of 80% of data within 25% of the mean to ensure baseline data stability prior to intervention implementation (Lane & Gast, 2014).

Praise training condition

Once baseline conditions were met (i.e., student on-task behaviors were stable), the first author and primary data collector met with both teacher participants and trained them using explicit instruction (Archer & Hughes, 2011) on the importance of specific and contingent praise (note: praise variety was not mentioned during this training). The first author trained teacher participants on the definition and use of BSP; research efficacies of BSP on student behaviors were highlighted, and the teacher participants practiced delivering BSP with their target students’ name. The trainer delivered positive and corrective verbal feedback. Participants were then told that prior to each observation, they would be handed an Apple Watch™ (series 4) that had a Periodic Timer application (Kelin, 2014). The timer application was programmed to deliver a vibratory cue (i.e., tactile prompt) every 75 s. Previous research suggests tactile prompting effectively increases teacher praise rates (Markelz, Riden, & Hooks, 2021; Markelz, Taylor, et al., 2019).

Participants practiced putting the Apple Watch on and delivering BSP every tactile prompt. Participants were instructed to deliver specific praise to their target student following each tactile prompt, contingent on on-task behaviors (e.g., following directions, participating in the activity). Participants were informed that they could deliver BSP at any time during observations; however, the Apple Watch prompted delivery at a minimum of 10 statements based on previous research on BSP in childhood settings (LaBrot et al., 2016), and previous recommendations of 6 to 10 praise statements per 15 min (Sutherland et al., 2000). The trainer instructed teacher participants that to reach mastery levels of praise delivery, they would need to meet the goal of at least 10 BSP statements per observation for 3 days before entering the treatment condition. The training lasted approximately 30 min.

Treatment condition

After each participant demonstrated mastery in delivering specific and contingent praise to their target student during observation sessions, an email was sent describing the difference between varied and non-varied praise. Teacher participants were informed that observation sessions would now alternate between T1 and T2 conditions. When the data collector handed teacher participants the Apple Watch prior to observation, they informed the teacher which treatment was in place for that session. A research team member randomly assigned T1 and T2 condition orders without knowledge of which condition was varied or non-varied praise. To meet alternating treatments design standards without reservations (What Works Clearinghouse, 2020), five sessions per treatment condition were scheduled with no more than two consecutive treatments.

Data Analysis

We used Barton’s (2021) Visual Analysis Tool and Lanovaz et al.’s (2019) visual structured criterion (VSC) for alternating treatment designs to determine the presence of a functional relation between experimental conditions and phases. The Visual Analysis Tool (Barton, 2021) is a spreadsheet that guides users through the visual analysis process to help determine whether a functional relation is present and how confident the user can be in that determination. The tool prompts users to consider the overlap between conditions, differentiation between conditions, and the magnitude and trend of differentiation. We computed the nonoverlap of all pairs (NAP; Parker & Vannest, 2009) to assess overlap and judged differentiation between conditions by evaluating stability, trend, and effect size. We assessed stability by determining whether 80% of data points fell within 25% of the median of each phase (Lane & Gast, 2014) and used the split-middle method (White & Haring, 1980) to identify trend. To use the split-middle method, one finds the mid-rate, mid-date, and middle point of the mid-rate and mid-date for each phase; then draws a trend line between the middle point of the mid-rate and mid-date (Ledford & Gast, 2018).

We also used Lanovaz et al.’s (2019) visual structured criterion (VSC) for alternating treatments designs to supplement visual analysis. In contrast to nonoverlap methods used in Barton’s (2021) tool (i.e., NAP; Parker & Vannest, 2009), which compare overlap on a point-by-point basis and do not account for trend, the VSC compares the relative position of points and paths (Lanovaz et al., 2019). To use the VSC, one counts the number of times that a data path and/or points for one treatment condition fall above the path and points for a second treatment condition at each session. Then, that number is compared with a predetermined cutoff value based on the number of data points and comparisons (Lanovaz et al., 2019).

To report a quantitative index of effectiveness (Ledford & Gast, 2018) that captures the magnitude of behavior change between T1 and T2 conditions, we calculated the weighted average difference between success observations (ADISO; Manolov & Onghena, 2018). To do so, T1 and T2 data were entered into https://manolov.shinyapps.io/ATDesign/ where an ADISO score was calculated. The ADISO score is expressed in the same measurement units as the dependent variable. Thus, the ADISO score represents a weighted change in the percentage of on-task behavior between T1 and T2 conditions.

Visual and Statistical Analysis Agreement

The second and third authors conducted independent visual analyses on the graphed data collected for each participant to determine if there were functional relations between baseline and treatment conditions and T1 versus T2 conditions. Barton’s (2021) Visual Analysis Tool was used to examine level changes, trend, variability, overlap, and immediacy of change. The second author earned his PhD in special education and is a board certified behavior analyst-doctoral®. The third author earned her PhD in special education and completed the Institute of Education Sciences/The National Center for Special Education Research (IES/NCSER) Summer Research Training Institute on Single-Case Intervention Research Design and Analysis. After the independent visual analyses were completed, agreement across 66 coding opportunities within the Visual Analysis Tool was 97%.

IOA

To ensure observers remained reliable using the BSP-OT, we gathered IOA data for student on-task behavior and praise characteristics across 27.3% (n = 6) of total observations (n = 22) for Dyad A, and 36.8% (n = 7) of total observations (n = 19) for Dyad B. Similar to observer training, IOA scores were calculated with a priori criterion of acceptable kappa reliability at .80. For Dyad A, baseline κ reliability was .86, .89, and .93 for 33% (n = 3) of observations. During the mastery training phase, overall κ was .91 for 33% (n = 1) of observations. T1 reliability for Dyad A was .93 for 25% (n = 1) of observations. T2 reliability for Dyad A was .92 for 25% (n = 1) of observations.

For Dyad B, baseline κ reliability was .95, .85, and .97 for 60% (n = 3) of observations. During the mastery training phase, kappa reliability was .92 for 25% (n = 1) of observations. T1 reliability was .87 for 25% (n=1) and T2 reliability was .96 for 25% (n= 1) of observations.

Treatment Fidelity

During intervention conditions, data were analyzed daily to confirm the only praise characteristic manipulated was varied and non-varied BSP. Based on previous research on low rate BSP (Jenkins et al., 2015; Markelz, Riden, Floress, et al., 2022) and previous praise rate recommendations (Floress et al., 2020), a minimum of 10 BSP statements toward the target student per 15-min session was set so that sufficient data could be observed. The number of GP statements was recorded to document if anomalous rates of GP occurred during any particular session. A post hoc paired samples t test was conducted on the number of GP statements between T1 and T2 for each Dyad. There was no statistical difference between the two treatments for Dyad A, t(4) = −1.500, p = .208, and Dyad B, t(4) = −0.767, p = .486.

Contingent BSP was measured using the BSP-OT to confirm that consistent BSP contingency was being delivered. Due to the assumption of independence of groups not being met, Wilcoxon signed-rank tests were used to examine post hoc statistical differences in praise characteristics. Results indicated no statistical difference for BSP contingency between T1 and T2 for both Dyads (z = −.074, p = .941). In addition, the post hoc Wilcoxon signed-rank test indicated no significant differences for the number of BSP statements per T1 and T2 sessions for both Dyads (z = −.291, p = .771).

A post hoc paired samples t test did confirm a statistical difference of BSP variety between T1 and T2 for Dyad A, t(4) = −5.48, p = .005, and Dyad B, t(4) = −7.64, p = .002. The Wilcoxon signed-rank analysis confirmed this significant difference in praise variety between T1 and T2 (z = −2.316, p = .021). Treatment fidelity analyses suggest the only praise characteristic manipulated between T1 and T2 was BSP variety.

Results

Visual analysis indicates a positive effect of praise (both varied and non-varied) on participants’ on-task behavior, but fails to indicate a clear difference in effectiveness between T1 and T2 for either participant. Alli’s data are presented in Figure 1 and Brice’s data are presented in Figure 2. Teachers’ praise data (number of BSP statements and variety percentage) are summarized in Tables 1 and 2.

Figure 1.

Percentage of On-Task Behavior Across Conditions for Alli.

Figure 2.

Percentage of On-Task Behavior Across Conditions for Brice.

Table 1.

Praise Statement Data for Dyad 1: Ms. Alisha and Alli.

Session	Phase	Treatment	BSP frequency	Percentage of variety
1	Baseline		0	0
2	Baseline		0	0
3	Baseline		1	0
4	Baseline		1	0
5	Baseline		0	0
6	Baseline		0	0
7	Baseline		0	0
8	Baseline		1	0
9	Baseline		0	0
10	Training		14	36
11	Training		12	41
12	Training		13	46
13	Intervention	T2	14	14
14	Intervention	T1	13	38
15	Intervention	T1	15	33
16	Intervention	T2	13	15
17	Intervention	T2	14	14
18	Intervention	T1	14	36
19	Intervention	T2	10	0
20	Intervention	T1	15	40
21	Intervention	T2	12	0
22	Intervention	T1	13	46

Note. BSP = behavior-specific praise; T1 = varied praise condition; T2 = non-varied praise condition.

Table 2.

Praise Statement Data for Dyad 2: Ms. Brenda and Brice.

Session	Phase	Treatment	BSP frequency	Percentage of variety
1	Baseline		0	0
2	Baseline		1	0
3	Baseline		2	0
4	Baseline		2	0
5	Baseline		1	0
6	Training		12	41
7	Training		8^a	37
8	Training		12	58
9	Training		13	46
10	Intervention	T1	11	63
11	Intervention	T1	9^a	66
12	Intervention	T2	11	0
13	Intervention	T2	12	16
14	Intervention	T1	11	45
15	Intervention	T2	13	15
16	Intervention	T1	10	60
17	Intervention	T2	11	0
18	Intervention	T1	13	54
19	Intervention	T2	12	16

Note. BSP = behavior-specific praise; T1 = varied praise condition; T2 = non-varied praise condition.

Participant did not meet minimum BSP criterion and was retrained prior to subsequent session.

Dyad A

During baseline, Ms. Alisha provided an average of less than 1 BSP statement to Alli per observation (range 0–2), and Alli was on-task an average of 49% of the time (range 27%–76%). Alli’s baseline data were variable, and a split middle trend analysis indicated a decelerating trend. During training, Ms. Alisha provided Alli an average of 13 BSP statements per observation (range 12–14) and her BSP statements averaged 41% variety (range: 36%–46%). Alli’s percentage of time-on-task increased to an average of 70% (range: 60%–82%).

During T1 sessions (i.e., varied praise), Ms. Alisha provided an average of 14 BSP statements per observation (range: 13–15) with an average of 39% variety (range 33–46%). During T2 sessions (i.e., non-varied praise), she provided an average of 13 BSP statements per observation (range 10–14) with an average of 9% variety (range: 0–15%). Alli’s average percentage of time on task rose to 80% (range 64–93%) during T1 sessions and 74% (range 62–80%) during T2 sessions. Split middle trend analyses indicated that Alli’s on-task behavior showed an accelerating trend in both T1 and T2 sessions.

We used visual analysis guided by Barton’s (2021) Visual Analysis Tool to determine that a functional relation was present between both treatment conditions and on-task behavior. For both T1 and T2, there was low overlap between data in the baseline and praise conditions (NAP = 93% for both comparisons), and changes in level and trend provided clear differentiation between baseline and T1 and T2. The level of on-task behavior increased in T1 and T2 and the trend changed from decelerating during baseline to accelerating.

Visual analysis and the VSC (Lanovaz et al., 2019) fail to indicate a functional relation between T1 and T2 conditions. There was a high degree of overlap between T1 and T2 data for Alli (NAP = 30%) and moderate overlap between T2 and T1 data (NAP = 70%). Given undifferentiated intervention conditions, the Visual Analysis Tool indicated no functional relation between T1 and T2 for Alli. The VSC (Lanovaz et al., 2019) supported this determination. The T1 data points and/or data path were higher than T2 in 7 out of 8 comparisons, which falls below the cutoff of 8 out of 8 comparisons (Lanovaz et al., 2019). Although a functional relation between treatments was unestablished, the ADISO calculation demonstrated that T1 had a +8.5% difference in on-task behavior compared with T2.

Dyad B

During baseline, Ms. Brenda provided an average of 1 BSP statement for Brice per observation (range 0–2). Brice was on-task an average of 28% of sessions (range 22–36%) and his baseline data were stable. During training, Ms. Brenda provided an average of 12 BSP per observation (range 8–13) and her BSP statements averaged 46% variety (range 37–58%). Brice’s percentage of time on task increased to an average of 41% during training (range 33–53%).

During T1 sessions (i.e., varied praise), Ms. Brenda provided an average of 11 BSP statements per observation (range 9–13) with an average of 58% variety (range 45–66%). During T2 sessions (i.e., non-varied praise), she provided an average of 12 BSP statements per observation (range 11–13) with an average of 9% variety (range 0–16%). Brice’s average percentage of time on task was 66% (range 42–84%) during T1 sessions and 64% (range 44–80%) during T2 sessions. Split middle trend analyses indicated that Brice’s on-task behavior showed an accelerating trend in both T1 and T2 sessions.

Barton’s (2021) Visual Analysis Tool suggested a functional relation between the intervention and on-task behavior. For both T1 and T2, there was no overlap between data in baseline and treatment conditions (NAP = 100% for both comparisons); and differences in level and trend provide clear differentiation between baseline and each treatment condition.

There was substantial overlap in Brice’s data in the two treatment conditions (NAP=46% for T1 vs. T2 and NAP=54% for T2 vs. T1) and undifferentiated conditions. As a result, the Visual Analysis Tool indicated no functional relation between T1 and T2. We were unable to confirm this determination using the VSC because there were 7 comparison points between conditions and 8 are required for VSC (Lanovaz et al., 2019). The AIDSO calculation demonstrated that T1 had a +1.6% difference in on-task behavior compared with T2.

Discussion

Results from this study are the first in teacher praise research to examine the efficacy of praise variety on student on-task behaviors. Increases in student on-task behaviors between baseline and both T1 and T2 conditions support consensus in the research community that BSP may positively affect a variety of student behaviors (Ennis et al., 2020). Although praise research has been conducted for decades, few methodologically sound studies exist to classify BSP as an evidence-based practice according to the Council for Exceptional Children (CEC) and the What Works Clearinghouse quality indicators and standards (Moore et al., 2019). Results from this study contribute to the growing body of rigorous research which supports BSP as a potential evidence-based practice according to the CEC guidelines (Royer et al., 2019).

The visual and statistical examination of varied versus non-varied praise on student on-task behaviors indicates no functional relation in efficacy. Varied praise did produce slightly higher on-task behavior percentages for both student participants; however, insufficient change to claim differences in efficacy. Given the novelty of praise variety research, we recommend further examination between varied and non-varied praise to build a larger literature base. Because challenges with classroom management contribute to teacher stress and decreased self-efficacy (Bottiani et al., 2019) and BSP is an effective, efficient, low-intensity strategy (Ennis et al., 2018), further research into BSP characteristics is warranted.

Future research should acknowledge a potential ceiling effect for varied praise percentages. Participants in this study used a total of 13 different praise statements across all sessions (i.e., good, great, awesome, thank you, appreciate, like, wonderful, love, nice, perfect, fantastic, excellent, super). On average, teacher participants used only five varied statements during T1 conditions. With a ceiling effect on the number of unique statements, fluctuations in total statements may misrepresent desired levels of percent varied. For example, if a teacher uses two unique BSP statements out of 4 total BSP statements; the percent varied for that session is 50%. However, if the teacher during the next observation uses five unique BSP statements, but 15 statements in total, the percent varied for that session is 33%. The use of more BSP statements is encouraged and aligned with best practices; however, the teacher is “penalized” with a lower percent variety. In other words, the more BSP statements a teacher delivers, the more difficult it is to have higher percentages of praise variety.

In addition to the ceiling effect on the number of unique statements, previous research suggests cognitive load may also play a factor in limiting the number of unique statements (Markelz, Scheeler, et al., 2019). In a classroom with competing stimuli, teachers are often focused on their pedagogy, curricular content, and reacting to management issues (Maag, 2001). One explanation for consistently higher GP rates over BSP rates is that it takes less cognitive effort to deliver “Good job” as opposed to identifying a specific behavior to praise (Markelz, Riden, Floress, et al., 2022). The same may be true for delivering unique BSP statements. Teachers may deliver repeated BSP statements because it is easier than producing a unique BSP statement every time. Based on these realities of delivering praise in a busy classroom, it is unrealistic to expect teachers to use proportionally higher numbers of unique statements as total statements increase.

In a study that examined teachers’ natural rates of praise characteristics, praise variety was around 50% on average (Markelz, Riden, Floress, et al., 2022). Participants within that study, however, delivered meager total BSP statements (M = 1.6 per 15-min observation). Thus, simply delivering two unique statements out of three total BSP statements would result in a 66% variety. Teacher participants in the current study averaged 39% and 58% during varied praise treatment conditions; yet, also averaged 14 and 11 BSP statements, respectively. The question arises, which is more important, praise variety or the number of BSP statements? Preliminary evidence from this study suggests the number of total BSP statements supersedes higher percentages of praise variety. However, we caution any definitive declarations on the importance of praise variety through descriptive words as a salient characteristic on student behaviors until more research is conducted.

Future researchers of praise variety should consider that although the BSP-OT measures praise variety by differentiation in descriptive words, Floress and Beschta (2018) identified various student behaviors as means of varying praise statements. For explicit operationalized behaviors (e.g., hand raising), descriptive word variation may be a more appropriate method of varying praise. For example, “Great job raising your hand before speaking,” and “I really appreciate you raising your hand and not calling out.” Yet for more ambiguous behaviors, like “on-task,” descriptive word differentiation and various student behaviors could contribute to praise variety. For example, a teacher could say “I love how focused you are on your assignment right now,” or “Great job working hard today.” Both praise statements target on-task behavior and use different descriptive words (i.e., love and great). The statements also praise two different behaviors (i.e., focused and working hard). Although this study lacked control for the praise of varied student behaviors (as long as they targeted on-task), future inquiry should examine possible interactions between these two modes of praise variety and their possible contribution to praise efficacy.

Implications

Current evidence that teachers may use low-rate BSP (Floress et al., 2022) suggests teachers might not connect best practice with actual practice. Multiple interventions have been documented to successfully increase teachers’ BSP rates with components like didactic training, self-monitoring, performance feedback, and tactile prompting (Markelz et al., 2018; Zoder-Martell et al., 2019). Preservice teacher preparation programs or professional development for in-service teachers must provide opportunities for teachers to learn about and practice BSP to counter persistent low-level use. Future research should continue to examine the maintenance and generalized use of BSP following intervention to provide guidance on best practices in sustainable and efficient teacher training.

Markelz, Riden, Floress, and colleagues (2022) discussed future research to determine normative guides of praise characteristics to provide recommendations to teachers and teacher trainers. Regarding frequency of BSP delivery, ranges have been recommended to offer flexibility such as 6 to 10 BSP statements class wide per 15 min (Sutherland et al., 2000). Other researchers have recommended similar ranges like 18 to 30 BSP class-wide per hour (Floress et al., 2020). At the secondary level, researchers have recently identified using BSP once per 2 min as superior to once per 4 min in increasing academic engaged behavior and reducing disruptive behavior (O’Handley et al., 2022). In line with previous research, we also recommend at least 1 BSP statement class-wide every 2 min. However, a denser schedule of reinforcement may be appropriate for a particular student given the frequency of student behavior and reinforcement goals.

When discussing a normative guide dictating optimal praise variety, either in professional development or in future research, we suggest using a ratio (unique BSP statements to total BSP statements) as opposed to a percentage. Unsimplified ratios are more descriptive than percentages. An unsimplified ratio provides the number of unique BSP statements as well as the number of total BSP statements, while percentages obscure actual counts and provide only a proportion of the total. Using un-simplified ratios underscores the importance of the rate of praise as well as the proportion of unique praise statements. We acknowledge, however, that more research is needed to examine varying ratio effects on student behaviors before definitive guidelines are established.

Limitations

There are several limitations to this study that require discussion. First, the BSP-OT developed and assessed for reliability (Markelz et al., 2020) was amended for this study. To measure teacher praise characteristics and their effect on student on-task behavior, we added an additional row to the tool that allowed data recorders to capture student on-task data with whole interval recording. Tool amendment may have impacted reliability; however, acceptable IOA data suggest the tool was reliable and recorded teacher praise characteristics as well as student on-task behavior.

Second, we collected data using a whole interval recording approach, which can underestimate the true occurrence of behavior and thus is likely to underestimate the level of the behavior (Fiske & Delmolino, 2012). A behavior is marked as occurring only if it occurs for the duration of the entire interval. For example, a student may have been on task for 19-s within an interval, however, the 1-s non-response (i.e., off-task) is coded for the entire interval as a non-occurrence. In other words, on-task behaviors that occurred for a fraction of the interval escape capture. Although we considered an underestimation of behavior an acceptable limitation, future research should consider other data collection methods (e.g., partial interval recording).

Third, we used the VSC for alternating treatments design (Lanovaz et al., 2019) as a supplement to visual analysis in interpreting Alli’s data, but we were unable to use the procedure for Bryce’s data. We followed single case design quality guidelines and used random assignment of treatment condition to session and collected data across five sessions for each intervention (T1 and T2). Based on random assignment, Bryce received T1 for his first two intervention sessions, but this session order in combination with the limited number of total sessions resulted in only seven possible comparisons between T1 and T2. We were unable to use the VSC as a supplement for Bryce’s data as the VSC requires a minimum of 8 comparisons between conditions to make a determination about functional relation. We recommend researchers adhere to the comparison requirement or extend the total number of sessions.

Finally, we did not collect social validity data from teacher participants on their perceived differences between varied and non-varied praise conditions. Measuring teacher’s acceptability of treatment conditions could have provided a more nuanced examination of willingness to deliver higher percentages of praise variety. Future research should include social validity measures when conducting praise variety research to explore potential barriers like cognitive overload.

Conclusion

This study’s findings add to the evidence base supporting BSP as an effective practice to change student behavior. Participating teachers significantly increased their rate of BSP delivery following training, and visual analysis of results indicated that increased praise was correlated to increased on-task behavior for student participants. In addition, characteristics of praise, such as variety, may increase praise efficacy. Although a functional relation between varied and non-varied praise was not identified, preliminary results from this study warrant further investigation into praise variety as a salient characteristic of BSP.

Supplemental Material

sj-pdf-1-pbi-10.1177_10983007221126568 – Supplemental material for The Effects of Varied and Non-Varied Praise on Student On-Task Behaviors

Supplemental material, sj-pdf-1-pbi-10.1177_10983007221126568 for The Effects of Varied and Non-Varied Praise on Student On-Task Behaviors by Andrew M. Markelz, Benjamin S. Riden, Stephanie Morano, Alicia L. Hazelwood and April M. Taylor in Journal of Positive Behavior Interventions

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 iD

Benjamin S. Riden

Supplemental Material

Supplemental material for this article is available on the Journal of Positive Behavior Interventions website with the online version of this article.

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