Student Demographics:

• White: Approximately 64%

• Hispanic: Approximately 23%

• African American: Approximately 6%

• Asian: Approximately 1%

• Two or More Races: Approximately 4%

• Native American: Less than 1%

• Pacific Islander: Less than 1%
  

Additional Information:

• Free or Reduced-Price Lunch Eligibility: Approximately 35% of students are eligible.
  

Student-Teacher Ratio: Approximately 13:1, aligning with the Wisconsin state average.

In districts like Waukesha, Wisconsin, these programs integrate English Language Learners (ELLs) and native English speakers, promoting equity by valuing home languages and cultures. Research indicates that participants often match or exceed peers in monolingual programs academically, while gaining cognitive and sociocultural advantages in a globalized society.

Waukesha served as an important station on the Underground Railroad and was a center of abolitionist activity before the Civil War, including the publication of the antislavery newspaper The American Freeman from 1844 to 1848.

When a student walks into class carrying anxiety, stress, or emotional challenges, it shows up in their work, their behavior, and even how they interact with their peers. I’ve learned that supporting their emotional health isn’t a "nice extra"—it’s essential. Through this research, I’m hoping to better understand what students are going through and find practical ways to support them more effectively.

My goal isn’t just to help them succeed in class, but to help them feel safe, supported, and confident—because that’s when real learning happens. If we want to build stronger students, we have to start by paying attention to how they feel.

I spend three hours daily with them, and during this time, we create a connection that helps me notice what motivates them and how they respond to school instructions.

In this study, I’m exploring how focusing on and recognizing positive behavior can help my students become more responsible and engaged learners rather than defensive or offensive.

Besides brain breaks I have tried small talks and positive phone calls in a very limited ways.

The participant group comprised two male and two female students, all aged 13 years.

Pseudonyms were assigned to protect participant confidentiality: Emiliano, Aymar, Jaiden, and Sophia.

These students were classified as English Language Learners (ELLs), with foundational proficiency in Spanish as their heritage language, and demonstrated academic performance below grade-level benchmarks across core subjects.

This approach ...

1. represented a practitioner-led, classroom-based inquiry that enabled me to investigate phenomena in my own teaching context and implement meaningful changes to improve it.

2. allowed me to address an authentic classroom challenge and provided strong motivation for change, as I routinely observed recurring patterns in student behavior and sought to explore how increased positive praise (the intervention) might influence responsibility and engagement among my targeted 8th-grade students.

3. embodied a cyclical and participatory process, as I was fully immersed in the study rather than observing from afar—I systematically planned, acted, observed, and reflected throughout the teaching and intervention implementation.

4. facilitated systematic data collection to evaluate the impact of the intervention, as I quantitatively tracked the frequency of on-task and off-task behaviors among the four participants to assess whether positive praise reduced disruptive actions.

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1 - Kemmis, S., McTaggart, R., & Nixon, R. (2014). The Action Research Planner: Doing Critical Participatory Action Research. Springer.

Phase A1: Initial Baseline I observed and recorded the target disruptive behaviors in the natural classroom environment. I collected data consistently over three days using a behavior checklist or frequency tally. This step helped me see how students typically behaved before the intervention, providing a clear baseline for future comparisons.

Phase B1: First Intervention I introduced the positive praise intervention and applied it consistently each day. I continued tracking the same disruptive behaviors for three days using the identical checklist. This allowed me to monitor initial changes and assess the immediate impact of praise on engagement.

Phase A2: Return to Baseline (Withdrawal) I completely withdrew the positive praise intervention and returned to standard classroom routines for three days. Data collection continued unchanged. By observing whether disruptive behaviors reverted to baseline levels, I could determine if prior improvements were truly linked to the praise.

Phase B2: Second Intervention (Replication) I reintroduced the positive praise intervention, delivering it in the same manner as Phase B1.Behavior data were collected over another three days.

Extreme case sampling was further employed to select the four students, who exhibited pronounced needs for ongoing motivation, reassurance, and positive reinforcement to maintain engagement and responsibility. In the absence of such support, they tended to disengage from learning activities and display defensive behaviors, contributing to a tense and disrespectful classroom environment.

The behavior checklist¹ enabled me to:

• systematically track specific, predefined behaviors (e.g., Disruptive, Off-Task, Defiant) across students and days;

• contrast conditions ("No Positive Praise Given" vs. "Positive Praise Given" weeks), which allowed me to compare behavior patterns before/after the Postive Praise intervention;

• collect baseline data, monitor the use of the intervention, and evaluate the observed behaviors;

• keep track of the behaviors of my targeted students —Emiliano, Aymar, Jaiden, and Sophia — and how they responded to the positive praise intervention, even in my busy classroom.

• collect countable data that showed patterns in behavior change.

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1 - A template of this checklist is located [HERE].

During the data collection process, I gained valuable insights into how adolescents behave during the school day. By paying close attention to rewards and students’ intentions, I was able to observe clear differences in their reactions—both when they put effort into producing high-quality work and when they disengaged or gave up. The group of students I am working with tends to be highly reactive, and I noticed that many of their responses stem from the mind’s natural defense and offense stages. Implementing intentional rewards and consistently focusing on positive behaviors contributed to a noticeable reduction in emotional outbursts and helped create a more supportive classroom environment.

Personal Reflection on Teaching Practice

Throughout this process, I also reflected on my own strengths and areas for growth as a teacher. This was my first year leading a classroom independently, as last year I had the support of a coach who co-taught with me. Engaging in four weeks of structured data collection and deep reflection allowed me to better understand my instructional decisions and their impact on student behavior.

Evidence of Growth and Concluding Thoughts

At the end of this cycle, I can clearly see and feel the growth—both in my students and in myself. I conclude that intentional rewards, a focus on positive behavior, and systematic data collection played a significant role in shifting the dynamics of my classroom in a meaningful and sustainable way.

Creswell, J. W. (2015). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (5th ed.). Pearson.Hott, B. L., Brigham, F. J., &

Peltier, C. (2021). Research methods in special education. SLACK Incorporated.

Procedure for Implementing an ABAB Design

1. Defined the Target Behavior: Clearly identified and described the specific behavior (dependent variable) to be measured and changed.

2. Selected the Intervention: Chose a well-defined, replicable intervention (independent variable) to influence the target behavior.

3. Phase A1 – Initial Baseline: Observed and recorded the behavior under natural conditions without the intervention. Collected consistent data until a stable pattern emerged.

4. Phase B1 – First Intervention: Introduced and applied the intervention consistently while continuing to measure the behavior.

5. Phase A2 – Return to Baseline: Withdrew the intervention and measured the behavior to check for reversion to baseline levels.

6. Phase B2 – Second Intervention: Reintroduced the intervention identically and monitored for replication of earlier improvements.

7. Data Collection and Monitoring: Used consistent tools, timing, and conditions to record data continuously across all phases.

8. Analyzed Results: Compared data across phases (A1, B1, A2, B2) for patterns—improvement during intervention phases and reversion during withdrawal—to evaluate the intervention’s effectiveness.

• is a primary, low-effort tool for delivering immediate positive reinforcement—e.g., verbally acknowledging on-task behavior or effort, which increases the likelihood of those behaviors recurring;

• and acknowledgment systems (e.g., specific, contingent verbal praise) create a positive school climate, teaching and reinforcing expectations for all students;

• is tailored and genuine, fostering a student's intrinsic motivation, self-efficacy, and belonging.

The selected intervention was the consistent use of positive praise. Each day, I provided each targeted student with at least one specific positive comment focused on their effort or attitude (e.g., “I’m glad you’re putting effort into the classwork,” “I notice you’re trying your best,” or “I appreciate your positive attitude today”).

Praise emphasized effort rather than innate ability, aligning with growth-mindset principles.

This approach drew from Positive Behavioral Interventions and Supports (PBIS), which prioritizes proactive, preventive strategies over reactive discipline. When implemented faithfully, PBIS has been shown to improve student behavior and classroom climate.

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1 - The idea of positive praise—using verbal approval or specific compliments to reinforce desired behaviors—originated in behaviorist psychology, particularly the work of B.F. Skinner² in the mid-20th century.

2 - Skinner, B. F. (1953). Science and Human Behavior. Macmillan.

Behaviors

1. Disruptive (D1)¹: talking, making noises, interrupting instruction.

2. Defiant/Disrespectful (D2): arguing, refusing directions, negative attitude.

3. Off-Task (O1): avoiding work, distracting others, not participating.

4. Peer Conflict (P1): teasing, blaming, arguing with classmates.

5. Technology Misuse (T1): using devices for non-academic purposes.

6. Dishonesty (H1): lying, cheating, hiding mistakes.

7. Emotional Outburst (E1): yelling, walking out, shutting down.

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1 - The letters in parentheses are the identification codes I used during data collection.

Key Findings:
Higher teacher praise-to-reprimand ratios (PRRs) were strongly linked to better outcomes in a linear fashion (no optimal threshold found):

• Class-wide on-task behavior increased by 60–70 percentage points as PRRs rose from near 0:1 to mostly/all praise.

• For students at risk for emotional/behavioral disorders: on-task behavior rose 60–70%, disruptive behavior dropped sharply (near zero at high PRRs), and grades improved up to one full letter grade.

Implications:
Increasing positive praise relative to reprimands is a simple, low-effort strategy that significantly improves engagement, reduces disruptions, and boosts academic performance in middle schools—especially for at-risk students. Teachers should aim for at least a 1:1 ratio, ideally much higher.

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Ennis, R. P., Royer, D. J., Lane, K. L., & Dunlap, K. D. (2020). Behavior-Specific Praise in Pre-K–12 Settings: Mapping the 50-Year Knowledge Base. Behavioral Disorders, 45(3), 131–147.

Ethics is thus said to be the science of conduct. As a matter of fact, it deals with certain standards of human conduct and morals." (2016, p. 79)

With this idea in mind, I followed Cohen et al.’s (2013) Seven Ethical Guidelines for Researchers when I planned and executed my research. 

I pledge that I …

1. did not coerce my participants into participating;

2. obtained informed consent from each participant;

3. provided the right to withdraw from my study and made it explicit;

4. explained the benefits and/or “degree of harm” that may come to my participants;

5. guaranteed confidentiality, anonymity and non-traceability of the collected data;

6. recognized and controlled my bias; and most importantly,

7. did not promote an agenda.

Overall Pattern

• Total possible student-days: 4 students × 17 days = 68

• Total off-task marks ("x"): 47

• Off-task behavior occurred on 69% of all student-days – slightly higher than disruptive (D1) and equally pervasive early on.

By Intervention Condition

• No Praise weeks (Weeks 1 & 3: 7 days): Off-task on 28/28 student-days (100%) – universal across all students.

• Positive Praise weeks (Weeks 2 & 4: 10 days): Off-task on 19/40 student-days (48%)

Strong overall reduction: Positive praise weeks saw off-task behavior roughly cut in half compared to No Praise weeks. This is one of the clearest intervention effects in your data.

Individual Student Patterns

• Emiliano: Off-task on 15/17 days (88%). Present every day in Week 1, most days in Week 2 (4/5), both in Week 3, and 4/5 in Week 4. Some reduction during praise (especially Week 2), but still very frequent.

• Aymar: Off-task on 14/17 days (82%). Daily in Week 1, 4/5 in Week 2, both in Week 3, mixed in Week 4. Noticeable gaps during praise weeks.

• Jaiden: Off-task on 12/17 days (71%). Daily in Week 1, only 3/5 in Week 2 (biggest single-week drop), 1/2 in Week 3, 4/5 in Week 4. Praise appears highly effective for him here.

• Sophia: Off-task on 14/17 days (82%). Daily in Week 1, 3/5 in Week 2, both in Week 3, 3/5 in Week 4. Consistent moderate reduction during praise periods.

Big-Picture Takeaways

1. Positive praise had a substantial positive impact on reducing off-task behavior group-wide — from universal (100%) in No Praise to about half during Positive Praise.

2. All four students improved during praise weeks, with Jaiden showing the strongest response (sharp drop in Week 2) and Emiliano the least (still high frequency, though some days cleared).

3. Compared to Disruptive (D1):

   • Off-task was more uniformly responsive to praise across students.

   • It started at ceiling (everyone every day in Week 1) and showed clearer gaps in green columns.

4. The visual "thinning" of "x" marks in Weeks 2 and 4 (especially for Jaiden and Sophia) provides compelling evidence that intentional positive reinforcement increased engagement and participation for most students.

5. For Emiliano and Sophia, while off-task decreased, it remained frequent enough to affect classroom flow — suggesting praise helped but was not sufficient alone for full engagement.

Overall Pattern

• Disruptive incidents were extremely common across all four students and nearly all 17 school days.

• Total possible student-days: 4 students × 17 days = 68

• Total disruptive marks ("x"): 44

• This means disruptive behavior occurred on 65% of all student-days – a dominant challenge in the classroom.

By Intervention Condition

• No Praise weeks (Week 1 red + Week 3 red): 7 days total

  • Disruptive marks: 26 out of possible 28 student-days (93%)

• Positive Praise weeks (Week 2 green + Week 4 green): 10 days total

  • Disruptive marks: 18 out of possible 40 student-days (45%)

Clear overall reduction: When positive praise was intentionally given, the group was roughly half as likely to exhibit disruptive behavior on any given day.Individual

Student Patterns

• Emiliano: Disruptive on 15 of 17 days (88%). Present almost every day regardless of condition (5/5 in Week 1, 5/5 in Week 2, 1/2 in Week 3, 4/5 in Week 4). Praise had little to no visible effect on his disruptive talking/noises.

• Aymar: Disruptive on 9 of 17 days (53%). Some reduction during praise weeks, but still frequent.

• Jaiden: Disruptive on 11 of 17 days (65%). Noticeable drop during Positive Praise periods (especially Week 2: only 3/5 days vs. 5/5 in Week 1).

• Sophia: Disruptive on 11 of 17 days (65%). Similar to Jaiden — high in Week 1, reduced in Week 2, low in short Week 3, moderate in Week 4.

Big-Picture Takeaways

1. Positive praise was associated with a substantial group-level decrease in disruptive behavior (from ~93% to ~45% of student-days). This supports your reflection that intentional positive reinforcement helped create a less tense, more respectful classroom atmosphere.

2. The intervention worked well for Jaiden and Sophia, and moderately for Aymar.

3. Emiliano stands out as an outlier — his disruptive behavior persisted at near-ceiling levels throughout. This suggests that while whole-class positive praise helped the majority, Emiliano likely needed additional, more targeted strategies (e.g., individual behavior plan, proximity control, private verbal cues, or deeper relationship-building).

4. The visual density of "x" marks tapering after Week 1 (especially in the green columns) provides strong illustrative evidence of classroom improvement over time — even if full elimination wasn't achieved.

Overall Pattern

• Total possible student-days: 4 students × 17 days = 68

• Total misuse marks ("x"): 44 (from precise count in this view)

• Misuse occurred on 65% of all student-days – comparable to disruptive and off-task behaviors.

By Intervention Condition

• No Praise weeks (Weeks 1 & 3: 7 days): Misuse on 26/28 student-days (93%)

• Positive Praise weeks (Weeks 2 & 4: 10 days): Misuse on 18/40 student-days (45%)

Consistent strong reduction: Positive praise weeks show misuse roughly halved, mirroring the pattern seen in off-task behavior.

Individual Student Patterns

• Emiliano: Misuse on 15/17 days (88%). Daily in Week 1, 4/5 in Week 2, 1/2 in Week 3, 5/5 in Week 4. Very little change — persistent across conditions.

• Aymar: Misuse on 14/17 days (82%). Daily in Week 1, 4/5 in Week 2, both in Week 3, 4/5 in Week 4. Some reduction during praise, but still high.

• Jaiden: Misuse on 9/17 days (53%). Daily in Week 1 (5/5), only 2/5 in Week 2, 0/2 in Week 3, 2/5 in Week 4. Strongest response — misuse sharply dropped and stayed relatively low during/after praise introduction.

• Sophia: Misuse on 12/17 days (71%). Daily in Week 1, 2/5 in Week 2, 1/2 in Week 3, 4/5 in Week 4. Clear early reduction with praise, though some increase in Week 4.

Big-Picture Takeaways

1. Positive praise was associated with significantly better device management for the group as a whole — from near-universal misuse in No Praise to about half during Positive Praise.

2. Jaiden benefited most dramatically — his device misuse nearly vanished during the initial praise week and remained low.

3. Emiliano showed almost no improvement — technology misuse remained a daily issue regardless of praise, aligning with his patterns in disruptive and off-task behaviors.

4. The visual thinning of "x"s in the green columns (especially Week 2) is striking for Jaiden and Sophia, providing clear evidence that praise helped foster greater responsibility with technology.

5. Technology misuse appears closely linked to motivation and engagement — when students received regular positive reinforcement, they were more likely to stay on academic tasks with their devices.

Cross-Behavior Comparison (D1, O1, T1)

• Most responsive to praise: Off-Task (O1) and Technology Misuse (T1) — both showed ~50% reduction group-wide and strong individual gains for Jaiden/Sophia/Aymar.

• Least responsive: Disruptive talking/noises (D1) — improved for three students but remained near-ceiling for Emiliano.

• Emiliano's profile: Consistently high across D1, O1, and T1 — suggesting his needs go beyond positive reinforcement (possible attention-seeking, impulse control, or relationship factors).

• Overall intervention impact: Praise clearly supported increased focus, participation, and appropriate technology use for the majority, contributing to the more supportive environment you observed.

Overall Pattern

• Total possible student-days: 4 students × 17 days = 68

• Total defiant marks ("x"): 31

• Defiance occurred on 46% of all student-days – noticeably lower frequency than D1 (disruptive ~65%), O1 (off-task ~69%), or T1 (tech misuse ~65%). This was the least common of the major tracked behaviors.

By Intervention Condition

• No Praise weeks (Weeks 1 & 3: 7 days): Defiance on 20/28 student-days (71%)

• Positive Praise weeks (Weeks 2 & 4: 10 days): Defiance on 11/40 student-days (28%)

Strongest relative reduction: Defiance dropped by more than half (from 71% to 28%) during Positive Praise – the largest proportional improvement of any behavior category.

Individual Student Patterns

• Emiliano: Defiance on 11/17 days (65%). Daily in Week 1 (5/5), 4/5 in Week 2, 1/2 in Week 3, 1/5 in Week 4. Clear reduction during praise periods – one of the few behaviors where Emiliano showed meaningful improvement.

• Aymar: Defiance on 7/17 days (41%). Sparse overall – 3/5 in Week 1, 3/5 in Week 2, 0/2 in Week 3, 1/5 in Week 4. Low baseline, stayed low.

• Jaiden: Defiance on 9/17 days (53%). 4/5 in Week 1, 3/5 in Week 2, 0/2 in Week 3, 2/5 in Week 4. Reduced during praise, especially in short Week 3.

• Sophia: Defiance on 10/17 days (59%). Daily in Week 1, 2/5 in Week 2, 1/2 in Week 3, 2/5 in Week 4. Strong early reduction with praise introduction.

Big-Picture Takeaways

1. Positive praise had the most pronounced effect on reducing defiance/disrespect – the behavior most directly tied to classroom climate and relationships. This strongly supports your reflection about creating "a more supportive classroom environment" and reducing "emotional outbursts" and "tense, disrespectful atmosphere."

2. Even Emiliano improved noticeably here – unlike his persistent patterns in disruptive talking (D1), off-task (O1), and tech misuse (T1). Positive reinforcement appeared particularly effective at curbing his arguing/refusals/negative attitude.

3. The visual pattern shows dense marks in Week 1 (red) that thin dramatically in Week 2 (green) for most students, with relatively few marks afterward.

4. Defiance seems highly responsive to relational cues like praise and affirmation – more so than passive avoidance behaviors (off-task/tech) or active disruption (talking/noises).

5. This lower-frequency but high-impact behavior (when it occurs) showed the clearest link to your intervention, suggesting intentional positive reinforcement built mutual respect and compliance.

Emerging Overall Story Across Behaviors

• Praise worked best on: Defiant/disrespectful (D2) and relational climate; also strongly on off-task (O1) and tech misuse (T1) for most students.

• Praise had limited effect on: Active disruptive talking/noises (D1), especially for Emiliano.

• Emiliano's unique profile: Improved in defiance but not in talking, off-task, or tech – indicating praise helped his attitude/compliance but not his impulse to interrupt or disengage.

• Group progress: Across nearly all behaviors, Positive Praise weeks were markedly better – supporting your conclusion that "intentional rewards, a focus on positive behavior, and systematic data collection played a significant role in shifting the dynamics."

Overall Pattern

• Total possible student-days: 4 students × 17 days = 68

• Total outburst marks ("x"): 23

• Outbursts occurred on 34% of all student-days – the lowest frequency among the major categories (compared to ~65–69% for D1/O1/T1 and ~46% for D2). These were relatively rare but significant when they happened.

By Intervention Condition

• No Praise weeks (Weeks 1 & 3: 7 days): Outbursts on 17/28 student-days (61%)

• Positive Praise weeks (Weeks 2 & 4: 10 days): Outbursts on 6/40 student-days (15%)

Dramatic reduction: Outbursts dropped by more than three-quarters during Positive Praise – one of the strongest intervention effects in your data.

Individual Student Patterns

• Emiliano: Outbursts on 7/17 days (41%). 3/5 in Week 1, 2/5 in Week 2, 1/2 in Week 3, 1/5 in Week 4. Noticeable decrease after praise introduction, though still present occasionally.

• Aymar: Outbursts on 9/17 days (53%). Daily in Week 1 (5/5), 2/5 in Week 2, 1/2 in Week 3, 1/5 in Week 4. Strong early reduction with praise.

• Jaiden: Outbursts on 6/17 days (35%). 4/5 in Week 1, 1/5 in Week 2, 0/2 in Week 3, 1/5 in Week 4. Sharpest drop – nearly eliminated during praise periods.

• Sophia: Outbursts on 8/17 days (47%). Daily in Week 1, 1/5 in Week 2, 1/2 in Week 3, 1/5 in Week 4. Clear reduction starting in Week 2.

Big-Picture Takeaways

1. Positive praise was highly effective at reducing emotional outbursts – from frequent in No Praise weeks to rare during Positive Praise. This directly supports your observation of "a noticeable reduction in emotional outbursts" when focusing on positive behaviors.

2. All four students showed fewer outbursts during praise periods, with Jaiden again responding most dramatically (almost complete elimination after Week 1).

3. Even Emiliano, who showed persistence in other behaviors (D1, O1, T1), had fewer outbursts with praise – suggesting positive reinforcement helped de-escalate his emotional reactivity.

4. The visual pattern is striking: dense marks clustered in Week 1 (red), with very sparse "x"s afterward (especially in green columns). This illustrates a rapid shift toward emotional regulation.

5. Outbursts appear strongly tied to the classroom emotional climate. Intentional praise likely fostered greater feelings of support and validation, preventing escalation into yelling, shutdown, or walking out.

Overall Story Across All Behaviors

• Strongest praise effects: Emotional Outbursts (E1) and Defiant/Disrespectful (D2) – both relational/intense behaviors showed the largest reductions.

• Solid effects: Off-Task (O1) and Technology Misuse (T1) – engagement-focused behaviors halved.

• Limited effect: Disruptive talking/noises (D1) – persistent for Emiliano, moderate improvement for others.

• Group-wide progress: Every behavior category improved during Positive Praise weeks, contributing to the "more supportive classroom environment" you described.

• Individual nuance: Jaiden and Sophia often showed the strongest gains; Aymar moderate; Emiliano improved in attitude/emotion (D2/E1) but needed more for talking/off-task.

The data from the behavior observation checklists reveals that intentional positive praise significantly reduced negative incidents across most categories, with emotional outbursts and defiant/disrespectful behaviors showing the most dramatic improvements (dropping to 15–28% of student-days during praise weeks compared to 61–71% without praise). While off-task behavior, technology misuse, and disruptive talking also decreased substantially for the group, Emiliano's persistent disruptive talking highlights the need for additional individualized strategies alongside whole-class positive reinforcement.

D1 – Disruptive (talking, noises, interrupting instruction)

1. No Praise: 93% of student-days

2. Positive Praise: 45% of student-days

3. Overall: ~52% reduction

4. Note: Strong group effect, but Emiliano persisted at ~90%.

O1 – Off-Task (avoiding work, distracting others, not participating)

1. No Praise: 100% of student-days

2. Positive Praise: 48% of student-days

3. Overall: ~52% reduction

4. Note: Universal in No Praise; halved with praise. Jaiden improved most dramatically.

T1 – Technology Misuse (non-academic device use)

1. No Praise: 93% of student-days

2. Positive Praise: 45% of student-days

3. Overall: ~52% reduction

4. Note: Similar to O1; strong for Jaiden and Sophia, limited for Emiliano.

D2 – Defiant/Disrespectful (arguing, refusing directions, negative attitude)

1. No Praise: 71% of student-days

2. Positive Praise: 28% of student-days

3. Overall: ~61% reduction

4. Note: One of the strongest effects; improved respect and compliance across all students.

E1 – Emotional Outburst (yelling, walking out, shutting down)

1. No Praise: 61% of student-days

2. Positive Praise: 15% of student-days

3. Overall: ~75% reduction

4. Note: Most dramatic improvement; outbursts became rare during praise weeks.

Overall Takeaways

• Average reduction across behaviors: ~58% fewer incident-days during Positive Praise.

• Most responsive: Emotional Outbursts (E1) and Defiant/Disrespectful (D2) – relational and high-impact behaviors.

• Solid gains: Off-Task (O1) and Technology Misuse (T1) – praise increased focus and engagement.

• Mixed: Disruptive talking/noises (D1) – group improvement but persistent for Emiliano.

• Individual variation: Jaiden and Sophia showed the strongest gains; Aymar moderate; Emiliano improved in attitude and emotion but needed more support for talking, off-task, and tech issues.

Specifically, behaviors tied to emotional regulation and relationships—emotional outbursts (dropping from 61% to 15% of student-days) and defiant/disrespectful actions (71% to 28%)—showed the most dramatic improvements, while off-task behavior, technology misuse, and general disruptive talking were roughly halved during Positive Praise weeks.

All students benefited to some degree, with Jaiden and Sophia responding most strongly overall, Aymar moderately, and Emiliano showing clear gains in attitude and emotional control but persistent disruptive talking that suggests a need for additional targeted supports beyond whole-class praise.

In short, Positive Praise proved an effective, low-effort strategy for improving engagement and reducing escalation for this group of reactive English Language Learners, validating its role in shifting classroom dynamics toward greater supportiveness and responsibility.

1. Prepare the Raw Data: Organize the behavioral checklist in a spreadsheet (e.g., Excel) with rows for students and behaviors, and columns for each observation day. Use "x" to mark incidents and blank cells for no incident. Include headers indicating intervention conditions (e.g., "No Positive Praise" vs. "Positive Praise") and color-code phases for visual clarity.

2. Count Incidents: For each behavior and student, manually or via formulas count "x" marks separately for No Praise phases (e.g., Weeks 1 & 3) and Positive Praise phases (e.g., Weeks 2 & 4). Calculate total possible student-days per phase (students × days).

3. Compute Percentages: Determine the percentage of student-days with incidents for each phase: (incidents / possible student-days) × 100. Compute overall percentages and reduction (No Praise % – Positive Praise %).

4. Create Behavior-Specific Matrices: Generate visual checklists (one per major behavior) showing daily "x" marks, color-coded phases, and student rows. Use these to identify patterns visually.

5. Summarize by Behavior: Compile a non-tabular summary listing each behavior with No Praise %, Positive Praise %, reduction, and key notes (e.g., individual outliers like persistent cases).

6. Synthesize Findings: Compare effects across behaviors, noting strongest reductions (e.g., relational/emotional) and limitations (e.g., persistent issues for specific students). Discuss implications for classroom practice and future targeted interventions.