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Reducing Procrastination in Mathematics: A Study on the Effectiveness of Progressive Peer-Assisted Learning (PPAL) among College Students

Paul John B. Panganiban*, Janneth Q. Rondina

Department of Mathematics Education, University of Science and Technology of Southern Philippines, Cagayan de Oro City, Philippines

Abstract: This study examined the influence of Progressive Peer-Assisted Learning (PPAL) on students’ academic procrastination using a quasi-experimental pretest-posttest control group design. The participants were two intact classes of social work students enrolled in Mathematics in the Modern World at Christ the King College, Gingoog City, Philippines. A researcher-developed questionnaire on academic procrastination, validated and tested for reliability, was used to collect data. The control group experienced traditional collaborative learning, while the experimental group experienced PPAL. In PPAL, students initially worked individually to solve problems and defended their solutions in class to earn points. They then progressively formed groups of two and three for subsequent tasks, earning additional points for correctly defended answers. Group membership changed each cycle, and students returned to individual work once tasks were completed. Data were analyzed using means, standard deviations, and ANCOVA. Results indicated that students exposed to PPAL exhibited significantly lower academic procrastination. These findings suggest that PPAL can reduce procrastination and have important implications for tertiary mathematics instruction, particularly for non-mathematics majors prone to task avoidance. Incorporating PPAL may enhance engagement and peer interaction. However, given the quasi-experimental design, use of intact classes from a single institution, and the relatively short duration of the intervention, caution is warranted in generalizing results or assessing sustained effects. Future research could employ randomized controlled designs, examine long-term effects on procrastination and achievement, explore applicability across disciplines, and use qualitative methods to investigate students’ experiences and perceptions of peer-assisted learning.

Keywords: Tertiary Mathematics, Academic Procrastination, Collaboration learning, Progressive Peer-Assisted Learning

Received December 16, 2025; Revised January 18, 2026; Accepted January 25, 2026

Citation: Paul John B. Panganiban, Janneth Q. Rondina, Reducing Procrastination in Mathematics: A Study on the Effectiveness of Progressive Peer-Assisted Learning (PPAL) among College Students. Journal of Innovations in Teaching and Learning, 6 (1). 9-14. January 2026. doi:10.12691/jitl-6-1-2

1. Introduction

Academic procrastination, defined as the voluntary delay of academic tasks despite awareness of potential negative consequences [1], significantly undermines both the quality of student learning and overall well-being [2,4,5]. While often characterized as a failure in self-regulation or the inability to complete tasks promptly [3], the phenomenon is increasingly prevalent in educational settings [8]. Recent data indicates that approximately 70% of university students experience moderate procrastination, while 14% exhibit high levels of the behavior, particularly when preparing for examinations or assignments [6,7]. Notably, specific disciplines show varying rates; Setiyowati et al. found that Mathematics students reported the highest incidence (44%), followed by Physics (31%), identifying poor mathematical performance as a primary concern [9]. Although chronic procrastination affects roughly 20% of the general adult population, the figures are markedly higher among students, with estimates ranging from 75% to 95% across various college cohorts [10,11,12]. Given its pervasive nature, scholars emphasize the necessity of expanding research beyond the university level to include younger learners [4], as procrastination remains a critical issue across all educational stages.

Currently, academic procrastination is highly prevalent among students, driven by a complex interplay of internal and external factors. Primary contributors include stress, social challenges, low commitment, insufficient institutional support, and deficient time management skills [13]. Within the specific domain of mathematics, research identifies self-efficacy, motivation, and interest as the most dominant predictors of procrastinatory behavior [14]. Furthermore, internal variables such as learning styles, attitudes toward the subject, and levels of concentration significantly influence task initiation [15].

The literature further delineates the psychological drivers of delay, noting that students may avoid tasks due to distractions, a lack of focus, or even as a maladaptive strategy to minimize perceived task difficulty [16]. Notably, a significant relationship exists between mathematics anxiety and procrastination; students experiencing high anxiety levels are more likely to exhibit avoidance behaviors, which ultimately hinders achievement [17,18]. In addition, students with low academic self-efficacy are particularly prone to procrastinate, avoiding or postponing challenging assignments [19], making self-efficacy a crucial foundation for effective task engagement. Time management is also closely linked to procrastination; Aribas found a moderate negative correlation [20]. Moreover, Yun emphasized that motivated and confident students are less likely to delay tasks [21], while higher academic interest fosters engagement and commitment, reducing procrastination [22]. Conversely, lack of interest leads to disengagement and task avoidance [23], underscoring the protective role of academic interest. Given that procrastination in mathematics is closely linked to poor self-regulated learning and reduced cognitive strategy use [24,25], addressing these behaviors is vital for improving long-term academic outcomes and student achievement.

One strategic approach to mitigating academic procrastination involves identifying its underlying causes and implementing targeted interventions [26]. Collaborative learning has emerged as a promising strategy to enhance student engagement and reduce procrastination in mathematics; however, there is a distinct lack of research on structured grouping methods specifically tailored for college-level mathematics. Traditional instructional methods often fail to maintain active student engagement, frequently resulting in increased procrastination and diminished academic performance [27]. To address this gap, the present study implemented an intervention rooted in social interdependence theory, operationalized through Salazar’s grouping method [28].

For theoretical clarity, this approach is termed Progressive Peer-Assisted Learning (PPAL). PPAL is defined as a structured instructional cycle consisting of three distinct phases: Individual Mastery, Collaborative Mastery, and a Return to Individual Work. This study aims to provide educators with evidence-based insights for designing instructional strategies that effectively curtail procrastination in mathematics curricula. Consequently, this research investigates the following question: What is the main effect of Progressive Peer-Assisted Learning (PPAL) on reducing academic procrastination in mathematics compared to a control group?

2. Methods

2.1. Research Design

This study employed a quasi-experimental pretest-posttest control group design. In this framework, the experimental group received a pretest, the Progressive Peer-Assisted Learning (PPAL) intervention, and a subsequent posttest. Conversely, the control group completed the pretest and posttest but did not receive the treatment. This design involves selecting an experimental group to undergo the intervention and a control group with comparable characteristics to serve as a baseline [29,30]. To strengthen internal validity, it is ideal for the groups’ mean pretest scores to be statistically similar (p > .05). By ensuring similarity between the treatment and control groups, any differences in posttest scores can be attributed to the intervention received by the treatment group [31].

2.2. Participants

The research was conducted at Christ the King College in Gingoog City, Philippines, a private higher education institution. The participants comprised two intact sections of first-year Social Work students enrolled in the "Mathematics in the Modern World" course during the 2024–2025 academic year. The control group consisted of 35 students, while the experimental group included 39 students. Although the sections were assigned to their respective conditions randomly, the use of pre-existing classes rather than the random assignment of individual students is characteristic of a quasi-experimental design [32]. Both groups completed pretests and posttests to evaluate the efficacy of the intervention within this naturalistic classroom setting.

2.3. Instruments

The Academic Procrastination Scale utilized in this study was a researcher-developed instrument adapted from existing measures. This self-report questionnaire comprised 46 items categorized into five dimensions: Mathematics Anxiety (10 items), Academic Self-Efficacy (10 items), Time Management (8 items), Academic Motivation (9 items), and Academic Interest (9 items). Responses were recorded on a five-point Likert scale, ranging from strongly disagree to strongly agree. To ensure content validity, four experts conducted a face validity evaluation. Furthermore, a pilot test involving 39 students distinct from the study's intact sections yielded Cronbach’s alpha coefficients of .924, .935, .913, .962, and .981 for the respective dimensions. These values indicate strong internal consistency across all scales.

Representative items from the Academic Procrastination Scale include: Mathematics Anxiety (“I delay starting math assignments because I worry about failing”; “I experience sweating or shaking during graded math work”); Academic Self-Efficacy (“I delay starting important academic tasks because I’m not confident I can do them well”); and Time Management (“I fail to plan my study schedule in advance, which causes me to delay starting assignments until the last minute”). Furthermore, the scale assesses Academic Motivation through items such as “I avoid academic work when I do not feel intrinsically motivated to learn the content,” and Academic Interest via items like “I procrastinate on tasks that do not align with my personal academic interests.”

2.4. Data Collection

Data for this study were collected using a researcher-developed questionnaire. At the onset of the study, a pretest was administered to both the control and experimental groups on the same day. Following the pretest, the intervention period commenced. In the control group, the instructor discussed mathematical concepts followed by traditional seatwork activities. In contrast, the experimental group engaged in the Progressive Peer-Assisted Learning (PPAL) model. This method utilized a progressive grouping sequence designed to promote individual accountability while gradually fostering collaborative mastery.

During the initial stage, students worked independently on assigned tasks and were required to present and defend their solutions. Students who successfully demonstrated a correct solution earned five incentive points, serving as formative reinforcement. Upon mastery, students advanced to the second stage by forming dyads. In these pairs, students collaboratively solved problems and defended their work; successful dyads earned an additional five points per member and were then permitted to expand into a triad by selecting a third peer. In the third stage, these groups of three engaged in collaborative problem-solving; successful defense earned a final five points per member, after which students returned to individual work. This cyclical progression—transitioning from individual mastery to dyads, then triads, and back to independent engagement—was repeated throughout the five-week intervention, with group membership rotating in each cycle to ensure varied peer interaction. Finally, a posttest was administered to both groups to evaluate the effects of the intervention.

2.5. Data Analysis

The data collected from the pretest and posttest were analyzed using descriptive statistics, specifically mean and standard deviation, to summarize the levels of academic procrastination across both groups. This provided a comprehensive overview of baseline variations and longitudinal trends. To determine whether significant differences existed in students’ procrastination levels following the intervention, an analysis of covariance (ANCOVA) was performed. ANCOVA is particularly appropriate for quasi-experimental designs as it statistically adjusts posttest scores based on pretest values, thereby mitigating bias resulting from initial group disparities [33,34]. In this model, the pretest served as the covariate, while the posttest functioned as the dependent variable (criterion). All statistical assumptions, including homogeneity of regression slopes and normality, were verified and satisfied prior to the final analysis.

2.6. Ethical Consideration

This study adhered to the core ethical principles of respect for persons, beneficence, and justice, as established in the Philippine National Ethical Guidelines for Health and Social Science Research (2023). Participation was entirely voluntary, and informed consent was obtained from all students prior to the administration of the pretest. To protect participant privacy, all data were anonymized, and students were explicitly assured that their responses would remain strictly confidential throughout the research process.

3. Results and Discussion

Table 1 presents the mean scores and standard deviations for academic procrastination in mathematics across the experimental and control groups. The data reveal that both groups exhibited moderate levels of procrastination at the baseline. Specifically, the control group (Traditional Collaborative Learning) obtained a mean of 2.81 (SD = 0.40), while the experimental group (PPAL) registered a slightly higher mean of 2.99 (SD = 0.36). These results indicate that prior to the intervention, students in both groups demonstrated comparable baseline levels with relatively low variability, suggesting initial group homogeneity. These findings mirror patterns reported in previous research; for instance, Hima observed moderate procrastination scores among high school students performing mathematical tasks [35]. Similarly, studies involving BSED Mathematics students have identified average levels of academic procrastination within that population [36].

Table 1. Mean and Standard Deviation of Students’ Academic Procrastination

In the posttest, divergent trends emerged between the two groups. The control group (Traditional Collaborative Learning) exhibited a slight increase in mean score from 2.81 to 2.89 (SD = 0.46), remaining within the moderate range. This marginal shift suggests that traditional collaborative strategies may have a limited impact on reducing procrastination, as evidenced by the minimal mean gain and increased score variability. In contrast, the PPAL group demonstrated a notable decrease in its mean score, falling from 2.99 to 2.67 (SD = 0.36). Given that lower mean values signify reduced levels of academic procrastination, this decline indicates a more substantial improvement attributable to the PPAL intervention. The stable standard deviation further suggests that this reduction was consistent across participants rather than being driven by outliers. These findings support the assertions of Rohani, who noted that both self-regulated learning and peer social support significantly mitigate academic procrastination; specifically, fostering supportive peer networks and enhancing self-regulation can effectively decrease task avoidance [37].

Taken together, these findings suggest that the PPAL model is more effective than traditional collaborative learning in mitigating academic procrastination. The structured progression, peer assistance, and performance-based incentives embedded within the PPAL framework likely enhanced student engagement, accountability, and self-regulation, resulting in more substantive behavioral change. While both groups remained within the same descriptive category, the direction and magnitude of the results favor the PPAL intervention. These outcomes underscore the potential pedagogical advantages of a structured peer-assisted approach over conventional collaborative methods in fostering timely task completion.

An analysis of covariance (ANCOVA) was conducted to examine the effect of collaboration approach on students’ academic procrastination in mathematics, with posttest scores as the dependent variable and pretest scores as the covariate. Prior to conducting ANCOVA, the underlying assumptions were examined.

Table 2. Assumption of Normality

As shown in Table 2 above, the assumption of normality was assessed using Shapiro-Wilk tests on the standardized residuals for each group (Control group: p = .055; Experimental group: p = .295), thus the null hypotheses are fail to reject. The data are consistent with a normal distribution, indicating the assumption was met [36]. Visual inspection of QQ-plots and histograms further confirmed that the residuals were approximately normally distributed.

Table 3. Assumption of homogeneity of regression slopes

Table 3 shows the assumption of homogeneity of regression slopes was tested by evaluating the interaction between the covariate (pretest scores) and the grouping variable. The interaction was not statistically significant F(1, 71) = 2.974, p = .057), confirming that the relationship between the covariate and the dependent variable was consistent across all treatment groups [32], indicating that the assumption was met.

The one-way ANOVA results presented in Table 4 confirm a statistically significant difference in the posttest mean scores in academic procrastination across the instructional groups, F(1, 71) = 6.778, p = .011. Since the p-value falls below the .05 threshold, the null hypothesis positing no significant difference between group means is rejected. The effect size (Partial eta squared, η² = .087) indicates a moderate effect size [38], suggesting that approximately 8.7% of the variance in academic procrastination can be attributed to group membership, specifically the Progressive Peer-Assisted Learning (PPAL) model. This suggest that the intervention in group differences had an impact on students’ level of academic procrastination. Furthermore, the relatively small within-group mean square (MS = 0.164) suggests that the model effectively controlled for individual variations. These empirical findings substantiate existing literature that characterizes academic procrastination not merely as a temporal delay, but as a multifaceted self-regulatory failure involving motivational, cognitive, and emotional dimensions [1,4,5].

Table 4. One – way ANCOVA Summary of Students’ Academic Procrastination

Through the lens of Self-Regulated Learning (SRL) theory, the success of PPAL stems from its rigorous emphasis on individual preparation and continuous self-monitoring. By mandating that students independently solve and defend tasks as a prerequisite for advancement, the model reinforces planning, effort regulation, and self-evaluation components historically shown to be inversely correlated with procrastination [19,20,36]. Thus, PPAL serves as a practical pedagogical framework that operationalizes SRL principles by rewarding high self-efficacy and superior time management [12,22,24].

The efficacy of the intervention is further explained by social interdependence theory. Unlike traditional collaborative methods that often suffer from diffused responsibility, PPAL fosters positive interdependence while mandating individual mastery. This staged progression provides the benefits of peer support while simultaneously mitigating social loafing, a behavior frequently linked to procrastination in group settings [11,36]. By embedding clear expectations and accountability mechanisms within the peer-interaction phases, PPAL ensures that social support leads to task completion rather than avoidance [11].

In addition, the incentive-based structure of PPAL differentiates it from conventional approaches by directly addressing student motivation and perceived competence. Given that motivational factors are primary predictors of procrastination in mathematics [2,14,21], the model’s immediate rewards and visible progression establish proximal goals. These short-term milestones counteract the psychological tendency to postpone tasks with distal or abstract outcomes [20,22]. Crucially, the requirement for students to return to individual work after group phases ensures that peer collaboration remains a support mechanism rather than a crutch, thereby sustaining personal responsibility for learning.

The significant results and moderate effect size provide empirical support for PPAL as an effective intervention for reducing academic procrastination. By integrating self-regulated learning processes, structured social interdependence, and incentive-based accountability, PPAL addresses key psychological and behavioral mechanisms underlying procrastination that are often overlooked in traditional collaborative learning models. This finding is consistent with recent systematic reviews emphasizing the need for theory-driven, structured interventions to effectively reduce academic procrastination in higher education settings [5,10].

3.1. Summary of Findings

The results indicate that both the control and experimental groups initially exhibited comparable and moderate levels of academic procrastination in mathematics, suggesting homogeneity at baseline. Following the intervention, divergent patterns emerged. Students exposed to Traditional Collaborative Learning showed a slight increase in procrastination levels, whereas those who participated in Progressive Peer-Assisted Learning (PPAL) demonstrated a meaningful reduction, reflected in lower posttest mean scores with stable variability.

The ANCOVA results confirmed a statistically significant difference in posttest academic procrastination between the two groups after controlling for pretest scores, with a moderate effect size. This indicates that participation in PPAL accounted for a substantive proportion of the variance in procrastination outcomes. Collectively, these findings provide empirical support for the effectiveness of PPAL in reducing academic procrastination among tertiary mathematics students. The results suggest that PPAL’s structured progression, emphasis on individual accountability, peer-assisted learning, and incentive-based mechanisms contribute to improved self-regulation and reduced tendencies toward task delay, outperforming traditional collaborative learning approaches.

3.2. Conclusion

This study provides compelling evidence that Progressive Peer-Assisted Learning (PPAL) serves as a potent intervention for reducing academic procrastination in tertiary mathematics education. By transitioning from traditional, isolated study habits to a structured, collaborative model, students demonstrated improved engagement and a heightened sense of accountability. These findings suggest that PPAL is not merely a grouping strategy, but a transformative pedagogical tool capable of fostering more disciplined and proactive learning behaviors.

However, the path toward broader implementation necessitates careful consideration of the study’s inherent limitations. Although the single-institution context and reliance on self-reported data offered a necessary and practical starting point, these constraints limit the generalizability and robustness of the findings. The relatively short duration of the intervention further limits the ability to assess sustained effects on academic procrastination. This underscores the need for greater methodological diversity in future research, including multi-institutional samples, and the use of longitudinal designs to strengthen causal inferences and enhance external validity. Moreover, the success of PPAL in this context invites a broader exploration of its utility.

3.3. Recommendations

Future studies are encouraged to employ randomized controlled designs, investigate the long-term effects of PPAL on procrastination and academic achievement, and examine its applicability across diverse academic disciplines and learner characteristics. Qualitative research may further enrich understanding by exploring students’ lived experiences and perceptions of peer-assisted learning environments.

ACKNOWLEDGEMENTS

The researcher expresses profound gratitude to all individuals who provided guidance and support in the completion of this study. Heartfelt appreciation is owed to my adviser, Dr. Janneth Q. Rondina, for her patient guidance, encouragement, and expert insights that were instrumental in bringing this study to fruition. Special thanks are extended to the expert validators for their invaluable contributions to the development and refinement of the research instrument. I am deeply thankful to my family for their unwavering support throughout this journey. Appreciation is also extended to Christ the King College – Gingoog City for granting permission to conduct the study and to all the participants whose involvement made this research possible. Above all, I give thanks to the Almighty for guidance, strength, and inspiration throughout this endeavor.

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