Smartphone usage and academic performance: A cross-sectional investigation among statistics students at MBSTU

 Corresponding author: Samiul Islam. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. Smartphone usage and academic performance: A cross-sectional investigation among statistics students at MBSTU Sharmin Sultana 1, Md. Ruknuzzaman 2, Md. Ayub Ali 1, Aysha Seddeque 3, Salma Siddiqua 4, Nibas Kumar Pal 1 and Samiul Islam 2, * 1 Department of Statistics, Faculty of Science, University of Rajshahi, Bangladesh. 2 Department of Statistics, Faculty of Science, Mawlana Bhashani Science and Technology University, Bangladesh. 3 Department of General Education, Faculty of Social Science, Northern University of Business & Technology Khulna, Bangladesh. 4 Department of Statistics, Faculty of Science, Khulna University, Bangladesh. World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 Publication history: Received on 02 June 2025; revised on 13 July 2025; accepted on 15 July 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.27.1.2574 Abstract Our research aimed to investigate the influence of smartphone usage on academic performance among statistics students at Mawlana Bhashani Science and Technology University (MBSTU), focusing on usage patterns, self-regulation, and associated academic and health outcomes. Data were collected from 102 undergraduate and postgraduate students using a stratified random sampling technique and a structured questionnaire covering demographics, smartphone habits, study behaviors, and academic results. Descriptive statistics revealed that smartphone usage is nearly universal, with daily use ranging from 2 to 15 hours (mean = 6.52, SD = 2.57), and students reported moderate ability to study effectively with smartphones but better study quality without them. Stepwise regression analysis showed that purposeful use of smartphones for academic activities was positively associated with academic performance, while excessive or non-academic use—especially during study sessions or late at night—was linked to reduced concentration, disrupted study habits, and lower academic achievement. Health-related issues, such as sleep disturbances, neck pain, and mental stress, were also prevalent among frequent users. The final regression model explained 58.2% of the variance in academic results, highlighting that the context and intent of smartphone use are more influential than total usage time. These findings support previous research indicating both the benefits and risks of smartphone integration in academic life, while offering discipline-specific insights for statistics education at MBSTU. Overall, this research provides targeted insights for educators and students, emphasizing the dual role of smartphones as both valuable academic tools and potential sources of distraction and health risk. Keywords: Academic Performance; Stratified Random Sampling; Self-Regulation; Stepwise Regression Analysis; Sleep Disturbances; Mental Stress 1. Introduction Smartphone usage has become an essential part of daily life, particularly among university students, who frequently use them for academic and non-academic purposes [1]. The rapid advancement of information technology has resulted in smartphones forming an integral part of modern education, and their influence on the way students communicate, search for knowledge, and get involved in academic life has become profound. Also, the rise of smartphones has changed the way in which users interact with technology, bringing about opportunities and challenges [2], [3], [4]. The fact that students are using smartphones at school and on campus has really characterized post-secondary education today. In addition, smartphones had been highly owned by almost all the university students in recent years when the rapid World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1578 growth of the internet and the usage of Internet-enabled mobile device have made a smartphone become a primary source to access information, communicate with people, and learning activities [4], [5], [6] . Those students choosing to specialize in statistics are particularly advantageous, as they bring access to statistical software, online databases, and collaborative platforms needed to learn complex quantitative concepts at their fingertips. Nevertheless, the increasing use of smartphones is also associated with worries about distraction, limited ability to focus, and negative effects on education [7], [8], [9]. Smartphone usage among university students is nearly ubiquitous, and its influence on academic performance has been the subject of extensive research. Smartphones have numerous versatile functions, particularly among university students as tools of communication, education, and entertainment [10]. Although some research has pointed out the positive effects of using mobile technologies for education e.g., better access to learning resources, enhanced communication and more flexible learning [5], [6], [11] and others show the risks of misuse or too much use of this technology, such as distraction, procrastination, and lower academic performances [1], [4], [12]. The timing and the context of smartphone use also matter; late use or use during class are linked to negative academic achievements, while strategic and mindful use can lead to better performance [4], [13]. However, the increasing worry following the use of smartphones and their negative effects on students’ GPAs is still one of the pressing issues in educational research [14]. Smartphones have become a norm on college campuses and are used by students for various reasons: access to educational materials, for communication and interacting with colleagues, online participation in discussions, and dealing with their academic time schedules [3], [15]. Smartphones offer valuable educational resources, but at the same time present distractions that could deter the ability to concentrate and stay focused in studying [8]. While these technological advancements are beneficial, there are some concerns about the adverse effects of the heavy reliance on smartphones, especially on academic achievement [3]. A substantial body of literature has examined the relationship between smartphone usage and academic performance. Meta-analyses and systematic reviews repeatedly show that PSU is negatively associated with academic achievement, but the magnitude of the effect tends to be small [8], [14], [16]. For instance, a researcher conducted a meta-analysis of 29 studies with 48,490 students and reported a small, yet significantly negative (r = −0.110) relationship between PSU and academic achievement, with even larger effects for younger students [8]. In common, it is found that heavy smartphone usage mainly for non-academic purposes led to lower academic achievement of students because of the distraction and time displacement [14]. The exploration of the influence of smartphone use on academic performance among undergraduate students at four tertiary institutions in Northwestern Nigeria [17]. They discovered that smartphones are highly integrated into the academic lives of the students but also present challenges. Their study found that an overwhelming majority (97.22%) of respondents used smartphones primarily for making calls, and most of them (98.23%) utilized them to chat with friends and family via apps like WhatsApp, indicating their key role in social communication. Notably, 76.26% of respondents also use smartphones for academic purposes, including reviewing materials and accessing online information, highlighting the dual roles of these devices in social and academic contexts. However, the study identified significant challenges: 74.24% of students reported a lack of ICT infrastructure, and 74.75% cited insufficient understanding of how to use smartphones for learning as major barriers. Nonetheless, the majority of participants (81.31%) believed they spent more time studying than on smartphones, and that smartphones facilitated their acquisition of material to aid in learning (86.11%). Regarding daily usage, 41.92% of students used their smartphones for 3–4 hours per day, 26.92% for 5–6 hours, and 17.93% for more than 6 hours, suggesting that excessive and uncontrolled use might lead to distraction. Longitudinal evidence on the causal effect of smartphone use on academic performance is supplied by studies investigating repeated measures of smartphone use (and other related aspects of mobile phone use) and gold standard measures of academic performance. Three years of data on Belgian university students was studies, and they reported that the more students used their smartphones, the less likely they were to pass exams and their exam scores were lower [13]. Laboratory investigation found that even though students used their phones at nearly equal rates, students who used their phones in lectures performed poorly on assessments; these findings emphasize the negative effects of multitasking and distraction [1], [12]. However, when smartphones are used for educational purposes, e.g., to access academic materials, attend online discussion and use productivity apps, they have been found to enhance academic performance [5], [6], [15]. It has been discovered that having profound self-regulation skills and high smartphone self-efficacy can enhance how students use these devices for academic purposes [11], [18], [19]. World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1579 Nevertheless, the downsides of smartphone addiction are now well-documented. Also some researcher observed that smartphone addiction positively correlated with higher academic-anxiety, procrastination, and lower academic performance [16], [20], [21]. A study showed that the academic anxiety is a full mediating mechanism between smartphone addiction and academic performance but the academic control is a moderator in this association [16]. Indeed, students who cannot control their use of smart phones seem to have impaired cognitive processes, limited ability to concentrate and problems with critical thinking [4], [7], [9]. The effects of smartphone engagement also depend on context and subject. For instance, the use of smartphones among students at a business and statistics school might be distinctive, and the influences of smartphones could have been moderated by other factors like self-regulation, attention, and the curriculum requirements [4], [11], [22]. However, despite such an understanding, little is known about the experiences of statistics students in institutions such as MBSTU. Significant deficiencies persist for statistics pupils at Mawlana Bhashani Science and Technology University, notwithstanding the heightened study on university students' smartphone and internet utilization. Primarily, the majority of research concerning smartphone or internet use and academic performance has focused on extensive student populations or certain disciplines such as medicine, commerce, or social sciences [2], [23], [24]. Research from MBSTU indicates that internet use for academic purposes enhances academic performance, but non-academic usage detracts from it [23]. Statistics students need certain software, data analysis instruments, and collaboration platforms that may influence their smartphone utilization for both academic and non-academic activities. These results fail to meet their requirements. Secondly, the majority of research on smartphone and internet use employs quantitative methodologies, such as self-reported surveys and regression analyses [2], [23], [25]. These strategies are advantageous; yet, they may neglect the ways in which children learn, manage distractions, and self-regulate in relation to cellphones. Mixed-methods research, integrating surveys with interviews or observational data, is advocated for a more comprehensive analysis of smartphone use, however it is hardly employed in statistics courses [25]. Self-regulation moderates the academic impact of smartphone use, representing a third gap [4], [26]. Certain studies indicate that selfregulation may mitigate smartphone distractions, although little research has investigated how statistics students acquire and use self-regulation. Academic success depends on the ability to set explicit goals, prioritize scholarly tasks, and resist non-academic smartphone use during study periods; nevertheless, the mechanisms via which self-regulation affects smartphone-related academic performance in certain student populations remain unclear [26]. The objective of this study is to investigate the influence of smartphone usage on the academic performance of statistics students at MBSTU. By examining smartphone usage patterns, self-regulation strategies, and their association with academic outcomes, this study aims to provide targeted insights that can inform educational interventions and policies for statistics students. Specifically, the study seeks to assess the extent of smartphone usage among these students, including total time spent, usage during study or class hours, and engagement in social media and gaming. Additionally, the study will explore students' perceptions and behaviors related to smartphone use, including its impact on sleep, concentration, physical discomfort, and emotional dependency. Another key objective is to analyze the relationship between smartphone usage patterns and academic outcomes. Furthermore, the research will determine whether high smartphone usage correlates with changes in study habits, reduced academic focus, or lower academic performance. The study will also identify demographic or lifestyle factors, such as age, gender, rural/urban background, and exercise habits, that may influence the relationship between smartphone usage and academic performance. Finally, the research aims to provide recommendations for both students and educators on promoting healthy smartphone habits that support academic success. Hence, in this study, the research questions are • RQ1: What is the relationship between smartphone usage and academic performance among statistics students at Mawlana Bhashani Science and Technology University in Bangladesh? • RQ2: Which patterns of smartphone use, including bedtime use, class use, social media, and gaming, are most associated with academic decline? • RQ3: Do students believe that smartphone usage affects their academic focus and time management? • RQ4: What physical or psychological effects, such as sleep disturbance, neck pain, and anxiety, are associated with frequent smartphone use among students? The remaining part of the study is organized as follows: Section 2 contains the materials and methodology, including a description of the dataset, different statistical tests, and an overview of the evaluation criteria for those statistical tests. Section 3 presents the results of analyzing the example dataset. Section 4 summarizes the concluding remarks and outlines directions for future research. World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1580 2. Material and Methods 2.1. Description of Data Set The study population comprised all undergraduate and postgraduate students in the Department of Statistics at Mawlana Bhashani Science and Technology University (MBSTU) in Bangladesh. From this population, a sample of 102 students was obtained to investigate the influence of smartphone usage on academic performance. A stratified random sampling technique was employed to ensure the sample accurately represented key subgroups (e.g., each year of study). Specifically, students were divided into strata based on their academic year, and participants were then randomly selected from each stratum in proportion to the size of the stratum. There were five strata in total: 1st-year undergraduate, 2nd-year undergraduate, 3rd-year undergraduate, 4th-year undergraduate, and 5th-year postgraduate students. The final sample of 102 students provided adequate representation for both descriptive and inferential analyses. The stratified random sampling technique involves dividing the target population into distinct strata, with samples selected from each stratum either by simple or systematic sampling. The number of individuals selected from each stratum is either fixed or proportional to the size of the stratum in the population. In this study, the sample size from each stratum was proportional to the size of the stratum in the population, calculated using the formula: ni=(Ni N)×n • ni: Sample size to be taken from stratum i • Ni: Population size of stratum i • N: Total population size (sum of all Ni) • n : Total desired sample size Figure 1 Students from department of Statistics at MBST World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1581 Figure 2 Stratified Sampling Diagram 2.2. Material Properties This cross-sectional study examined the relationship between MBSTU Statistics students' academic performance and smartphone usage. A pre-designed questionnaire containing four sections—demographics, smartphone use, study habits, and indicators of academic achievement—was utilized to gather data. The questionnaire covered aspects of smartphone use such as social networking, gaming, learning, and other activities. The study evaluated students' academic performance, emotional stress, smartphone distractions, and sleep patterns. Both closed-ended and open-ended questions were included in the survey. Participants were asked to record all aspects of their daily smartphone use, including social media, gaming, and schoolwork. Students were asked how often they used their smartphones for studying and how this affected their focus and performance. The additional survey examined people's mental stress levels, sleep habits, and the alleged negative impacts of smartphone use on academic performance, such as disturbed sleep and missed meals. To determine the effect of smartphone, use on academic achievement metrics like GPA, focus, and study quality, the data were analyzed descriptively. The effects of cellphones on sleep disturbances, psychological stress, and distractions were evaluated. These studies aim to understand how university students' use of smartphones influences their academic performance. 2.3. Statistical Analysis The data for this study were collected via a structured questionnaire and subsequently entered into SPSS (IBM, version 25) for comprehensive analysis. Descriptive statistics were first computed to summarize the demographic characteristics, smartphone usage patterns, study behaviors, and academic performance indicators of the participants. To assess the normality of the data distribution, the Shapiro-Wilk Test was conducted. The results indicated that the data approximated a normal distribution, as confirmed by the histograms and P-P plots. To examine the relationship between smartphone usage and academic performance, multiple statistical techniques were employed. Stepwise regression analysis was employed to identify the key predictors of academic performance, with a particular focus on smartphone usage time, frequency of social media engagement, and other relevant variables, including sleep quality and perceived distractions. Additionally, independent samples t-tests were performed to compare the academic performance (GPA) across different groups based on variables such as gender, smartphone usage during study hours, and sleep habits. These statistical analyses were used to uncover significant relationships between smartphone usage and academic performance among Statistics students at MBSTU. World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1582 2.4. Stepwise Regression Analysis Stepwise Regression is a method to select the most significant variables for inclusion in a regression model. It combines forward selection and backward elimination techniques, making it a highly efficient approach for identifying key predictors in a model [27]. In this study, Stepwise Regression was employed to identify significant predictors of the Perfusion Index. The process begins with all potential predictor variables. Iteratively adds or removes variables based on specific criteria, such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC). This ensures that only the most relevant predictors remain in the final model, thereby improving the model's explanatory power and interpretability [28]. Below is an illustration [29] of the Stepwise Regression process, showing the forward selection and backward elimination steps. The process proceeds as follows Figure 3 Stepwise Regression • Forward Selection: The model starts with no predictors. Predictors are added one by one based on their significance. • Backward Elimination: Starts with all predictors and removes those not statistically significant. In Stepwise Regression, the inclusion or exclusion of predictors is determined based on the p-value of each variable, typically with a threshold of p < 0.05 for inclusion and p > 0.10 for exclusion [30]. This method helps identify the most important predictors while avoiding overfitting [31]. 2.5. Association Test Analysis To examine the associations between categorical variables, such as smartphone usage during study sessions and selfreported academic performance, the Chi-square test of independence was employed. This test is used to determine whether there is a significant association between two categorical variables. The formula for the Chi-square statistic is given by 𝜒2=∑(𝑂𝑖−𝐸𝑖)2 𝐸𝑖 where • 𝜒2 is the Chi-square statistic, • 𝑂𝑖 represents the observed frequency for each category, • 𝐸𝑖 represents the expected frequency for each category. The Chi-square test was applied to determine whether significant associations exist between categorical variables such as academic performance, including study quality and self-reported GPA. A p-value less than 0.05 was considered statistically significant, indicating that the observed frequencies differed significantly from the expected frequencies, suggesting an association between the variables under investigation. World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1583 3. Results and Discussion The descriptive statistics in Table 1 provide an overview of smartphone usage and its perceived impact on academic performance among statistics students at MBSTU. The age of the participants ranged from 18 to 28 years, with an average age of 23.10 years (SD = 1.86), indicating that our sample represents a comparatively young student population. Regarding smartphone usage, students used their smartphones for 2 to 15 hours per day (M = 6.52 h, SD = 2.57), suggesting considerable variation in daily phone use. The number of mobile apps installed on their smartphones varied between 2 and 77, with a mean of 17.16 apps (SD = 15.74), demonstrating a wide range of smartphone engagement among respondents. In terms of the quality of study experience, students reported their ability to study effectively using a smartphone as moderate (mean = 5.73, SD = 2.09), which negatively affected academic focus. However, students indicated that study quality improved without utilizing the smartphone, with an average value of 6.67 (SD = 2.27), suggesting that, in general, students believe their study is better without the presence of a smartphone. These results highlight the various effects of smartphone use on students' academic experiences, as smartphones can influence not only study quality but also distractibility during academic tasks. Table 1 Descriptive Statistics of Smartphone Usage and Study Quality Among Statistics Students at MBSTU Variable Minimum Maximum Mean Standard Deviation Age (years) 18 28 23.10 1.86 Phone use per day (in hours) 2 15 6.52 2.57 Number of mobile apps used 2 77 17.16 15.74 Study quality rating with smartphone 1.0 10.0 5.73 2.09 Study quality rating without smartphone 1.0 10.0 6.67 2.27 Figure 4 Total Duration of Mobile Phone Usage in a Day Figure 4 illustrates the total duration of mobile phone usage in one day, represented through a pie chart. The data indicates that a significant portion of individuals (38.2%) use their mobile phones for more than 10 hours daily. The next largest group (21.6%) reports using their phones for 7-8 hours, while 19.6% spend 9-10 hours on their devices. Additionally, 10.8% use their phones for 5-6 hours, and 8.8% use them for less than 5 hours. World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1584 Figure 5 Use of smartphones during class time Figure 5, a bar graph, depicts smartphone usage during class time. It reveals that 39.216% of individuals never use smartphones during class, with 27.451% reporting usage sometimes. Furthermore, 20.588% use their smartphones rarely, 10.784% use them often, and only 1.961% use them always. These figures highlight the varying patterns of mobile phone usage, both in general daily use and within the academic context. Table 2 Frequency and Percentage Distribution of Health-Related Issues Among Smartphone Users Neck Pain Insomnia Sleep Disturbance Mental Stress Concentration Break Frequency Percent Frequency Percent Frequency Percent Frequency Percent Frequency Percent Never 25 24.5 30 29.4 30 29.4 14 13.7 7 6.9 Rarely 27 26.5 39 38.2 39 38.2 26 25.5 16 15.7 Sometimes 32 31.4 16 15.7 16 15.7 31 30.4 28 27.5 Often 10 9.8 9 8.8 9 8.8 21 20.6 30 29.4 Always 8 7.8 8 7.8 8 7.8 10 9.8 21 20.6 The table 2 presents the frequency and percentage distribution of health-related issues, such as neck pain, insomnia, sleep disturbance, mental stress, and concentration breaks, among participants who use smartphones. It shows that those who report higher frequencies of smartphone use may experience a variety of health-related concerns. For instance, neck pain was reported by 24.5% of participants as never experienced, while 26.5% reported it rarely. Those who use smartphones more frequently may face higher instances of neck pain, as excessive screen time and poor posture are common among smartphone users. Regarding insomnia, 38.2% of participants reported it rarely, with 15.7% reporting it sometimes. Frequent smartphone users often experience disrupted sleep patterns due to late-night screen exposure, which can negatively impact sleep quality. Sleep disturbance was reported as never occurring by 29.4% of participants, but a notable 38.2% experienced it rarely and 15.7% sometimes, suggesting that smartphone usage, particularly before sleep, may be linked to disturbances. Mental stress was reported frequently, with 30.4% of participants indicating it occurred sometimes, and 20.6% often, a common issue among individuals who are frequently on their phones due to work, social media, and communication pressures. Lastly, concentration breaks were World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1585 experienced by 29.4% of participants often and 27.5% sometimes, suggesting that smartphone distractions may significantly impact students' focus and academic performance. Overall, these findings highlight a clear association between smartphone usage and various physical and psychological issues, pointing to the potential negative effects smartphones may have on the well-being and concentration of users. Figure 6 The biggest distraction while studying with a smartphone Figure 7 Notice changes in study habits after using a personal phone The two pie charts presented in Figures 6 and 7 provide insights into the impact of smartphone usage on academic performance among students at MBSTU. Figure 6 depicts the primary distractions encountered by students while studying with a smartphone. The chart indicates that 66.7% of students perceive social media notifications as the biggest distraction, while 33.3% report that reduced concentration, attributed to smartphone use, is the primary cause of their study interruptions. Figure 7 explores the changes in study habits noticed by students after incorporating a personal phone into their study routines. A significant majority, 81.4%, observe a negative change in their study habits, whereas a smaller proportion, 18.6%, note a positive change. These findings underscore the substantial negative effect of smartphone usage on student concentration and academic habits, particularly through the distraction of social media notifications. World Journal of Advanced Research and Reviews, 2025, 27(01), 1577-1592 1592 [24] S. Venkatapathy, S. Lakshmi, R. Bhargavan, V. Santhi, and B. Rajesh, “Impact of smartphone usage on the academic performance among medical students,” Res. Santhi, B RajeshJ. Evol. Med. Dent. 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